Dataset Information

Meta-analysis of Calibration, Discrimination, and Stratum-Specific Likelihood Ratios for the CRB-65 Score.

ABSTRACT: BACKGROUND:The CRB-65 score is recommended as a decision support tool to help identify patients with community-acquired pneumonia (CAP) who can safely be treated as outpatients. OBJECTIVE:To perform an updated meta-analysis of the accuracy, discrimination, and calibration of the CRB-65 score using a novel approach to calculation of stratum-specific likelihood ratios. DESIGN:Meta-analysis of accuracy, discrimination, and calibration. METHODS:We searched PubMed, Google, previous systematic reviews, and reference lists of included studies. Data was abstracted and quality assessed in parallel by two investigators. The quality assessment used an adaptation of the TRIPOD and PROBAST criteria. Measures of discrimination, calibration, and stratum-specific likelihood ratios are reported. KEY RESULTS:Twenty-nine studies met our inclusion criteria and provided usable data. Most studies were set in Europe, none in North America, and 12 were judged to be at low risk of bias. The pooled estimate of area under the receiver operating characteristic curve was 0.74 (95% CI 0.71-0.77) for all studies. Calibration was good although there was significant heterogeneity; the pooled estimate of the ratio of observed to expected mortality for all studies was 1.04 (95% CI 0.91-1.19). The corresponding values for studies at low risk of bias where patients could be treated as outpatients or inpatients were 0.76 (0.70-0.81) and 0.88 (0.69-1.13). Summary estimates of stratum-specific likelihood ratios for all studies were 0.19 for the low-risk group, 1.1 for the moderate-risk group, and 4.5 for the high-risk group, and 0.13, 1.3, and 5.6 for studies at low risk of bias where patients could be treated as outpatients or inpatients. CONCLUSIONS:The CRB-65 is useful for identifying low-risk patients for outpatient therapy. Given a 4% overall mortality risk, patients classified as low risk by the CRB-65 had an outpatient mortality risk of no more than 0.5%.

SUBMITTER: Ebell MH

PROVIDER: S-EPMC6614215 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Meta-analysis of Calibration, Discrimination, and Stratum-Specific Likelihood Ratios for the CRB-65 Score.

Ebell Mark H MH Walsh Mary E ME Fahey Tom T Kearney Maggie M Marchello Christian C

Journal of general internal medicine 20190416 7

<h4>Background</h4>The CRB-65 score is recommended as a decision support tool to help identify patients with community-acquired pneumonia (CAP) who can safely be treated as outpatients.<h4>Objective</h4>To perform an updated meta-analysis of the accuracy, discrimination, and calibration of the CRB-65 score using a novel approach to calculation of stratum-specific likelihood ratios.<h4>Design</h4>Meta-analysis of accuracy, discrimination, and calibration.<h4>Methods</h4>We searched PubMed, Google ...[more]

PMID: 30993633

Similar Datasets

Project description:A likelihood ratio (LR) system is defined as the entire pipeline of the measurement and interpretation processes where probabilistic genotyping software (PGS) is a piece of the whole LR system. To gain understanding on how two LR systems perform, a total of 154 two-person, 147 three-person, and 127 four-person mixture profiles of varying DNA quality, DNA quantity, and mixture ratios were obtained from the filtered (.CSV) files of the GlobalFiler 29 cycles 15s PROVEDIt dataset and deconvolved in two independently developed fully continuous programs, STRmix v2.6 and EuroForMix v2.1.0. Various parameters were set in each software and LR computations obtained from the two software were based on same/fixed EPG features, same pair of propositions, number of contributors, theta, and population allele frequencies. The ability of each LR system to discriminate between contributor (H1-true) and non-contributor (H2-true) scenarios was evaluated qualitatively and quantitatively. Differences in the numeric LR values and their corresponding verbal classifications between the two LR systems were compared. The magnitude of the differences in the assigned LRs and the potential explanations for the observed differences greater than or equal to 3 on the log10 scale were described. Cases of LR < 1 for H1-true tests and LR > 1 for H2-true tests were also discussed. Our intent is to demonstrate the value of using a publicly available ground truth known mixture dataset to assess discrimination performance of any LR system and show the steps used to understand similarities and differences between different LR systems. We share our observations with the forensic community and describe how examining more than one PGS with similar discrimination power can be beneficial, help analysts compare interpretation especially with low-template profiles or minor contributor cases, and be a potential additional diagnostic check even if software in use does contain certain diagnostic statistics as part of the output.

Project description:BackgroundMissense (amino-acid changing) variants found in cancer predisposition genes often create difficulties when clinically interpreting genetic testing results. Although bioinformatics has developed approaches to predicting the impact of these variants, many of these approaches have not been readily applicable in the clinical setting. Bioinformatics approaches for predicting the impact of these variants have not yet found their footing in clinical practice because 1) interpreting the medical relevance of predictive scores is difficult; 2) the relationship between bioinformatics "predictors" (sequence conservation, protein structure) and cancer susceptibility is not understood.Methodology/principal findingsWe present a computational method that produces a probabilistic likelihood ratio predictive of whether a missense variant impairs protein function. We apply the method to a tumor suppressor gene, BRCA2, whose loss of function is important to cancer susceptibility. Protein likelihood ratios are computed for 229 unclassified variants found in individuals from high-risk breast/ovarian cancer families. We map the variants onto a protein structure model, and suggest that a cluster of predicted deleterious variants in the BRCA2 OB1 domain may destabilize BRCA2 and a protein binding partner, the small acidic protein DSS1. We compare our predictions with variant "re-classifications" provided by Myriad Genetics, a biotechnology company that holds the patent on BRCA2 genetic testing in the U.S., and with classifications made by an established medical genetics model [1]. Our approach uses bioinformatics data that is independent of these genetics-based classifications and yet shows significant agreement with them. Preliminary results indicate that our method is less likely to make false positive errors than other bioinformatics methods, which were designed to predict the impact of missense mutations in general.Conclusions/significanceMissense mutations are the most common disease-producing genetic variants. We present a fast, scalable bioinformatics method that integrates information about protein sequence, conservation, and structure in a likelihood ratio that can be integrated with medical genetics likelihood ratios. The protein likelihood ratio, together with medical genetics likelihood ratios, can be used by clinicians and counselors to communicate the relevance of a VUS to the individual who has that VUS. The approach described here is generalizable to regions of any tumor suppressor gene that have been structurally determined by X-ray crystallography or for which a protein homology model can be built.

Dataset Information

Meta-analysis of Calibration, Discrimination, and Stratum-Specific Likelihood Ratios for the CRB-65 Score.

Publications

Meta-analysis of Calibration, Discrimination, and Stratum-Specific Likelihood Ratios for the CRB-65 Score.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets