We identified several pre-arrest variables associated with failure to survive to discharge. This information should be shared with patients as part of a shared decision-making process regarding the use of do not resuscitate orders.
BackgroundThere is significant heterogeneity in reported sensitivities and specificities of diagnostic serological assays for Chagas disease, as might be expected from studies that vary widely according to setting, research design, antigens employed, and reference standard. The purpose of this study is to summarize the reported accuracy of serological assays and to identify sources of heterogeneity including quality of research design. To avoid associated spectrum bias, our analysis was limited to cohort studies.MethodsWe completed a search of PubMed, a bibliographic review of potentially relevant articles, and a review of articles identified by a study author involved in this area of research. Studies were limited to prospective cohort studies of adults published since 1985. Measures of diagnostic accuracy were pooled using a Der Simonian Laird Random Effects Model. A subgroup analysis and meta regression were employed to identify sources of heterogeneity. The QUADAS tool was used to assess quality of included studies and Begg's funnel plot was used to assess publication bias.ResultsEighteen studies and 61 assays were included in the final analysis. Significant heterogeneity was found in all pre-determined subgroups. Overall sensitivity was 90% (95% CI: 89%–91%) and overall specificity was 98% (95% CI: 98%–98%).ConclusionSensitivity and specificity of serological assays for the diagnosis of Chagas disease appear less accurate than previously thought. Suggestions to improve the accuracy of reporting include the enrollment of patients in a prospective manner, double blinding, and providing an explicit method of addressing subjects that have an indeterminate diagnosis by either the reference standard or index test.
PURPOSE In this study, we assessed whether multivariate models and clinical decision rules can be used to reliably diagnose infl uenza. METHODSWe conducted a systematic review of MEDLINE, bibliographies of relevant studies, and previous meta-analyses. We searched the literature for articles evaluating the accuracy of multivariate models, clinical decision rules, or simple heuristics for the diagnosis of infl uenza. Each author independently reviewed and abstracted data from each article; discrepancies were resolved by consensus discussion. Where possible, we calculated sensitivity, specifi city, predictive value, likelihood ratios, and areas under the receiver operating characteristic curve. RESULTSA total of 12 studies met our inclusion criteria. No study prospectively validated a multivariate model or clinical decision rule, and no study performed a split-sample or bootstrap validation of such a model. Simple heuristics such as the so-called fever and cough rule and the fever, cough, and acute onset rule were each evaluated by several studies in populations of adults and children. The areas under the receiver operating characteristic curves were 0.70 and 0.79, respectively. We could not calculate a single summary estimate, however, as the diagnostic threshold varied among studies. CONCLUSIONSThe fever and cough, and the fever, cough, and acute onset heuristics have modest accuracy, but summary estimates could not be calculated. Further research is needed to develop and prospectively validate clinical decision rules to identify patients requiring testing, empiric treatment, or neither. INTRODUCTIONA ccurate diagnosis of infl uenza is important for several reasons. If the probability of disease exceeds the treatment threshold or is below the testing threshold, no further testing is needed. If offi ce-based testing is performed, its interpretation depends on the pretest probability of disease. And, although a systematic review found that neuraminidase inhibitors are of only modest benefi t in patients with undifferentiated infl uenza-like illness, greater benefi t was seen in patients who actually had laboratory-confi rmed infl uenza.1 Accurate diagnosis is also helpful because it enables a more accurate prognosis, implicitly rules out other diagnoses, and guides patient education; however, 2 previous meta-analyses 2,3 showed that individual fi ndings on the history and physical examination have only modest accuracy for the clinical diagnosis of infl uenza (Table 1). These studies did fi nd that certain combinations of variables, such as the combination of fever plus cough, the combination of fever, cough, and acute onset, 3 and the combination of fever plus presentation within 3 days, 2 had positive likelihood ratios for infl uenza between 4.0 and 5.4. These results suggest that clinical decision rules (CDRs) that integrate data from several clinical fi ndings and are developed using multivariate methods might be helpful.Economic analyses have shown that diagnostic testing is cost-effective only when t...
Introduction:A clinical decision rule to improve the accuracy of a diagnosis of influenza could help clinicians avoid unnecessary use of diagnostic tests and treatments. Our objective was to develop and validate a simple clinical decision rule for diagnosis of influenza.Methods: We combined data from 2 studies of influenza diagnosis in adult outpatients with suspected influenza: one set in California and one in Switzerland. Patients in both studies underwent a structured history and physical examination and had a reference standard test for influenza (polymerase chain reaction or culture). We randomly divided the dataset into derivation and validation groups and then evaluated simple heuristics and decision rules from previous studies and 3 rules based on our own multivariate analysis. Cutpoints for stratification of risk groups in each model were determined using the derivation group before evaluating them in the validation group. For each decision rule, the positive predictive value and likelihood ratio for influenza in low-, moderate-, and high-risk groups, and the percentage of patients allocated to each risk group, were reported.Results: The simple heuristics (fever and cough; fever, cough, and acute onset) were helpful when positive but not when negative. The most useful and accurate clinical rule assigned 2 points for fever plus cough, 2 points for myalgias, and 1 point each for duration <48 hours and chills or sweats. The risk of influenza was 8% for 0 to 2 points, 30% for 3 points, and 59% for 4 to 6 points; the rule performed similarly in derivation and validation groups. Approximately two-thirds of patients fell into the low-or high-risk group and would not require further diagnostic testing.
We have developed and validated Classification and Regression Tree models that predict survival to discharge with good neurologic function or with mild deficits following in-hospital cardiopulmonary arrest. Models like this can assist physicians and patients who are considering do-not-resuscitate orders.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.