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Risk assessment for VTE Dr Roopen Arya King’s College Hospital V Prevention of VTE in hospitalised patients: Why the need for risk assessment for VTE? Identifying at-risk patient Counselling at-risk patient Prescribe thromboprophylaxis Risk Assessment • The highest ranking safety practice was the appropriate use of prophylaxis to prevent VTE in patients at risk. AHRQ “Making Health Safer: A Critical Analysis of Patient Safety Practices” 2001 • We recommend that every hospital develop a formal strategy that addresses the prevention of thromboembolic complications. This should generally be in the form of a written thromboprophylaxis policy especially for high risk groups. ACCP guidelines “ Prevention of VTE” 2004 Risk assessment models • Group-specific (‘opt-out’) • Individualized (‘opt-in’) – Risk stratification – Risk scores • Linked to ACTION of thromboprophylaxis VTE risk assessment in medical patients VTE Risk Assessment for Adult Surgical Patients Patient name: Hospital no: Please fill in this form, sign and file in notes Prescribe appropriate prophylaxis on drug chart DOB: Risk Category Surgery HIGH MODERATE LOW Tick Recommended Prophylaxis Hip fracture, hip or knee arthroplasty Major trauma /spinal cord injury Major surgery with additional risk factors (ARF) Major surgery upto age 59 years with no ARF Minor surgery with ARF Enoxaparin 40 mg daily + TED stockings +/Sequential compression device Minor surgery with no ARF Early mobilisation Tick Enoxaparin 40 mg daily + TED stockings Additional Risk Factors (ARF) Age >60 years Personal or family history of VTE Thrombophilia Active cancer or treatment Acute exacerbation of heart failure Recent MI or ischaemic stroke Acute on chronic respiratory disease Sepsis Contraindications Enoxaparin Creatinine >175 mol/l (CrCl< 30ml/min) use unfractionated heparin 5000 u BD Active bleeding Thrombocytopenia (platelet count<50) Known bleeding disorder Previous HIT or allergy to enoxaparin On therapeutic anticoagulation Tick Additional Risk Factors (ARF) Tick Acute inflammatory disorder Pregnancy and the post partum period Hormone therapy e.g. HRT/COCP Obesity (BMI >30kg/m2) Immobility Travel>3 hrs within 4 weeks of surgery Nephrotic syndrome Varicose veins Tick Contraindications Tick Mechanical measures (TEDs / SCD) SCD contraindicated if acute DVT present Severe peripheral vascular disease Severe dermatitis Leg oedema Leg deformity Peripheral neuropathy Recent skin graft Doctor’s name Doctor’s signature Timing: Duration: Date Thromboprophylaxis should start 6 hours post op and at 6pm daily thereafter. Epidural/spinal analgesia - placement or removal of catheter should be delayed for 12 hrs after administration of enoxaparin. Enoxaparin should not be given sooner than 4 hrs after catheter removal. At least 10 days prophylaxis is recommended for all high risk orthopaedic patients. Extended prophylaxis (28 days) is recommended for elective hip replacement and hip fracture patients. Extended prophylaxis is recommended for selected high-risk general surgery patients e.g. major cancer surgery. 2 High BMI (>30 mg/m ): use enoxaparin 40mg twice daily (or enoxaparin 60 mg bd if body weight >150kg) Sequential compression device (SCD): Consider in high-risk patients & those unable to receive LMWH due to high bleeding risk. VTE risk assessment in surgical patients Risk scoring for VTE: Kucher risk score Clinical Feature Score Active cancer (treatment ongoing or within 6 months or palliative) 3 Personal history of VTE 3 Thrombophilia 3 Recent major surgery 2 Advanced age (≥ 75 years) 1 Obesity (BMI >29) 1 Bed rest (medical inpatient/immobilized >3d in last 4 wks/paralysis) 1 Hormonal therapy (OCP/HRT) 1 Kucher, N. et al. N Engl J Med 2005;352:969-977 Primary end point: Freedom from VTE 100 Freedom from DVT or PE (%) 98 Intervention 96 94 P < 0.001 92 Control 90 Number at risk Intervention Control 0 30 60 90 1255 1251 977 976 900 893 853 839 Kucher, N. et al. N Engl J Med 2005;352:969-977 Time (days) Derivation and Validation of a Prediction Tool for Venous Thromboembolism (VTE): a VERITY Registry Study Study objective • to develop a multiple regression model for VTE risk, based on Kucher, and validate its performance • to employ the extensive VTE risk factor data recorded in a UK VTE treatment registry (VERITY) – VERITY enrolls patients presenting to hospital with suspected VTE UK multi-centre observational VTE registry of clinical management practices & patient outcomes Features of VERITY • National registry – outpatient VTE treatment • Full spectrum of VTE – DVT and PE • Records information on patients presenting with suspected and confirmed VTE • Expanded data on demographics, presentation, management & outcomes • Extensive risk factor data Statistical plan – model development • As a preliminary to a formal multiple regression analysis, the effects of the 8 Kucher risk factors on VTE risk were investigated individually by univariate analysis • Initial findings: univariate analysis (n=5928; 32.4% with diagnosis of VTE) suggested VTE risk was not accounted for by the 8 Kucher risk factors • An additional 3 risk factors were added (leg paralysis, smoking, IV drug use) and also patient sex, and the model was created with these 12 factors Statistical plan – model development • The multiple logistic regression model was developed using backward stepwise regression • The open source statistical package ‘R’ was employed to conduct the regression analysis Statistical plan – model performance • We tested the accuracy of the Kucher score and the new logistic regression model to classify patients by receiver operating characteristic (ROC) curve analysis, plotted as 1-specificity versus sensitivity for VTE diagnosis – The c statistic (area under the curve), representing the ability of the model to correctly classify patients, was estimated using the nonparametric method of Hanley and McNeil • We validated the model using a risk factor database of patients enrolled at an outpatient DVT clinic at King’s College Hospital Statistical plan – model performance • We interpreted the predicted probabilities from the logistic regression model as a risk score – each tenth of predicted risk was scored as 1 • i.e. lower tenth of risk = risk score of 1; upper tenth of risk = risk score of 10 • We assessed the degree of agreement between the observed rate and the predicted rate of VTE by plotting the risk score vs. observed VTE rate – Differences in the rates of VTE vs. increasing risk score were assessed using the χ2 test for trend Results - study populations VERITY n=55996 DVT O/P KCH n=928 Assessment cohort (n=5938) 8 risk factors known VTE status known Univariate regression analysis Development cohort (n=5241) 12 risk factors known VTE status known Multiple regression analysis Validation Cohort (n=915) 12 risk factors known VTE status known Results – baseline characteristics Assessment, development and validation cohorts Assessment Development Validation VTE negative VTE positive VTE negative VTE positive VTE negative VTE positive (N=4019) (N=1919) (N=3563) (N=1678) (N=712) (N=203) N N N % N % N % N % % % Female sex 2530 62.95 906 47.21 2254 63.26 777 46.31 497 69.80 110 54.19 Age75 1009 25.11 442 23.03 896 25.15 386 23.00 108 15.17 35 17.24 BMI30 1610 40.06 602 31.37 1446 40.58 527 31.41 70 9.83 8 3.94 Medical Inpatient 176 4.38 219 11.41 151 4.24 172 10.25 33 4.63 34 16.75 Major surgery 281 6.99 254 13.24 244 6.85 224 13.35 37 5.2 27 13.3 Hormonal factor 442 11.00 187 9.74 389 10.92 166 9.89 59 8.29 21 10.34 Personal History 524 13.04 503 26.21 458 12.85 454 27.06 139 19.52 43 21.18 Thrombophilia 25 0.62 42 2.19 21 0.59 40 2.38 9 1.26 3 1.48 Leg paralysis 154 3.83 145 7.56 124 3.48 121 7.21 4 0.56 2 0.99 Cancer 158 3.93 234 12.19 145 4.07 207 12.34 23 3.23 20 9.85 Smoking NK NK NK NK 704 19.76 430 25.63 83 11.66 33 16.26 IV drug use NK NK NK NK 21 0.59 101 6.02 10 1.40 9 4.43 Results – risk factor findings in multiple logistic regression model β coefficient SE P value Sex Male 1.044 0.086 <0.001 2.840 (2.40, 3.36) Age 75 0.477 0.107 <0.001 1.611 (1.31, 1.99) Obesity -0.334 0.071 <0.001 0.716 (0.62, 0.82) Inpatient 1.100 0.155 <0.001 3.005 (2.22, 4.07) Surgery 1.116 0.169 <0.001 3.054 (2.19, 4.25) Hormonal risk factor 0.309 0.166 0.063 1.361 (0.98, 1.89) Previous history 1.075 0.084 <0.001 2.931 (2.49, 3.45) Leg paralysis 1.337 0.220 <0.001 3.809 (2.47, 5.86) Cancer 1.670 0.178 <0.001 5.314 (3.75, 7.53) Intravenous drug abuse 2.660 0.324 <0.001 14.302 (7.58, 26.98) Smoking 0.174 0.066 0.009 1.190 (1.05, 1.35) Known thrombophilia 1.024 0.299 <0.001 2.784 (1.55, 5.00) Model Terms OR (95% CI) Pair-wise interactions for VTE risk in multiple logistic regression model β coefficient SE P value Sex Male x Age 75 -0.937 0.156 <0.001 0.392 (0.29, 0.53) Sex Male x Surgery -0.498 0.217 0.022 0.608 (0.40, 0.93) Sex Male x Leg paralysis -0.976 0.299 0.001 0.377 (0.21, 0.68) Sex Male x Cancer -0.699 0.242 0.004 0.497 (0.31, 0.80) Age 75 x Surgery -0.428 0.265 0.106 0.651 (0.39, 1.10) Age 75 x Leg paralysis -1.085 0.368 0.003 0.338 (0.16, 0.70) Obesity x Hormone 0.685 0.255 0.007 1.983 (1.20, 3.27) Inpatient x Surgery -1.184 0.279 <0.001 0.306 (0.18, 0.53) Hormone x Previous history -1.167 0.382 0.002 0.311 (0.15, 0.66) Hormone x Cancer -1.009 0.534 0.059 0.364 (0.13, 1.04) Previous history x Drug -1.009 0.559 0.071 0.365 (0.12, 1.09) Cancer x Drug -3.580 0.918 <0.001 0.028 (0.00, 0.17) Model Terms OR (95% CI) Receiver operating characteristic (ROC) curves for risk score prediction of VTE Kucher (––) c statistic 0.617 95% CI 0.599–0.634 VERITY (- - -) c statistic 0.720 95% CI 0.705–0.735 VERITY significantly better than Kucher (p<0.001) Proportion of patients with VTE vs. risk score Strong positive correlation between an increasing risk score and the percentage of VTE-positive cases in the development cohort VERITY score (P<0.001 by χ2 testrisk for trend). Kucher risk score Validation cohort: ROC curves for risk score prediction of VTE Kucher (––) c statistic 0.587 95% CI 0.542–0.632 VERITY (- - -) c statistic 0.678 95% CI 0.635–0.721 VERITY c statistic no different from development cohort (p=NS) Conclusions • The c statistic for this VERITY risk model (0.72) indicates a good test for likelihood of VTE diagnosis • This VERITY risk model was superior to Kucher for predicting the likelihood of a diagnosis of VTE in a cohort in whom the diagnosis was suspected • This risk model was validated in an independent VTE database • A prospective study is required to determine clinical value as a risk prediction tool for VTE at the time of hospital admission to assist in assessing prophylaxis needs