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5. Exercise Survival time and congestive heart failure A 69 year old woman, who is a retired high school teacher, has attended an outpatient appointment to review her test results. A month ago she presented with symptoms and signs of congestive heart failure. She has had long-standing essential hypertension, but had been otherwise healthy until now. An SHO reviews the test results, the patient’s medication use and the patient’s general wellbeing. During the appointment, she asked, "Heart failure sounds serious - is it? What do I have to look forward to?" The SHO decides to find out the average survival time for a patient with heart failure and comes into the library for a literature search. Source: Centre for Evidence Based Medicine Four particular factors are central to a prognosis question – the gender and age of the patient, the severity or stage of the target disease or condition and the existence of other diseases or conditions (comorbidities). Three of these elements are significant to this scenario – the age of the patient, the fact that she is a woman and the co-existence of hypertension – all of these factors should feature in the focused question and hence the search strategy. As for the stage of the disease – we have little to go on – it has only recently been diagnosed but this is not uncommon in prognosis questions. We are looking here at the course of untreated disease. Although the prognosis filter covers a wide range of prognosis-related issues we are specifically asked to identify the “average survival time” in this instance. Break down the above enquiry into the following components PATIENT/ POPULATION/ CONDITION INTERVENTION OUTCOME COMPARISON Any (3) Risk of death None (2) Survival Time (2) No intervention or treatment (2) Hypertension (2) Drug therapy Mortality (3) Female age 69 Medication Survival/death Congestive Heart Failure (2) Treatment for hypertension None 69 year old woman Survival (1) 56yr old female [??] Quality of Life (2) No treatment/treatment Aged/Female Survival rates Intervention Congestive heart failure, plus essential hypertension Aged Woman with Congestive Heart Failure and Hypertension or Non-intervention For some reason one of your responses recorded this woman as a 56 year old female. Unfortunately this confirms all my irrational prejudices as a 33 year old (!) male about a certain predilection for automatically subtracting over a decade from one’s age. The medical records, however, never lie! Now try to match the above components to the corresponding MeSH terminology PATIENT/ POPULATION/ CONDITION INTERVENTION OUTCOME COMPARISON ? Could have had the treatment that patient was receiving for her hypertension? Mortality (3) No intervention Survival Analysis (3) No intervention or treatment Any (covered by the filter) Intervention exp Hypertension/ Incidence Non-intervention Hypertension (3) Cohort studies Aged ("all aged <65 and over>") (2) Survival rate, survival Female (2) Any -mortality (sh) Quality of Life exp Heart Failure, Congestive/ Heart failure, Congestive (4) Drug therapy Prognosis (3) or Female and aging <65 to 75 years> Hypertension While all of the above outcomes are appropriate to a prognosis question only those specific to survival are in fact pertinent to this particular scenario. While doing this same exercise with our group of Trent librarians only last week we also encountered a fairly useful MeSH term “Disease progression” that doesn’t seem to have figured prominently in most of the prognosis filters. This is defined by the MeSH browser as: Disease Progression The worsening of a disease over time. This concept is most often used for chronic and incurable diseases where the stage of the disease is an important determinant of therapy and prognosis. Year introduced: 1995 By the way (as a side point) do you all know about the animated tutorials on MeSH now available via the MeSH Browser?. They cover: Searching with the MeSH Database Combining MeSH Terms Applying Subheadings and other features of the MeSH Database Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=mesh With broad subjects, you may need to use subheadings to narrow the search to a manageable number of hits. For narrower subjects, you may find that subheadings limit your search too much. Which subheadings (if any) are you going to use with the condition “HEART FAILURE, CONGESTIVE”? Participant comments: Complications, Diagnosis, Drug therapy, Etiology, Mortality and Physiopathology I thought of using mortality – but this is covered with the Ovid filter. (diagnosis had not sprung to mind until I read the next page) – this also seems to be covered with prognosis etc in filter. My initial search gave too many hits, so I decided to put Hypertension in too. Although final scenario does not mention hypertension, this is mentioned with relation to the patient. Although I also tried survival analysis, I realised that the filter covered this too. Tried also limiting to English/sex/age – but did not make much difference. Mortality None because Mortality appears to be the only strictly relevant subheading and is not sufficient on its own to identify all prognostic factors . What about diagnosis? No – not sufficiently specific to prognosis. While mortality is the obvious choice for a subheading the fact that this concept (albeit in a half-full/half empty way) is already captured in the concept of “survival analysis” means that it probably will not be required in this instance. Notwithstanding the comment above on the lack of specificity of the subheading “diagnosis” you will recall that in the module it describes how indexers are instructed to place materials on prognosis under the diagnosis subheading. Another parallel strategy for the search is to use the study design feature embodied in the one line filter “cohort-studies” – an optimal study will follow a group of patients with hypertension and compare it with those without hypertension and compare them for development of heart failure and subsequent mortality. We reproduce below our own strategy along with those from some of the course participants. Of course our strategy is not a “gold standard” merely an indicative strategy for comparison with your own! # Search History Results Display 1 congestive heart failure.mp. or exp Heart Failure, Congestive/ 59245 2 hypertension.mp. or exp HYPERTENSION/ 217859 3 1 and 2 4 exp SURVIVAL ANALYSIS/ or exp SURVIVAL/ or survival.mp. 5 3 and 4 445 6 limit 5 to (female and (middle aged <45 plus years> or aging <65 to 79 years> or "all aged <65 and over>" or "aged <80 and over>")) 211 7 exp *Heart Failure, Congestive/ 35301 8 exp *HYPERTENSION/ 120681 9 7 and 8 and 4 7022 275290 10 9 and 6 29 10 Using this strategy we retrieve the following relevant article: Unique Identifier 8622246 Authors Levy D. Larson MG. Vasan RS. Kannel WB. Ho KK. Institution Framingham (Mass) Heart Study, Massachusetts 01701, USA. Title The progression from hypertension to congestive heart failure.[see comment]. Comments Comment in: JAMA. 1996 May 22-29;275(20):1604-6; PMID: 8622253 Source JAMA. 275(20):1557-62, 1996 May 22-29. Abstract OBJECTIVES.- To study the relative and population-attributable risks of hypertension for the development of congestive heart failure (CHF), to assess the time course of progression from hypertension to CHF, and to identify risk factors that contribute to the development of overt heart failure in hypertensive subjects. DESIGN.- Inception cohort study. SETTING.- General community. PARTICIPANTS.- Original Framingham Heart Study and Framingham Offspring Study participants aged 40 to 89 years and free of CHF. To reflect more contemporary experience, the starting point of this study was January 1, 1970. EXPOSURE MEASURES.- Hypertension (blood pressure of at least 140 mm Hg systolic or 90 mm Hg diastolic or current use of medications for treatment of high blood pressure) and other potential CHF risk factors were assessed at periodic clinic examinations. OUTCOME MEASURE.- The development of CHF. RESULTS.- A total of 5143 eligible subjects contributed 72422 person-years of observation. During up to 20.1 years of follow-up (mean, 14.1 years), there were 392 new cases of heart failure; in 91% (357/392), hypertension antedated the development of heart failure. Adjusting for age and heart failure risk factors in proportional hazards regression models, the hazard for developing heart failure in hypertensive compared with normotensive subjects was about 2-fold in men and 3-fold in women. Multivariable analyses revealed that hypertension had a high population-attributable risk for CHF, accounting for 39% of cases in men and 59% in women. Among hypertensive subjects, myocardial infarction, diabetes, left ventricular hypertrophy, and valvular heart disease were predictive of increased risk for CHF in both sexes. Survival following the onset of hypertensive CHF was bleak; only 24% of men and 31% of women survived 5 years. CONCLUSIONS.- Hypertension was the most common risk factor for CHF, and it contributed a large proportion of heart failure cases in this population-based sample. Preventive strategies directed toward earlier and more aggressive blood pressure control are likely to offer the greatest promise for reducing the incidence of CHF and its associated mortality. This tells us that only 31% of women survive 5 years with hypertensive congestive heart failure. This doesn’t quite answer the clinician’s question which was on average survival time. However this article is definitely relevant and if your requester was to examine the full text you will see that the average survival time is 2.48 years. Participant Strategies Here are some of the strategies that you have used. Please paste your suggested strategy here: # Search History Results 1 exp Heart Failure, Congestive/ 17971 2 exp HYPERTENSION/ 43238 3 1 and 2 1139 4 incidence/ or exp mortality/ or mo.fs. 169277 5 exp prognosis/ or prognos$.tw. 264443 6 4 or 5 382544 7 exp cohort studies/ or cohort.tw. 242723 8 exp survival analysis/ 39616 9 7 or 8 269217 10 6 and 9 114236 11 3 and 10 78 # Search History Results 1 exp *heart failure, congestive/ 34428 2 limit 1 to (human and english language) 22062 3 exp AGED/ 1346637 4 exp female/ 4012571 5 2 and 3 and 4 6 exp *hypertension/ 7 5 and 6 8 essential hypertension.mp. 9 5 and 8 10 incidence/ or exp mortality/ or mo.fs. 385507 11 exp prognosis/ or prognos:.mp. 471606 12 10 or 11 762949 13 exp cohort studies/ or cohort.mp. 507037 14 exp survival analysis/ 51045 15 13 or 14 541177 16 12 and 15 185570 17 7 and 16 10 18 5 and 16 891 19 2 and 16 1805 20 7 or 9 124 21 18 or 19 1805 22 20 and 21 10 23 22 not 17 0 Set 17 looks to have reasonable results. 5686 119015 118 15350 13 1 Heart Failure, Congestive/mo [Mortality] 2 PROGNOSIS/ 201691 3 1 and 2 760 4 exp cohort studies/ 486726 5 3 and 4 # 3006 279 Search History Results 1 exp Heart Failure, Congestive/ 18118 1 Heart Failure, Congestive/mo [Mortality] 3006 2 incidence/ or exp mortality/ or mo.fs. 386505 3 exp prognosis/ or prognos$.tw. 473422 4 2 or 3 765434 5 exp cohort studies/ or cohort.tw. 508457 6 exp survival analysis/ 51372 7 5 or 6 542818 8 4 and 7 186240 9 1 and 8 1206 2 exp HYPERTENSION/ 43462 3 incidence/ or exp mortality/ or mo.fs. 170273 4 disease progression/ 25821 5 ((natural$ or disease$) adj (progress$ or course$ or histor$)).ti,ab,sh. 6 exp prognosis/ 22559 233970 7 (incidence or mortality or prognos$ or predict$ or course or outcome$).ti,ab,sh. 636120 8 or/3-7 787700 9 exp survival analysis/ 39950 10 exp cohort studies/ 230028 11 (cohort$ or follow-up or compar$ or longitudinal$ or 1121484 prospective$ or multivariate or reproducib$).ti,ab,sh. 12 or/9-11 1167263 13 8 and 12 397018 14 1 and 2 and 13 224 15 limit 14 to english language 185 16 limit 15 to female 119 17 limit 16 to aging <65 to 79 years> 44 # Search History Results 1 hypertension.mp. or exp HYPERTENSION/ 214880 *Heart Failure, Congestive/mo, co, pp, di, dt, et 2 [Mortality, Complications, Physiopathology, Diagnosis, Drug Therapy, Etiology] 20090 3 1 and 2 1614 4 limit 3 to (english language and female and aging <65 to 79 years>) 172 5 incidence/ or exp mortality/ or mo.fs. 386964 6 exp prognosis/ or prognos$.tw. 474384 7 5 and 6 94656 8 exp cohort studies/ or cohort.tw. 509279 9 exp survival analysis/ 51500 10 8 or 9 543721 11 7 and 10 40886 12 exp case-control studies/ or exp follow-up studies/ 501042 13 11 or 12 517621 14 4 and 13 30 Ovid Prognosis strategy: 1. incidence/ 2. or exp mortality 3. (incidence or mortality or prognos$ or predict$ or course or outcome$).ti,ab,sh. 4. mo.fs. 5. exp prognosis 6. exp disease progression 7. ((natural$ or disease$) adj (progress$ or course$ or histor$)).ti,ab,sh. 8. or/1-7 9. exp cohort studies 10. (cohort or follow-up or compar$ or longitudinal or prospective$ or multivariate or reproducib$).ti,ab,sh. 11. exp survival analysis 12. or/9-11 1. 8 and 12 I didn’t Major or Focus MeSH disease Subject headings: too sensitive. I combined Heart Failure, Congestive with Hypertension on the basis that Hypertension and gender might influence disease progression. I limited to English and Aging, on the basis that Aged was too narrow to encompass longer term prognosis for this patient. Bearing in mind the context, I didn’t search for freetext terms and I did limit to English language, in order to identify a manageable number of references for a SHO to manage. Re. the Filter - I tried to combine the best of all three suggested ‘prognosis’ strategies. I’d found during the reading exercise that none quite hit the mark on the basis that: exp prognosis/ found more than prognosis$ (hence Univ. of Alberta strategy wasn’t best for me) HIRU’s “mid-range” strategy didn’t appear to include Cohort Studies, identified by HIRU as the single most useful term for a prognosis study. .ti,ab,sh. found more than .tw. Questions re. HIRU “mid-range” strategy: Why incidence and not exp Morbidity which includes Incidence and Prevalence? Exp Mortality includes mortality (MeSH) Exp Prognosis/ finds more that prognos$ Why does HIRU strategy not include Exp Cohort-Studies/ which a. includes Follow-Up Studies and b. has been identified by HIRU as single most useful term for a prognosis study? The above questions highlight the way in which McMaster HIRU strategies are conducted. They are looking for optimal permutations and therefore the strategies are computer generated not using librarian-guided intuition. This means that the alternatives that our participant suggests are not regarded as providing an acceptable response with regard to specificity. Questions re. University of Alberta Advanced Strategy: Why is follow-up in (1) separate from exp cohort studies in (5) which includes Follow-up Studies? For the same reason as above, the performance of the sensitivity/specificity trade-off is regarded as less acceptable in line 5 than in line 1. The researchers prefer to report individual terms so that when implemented the searchers can choose to drop particular lines as and when required. Note: Since this Unit was released the McMaster team has produced a new study looking specifically at new terms for a prognosis filter: BMC Med. 2004 Jun 9;2(1):23. Developing optimal search strategies for detecting clinically sound prognostic studies in MEDLINE: an analytic survey. Wilczynski NL, Haynes RB; The Hedges Team. Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, L8N 3Z5, Canada. [email protected] BACKGROUND: Clinical end users of MEDLINE have a difficult time retrieving articles that are both scientifically sound and directly relevant to clinical practice. Search filters have been developed to assist end users in increasing the success of their searches. Many filters have been developed for the literature on therapy and reviews but little has been done in the area of prognosis. The objective of this study is to determine how well various methodologic textwords, Medical Subject Headings, and their Boolean combinations retrieve methodologically sound literature on the prognosis of health disorders in MEDLINE. METHODS: An analytic survey was conducted, comparing hand searches of journals with retrievals from MEDLINE for candidate search terms and combinations. Six research assistants read all issues of 161 journals for the publishing year 2000. All articles were rated using purpose and quality indicators and categorized into clinically relevant original studies, review articles, general papers, or case reports. The original and review articles were then categorized as 'pass' or 'fail' for methodologic rigor in the areas of prognosis and other clinical topics. Candidate search strategies were developed for prognosis and run in MEDLINE - the retrievals being compared with the hand search data. The sensitivity, specificity, precision, and accuracy of the search strategies were calculated. RESULTS: 12% of studies classified as prognosis met basic criteria for scientific merit for testing clinical applications. Combinations of terms reached peak sensitivities of 90%. Compared with the best single term, multiple terms increased sensitivity for sound studies by 25.2% (absolute increase), and increased specificity, but by a much smaller amount (1.1%) when sensitivity was maximized. Combining terms to optimize both sensitivity and specificity achieved sensitivities and specificities of approximately 83% for each. CONCLUSION: Empirically derived search strategies combining indexing terms and textwords can achieve high sensitivity and specificity for retrieving sound prognostic studies from MEDLINE. This article is available free at: http://www.biomedcentral.com/1741-7015/2/23 You will notice that the authors are suggesting a new one-line filter “explode epidemiologic studies”. This would have picked out our article above as it includes the study design filter “cohort-studies” Table 1 Single term with the best sensitivity (keeping specificity ≥50%), best specificity (keeping sensitivity ≥50%), and best optimization of sensitivity and specificity (based on the lowest possible absolute difference between sensitivity and specificity) for detecting studies of prognosis in MEDLINE in 2000 Search term Sensitivity Specificity Precision Accuracy OVID (%) (%) (%) (%) search* Development Development Development Development Validation Validation Validation Validation Diff (95% Diff (95% Diff (95% Diff (95% CI)† CI)† CI)† CI)† ‡exp 64.9 78.6 1.1 78.6 73.4 8.5 (-4.6 to 21.7) 79.1 0.5 (-0.3 to 1.2) 1.4 0.3 (-0.2 to 0.7) 79.1 0.5 (-0.3 to 1.2) epidemiologic studies *The search strategy is reported using Ovid's search engine syntax for MEDLINE. The PubMed syntax is epidemiologic studies [MeSH]. †Diff = Difference, comparing the development and validation data sets using the iterative method of Miettinen and Nurminen for two independent binomial proportions. None of the differences were statistically significant. ‡exp = explode, a search term that automatically includes closely related indexing terms. Table 2 Combination of terms with the best sensitivity (keeping specificity ≥50%), best specificity (keeping sensitivity ≥50%), and best optimization of sensitivity and specificity (based on abs [sensitivity-specificity] < 1%) for detecting studies of prognosis in MEDLINE in 2000 Search Sensitivity (%) Strategy OVID Development search* Validation Diff (95% CI)† Specificity (%) Development Validation Diff (95% CI)† Precision (%) Development Validation Diff (95% CI)† Accuracy (%) Development Validation Diff (95% CI)† Best Sensitivity incidence.sh. OR exp mortality 90.1 79.7 1.7 79.7 82.3 79.7 1.6 79.7 -7.8 (-17.9 to 2.3) 0 -0.1 (-0.5 to 0.5) 0 prognos:.tw. 52.3 94.1 3.3 93.9 OR first episode.tw. 48.1 94.2 3.2 94.0 -4.2 (-18.6 to 10.3) 0.1 (-0.3 to 0.5) -0.1 (-1.3 to 1.3) 0.1 (-0.4 to 0.5) prognosis.sh. 82.9 83.7 1.9 83.7 OR diagnosed.tw. 73.4 84.1 1.8 84.0 OR follow-up studies.sh. OR prognos:.tw. OR predict:.tw. OR course:.tw. Best Specificity OR cohort.tw. Best Optimization of Sensitivity & Specificity OR cohort:.mp. -9.5 (-21.5 to 2.5) -0.4 (-0.2 to 1.1) -0.1 (-0.7 to 0.5) 0.3 (-0.2 to 1.0) OR predictor:.tw. OR death.tw. OR exp models, statistical *Search strategies are reported using Ovid's search engine syntax for MEDLINE. The PubMed syntax is embedded in PubMed's Clinical Queries (see Discussion). †Diff = Difference, comparing the development and validation data sets using the iterative method of Miettinen and Nurminen for two independent binomial proportions. None of the differences were statistically significant. sh = subject heading; exp = explode, a search term that automatically includes closely related indexing terms; : = truncation; tw = textword (word or phrase appears in title or abstract); mp = multiple posting (term appears in title, abstract, or MeSH heading).