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Diagnostic accuracy of the STRATIFY clinical prediction rule for falls: a systematic review and meta-analysis.

ABSTRACT: BACKGROUND: The STRATIFY score is a clinical prediction rule (CPR) derived to assist clinicians to identify patients at risk of falling. The purpose of this systematic review and meta-analysis is to determine the overall diagnostic accuracy of the STRATIFY rule across a variety of clinical settings. METHODS: A literature search was performed to identify all studies that validated the STRATIFY rule. The methodological quality of the studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. A STRATIFY score of ?2 points was used to identify individuals at higher risk of falling. All included studies were combined using a bivariate random effects model to generate pooled sensitivity and specificity of STRATIFY at ?2 points. Heterogeneity was assessed using the variance of logit transformed sensitivity and specificity. RESULTS: Seventeen studies were included in our meta-analysis, incorporating 11,378 patients. At a score ?2 points, the STRATIFY rule is more useful at ruling out falls in those classified as low risk, with a greater pooled sensitivity estimate (0.67, 95% CI 0.52-0.80) than specificity (0.57, 95% CI 0.45 - 0.69). The sensitivity analysis which examined the performance of the rule in different settings and subgroups also showed broadly comparable results, indicating that the STRATIFY rule performs in a similar manner across a variety of different 'at risk' patient groups in different clinical settings. CONCLUSION: This systematic review shows that the diagnostic accuracy of the STRATIFY rule is limited and should not be used in isolation for identifying individuals at high risk of falls in clinical practice.

SUBMITTER: Billington J

PROVIDER: S-EPMC3460792 | biostudies-other | 2012

REPOSITORIES: biostudies-other

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Diagnostic accuracy of the STRATIFY clinical prediction rule for falls: a systematic review and meta-analysis.

Billington Jennifer J Fahey Tom T Galvin Rose R

BMC family practice 20120807

<h4>Background</h4>The STRATIFY score is a clinical prediction rule (CPR) derived to assist clinicians to identify patients at risk of falling. The purpose of this systematic review and meta-analysis is to determine the overall diagnostic accuracy of the STRATIFY rule across a variety of clinical settings.<h4>Methods</h4>A literature search was performed to identify all studies that validated the STRATIFY rule. The methodological quality of the studies was assessed using the Quality Assessment o ...[more]

PMID: 22870921

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Project description:BackgroundSTRATIFY is a prediction tool developed for use in for hospital inpatients, using a 0-5 score to predict patients who will fall. It has been widely used as part of hospital fall prevention plans, but it is not clear how good its operational utility is in a variety of settings.Objectives(i) to describe the predictive validity of STRATIFY for identifying hospital inpatients who will fall via systematic review and descriptive analysis, based on its use in several prospective cohort studies of hospital inpatients; (ii) to describe the predictive validity of STRATIFY among inpatients in geriatric rehabilitation via meta-analysis and (iii) in turn, to help practitioners and institutions wishing to implement interventions to prevent in-hospital falls.Methodsa systematic literature review of prospective validation studies of STRATIFY for falls prediction in hospital inpatients. For inclusion, studies must report prospective validation cohorts, with sufficient data for calculation of sensitivity (SENS), specificity (SPEC), negative and positive predictive value (NPV and PPV), total predictive accuracy (TPA) and 95% confidence intervals (CI). We performed meta-analysis using precision-weighted fixed- and random-effects models using studies that evaluated STRATIFY among geriatric rehabilitation inpatients.Measurementskey features of the patient population, setting, study design and numbers of falls/fallers were abstracted. SENS, SPEC, PPV, NPV, TPA and 95% CI were reported for each cohort. Pooled values and chi-squared test for homogeneity were reported for a meta-analysis of studies conducted in geriatric rehabilitation settings.Resultsforty-one papers were identified by the search, with eight ultimately eligible for inclusion in the systematic review and four for inclusion in the meta-analysis. The predictive validity of STRATIFY, using a random-effects model, for the four studies involving geriatric patients was as follows: SENS 67.2 (95% CI 60.8, 73.6), SPEC 51.2 (95% CI 43.0, 59.3), PPV 23.1 (95% CI 14.9, 31.2), NPV 86.5 (95% CI 78.4, 94.6). The Q((3)) test for homogeneity was not significant for SENS at P = 0.36, but it was significant at P < 0.01 for SPEC, PPV and NPV. TPA across all four studies varied from 43.2 to 60.0.Conclusionthe current study reveals a relatively high NPV and low PPV and TPA for the STRATIFY instrument, suggesting that it may not be optimal for identifying high-risk individuals for fall prevention. Further, the study demonstrates that population and setting affect STRATIFY performance.

Project description:PurposeEarly clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis.MethodsA systematic search was performed in PubMed, Embase.com and Scopus. Studies targeting sepsis, severe sepsis or septic shock in any hospital setting were eligible for inclusion. The index test was any supervised machine learning model for real-time prediction of these conditions. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology, with a tailored Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist to evaluate risk of bias. Models with a reported area under the curve of the receiver operating characteristic (AUROC) metric were meta-analyzed to identify strongest contributors to model performance.ResultsAfter screening, a total of 28 papers were eligible for synthesis, from which 130 models were extracted. The majority of papers were developed in the intensive care unit (ICU, n = 15; 54%), followed by hospital wards (n = 7; 25%), the emergency department (ED, n = 4; 14%) and all of these settings (n = 2; 7%). For the prediction of sepsis, diagnostic test accuracy assessed by the AUROC ranged from 0.68-0.99 in the ICU, to 0.96-0.98 in-hospital and 0.87 to 0.97 in the ED. Varying sepsis definitions limit pooling of the performance across studies. Only three papers clinically implemented models with mixed results. In the multivariate analysis, temperature, lab values, and model type contributed most to model performance.ConclusionThis systematic review and meta-analysis show that on retrospective data, individual machine learning models can accurately predict sepsis onset ahead of time. Although they present alternatives to traditional scoring systems, between-study heterogeneity limits the assessment of pooled results. Systematic reporting and clinical implementation studies are needed to bridge the gap between bytes and bedside.

Project description:ObjectiveTo determine the diagnostic accuracy of serological tests for coronavirus disease-2019 (covid-19).DesignSystematic review and meta-analysis.Data sourcesMedline, bioRxiv, and medRxiv from 1 January to 30 April 2020, using subject headings or subheadings combined with text words for the concepts of covid-19 and serological tests for covid-19.Eligibility criteria and data analysisEligible studies measured sensitivity or specificity, or both of a covid-19 serological test compared with a reference standard of viral culture or reverse transcriptase polymerase chain reaction. Studies were excluded with fewer than five participants or samples. Risk of bias was assessed using quality assessment of diagnostic accuracy studies 2 (QUADAS-2). Pooled sensitivity and specificity were estimated using random effects bivariate meta-analyses.Main outcome measuresThe primary outcome was overall sensitivity and specificity, stratified by method of serological testing (enzyme linked immunosorbent assays (ELISAs), lateral flow immunoassays (LFIAs), or chemiluminescent immunoassays (CLIAs)) and immunoglobulin class (IgG, IgM, or both). Secondary outcomes were stratum specific sensitivity and specificity within subgroups defined by study or participant characteristics, including time since symptom onset.Results5016 references were identified and 40 studies included. 49 risk of bias assessments were carried out (one for each population and method evaluated). High risk of patient selection bias was found in 98% (48/49) of assessments and high or unclear risk of bias from performance or interpretation of the serological test in 73% (36/49). Only 10% (4/40) of studies included outpatients. Only two studies evaluated tests at the point of care. For each method of testing, pooled sensitivity and specificity were not associated with the immunoglobulin class measured. The pooled sensitivity of ELISAs measuring IgG or IgM was 84.3% (95% confidence interval 75.6% to 90.9%), of LFIAs was 66.0% (49.3% to 79.3%), and of CLIAs was 97.8% (46.2% to 100%). In all analyses, pooled sensitivity was lower for LFIAs, the potential point-of-care method. Pooled specificities ranged from 96.6% to 99.7%. Of the samples used for estimating specificity, 83% (10 465/12 547) were from populations tested before the epidemic or not suspected of having covid-19. Among LFIAs, pooled sensitivity of commercial kits (65.0%, 49.0% to 78.2%) was lower than that of non-commercial tests (88.2%, 83.6% to 91.3%). Heterogeneity was seen in all analyses. Sensitivity was higher at least three weeks after symptom onset (ranging from 69.9% to 98.9%) compared with within the first week (from 13.4% to 50.3%).ConclusionHigher quality clinical studies assessing the diagnostic accuracy of serological tests for covid-19 are urgently needed. Currently, available evidence does not support the continued use of existing point-of-care serological tests.Study registrationPROSPERO CRD42020179452.

Project description:Diagnostic clinical prediction rules (CPRs) are developed to improve diagnosis or decrease diagnostic testing. Whether, and in what situations diagnostic CPRs improve upon clinical judgment is unclear.We searched MEDLINE, Embase and CINAHL, with supplementary citation and reference checking for studies comparing CPRs and clinical judgment against a current objective reference standard. We report 1) the proportion of study participants classified as not having disease who hence may avoid further testing and or treatment and 2) the proportion, among those classified as not having disease, who do (missed diagnoses) by both approaches. 31 studies of 13 medical conditions were included, with 46 comparisons between CPRs and clinical judgment. In 2 comparisons (4%), CPRs reduced the proportion of missed diagnoses, but this was offset by classifying a larger proportion of study participants as having disease (more false positives). In 36 comparisons (78%) the proportion of diagnoses missed by CPRs and clinical judgment was similar, and in 9 of these, the CPRs classified a larger proportion of participants as not having disease (fewer false positives). In 8 comparisons (17%) the proportion of diagnoses missed by the CPRs was greater. This was offset by classifying a smaller proportion of participants as having the disease (fewer false positives) in 2 comparisons. There were no comparisons where the CPR missed a smaller proportion of diagnoses than clinical judgment and classified more participants as not having the disease. The design of the included studies allows evaluation of CPRs when their results are applied independently of clinical judgment. The performance of CPRs, when implemented by clinicians as a support to their judgment may be different.In the limited studies to date, CPRs are rarely superior to clinical judgment and there is generally a trade-off between the proportion classified as not having disease and the proportion of missed diagnoses. Differences between the two methods of judgment are likely the result of different diagnostic thresholds for positivity. Which is the preferred judgment method for a particular clinical condition depends on the relative benefits and harms of true positive and false positive diagnoses.

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Diagnostic accuracy of the STRATIFY clinical prediction rule for falls: a systematic review and meta-analysis.

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Diagnostic accuracy of the STRATIFY clinical prediction rule for falls: a systematic review and meta-analysis.

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