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Chapter I Critically Ill Child Critically ill child Critically ill child means a child who is in a clinical state which may result in cardiac arrest or severe neurologic complication, if not recognized promptly. This term does not refer to any particular disease, but many diseases can lead onto "critically ill state". Whether a child presents with a primary cardiovascular, respiratory, neurologic, infectious or metabolic disorder, the goal is early recognition of respiratory and circulatory insufficiency (Kinsella.1993). Acute life threatening condition can result from a serious illness affecting one or more of the body systems necessary to maintain life or a serious injury; trauma, burn, near drowning, poisoning which leads to derangement in physiology with the potential to result in significant morbidity and mortality without prompt and appropriate intervention (Fletcher.1987). Factors Potentially Affecting Outcomes of Critical Illness in Children Baseline Illness Children with chronic and complex diseases are at increased risk for in-hospital clinical deterioration and are over-represented in the ICU (Mestrovic, et al. 2007). Studies have shown prolonged ICU stay and higher mortality rates in children with ventilator-associated pneumonia (Srinivasan ,et al .2009), bone marrow transplant (Van Gestel, et al.2008), hypoplastic left heart syndrome (HLHS) (Gordon, et al.2008), hemophagocytic lymphohistiocytosis (HLH) (Karapinar, et al.2009), hyperglycemia (Klein, et al. 2008) and acute kidney injury(Hayes,et al.2009). The quality of life of patients with other chronic condition did 4 Chapter I Critically Ill Child not differ significantly from children who did not suffer from a chronic condition (Mestrovic, et al.2007). Acute Severity of Illness Acute Severity of illness on ICU admission is highly correlated with ICU survival in children (Pearson, et al.1997). In critically ill children, the Severity of illness at PICU admission may be related to functional outcomes as assessed by the PCPC (Pediatric Cerebral Performance Category) and POPC (Pediatric overall performance category) (Fiser, et al.2000). Increased Severity of illness may necessitate a longer PICU stay. Longer PICU stay is associated with cognitive morbidity (Nelson, et al. 2006) and increased mortality (Marcin, et al. 2001). Development of the PRISM III scoring system has greatly enhanced the paediatric intensivist’s ability to measure outcomes in the PICU objectively. It permits the quantification of severity of illness through the development of probability models predicting mortality risks (Lemeshow and Le Gall. 1994). Until recently, the Pediatric Risk of Mortality (PRISM) score (A physiology based predictor for pediatric patients) was the only Severity of illness assessment in the PICU. The Pediatric Index of Mortality (PIM) score (which is more recent than PRISMIII score) was introduced later which analyzes the condition of the patient directly upon arrival in the PICU (Gemke and Van Vught. 2002). Some pediatric intensivists considered that a good tool to estimate the severity of cases of MODS observed in PICUs was needed to describe correctly the clinical course of illnesses observed in critically ill children. Thus, it was thought appropriate to undertake a research program to create and validate a score 5 Chapter I Critically Ill Child for MODS in children. That score was named the pediatric Logistic Organ Dysfunction (PELOD) score (Lacroix and Cotting, 2005). Unlike the PRISM system (developed to be a predictive marker), the PELOD system was designed to be an outcome measure. Furthermore, PRISM includes parameters related to different organ dysfunctions but does not separate each dysfunction. The PELOD score describes the number and severity of organ dysfunctions during the child’s stay in the PICU, indepe ndent of cause (Leteurtre, et al. 2003). Interventions Less severe forms of critical illness require less intense forms of resuscitation. These include urgent intubation, mechanical ventilation, circulatory support with inotropes and administration of intravenous fluids to restore circulation. Acquired morbidity in the ICU can follow drug administration (Tobias. 2005), transfusion (Bateman, et al. 2008), mechanical ventilation (Albuali, et al. 2007), monitoring, anticoagulation and other therapies (Cengiz, et al. 2005). The better survival outcomes observed in children could be due to differences in patient characteristics, pre arrest conditions, earlier recognition and treatment of the cardiac arrest, interventions during CPR, and post resuscitation care. Children were monitored in an intensive care unit before the arrest more frequently than adults, perhaps because of the increased emphasis on early recognition and treatment of respiratory failure and shock in pediatric advanced life-support resuscitation training (Buist, et al. 2002). In-ICU cardiac arrest is associated with high in-hospital mortality and subsequent morbidity in survivors. Prearrest renal dysfunction and 6 Chapter I Critically Ill Child epinephrine infusion were associated with increased in-hospital mortality. The use of post-arrest ECMO within 24 hrs was associated with reduced mortality. Rigorous prospective evaluation of the role of ECMO following cardiac arrest is needed (De Mos, et al. 2006). Several studies showed that patients who received more than two doses of standard-dose epinephrine did not survive (Young and Seidel. 1999). Termination of resuscitation has been recommended if there is no Return of Spontaneous circulation (ROSC) after two doses of epinephrine. These studies conflict with De Mos, et al. (2006) where number of epinephrine boluses given during cardiac arrest was not significantly associated with ICU or hospital survival. In studies where there has been documented long-term survival after >2 doses of epinephrine, most were severely neurologically impaired (Christiansen, et al. 1997). Timing of Treatment Early applications of ICU interventions to treat patients with evolving critical illness have been associated with improved outcome (Buist, et al. 2002). Increasing interest and application of early interventions to improve patient outcome include earlier identification and referral of children with evolving critical illness, Medical Emergency Teams, and other ICU expertise (Pronovost, et al. 2002). This may improve survival but does not necessarily equate to improved cognitive or functional outcomes. These interventions may be improving timeliness of admission to ICU and thus improving the outcomes of care, by permitting intubation and other resuscitation to be delivered in the controlled ICU environment (Parkhe, et al. 2002). 7 Chapter I Critically Ill Child Other Risk Factors More than half of pediatric cardiac arrest cases occurred in patients before age two (Engdahl, et al. 2003). More than 99% of all child deaths occur in developing countries, and 10.1 million of the 10.9 million deaths in children younger than five years are preventable (Fuhrman and Zimmerman. 2005). Other risk factors identified include: cause of the arrest, patient location at the time of the arrest and length of stay in hospital prior to ICU admission (Holleran. 2002). Nature of the ICU The nature of the PICU itself can affect patient outcomes. A study evaluating changes of outcomes within a single ICU over a 10-year period showed that increased ICU activity is significantly associated with prolonged severity-adjusted survival (Kvale and Flaatten.2002). Another study comparing the efficiency of open ICUs with closed ICUs showed that ICU and hospital length of stay and days on mechanical ventilation were lower in closed ICUs (Multz , et al. 1998). A study in 2007 showed that patients with acute lung injury in a closed-model ICU have reduced mortality (Treggiari , et al. 2007). A systematic review by Kane, et al. (2007) examined nursing staff patterns and their effects on patient outcomes and showed a significant association between nurse staffing and hospital-related mortality. This review suggests that higher quality of hospital care, effective nurse retention strategies and implementation of collaborative evidence-based medicine lead to better patient outcomes. 8 Chapter I Critically Ill Child Table (1): Criteria for PICU and PIICU admission and discharge. Admission criteria PICU Discharge criteria Patients who need invasive Patient may be monitoring: arterial and central venous PICU once the catheters, pulmonary arterial lines, ICP reversed itself catheters. provided in environment: Patients with evidence of: Respiratory impairment of failure. discharged from the disease process has and care can be a less intensive Patient no longer requires invasive monitoring. Cardiovascular compromise: shock, Patient can protect his/her airway (cough and gag reflexes) Patient is hypotension, hypertensive crisis. hemodynamically stable. Acute neurologic deterioration: coma, status epilepticus, increased ICP. Acute renal failure requiring dialysis or CVVH. Bleeding disorders massive transfusions. PIICU that necessitate Patients who do not require respiratory assistance for acute respiratory failure but may require continuous noninvasive monitoring of vital signs, BP, SaO2, Tco2, Tcco2. Patient may be discharged from the PIICU when it has been determined that care can be provided in general care areas. Patients who require chronic respiratory support via tracheostomies or noninvasive ventilation. Patients who are in early cardiovascular failure and require monitoring of vital signs (noninvasive monitoring). Patients with acute neurologic injury but with a patent airway that they can protect themselves. Patients with MSOD who do not need a PICU, but nursing care is not available elsewhere (e.g., trauma victims, DKA). BP = blood pressure; CVVH = continuous venovenous hemofiltration; DKA = diabetic ketoacidosis; ICP = intracranial pressure; MSOD = multisystem organ dysfunction; PICU = pediatric intensive care unit; PIICU = pediatric intermediate 9 Chapter I Critically Ill Child intensive care unit; SaO2 = arterial oxygen saturation; Tco2 = transcutaneous oxygen; Tcco2 = transcutaneous carbon dioxide (Frankel. 2004). Common problems in PICU: Malnutrition: Malnutrition in the pediatric ICU population is a widely acknowledged problem that may intensify underlying illnesses, increase the risk of complications and affect growth and development (Pollack, et al.1985). Nutritional assessment upon admission to the ICU is necessary to identify children at risk and to guide nutritional support during ICU stay. The repertoire of routine laboratory parameters includes several markers (e.g., albumin, urea, triglycerides, electrolytes) that can provide useful and easily obtainable information regarding nutritional status and requirements (Selberg and Sel.2001). Abnormalities in these parameters reflect derangements in several metabolic pathways and may represent the severity of depletions occurring during critical illness. Previously, we have shown the nutritional status of children admitted to an ICU to deteriorate during admission (Hulst, et al. 2004). Nutritional support is essential in the care of critically ill children because inadequate feeding can increase morbidity and mortality rates (Biolo, et al. 1997). Sepsis: Sepsis remains an important health problem in children, as it is in adults (Angus, et al. 2001). Systemic inflammatory response syndrome (SIRS), sepsis, sever sepsis, and septic shock are frequently observed in 10 Chapter I Critically Ill Child the PICU. SIRS has been reported with an incidence up to 82% in the PICU (Despond, et al. 2001). Moreover, infection may lead to multiple organ dysfunction syndrome (MODS; defined as more than two organ dysfunctions); this is particularly true in children with septic shock who receive delayed treatment or have primary or acquired immunodeficiency (Carcillo. 2003). Pain: Pain is a complex phenomenon that received little attention in pediatrics until the late 1970s. Until recently, many health care professionals mistakenly believed that the child's immature nervous system rendered him or her in capable of feeling pain or assumed that the child was pain free. Several studies documented the inadequacy of analgesia provided to pediatric patients (e.g. following cardiovascular surgery). In addition, there was a lack of scientific information regarding the safety and efficacy of analgesics in young children and difficulty in identifying pain behaviors in infant and children. Clearly children can fed pain, and adequate analgesia is required in critical care unit (Hazinski. 1999). Hyperglycemia: Despite potential positive effects, prolonged hyperglycemia in critically ill adults has been shown to be associated with a number of deleterious consequences contributing to greater risks of morbidity and mortality, even in the absence of preexisting diabetes mellitus (Umpierrez, et al. 2002). Elevated glucose concentrations have been associated with increased risks of congestive heart failure, cardiogenic shock, and poor functional 11 Chapter I Critically Ill Child recovery after stroke as well as increased risks of dying after myocardial infarction and ischemic stroke among nondiabetic patients. Even among non-critically ill adult patients admitted to general patient care units, patients with newly diagnosed hyperglycemia had a significantly higher mortality rate and a lower functional outcome compared with known diabetic patients or normoglycemic patients (Capes, et al. 2001). Hyperglycemia may be less prevalent among children because diabetes mellitus is much less common in this age group (Green, et al. 2003). Furthermore, hyperglycemia in the pediatric population may have different effects on morbidity and mortality compared with adults as a consequence of different metabolic demands, differences in comorbid conditions, or age-dependent factors (Agus and Jaksic. 2002). Psychosocial problem: The hospitalization of a child for even a minor illness is stressful for both child and family. Life-threatening illness or injury magnifies the stress. The sensitive, empathetic nurse can mitigate some of the most frightening aspects of critical care, making the experience more positive and perhaps even growth promoting. Intervention should be aimed at enhancing individual coping skills sot that the child and family can apply them to other stress full experiences and situations in the future. The physicians and nurses are life lines controlling the life of the child and access to the child at all times, the nurse must convey sensitivity, compassion and support to the family and must support the family as the family attempts to support the child (Hazinski. 1999). Monitoring the critically ill or injured child: 12 Chapter I Critically Ill Child Hemodynamic and physiologic monitoring are basic elements in the evaluation and care of any critically ill or injured patient. Standard monitoring typically includes automated non-invasive blood pressure measurement, electrocardiographic monitoring, pulse oximetry, and urine output. However, these measures may fail to identify states of "compensated" shock or other pathologic processes in a reliable and timely manner (Martin and M.J. 2007). Modern intraoperative and intensive care practices have incorporated more advanced and invasive monitoring techniques, such as arterial catheterization and blood gas analysis, central venous catheterization, pulmonary artery catheterization, and intracranial pressure monitoring. Although these have become standard and accepted techniques, there are significant limitations and risks associated with their use in pediatric patients, including the availability and proper sizing of equipment, technical difficulties with vascular access, clinical expertise in interpretation of the data, and procedural-related pain and distress. In addition, the quality and clinical utility of the data provided by these monitoring systems is highly variable (Martin and M.J. 2007). A variety of newer noninvasive advanced monitoring systems have been developed and there is a growing body of experience with the clinical utility of these systems. The use and applications of ultrasound in critical care is expanding exponentially. Thoracic ultrasound can quickly and reliably assess for pneumothorax, hemothorax, cardiac contractility and volume, pulmonary hypertension, and intravascular volume status (Martin and M.J. 2007). Thoracic bioimpedance technology uses a transcutaneous electrical current to noninvasively assess thoracic blood volume, stroke volume, and cardiac output. These measurements have 13 Chapter I Critically Ill Child been shown to correlate well with invasive and echocardiographic measures of cardiac output (Keren, et al. 2007). Gastric perfusion measures (pH and PCO2) have been validated as an early marker of hyperfusion and shock, but require a specially designed nasogastric tube and equipment. New sublingual probes can noninvasively provide equivalent data to gastric tonometry (Creteur. 2006). Transcutaneous tissue perfusion measures of oxygen and CO2 tension can reliably estimate arterial blood gas measurements under uniform flow conditions. In pathologic flow states such as severe shock, these measures can provide evidence of local tissue hypoperfusion and can be used to guide therapy and the adequacy of resuscitation (Martin, et al. 2005). Near-infrared noninvasively spectroscopy measure tissue technology perfusion is and being guide used to therapeutic interventions in a variety of sites, including skeletal, muscle, and brain (Lima and Bakker. 2005). 14 Chapter I I Critically Ill Child CHAPTER II Scoring system in pediatric intensive care unit Critically ill patients are typically characterized by disturbances of body homeostasis. Both in adults and children, these disturbances can be estimated by measuring how much apart one or many physiologic variables are from the normal range. Scoring systems can be constructed with such variables. Many types of scores have been developed (Lacroix and Cotting .2005). The practice of paediatric critical care is dynamic and evolving. Paediatric population is a vulnerable group necessitating standard care for medically and surgically ill children.(Gomke, et al.1994) In context of intensive care, a rational and objective way to define and quantify severity of illness is through the development of probability models predicting mortality risks. Such predictive models or scoring system have been developed for all age groups including paediatric. (Lemeshow, et al.1993). Scoring systems are arrived at evaluation of the patient’s mortality risk in the ICU by assigning a score to patient and predicting the outcome. However, patient’s mortality is not only affected by ICU performance but also depends on many other factors such as demographic and clinical characteristic of population,infra structure and non medical factors (management and organization), case mix and admission practice. (Bertolini ,et al.1988). 15 Chapter I I Critically Ill Child These scoring systems assign a relative value or indication of severity to a clinical condition. The score itself can be used for various purposes, such as measuring clinical conditions or states and severity of illness for the purpose of assessing the risk of a disease or designated outcome. The most commonly measured pediatric ICU outcomes are mortality, length of stay, functional outcome, and organ dysfunction (Marcin and Pollack. 2007). Scoring system in paediatric intensive care units: Past, present & future Initially scoring systems were developed for trauma patients and were either specific anatomical methods (abbreviated injury scale 1969, burn score 1971, injury severity score 1974) or specific physiological methods (trauma index 1971, Glasgow coma scale 1974, trauma score 1981 and sepsis score 1983). Therapeutic Intervention Scoring System (TISS) In 1974 Therapeutic intervention scoring system (TISS) was introduced by Cullen D J et al to quantitate severity of illness according to the therapeutic interventions received by the patients. Each intervention had a value of 1-4 points based upon the complexity and invasiveness of intervention with a total score of 70 interventions.TISS has been utilized for many purposes which include:1- Determining the severity of illness 2- Establishing nurse patient ratio in ICU. 16 Chapter I I Critically Ill Child 3- Assessing current utilization of hospital intensive care beds. 4- Establishing future need and numbers of ICU beds particularly in response to request for certification of need. TISS was found to be a useful tool for obtaining comparable data which could be utilized for administrative, management and clinical purposes, within and between hospital settings.( Keene and Culten.1983) Unfortunately, the TISS score is heavily influenced by diagnosis, indicating the TISS score depends on the monitoring and therapeutic philosophies of the physicians and institutions using it (Pollack .1987). Compared with other predictors, it cannot quantify mortality risk. Efforts to evolve the TISS score by combining physiological dysfunction with therapies has been relatively unsuccessful ( Cullen, et al.1984). The Acute Physiology and Chronic Health Evaluation (APACHE) system The acute physiology and chronic health evaluation (APACHE) system was introduced (for adult patients) in 1981 by an expert panel of physicians who selected and weighed 34 laboratory and clinical measurements based on perceived impact on mortality. It consisted of 2 parts: An acute physiology score that reflected the degree of physiologic derangements and a chronic health evaluation that reflected patient’s status before the acute illness. There are now three APACHE system i.e. I, II,III. An increasing APACHE II score reflects increased severity of disease and a higher risk of hospital death.But the system was neither designed nor intended to predict for individual patients and it has an error 17 Chapter I I Critically Ill Child rate of approximately 15% for the prediction of hospital mortality using 0.50 decision point. APACHE III was introduced in 1991 to expand and improve the prognostic estimate provided by APACHE II. (Knaus, et al.1991). APACHE III system consists of points for physiologic abnormalities, age and chronic health status. Scoring is based on a degree of abnormality in 17 physiologic variables (APS), which reflects value for vital signs, laboratory tests and neurological status. In addition, points are added based on age and 7 comorbid conditions shown to have a significant impact on short term mortality. (Knaus, et al.1993) The APACHE system is appropriate for adult ICUs. However the changing physiology with growth and development within the wide spectrum of ages of pediatric patients prevents its direct application to PICUs.The limited number of patients and diverse conditions make diagnostic subgroups difficult to study ( Pollack, et al.1984). However in the recent past some scoring systems have been developed for PICU mortality prediction like for example Physiologic stability index ( PSI ). Physiologic Stability Index (PSI). Physiologic Stability index (PSI) was developed by a group of paediatric intensivists in 1984 from TISS as TISS only indirectly reflects the severity of illness by assessing therapeutic needs. PSI assesses the severity of acute illness in the total population of paediatric intensive care unit patients by quantitating the degree of derangement in 34 variables from 7 major physiologic systems. Each variable was assigned a score of 18 Chapter I I Critically Ill Child 1(abnormality worth concern but not to change therapy), 3 (need to change therapy), and 5 (life threatening). (Table 1) This reflected the clinical importance of derangements but not necessarily the amount of deviation from the normal value. The most abnormal value of a variable recorded within 24 hours was used ( Yeh, et al.1984). Table (2) Physiologic stability Index: Physiologic Systems (7) and Variables (34) (1)Cardiovascular: systolic blood pressure, diastolic blood pressure, heart rate, cardiac index, C(a-v)O2, CVP, PCWP (2)Respiratory: respiratory rate, PaO2, PaO2/FIO2, PaCO2 (3) Neurologic: Glasgow coma score, intracranial pressure, seizures, pupils (4) Hematologic: hemoglobin, WBC count, platelet count, PT/PTT, FSP (5) Renal: BUN, creatinine, urine output (6) Gastrointestinal: AST/ALT, amylase, total bilirubin, albumin (7) Metabolic: sodium, potassium, calcium, glucose, osmolality, pH, HCO3 Points for each variable: 0, 1, 3, 5 reflect clinical importance of derangement, with more abnormal having higher point value 19 Chapter I I Critically Ill Child not intended to reflect magnitude of deviation from the normal value PSI however, is time consuming; requiring the use of 34 variables from 7 physiologic systems and also it is a subjective score. A total of 294 clinical classification system (CCS) class III and IV patients in a PICU were evaluated by using PSI / TISS ratio. Non survivors had significantly higher (p < 0.01) PSI and TISS scores than survivors. Medical patients had the highest PSI / TISS ratio scores while, cardiovascular patients had lowest PSI / TISS ratio scores. (Pollack, et al.1984) PIM II score: The PIM II scoring system was developed on 20,787 patients from 14 ICUs and pediatric ICUs from Australia, New Zealand, and the United Kingdom (Slater, et al. 2003). PIM II requires 10 variables collected from the time of admission up to the first hour after admission. Compared with PRISM III, PIM II is less likely to be biased by the quality of treatment after admission to the pediatric ICU because variables are collected within the first hour rather than the first 12 hours after admission; however, PIM II does include predictor variables that are pediatric-ICU interventions (i.e., mechanical ventilation), which is also subject to bias from different intervention thresholds (Marcin and Pollack. 2007). PIM II has also been tested for reliability and has been shown that some training is necessary to ensure accurate and reliable data abstraction (Vankeulen, et al. 2005). Similar to PRISM III, most tests of discrimination have shown PIM II to be accurate in discriminating 20 Chapter I I Critically Ill Child nonsurvivors from survivors (c-statistic range from 0.74 to 0.90) but have inconsistent calibration statistics (Slater and Shann. 2004). The main weakness of PIM II score is that is has not been tested in North America and in many other countries around the world. On the other hand, the PIM score can be used for the same purpose as the PRISM score. More studies are required before one can conclude that the PRISM III is better than the PIM II or vice versa (Lacroix and Cotting., 2005). PIM II is calculated from the information collected at the time a child is admitted to your ICU. Because PIM II describes how ill the child was at the time you started intensive care, the observations to be recorded are those made at or about the time of first contact may be in your ICU, your emergency department, a ward in your own hospital, or in another hospital (e.g. on a retrieval). If information is missing (e.g. base excess is not measured) record zero, except for systolic blood pressure, which should be recorded as 120. include all children admitted to your ICU (consecutive admissions) (Slater, et al. 2003). 21 Chapter I I Critically Ill Child Table (3): PIM II (Pediatric index of mortality) Variables: 1. Systolic blood pressure, mmHg (unknown= 120)1 2. Pupillary reactions to bright light (3mm and both fixed = 1, other or unknown= 0)2 3. PaO2, mmHg (unknown= 0) FIO2 at the time of PaO2 if oxygen via ETT or headbox (unknown= 0). 4. Base excess in arterial or capillary blood, mmol/1 (unknown = 0). 5. Mechanical ventilation at any time during the first hour in ICU (no=0, yes = 1)3. 6. Elective admission to ICU (no=0, yes = 1)4 7. Recovery from surgery or procedure is the main reason for ICU admission (no=0, yes = 1)5 8. Admitted following cardiac bypass (no=0, yes =1)6 9. High risk diagnosis. Record the number in brackets. If in doubt record 0. [0] None. [1] Cardiac arrest preceding ICU admission7. [2] Severe combined immune deficiency. [3] Leukemia or lymphoma after first induction [4] Spontaneous cerebral hemorrhage 8 [5] Cardiomyopathy or myocarditis [7] HIV infection [8] Liver failure is the main reason for ICU admission 10 [9] Nero- degenerative disorder 11 10. Low risk diagnosis. Record the number in brackets. If in doubt record 0. [0] None [1] Asthma is the main reason for ICU admission. [2] Bronchiolitis is the main reason for ICU admission. 12 22 Chapter I I Critically Ill Child [3] Croup is the main reason for ICU admission. [4] Obstructive sleep apnea is the main reason for ICU admission 13 [5] Diabetic keto- acidosis is the main reason for ICU admission. Logit = (-4.8841) + (values Beta)+ (0.01395 (absolute (SBP-120) + (0.1040 (absolute base excess) + (0.2888 (1000 Fio2/PaO2) predicted death rate = e Logit/ (1 + e Logit) (Slater, et al. 2003). Coding rules. These rules must be followed carefully for PIM I to perform reliably: 1. Record SBP as 0 if the patients are in cardiac arrest, record 30 if the patient is shocked and the blood pressure is so low that it cannot be measured. 2. Papillary reactions to bright light are used as an index or brain function. Do no record an abnormal finding if this is due to drugs, toxins or local eye injury. 3. Mechanical ventilation includes mask or nasal CPAP or BiPAP or negative pressure ventilation. 4. Elective admission. Include admission after elective surgery or admission for an elective monitoring, or review of home ventilation. An ICU admission or an operation is considered elective if it could be postponed for more than 6 h without adverse effect. 5. Recovery from surgery or procedure includes a radiology procedure or cardiac catheter. Do not include patients admitted from the operating theatre where recovery from surgery is not the main reason for ICU admission (e.g. a patient with a head injury who is admitted from theatre after insertion of an ICP monitor; in this patient the main reason for ICU admission is the head injury). 23 Chapter I I Critically Ill Child 6. cardiac bypass. These patients must also be coded as recovery from surgery. 7. Cardiac arrest preceding ICU admission includes both in- hospital and out- of hospital arrests. Requires either documented absent pulse or the requirement for external cardiac compression. Do not include past history of cardiac arrest. 8. Cerebral hemorrhage must be spontaneous (e.g. from aneurysm or AV malformation). Do not include traumatic cerebral hemorrhage or intracranial hemorrhage that is not intracerebral (e.g. subdural hemorrhage). 9. Hypoplastic left heart syndrome. Any age, but include only cases where a Norwood procedure or equivalent is or was required in the neonatal period to sustain life. 10.liver failure acute or chronic must be the main reason for ICU admission. Include patients admitted for recovery following liver transplantation for acute or chronic liver failure. 11.Neuro- degenerative disorder. Requires a history of progressive loss of milestones or a diagnosis where this will inevitably occur. 12.Bronchiolitis. Include children who present either with respiratory distress or central apnea where the clinical diagnosis is bronchiloitis. 13.Obstructive sleep apnea. Include patients admitted following adenoidectomy and /or tonsillectomy in whom obstructive sleep apnea is the main reason for ICU admission (and code as recovery from surgery) (Slater, et al. 2003). 24 Chapter I I Critically Ill Child To reduce the number of physiologic variables required for severity of illness assessment and to obtain an objective weighting of remaining variables, a second generation score called pediatric risk of mortality(PRISM) has been devised by Pollack MM et al in 1988 (Pollack, et al.1988). Pediatric Risk Of Mortality (PRISM) Pediatric risk of mortality (PRISM) score allows for mortality risk assessment in the paediatric ICU. PRISM was developed from PSI to reduce the number of variables from 34 to 14 and number of ranges from 75 to 23 without losing the predictive power (Table 4). It is institution independent and can be used within limits to compare different intensive care units. (Balakrishnan, et al.1992). In 1996 physiological variables and their ranges as well as diagnostic and other risk variables reflective of mortality risk were re evaluated by Pollack MM et al to update and improve the performance of second generation PRISM score. Thus PRISM III was developed.( Pollack, et al.1996) Table (4) PRISM 25 Chapter I I Critically Ill Child Parameters: (1) Systolic blood pressure and age (2) Diastolic blood pressure (3) Heart rate (4) Respiratory rate (5) PaO2 to FIO2 ratio (6) PaCO2 (7) Glasgow coma score (8) Pupillary reactions to light (9) PT and PTT (10) Total serum bilirubin (11) Serum potassium (12) Serum total calcium (13) Glucose (14) Bicarbonate PRISM III The pediatric Risk of Mortality (PRISM) III score was first developed in 1996. It is a commonly used mortality prediction model. (Marcin and Pollack, 2007). The PRISM III scoring system was originally developed on 11, 165 patients from 32 different pediatric ICUs in the United states and is currently being updated on approximately 15,000 patients. Over 100 pediatric ICUs have software that enables the collection and computation of PRISM III- related outcomes. With approximately 350 pediatric ICUs in the United States (Randolph, et al. 2004). This represents a participation rate of more than 25% of pediatric ICUs. Mortality risk assessments are made using the first 12 hours of physiologic, demographic, and diagnostic data. Twelve hours of data collection ensure that over 905 of laboratory tests will be captured at least once of all the tests collected in the first 24 hours after admission.(Pollack, et al. 1997) PRISM-APS is a score to measure physiologic status on a more refined 26 Chapter I I Critically Ill Child scale to distinguish changes in physiologic stability that might not be large enough to alter mortality risk (Marcin and Pollack, 2007). The variables that were most predictive of mortality as indicated by the highest PRISM scores were minimum systolic BP, abnormal pupillary reflexes and stupor/coma were retained from PRISM score. Variables in the original PRISM that were not included in PRISM III are diastolic BP, respiratory rate, PaCO2/F1O2, serum bilirubin and calcium concentration. The PRISM III score is an improved version of the PRISM score developed at the Children’s National Medical Center in Washington, DC based on data collected at 32 pediatric intensive care units using 11,165 admissions. The PRISM III score contains 17 variables or signs of cardiovascular, neurologic, or vital functions (systolic blood pressure, heart rate, Glasgow com score, pupillary reflexes, temperature), acid base status (pH, CO2, Pco2 and Pao2), chemistry tests (glucose, potassium, creatinine, blood urea nitrogen), hematology tests (white blood cell count, platelet count, prothrombin time, partial thromboplastin time), and other factors like operative status and some types of diseases (Pollack, et al. 1996). Subscores: (1) cardiovascular and neurologic vital signs: 5 measures (2) acid-base and blood gas: 5 measures (3) chemistry tests: 4 measures (4) hematology tests: 3 measures (with PT and PTT counted as one) Grading variables: 27 Chapter I I Critically Ill Child Use the highest and/or lowest values for scoring. 28 Chapter I I Critically Ill Child Table (5) Cardiovascular and Findings Neurologic Vital Signs Systolic blood pressure neonate AND > 55 mm Hg neonate AND 40 -55 mmHg neonate AND < 40 mm Hg infant AND > 65 mm Hg infant AND 45 -65 mm Hg infant AND < 45 mm Hg child AND > 75 mm Hg child AND 55 -75 mm Hg child AND < 55 mm Hg adolescent AND > 85 mmHg adolescent AND 65 -85 mm Hg adolescent AND < 65 mm Hg Heart rate neonate AND < 215 beats/minute neonate AND 215 - 225 bpm neonate AND > 225 beats/minute infant AND < 215 beats/minute infant AND 215 - 225 bpm infant AND > 225 beats/minute child AND < 185 beats/minute child AND 185 - 205 bpm child AND > 205 beats/minute adolescent AND < 145 beats/minute adolescent AND 145 - 155 bpm adolescent AND > 155 beats/minute Points 0 3 7 0 3 7 0 3 7 0 3 7 temperature < 33°C 33 - 40°C > 40°C 0 3 4 0 3 4 0 3 4 0 3 4 3 0 3 mental status Glasgow coma score >= 8 Glasgow coma score < 8 0 5 pupillary response both reactive 1 reactive AND (1 fixed AND > 3 mm) both fixed AND both > 3 mm 29 0 7 11 Chapter I I Critically Ill Child where: The heart rate should not be monitored during crying or iatrogenic agitation. Pupillary size should not be assessed after iatrogenic dilatation. Body temperature may be rectal, oral, axillary or blood. Mental status should not be scored within 2 hours of sedation, paralysis or anesthesia. If sedation, paralysis or anesthesia is continuous, score based status prior to sedation, paralysis or anesthesia. Table (6) Acid-Base and Blood Gases Acidosis Ph PCO2 total CO2 PaO2 Findings points pH > 7.28 AND total CO2 >= 17 mEq/L (pH 7.0 - 7.28) OR (total CO2 5 - 16.9 mEq/L) pH < 7.0 OR total CO2 < 5 0 2 6 < 7.48 7.48 - 7.55 > 7.55 < 50 mm Hg 50 - 75 mm Hg > 75 mm Hg <= 34 mEq/L > 34 mEq/L 42.0 - 49.9 mm Hg < 42 mm Hg 0 2 3 0 1 3 0 4 3 6 where: PaO2 requires arterial blood. PCO2 can be measured from arterial, venous or specimens. 30 capillary Chapter I I Critically Ill Child Table (7) Chemistry Tests glucose <= 200 mg/dL > 200 mg/dL points 0 2 Potassium <= 6.9 mEq/L > 6.9 mEq/L 0 3 neonate AND <= 0.85 mg/dL neonate AND > 0.85 mg/dL infant AND <= 0.90 mg/dL infant AND > 0.90 mg/dL child AND <= 0.90 mg/dL child AND > 0.90 mg/dL adolescent AND <= 1.30mg/dL adolescent AND > 1.30 mg/dL neonate AND <= 11.9 mg/dL neonate AND > 11.9 mg/dL not neonate AND <= 14.9 mg/dL not neonate AND > 14.9 mg/dL 0 2 0 2 0 2 0 2 0 3 0 3 Creatinine BUN Findings where: • Whole blood measurements for glucose are increased 10% over serum; for potassium 0.4 mEq/L. Table (8) Hematologic Tests white blood cell count platelet count PT and PTT Findings >= 3,000 per μL < 3,000 per μL > 200,000 per μL 100,000 - 200,000 per μL 50,000 - 99,999 per μL < 50,000 per μL neonate AND PT <= 22 seconds AND PTT <= 85 seconds neonate AND (PT > 22 seconds OR neonate and PTT > 85 seconds) not neonate AND PT <= 22 seconds AND PTT <= 57 seconds not neonate AND (PT > 22 seconds OR PTT > 57 seconds) where: 31 Points 0 3 0 2 4 5 0 3 0 3 Chapter I I Critically Ill Child The upper limit of the normal reference ranges for PT and PTT are not given. Other factors to document: (1) nonoperative cardiovascular disease (2) chromosomal anomaly (3) cancer (4) previous ICU admission during current admission (5) pre-ICU CPR during current admission (6) post-operative (not including catheterizations) during past 24 hours (7) acute diabetes with ketoacidosis or other severe complication (8) admission from inpatient unit (do not count if in ICU for < 2 hours or if transferred from surgical recovery room) Cardiovascular and neurologic subscore = (points for systolic pressure) + (points for temperature) + (points for mental status)+ (points for heart rate) + (points for pupillary reflex) Acid-base and blood gas subscore = (points for acidosis) + (points for pH) + (points for PaCO2)+ (points for total CO2) + (points for PaO2) Chemistry subscore = (points for glucose) + (points for potassium) + (points for creatinine)+ (points for blood urea nitrogen) Hematology subscore = (points for WBC count) + (points for platelet count) + (points for PT and PTT testing) Total PRISM III score = (cardiovascular and neurologic subscore) + (acid base and blood gas subscore) + (chemistry subscore)+ (hematology subscore) 32 Chapter I I Critically Ill Child Interpretation: Minimum subscore and total score: 0 Maximum cardiovascular and neurologic subscore: 30 Maximum acid-base and blood gas subscore: 22 Maximum chemistry subscore: 10 Maximum hematology subscore: 12 Maximum total PRISM III score: 74 The higher the total score, the worse the prognosis. A rising score indicates deterioration. If performed during the first 12 hours in the ICU, the score is designated PRISM-12. If performed during the first 24 hours in the ICU, it is designated PRISM-24. Predictive equations: • Predictive equations for prognosis are available for the 12 hour and 24 hour scores PRISM III is a widely accepted and is a standard against which other scores are compared. However there some problems with the use of PRISM III: - A lot of information is needed to calculate it and many units do not calculate it routinely.Worst reading of 12/ 24 hours is used and a lot of deaths occur (in one study over 40%) with in first 24 hours, so the score may be diagnosing death rather predicting it. 33 Chapter I I Critically Ill Child There may be blurring of differences of 2 units as patient in a good unit may recover rapidly and score may be lower and the same patient in a bad unit might have had higher score due to poor management and high mortality of bad unit may be interpreted as due to sicker patients. The time spent in the hospital before coming to ICU could improve the PRISM score and predict lower than actual mortality ( lead time bias). ( Shann, et al.1997) Uses of models of mortality prediction including PRISM III: These models including PRISM III are most applicable to groups of patients (e.g. to assess institutional performance). These models help us to investigate best ways of organizing PICU by comparing different units.(pollock, et al.1996)They also help us to monitor effect of change in practice by observing trends within the unit over a time.(shann, et al.1997) They can also be used for controlling severity of illness for various clinical trials. ( Pollack, et al.1996) They can be applied for resource utilization (rationing intensive care). PRISM III takes 24 hours to complete and can’t be used in regulating admission to PICU. (Teres and Lemeshow.1994) They have been used to assess relation between severity of illness and length of stay or cost. In 1997 Pollack et al, developed a physiology based measure of physiologic instability for use in pediatric patients that has an expanded scale compare with the prism III score and called as the Pediatric 34 Chapter I I Critically Ill Child risk of mortality Ill-acute physiology score (prism Ill-APS) . It has 59 ranges of 21 physiologic variables .It was designed to have a broad severity scale from 0-356, with the higher values indicating higher instability.Data were collected from consecutive admissions to 32 Pediatric ICU’s (11165 admissions, 543 deaths). (Pollack, et al.1997) Most patients who had PRISM Ill-APS score of greater than 80 had mortality greater than 97%. It concluded that the PRISM Ill-APS score is an expanded measure of physiologic instability that has been validated against mortality. Compared with prism III, prism Ill-APS should be more sensitive to small changes in physiologic status even those that may not contribute significantly to mortality risk. Patient assessment for future studies for issues e.g. effectiveness of drugs or for other purposes might be more concerned with the physiologic status. However even this should not be used for quality assessments or calculating risk of individual patients. (Pollack, et al.1997) Multiple Organ Dysfunction score (MODS ) scores: The diagnosis of MODS is supported by the observation in a critically ill patient of the simultaneous dysfunction of at least two organ systems. Up to seven organs have been considered. Respiratory, cardiovascular, neurologic, hematologic, renal, hepatic, and gastrointestinal. The diagnostic criteria of these dysfunctions were defined by Wilkinson, et al. (1986) and Proulx, et al. (1996). These definitions were updated by experts who met in San Antonio in 2002 (Goldstein, et al.2005) and in Boston in 2004 (Brilli and Goldstein. 2005). It must be underlined that the diagnostic accuracy of the variables considered in these definitions have never been scientifically validated, 35 Chapter I I Critically Ill Child but these diagnostic criteria of pediatric MODS are extensively used by practitioners and investigators (Lacroix and Cotting, 2005). In critically ill adults, at least three quantitative scoring systems estimating the severity of cases of MODS have been developed and validated: the Multiple Organ Dysfunction score (Marshall, et al. 1995), the Logistic Organ Dysfunction score (Le Gall, et al. 1995), and the Sepsis Organ Failure Assessment (SOFA) score (Vincent, et al. 1996). In children, the number of dysfunctional organs is frequently used to describe the severity of cases of Pediatric MODS, the rationale being that MODS with more organ dysfunctions should be more severe. This ordinal scale has been named the pediatric MODS score by some physicians. There can be zero to six or seven organ dysfunctions (the gastrointestinal dysfunction is not retained by most experts). MODS score is not without interest. There is indeed a relationship between the number of organ dysfunctions and the mortality rate (Lacroix and Cotting, 2005). Such scoring systems can be used to measure sequentially organ dysfunction among a cohort of patients receiving different therapies for a similar disease process of different cohorts of patients at different hospitals. These scores can then allow comparisons to be made between these cohorts to allow inferences to be made between the effects of different therapies of different hospitals on rates of organ dysfunctions, respectively. Such scores may also allow one to draw inferences about pathophysiology and disease progress (Marcin and Pollack. 2007). The most commonly used score that estimates severity of multiple organ dysfunction syndrome in pediatrics is the Paediatric Logistic Organ Dysfunction score (Marcin and Pollack. 2007). 36 Chapter I I Critically Ill Child PELOD score: Development and validation: Some pediatric intensivists considered that a good tool to estimate the severity of cases of MODS observed in PICUs was needed to describe correctly the clinical course of illnesses observed in critically ill children. Thus, it was thought appropriate to undertake a research program to create and validate a score for MODS in children. That score was named the pediatric Logistic Organ Dysfunction (PELOD) score (Lacroix and Cotting. 2005). It is initially described in 1999 (Leteurtre, et al. 1999). Expert opinion and data analyses on 594 patients admitted to 3 academic pediatric ICUs and contains 12 variables. The paediatric Logistic Organ Dysfunction score was validated on a sample of 1806 patients from Canada, France, and Switzerland as a score associated with mortality (Leteurtre, et al. 2003). It was then validated on a sample of 209 patients from north India (Thukral, et al. 2007). The score can also be used sequentially on a daily basis to measure changes in organ dysfunction with time thus adding to the utility of the score as a measurement of changes in organ function status over time (Thukral, et al. 2007). Variables were only measured if clinical status of patients justified their knowledge. If a variable was not measured, it was assumed to be within the normal range. The most abnormal values were retained for the statistical analysis. All physiologic data accumulated during the preterminal period in dying patients or the last 2 hrs of life were not considered for analysis. Physiologic variables for which values are age dependent were stratified into four age groups: neonates (7 days or 1 month), infants (1-12 months) children (12-14 months), and adolescents 37 Chapter I I Critically Ill Child ( 144 months). Moreover, the weight of each variable to predict death was estimated independently. Four levels of increasing severity were found by cluster analysis, and values of 0,1,10 or 20 points were attributed to these levels. Then, the severity and the weight of each organ dysfunction were integrated into the PELOD score using multivariate logistic regression. Six systems contributed to the PELOD score, and 12 variables were retained . (Lacroix and Cotting. 2005). A daily PELOD score was also studied during the first 5 days in the PICU. When a variable was not measured, it was assumed to be either identical to the last measurement (if the physician considered that the value of the variable did not change) or normal (if the physician considered that the value of the variable was normal). The most abnormal values during the day under evaluation were retained for analysis. The predictive value of the daily PELOD score was quite good (Lacroix and Cotting. 2005). 38 Chapter I I Critically Ill Child Table (9): An example of a composite score, the Pediatric Logistic Organ Dysfunction (PELOD) score Points by level of severity for each system Organ systems and variables 0 Respiratory system Pao2 in mmHg/FIO2 ratio (kPa/%)a > 70 (9.3) and b Paco2 (mmHg or kPa) ≤ 90 (11.7) and c Mechanical ventilation No ventilation Cardiovascular systemd Heart rate (beats/min) < 12 yrs ≥ 12 yrs Systolic blood pressure, mmHg < 1 month 1 month – 1 yr 1-12 yrs ≥ 12 yrs Neurologic system Glasgow coma scoree Pupillary reactionf Hepatic system ALT or SGOT, IU/L Prothrombin time (% of standard), INR Renal system: creatinine, µmol/L (mg/dL): < 7 days 7 days – 1yr 1-12 yrs ≥ 12yrs Hematologic system White blood cell count (109/L) Platelet count (109/L) 1 10 20 ------Ventilation ≤ 70 (9.3) ---or ---> 90 (11.7) ------- ≤ 195 ≤ 150 and > 65 > 75 > 85 > 95 ------- ------- ------------- > 195 > 150 or 35-65 35-75 45-85 55-95 12-15 and Both reactive 7-11 ---- 4-6 or Both fixed 3 ---- < 950 and > 60 or < 1.4 ≥ 950 or ≤60 or <1.4 ------- ------- < 140 (1.59) < 55 (0.62) < 100 (1.13) < 140 (1.59) ------------- ≥ 140 (1.59) ≥ 55 (0.62) ≥ 100 (1.13) ≥ 140 (1.59) ------------- > 4.5 and ≥ 35 1.5 – 4.4 or < 35 < 1.5 ---- ------- ALT, alanine aminotransferase; SGOT, serum glutamic-oxaloacetic transaminase; INR, international normalized ratio. (Leteurtre, et al. 2003) 39 < 35 < 35 < 45 < 55 Chapter I I Critically Ill Child Use arterial measurement only for Pao2; bPaco2 may be measured from arterial, capillary or venous samples; cthe use of the mask ventilation is not considered as mechanical ventilation; ddo not assess heart rate and systolic blood pressure during crying or iatrogenic agitation; euse the lowest value (if the patient is sedated, record the estimated Glasgow coma score before sedation; assess only patients with known or suspected acute central nervous system disease); fpupillary reactions: nonreactive pupils must be > 3mm and do not assess after iatrogenic papillary dilatation (Leteurtre, et al. 2003). All variables must be measured at least one. If they are not measured, they are assumed to be within the normal range for scoring purposes. If they are measured more than once in 24 hrs, the most severe value is used in calculating the score. To calculate the PELOD score, each organ dysfunction receives points for the single variable associated with the most points for example, if the worst heart rate of the day is 200 beats/min (10 PELOD points) and the systolic blood pressure remains at 30mmHg (20 PELOD points), then 20 PELOD points are assigned. The points are not added to obtain 30 PELOD points for the organ dysfunction (the maximum number of points for an organ is 20, and the maximum PELOD score is 71). For the daily PELOD, when a variable is not measured, it is assumed to be either identical to the last measurement (if the physician considers that the value of the variable did not change) or normal if the physician considers that the value of the variable is normal). The equation to estimate probability of death is: probability death = 1 ÷ (1 + exp [7.64 – 0.30 x PELOD]) (Leteurtre, et al. 2003). 40 Chapter I I Critically Ill Child Limitations of Scoring Systems: There are limitations to acuity scoring systems in assessing quality improvement. For example, if a hospital is using a mortality scoring system, it may be able to demonstrate that its pediatric ICU has good outcomes (effective care), but its pediatric ICU care may not be patient centered or equitable. Furthermore, many of the acuity scoring systems do not account for factors known to affect outcomes. Measures of patient length of stay do not account for patient and/or physician preference, patients' social situations, and bed availability. To this end, a pediatric ICU may be inappropriately identified as "not efficient" (long length of stay) if other unmeasured factors are not considered; therefore, although acuity scoring systems are important research tools and improve the accuracy of benchmarking, there are limitations to the systems' ability to themselves achieve quality improvement (Marcin and Pollack. 2007). Limitations of the PELOD score: First, treatment bias may be a problem because the PELOD score includes data that can be modulated by the care provided during PICU stay. Thus, the PELOD score cannot differentiate therapy and severity of disease, but this bias is unavoidable unless one is ready to give no treatment to critically ill children. Second, the PELOD score is not validated to predict post-ICU morbidity and mortality and mortality; further studies are required before the PELOD score can be used as a surrogate outcome of post ICU morbidity and mortality (Lacroix and Cotting. 2005). Third, the points given to each criterion of the score (0,1,10 or 20) can not add up to a sum with predicted risk of mortality between 5% and 15% (Garncia, et al. 2006). 41 Chapter I I Critically Ill Child PELOD score and sepsis: Recently, further evaluation of the PELOD score was done in the context of sepsis. Four "septic states" have been defined in critically ill patients: systemic inflammatory response syndrome (SIRS), sepsis, severe sepsis, and septic shock (Hayden. 1994). One can ask if knowing both the PELOD score and the worst septic state observed during PICU stay improves our capacity to predict the risk of death. To address this question, we tested the hypothesis that the risk of death increases with the PELOD score and with the severity of the worst septic state and that this increase is higher if one takes into account both the PELOD score and the worst septic state (Leclerc, et al. 2005). Septic states were prospectively recorded during the development study of the PELOD score. The hazard ratios of deaths were 7.43 for SIRS and sepsis (we combined hazard rations of SIRS and sepsis because they were similar), 27.40 for sever sepsis, and 61.40 for septic shock. If we took into account the hazard ratio of the PELOD score (1.096 PELOD point ), the adjusted hazard ratios became 9.04 for SIRS/sepsis, 18.8 for severe sepsis, and 32.6 for septic shock. The combined hazard ratio (HR) of death was calculated with the following equation: (HR PELOD point x HRseptic state). For example, a patient in severe sepsis presents with a PELOD score of 24. Its combined hazard ratio would be 169.6 (1.096 24 x 18.8). This is significantly higher than the hazard ratio of 27.40 that we got for severe sepsis alone, without any reference to the PELOD score. Therefore, we can conclude that there is some accrual in the information collected if one takes into account both the PELOD score and the worst 42 Chapter I I Critically Ill Child septic state of a group of critically ill children (Lacroix and Cotting, 2005). PELOD score as surrogate outcome: Presently, it is the incidence rate of death in ICUs that is considered the standard outcome measure for clinical trials run in critically ill adults. The main justification for such a choice is that death is hard data and that it is quite difficult to bias such outcome. The feasibility of a randomized clinical trial run in PICUs may be a problem if one chooses mortality rate as the primary outcome measure because death is a rare event in PICUs: death rate is between 20% and 40% in adult ICUs, whereas it is 4% to 6% in PICUs (Leteurtre, et al. 2003). Such a low rate of death should increase significantly the sample size required to complete pediatric trials. Indeed, the number of patients required may be so huge that it could be impossible to collect them. The incidence rate of MODS in PICUs is about 18% to 25% (Proulx, et al. 1996); this is significantly higher than the death rate. Thus, it makes sense to ask the question: can the PELOD score be a good surrogate outcome of death for clinical studies run in PICUs? (Lacroix and Cotting. 2005). By definition, a surrogate outcome is an outcome measure used instead of the gold standard. Its relationship to the gold standard must be good; this is the case for MODS and PELOD scores because MODS is present in almost all dying critically ill children. There must be a meaningful cause effect relationship between the surrogate outcome and the gold standard. MODS is the cause of death of most critically ill children who die in the PICU (Proulx, et al. 1996). 43 Chapter I I Critically Ill Child The best way to check if there is really a cause effect relationship between MODS or PELOD score and death would be to test the hypothesis that improving MODS or PELOD score decreased death rate; this remains to be done. The prevalence or incidence rate of the surrogate outcome should be significantly greater than it is for the gold standard; the incidence rate of MODS is at least three to four times the death rate in PICUs. The scientific value of surrogate outcome must be well estimated; this is the case of the PELOD score (Lacroix and Cotting. 2005). Surrogate outcomes are frequently misused (Fleming and DeMets. 1996). For example, it might look appropriate to use as a surrogate outcome the blood level of protein C in a randomized clinical trial on the efficacy of activated protein C. This is inappropriate because the link between the blood level and clinically relevant outcome like survival or severity of MODS is not well established. Actually, the blood level of activated protein C would be a good measure of the compliance to the research protocol, but it can not be considered as a reliable surrogate outcome to death. On the other hand, a surrogate outcome can be better than the gold standard in some circumstances. As stated by Zygun, et al. (2005), "death cannot predict the neurologic outcome of severe brain injury, while there are data suggesting that a MOD [Multiple Organ Dysfunction] score can be in critically ill adults with severe brain injury". That there is such relationship between the PELOD score and neurologic outcome or the quality of life of critically ill children who survive a PICU stay remains to be determined. On the other hand, it is clear that using a severity scale like the PELOD score as a surrogate outcome of death should have indeed a great impact on the sample size of 44 Chapter I I Critically Ill Child clinical trials undertaken in PICUs. Thus, it makes sense to use the PELOD score as a surrogate outcome in some randomized clinical trial run in PICUs (Lacroix and Cotting. 2005). The PELOD and the daily PELOD scores seem to be valid measures of the severity of illness in the PICU. The delta PELOD score ( PELOD) equals the worst PELOD after randomization minus daily PELOD score at any of randomization) may also be useful to chart the course critical illness (Marshall. 1999). P-MODs: Dr. Graciano and colleagues (Graciano, et al. 2005), report the development and validation an additional pediatric organ dysfunction score, the pediatric Multiple organ Dysfunction Score (P-MODS). This score was developed using electronic medical records from a large database of patients at a single institution over a 3.5-yr time period (n=6456). Twenty- two candidate variables were analyzed from five organ systems (cardiovascular, respiratory, hepatic, hematologic, and renal). Although neurologic, dysfunction has been recognized to be a feature of multiple organ dysfunction syndrome, the authors omitted the variable from their analysis due to concerns about confounding by the use of sedative or paralytic agents in their patients. The severity of organ dysfunction was graded on a scale from 0 to 4 for the variables representing each organ system. The variables used for the final score included the most abnormal values recorded for arterial lactate, PaO2/FIO2 ratio, total bilirubin, fibrinogen, and blood urea nitrogen during the PICU stay. When used to 45 Chapter I I Critically Ill Child predict mortality in the validation set, the calibration across mortality risk groups was adequate (Dominguez and Huh. 2005). The P-MODS appears to have face validity in that the organ systems contained in the score have been recognized to represent organ dysfunction and the score is scaled such that worsening values in the organ system domains represent increasing organ dysfunction. The score appears to have content validity in that the variables in each domain of the score represent organ dysfunction in the relevant organ system. Finally, the authors have sought to demonstrate validity by using the score to predict mortality (since multiple organ system dysfunction is often associated with mortality in ICU patients and there is no goldstandard criterion measure of organ dysfunction) (Dominguez and Huh. 2005). Score Lactic acid, mmol/L Pao2/FIO2 Bilirubin a mol/L mg/dL Fibrinogen b mol/L mg/dL BUNc mol/L mg/dL 0 1 150 1 1-2 150-100 2 2-5 100-75 3 5-7.5 75-50 8.5 0.5 8.5-34.2 0.5-2.0 34.285.5 2.0-5.0 85.5-171 171 5.0-10.0 10 4.40 4.40-3.70 150 150-125 7.10-14.3 7.10 20-40 20 3.70-3.0 125-100 14.321.4 40-60 4 7.5 50 3.0-2.20 100-75 2.20 75 21.428.5 60-80 28.5 80 Table (10): Pediatric Multiple Organ Dysfunction Score (P-MODS) (Graciano, et al.2005). The pediatric cerebral Performance category (PCPC) and the Pediatric Overall Performance Category (POPC) are modified functional outcome scores developed from the Glasgow Outcome Scales to assess 46 Chapter I I Critically Ill Child the short- term functional outcome of pediatric intensive care (Fiser, et al. 2000). The scales can be used to assess changes in cognitive (PCPC) and physical disabilities (POPC). These scales were internally validated on 1469 subjects admitted to a single institution's ICU from 1989 to 1990. The potential utility of these measures lies in their ability to compare the effects of different process of care and their resulting effects on functional outcomes. Recently, PCPC and POPC were shown to correlate with a higher severity of illness (higher PRISM mortality predictions) and longer length of stays on a sample of 11, 104 patients from 16 Pediatric ICUs (Fiser, et al. 2000) . More importantly, the scores correlate with the Stanford- Binet intelligence Scale IV (measured at hospital discharge), the Bayley Scales of infant Development II (measured at hospital discharge), and the Vineleand Adaptive Behavior Scales (measured at 1 month and 6 months after discharge) (Fiser, et al. 2000). However, in general, functional outcome scores are difficult to use as a measure of quality of care because they do not discriminate between the effects of the disease and its treatment (Marcin and Pollack. 2007). Other Scores Used in the ICU: In addition to mortality, functional outcome, and organ dysfunction scores, there are more diagnostic specific scores that have been used in the pediatric ICU for research and quality assessments. Most scores have been developed on adult patients. General severity of illness scores include the Acute Physiology and Chronic Health Evaluation (APACHE 47 III and APACHE IV) Chapter I I Critically Ill Child (Zimmernem, et al. 2006). Score and the Mortality Prediction Model (MPM III) (lemeshow, et al. 1993). Adult trauma scores include the Revised Trauma score (RTS) (Eichelberger, et al. 1993). The injury severity Score (ISS) (Baker and O'Neill. 1976). And the Trauma score and injury severity score (TRISS), which is the combination of the physiologic RTS and anatomic ISS (Champion, et al. 1990). A severity characterization of Trauma (ASCOT). (Champion, et al.1990). And the international Classification of Disease Injury Severity Score (ICISS) (Osler, et al. 2002). Although there exists a pediatric Trauma score, it is more complicated and only mildly improved performance compared with the adult RTS (Kaufmann, et al. 1990). There is also a plethora of scoring systems developed for specific pediatric diagnoses that have a variety of applications in the pediatric ICU. These include, but are not limited to, CROUP scores (Leipzig, et al. 1979), asthma scores (Carroll, et al. 2005), pediatric respiratory failure (Timmons, et al. 1995), and meningococcemia (Flaegstad, et al. 1995). 48 Chapter I I Critically Ill Child CHAPTER III Lactate Many variables measured in critically ill patients have been used to estimate severity of disease, prognosticate morbidity and mortality, evaluate costs of treatment, and finally indicate specific treatment and monitor the adequacy of treatment and its timing. Although in our mind strongly linked to tissue hypoxia, lactate levels follow many more metabolic processes not related to tissue hypoxia and, therefore, subject to many disturbances found in various clinical situations. (Bakker, et al. 2013). History of lactate The first description of lactate originates from 1780 when Karl Scheele found lactate in sour milk. It took almost 70 years before the German physician-chemist Joseph Scherer demonstrated the presence of lactate in human blood. Where Scherer analysed blood drawn from a young woman who had just died from what we now call septic shock, it was Carl Folwarczny in 1858 who demonstrated the presence of lactate in the blood . (Kompanje, et al. 2007). Metabolism of lactate Lactate is a crucial metabolite in the two main energy (ATP)producing processes that power life: glycolysis and oxidative phosphorylation (OxPhos). Glycolysis, process that occurred very early in evolution (approximately 3 billion years ago), converts glucose into two 49 Chapter I I Critically Ill Child molecules of pyruvate with the concomitant generation of 2 ATP. When atmospheric oxygen levels rose (1 billion years ago), mitochondria developed to generate far more energy from glucose (36 ATP molecules for 1 glucose molecule), although following a much more complicated process (Krebs cycle and OxPhos). Glycolysis and OxPhos steadily metabolize glucose when conditions are stable (Figure 1). 50 Chapter I I Critically Ill Child Figure (1) Lactate at the cellular level. Usually not oxygen shortage per se, but acute energy requirements is a key determinant of lactate levels. a-Under stable conditions, glucose is converted to pyruvate, generating 2 ATP, and pyruvate is then subsequently fully oxidized to CO2 generating ~36 ATP. b-Under stress, glycolysis can increase by a factor 100 to 1,000, provided that glucose is present and pyruvate is converted to lactate. Irrespective of optimal mitochondrial function and oxygenation, such a rate of pyruvate production will saturate the mitochondrial tricarboxylic acid cycle and oxidative phosphorylation (OxPhos). c-During recovery, lactate is converted back to pyruvate and fully oxidized. Pyruvate is the molecule that links these two reactions. Because the rate of glycolysis can increase two to three orders of a magnitude faster than OxPhos, glycolysis can briefly provide far more ATP. Excess pyruvate will rapidly accumulate and is diverted to lactate in order for glycolysis to proceed (Figure 1b). During recovery (Figure 1c) lactate is converted into pyruvate. In both directions this is catalyzed by the enzyme lactate dehydrogenase (LDH). Thus, when rapidly large amounts of energy are required, such as under circumstances of cellular stress, lactate serves as a critical buffer that allows glycolysis to accelerate. Also, at the level of the organism 51 Chapter I I Critically Ill Child (Figure 2), lactate has a similar role as an intermediate fuel that is readily exchanged between various tissues, facilitated bya family of membranebound mono-carboxylate transporters (MCT). Over the past two decades, lactate shuttles between astrocytes, neurons, striated muscle, cardiac muscle, as well as the liver and kidneys have been demonstrated (Leverve. 1999). Figure (2) Lactate at the physiological level. The flexible use of glucose and lactate as fuels on the cellular level is mirrored at the organism level. All living tissues can consume glucose. From the glucose/lactate point of view, three sorts of tissues/cells exist: 1) cells that must produce lactate because they lack mitochondria, e.g., red blood cells; 2) tissues or cells that either produce or consume lactate depending on circumstances, i.e., all mitochondria-containing cells; 52 Chapter I I Critically Ill Child 3) tissues that can perform gluconeogenesis and export glucose that is resynthesized from lactate. The liver and the kidneys can only perform gluconeogenesis and export glucose. Only this so-called Cori cycle carries an energy penalty, whereas the other shuttles do not lead to “waste” of energy. It should be noted that whereas the Cori cycle involves energy consuming hepatic or renal gluconeogenesis to convert lactate into glucose, direct interorgan exchange of lactate itself does not carry an energy penalty and even exogenous lactate may serve as a suitable substrate . (Leverve.1999). Lactate and acidosis The metabolism of glucose during tissue hypoxia results in production of lactate, ATP, and water (Zilva.1978). The production of H+ originates from the hydrolysis of ATP to ADP. In the presence of oxygen and provided that OxPhos can keep up with glycolysis, these H+ ions can be used together with lactate in the OxPhos in the mitochondria and acidosis is thus less likely to develop. Stewart challenged the classic Henderson-Hasselbalch approach where the acidosis in his approach is the result of the dissociation of water to maintain acid–base equilibrium by the addition of the strong ion lactateto the circulation (Stewart.1983). There is however not a strong relationship between arterial pH and lactate levels. Even at higher lactate levels, only a weak, although significant, correlation exists. When evaluating the significance for patient outcome and the origin of the metabolic acidosis, it is probably more realistic to use the term: lactate associated metabolic acidosis, a 53 Chapter I I Critically Ill Child combination that also carries the highest risk of mortality . (Gunnerson ,et al.2006). Lactate and tissue hypoxia Many experimental studies have confirmed the relationship between tissue hypoxia and the generation of lactate by reducing the components of systemic oxygen delivery (haemoglobin level, oxygen saturation, and cardiac output) until the extraction of oxygen can no longer maintain oxygen availability to the cells to meet their demands (Zhang and Vincent.1993). At a critical level of oxygen delivery, oxygen consumption becomes limited by oxygen delivery, and this coincides with a sharp increase in lactate levels. Also, clinical data indicate the relationship between the presence of this supply dependent state of oxygen consumption and increased lactate levels similar to animal studies (Bakker and Vincent. 1991). In a landmark study, Ronco et al. showed that this phenomenon also was present in patients when oxygen delivery decreased until circulatory arrest during end-of-life care (Ronco, et al.1993). This has been confirmed in an experimental study of cardiac tamponade by Zhang et al. which showed that resolution of the supplydependent state of oxygen consumption by resolving the tamponade was associated with an increase in oxygen consumption to baseline levels and normalisation of lactate levels.(Zhang, et al.1993) Because the exchange of oxygen takes place in the microcirculation, alterations in microcirculatory perfusion also can result in limited oxygen availability. Particularly in sepsis, microcirculatory derangement or 54 Chapter I I Critically Ill Child shunting may lead to insufficient oxygen that is delivered to the cell, thereby increasing lactate levels (Zhang, et al. 1993). This is indirectly illustrated by the observation that improving capillary perfusion has been correlated to a reduction in lactate levels in patients with septic shock, independent of changes in systemic haemodynamic variables (De Backer, et al.2006). Given the near equilibrium reaction between lactate and pyruvate and its connection with the cellular oxidoreduction state, the lactate-topyruvate ratio (L:P) provides additional information as L:P is coupled to the cytoplasmic NADH:NAD+ (Levy, et al.1997). However, it must be noted that in contrast to lactate, pyruvate is far from trivial to reliably measure in clinical practice and therefore its use is limited in critically ill patients (Weil and Tang.2009). Lactate production in aerobic metabolism Aerobic glucose metabolism to lactate may be a preferred way to rapidly produce significant energy amounts. Therefore, stimulating increased aerobic glucose metabolism has been shown to increase lactate levels in the absence of tissue hypoxia. Most notably, the administration of epinephrine has long been shown to result in a dose-dependent increase in lactate levels (Griffith, et al.1939). Also, stimulation of the phosphofructokinase enzyme by alkalosis (respiratory and metabolic) has been shown to increase lactate levels (Zborowska-Sluis AND Dossetor.1967). Clinically often used therapeutic interventions also have been shown to increase aerobic lactate production (McMahon, et al.1988). 55 Chapter I I Critically Ill Child The aerobic production of lactate as an energy source is related to the very high lactate levels found in patients with lymphoma, a phenomenon referred to as the Warburg effect .When treating the lymphoma, both lactate levels and LDH respond to chemotherapy . (Warburg .1956). Recently, it has been shown that the activity of the Na+/K+ pump system, which requires significant amounts of ATP for its function, is related to increased lactate levels in both experimental and clinical conditions (Levy, et al.2005). unrelated to the presence of tissue hypoxia. Such enhanced glycolysis can be triggered by cytokine-mediated uptake of glucose (Taylor, et al.1992) or catecholamine-stimulated increased NaK-pump activity (McCarter, et al.2000) supported by both experimental and clinical studies (Haji-Michael, et al.1999). Still unresolved discussion, has focused on the presence of mitochondrial dysfunction in critically ill that could limit pyruvate metabolism (and thus increase lactate levels) in the absence of limited oxygen availability (Brealey, et al.2002). Infusion of Ringer’s lactate does not hamper the accuracy of lactate measurement (Didwania, et al.1997). Mechanism of hyperlactemia in sepsis and septic shock Traditionally, lactic acidosis in sepsis is attributed to anaerobic glycolysis due to inadequate oxygen delivery. However, it has become clear that the mechanism of hyperlactemia in sepsis is multifactorial and due to factors beyond hypoxic tissue injury alone (Bolton .2007) . 56 Chapter I I Critically Ill Child James et al. proposed that lactic acidosis refractory to standard resuscitation is frequently due to increased aerobic glycolysis in skeletal muscle secondary to epinephrine-stimulated Na+, K+-ATPase activity and not to anaerobic glycolysis from hypoperfusion , and warned that continued attempts at resuscitation targeting lactate levels could lead to unnecessary blood transfusion and use of inotropic agents (James, et al.1999). Furthermore, Gutierrez et al. emphasized that the etiology of prolonged lactic acidosis in sepsis is often multifactorial, making it an unreliable marker of oxygen debt and inadequate resuscitation (Gutierrez and Wulf.1996 ). Interestingly, it has been demonstrated that septic patients with hyperlactemia after 24 h of resuscitation had similar lactate production but lower lactate clearance than septic patients with normal blood lactate (Levraut, et al.1998). This finding raises doubts about the reliability of hyperlactemia as an indicator of the intensity of anaerobic metabolism in septic patients, suggesting instead that persistence of hyperlactemia during sepsis may be more representative of inadequate lactate clearance as opposed to pure lactate overproduction (Levraut, et al.1998). These findings questioned the notion that hyperlactemia in sepsis and septic shock is solely the result of hypoperfusion. Further support for the role of inadequate lactate clearance stems from Gibot et al. who used an endotoxemia model to demonstrate that lactate levels in sepsis can be elevated despite adequate systemic perfusion,blood pressure, and oxygen delivery. (Gibot.2012) 57 Chapter I I Critically Ill Child Similarly, in a pig model, Ven Genderen et al. showed that septic shock behaves differently from obstructive/circulatory shock that even when cardiac output and other systemic parameters are optimized, there continues to be regional microvascular oxygen mismatch in septic shock, as compared to obstructive/circulatory shock. The authors postulate that this regional microvascular oxygen mismatch leads to elevations in lactate that may be partially unresponsive to traditional resuscitative interventions that only target systemic parameters (Ven Genderen, et al.2014) . Likewise, Hernandez et al. showed lactate in septic shock to have a biphasic response, with an initial rapid improvement followed by a much slower normalization many hours later, and hypothesized that this slow and delayed response might be attributed to the microvascular oxygen mismatch as described by Ven Genderen et al., making further systemic resuscitation through traditional sepsis resuscitation bundles ineffective and perhaps detrimental (Hernandez, et al.2014). In fact, Marik attribute hyperlactemia in the later phase of sepsis to increased aerobic glycolysis due to a stress response and also label any attempts at using traditional sepsis therapies to normalize lactate during this later stage as flawed and potentially harmful (Marik.2013). With these findings, Rivers et al. warn against using only lactate clearance as a marker of sepsis recovery and state that lactate clearance, central venous oxygen saturation (ScvO2), and other markers are complementary and not mutually exclusive end points. (Rivers, et al. 2011) Thus, new insight regarding the mechanism for hyperlactemia in sepsis following adequate initial resuscitation and infection control 58 Chapter I I Critically Ill Child demonstrates that tissue hypoxia is not the sole etiology of hyperlactemia during late sepsis.Nevertheless, data supporting the clinical utility of lactate as a marker of early sepsis recovery is robust while the role of continued lactate monitoring beyond the initial resuscitation period into the stage of late sepsis and its potential to guide treatment during this later stage remains uncertain . (Bakker.2015) Lactate as a prognosticator in early sepsis management In their 2004 and 2008 sepsis guidelines, Dellinger et al. recommended measurement of lactate on initial presentation,with an elevated value signifying tissue hypoperfusion and necessitating aggressive resuscitation (Dellinger, et al .2004) Although their guidelines suggested measuring lactate only upon presentation, many clinicians and researchers have attempted to capitalize on the test’s theoretical diagnostic and predictive value by including additional measurements during the resuscitation process.(Nguyen, et al. 2010). For example, it was shown that lactate clearance greater than 10 % from initial measurement during the first 2 to 6 h of resuscitation predicted survival from septic shock and that protocols targeting lactate clearance of at least 10 % produced similar short-term survival rates to protocols using Central venous oxygen saturation (ScvO2) monitoring .Moreover, it was demonstrated that for every 10 % increase in lactate clearance, there was a corresponding 11 % decrease in inhospital mortality. (Nguyen, et al.2004) Similarly, septic patients with lactate clearance of greater than 20 % during the initial 8 h of resuscitation had a 22 % decline in the relative 59 Chapter I I Critically Ill Child risk of mortality, compared with patients having lactate clearances of less than 20 % (Jansen, et al.2010) . Since these initial studies are evaluating lactate as a marker of recovery in sepsis and septic shock, further research has evaluated the role of lactate monitoring during the early resuscitative period. For example, Puskarich et al. studied resuscitation during the initial 6 h of treatment and demonstrated that achieving an ScvO2 goal ≥70 % without obtaining a lactate clearance goal ≥10 % was associated with higher mortality than reaching the lactate clearance goal without the ScvO2 goal (Puskarich, et al.2012). Furthermore, these same authors showed that early lactate normalization (within 6 h) was a predictor of survival in patients being treated for sepsis and septic shock. ( Puskarich, et al.2013) Nguyen et al. investigated the addition of lactate clearance within the first 12 h of resuscitation to the severe sepsis resuscitation bundle and showed that including lactate clearance leads to an almost twofold increase in relative risk reduction of death. (Nguyen, et al. 2011) In response to the convincing literature supporting the utility of lactate clearance in early sepsis, the newest surviving sepsis guidelines for early goal-directed therapy (EGDT) now includes lactate clearance during the first 6 h of resuscitation as a goal of early resuscitation . (Jansen, et al.2010) Thus, research regarding lactate monitoring as a marker of recovery in severe sepsis and septic shock has proven fruitful but has primarily focused on the early resuscitation period . (Arnold, et al. 2009) 60 Chapter I I Critically Ill Child Lactate as a late prognosticator in sepsis management Literature evaluating the clinical and predictive value of lactate measurements beyond the initial 6-h resuscitation period in the medical management of sepsis is significantly less robust (Bakker, et al. 1996). In a study of 137 surgical intensive care unit (SICU) patients, Husain et al. showed elevated initial and 24-h lactate levels to be significant predictors of mortality, with mortality ranging from 10 to 67 % depending on whether lactate levels normalized or failed to normalize at 24 h, respectively (Husain, et al. 2003). In another study investigating SICU patients, Bakker et al. showed that lactate clearance measured 24 h after admission was a significant predictor of in-hospital mortality and that the duration of persistent lactic acidosis was more predictive of mortality than the initial lactate value (Bakker, et al. 1996). Similarly, Friedman et al. showed in a 35-patient study that survivors of severe sepsis admitted to the medical intensive care unit (MICU) or SICU had significantly lower lactate values at 24 h of resuscitation than nonsurvivors (Friedman, et al. 1995) Finally, Manikis et al. followed lactate measurements every 8 h for >72 h in 129 trauma patients and demonstrated serial lactate measurements and the duration of hyperlactemia to be reliable indicators of morbidity and mortality following trauma (Manikis, et al. 1995) In a 94-patient SICU sepsis study, Marty et al. measured lactate at time0 (T), T6, T12, and T24 and showed that the best predictor of death was the T24 clearance. These authors concluded that during the first 24 h in the ICU, hyperlactemia, even after the “golden hours,” is associated 61 Chapter I I Critically Ill Child with increased mortality, and lactate clearance-directed therapy should be considered for the first 24 h of treatment (Marty, et al. 2013) Similarly, in an 81-patient study, Herwanto et al. investigated the role of 6-, 12-, and 24-h lactate clearance in patients with sepsis and septic shock and found only the 24-h lactate clearance measurements to be associated with mortality (Herwanto, et al. 2014) . 62