A Bayesian decision support sequential model for severity of illness predictors and intensive care admissions in pneumonia

From Pre-test and Post-test Probabilities to Medical Decision Making

Background

A central goal of modern evidence-based medicine is the development of simple and easy to use tools that help clinicians integrate quantitative information into medical decision-making. The Bayesian Pre-test/Post-test Probability (BPP) framework is arguably the most well known of such tools and provides a formal approach to quantify diagnostic uncertainty given the result of a medical test or the presence of a clinical sign. Yet, clinical decision-making goes beyond quantifying diagnostic uncertainty and requires that that uncertainty be balanced against the various costs and benefits associated with each possible decision. Despite increasing attention in recent years, simple and flexible approaches to quantitative clinical decision-making have remained elusive.

Methods

We extend the BPP framework using concepts of Bayesian Decision Theory. By integrating cost, we can expand the BPP framework to allow for clinical decision-making.

Results

We develop a simple quantitative framework for binary clinical decisions (e.g., action/inaction, treat/no-treat, test/no-test). Let p be the pre-test or post-test probability that a patient has disease. We show that r * = ( 1 - p ) / p represents a critical value called a decision boundary. In terms of the relative cost of under- to over-acting, r * represents the critical value at which action and inaction are equally optimal. We demonstrate how this decision boundary can be used at the bedside through case studies and as a research tool through a reanalysis of a recent study which found widespread misestimation of pre-test and post-test probabilities among clinicians.

Conclusions

Our approach is so simple that it should be thought of as a core, yet previously overlooked, part of the BPP framework. Unlike prior approaches to quantitative clinical decision-making, our approach requires little more than a hand-held calculator, is applicable in almost any setting where the BPP framework can be used, and excels in situations where the costs and benefits associated with a particular decision are patient-specific and difficult to quantify.

Assessment of a Comparative Bayesian-Enhanced Population-Based Decision Model for COVID-19 Critical Care Prediction in the Dominican Republic Social Security Affiliates

Introduction: The novel coronavirus disease 2019 (COVID-19) has been a major health concern worldwide. This study aims to develop a Bayesian model to predict critical outcomes in patients with COVID-19.

Methods: Sensitivity and specificity were obtained from previous meta-analysis studies. The complex vulnerability index (IVC-COV2 index for its abbreviation in Spanish) was used to set the pretest probability. Likelihood ratios were integrated into a Fagan nomogram for posttest probabilities, and IVC-COV2 + National Early Warning Score (NEWS) values and CURB-65 scores were generated. Absolute and relative diagnostic gains (RDGs) were calculated based on pretest and posttest differences.

Results: The IVC-COV2 index was derived from a population of 1,055,746 individuals and was based on mortality in high-risk (71.97%), intermediate-risk (26.11%), and low-risk (1.91%) groups. The integration of models in which IVC-COV2 intermediate + NEWS ≥ 5 and CURB-65 > 2 led to a "number needed to (NNT) diagnose" that was slightly improved in the CURB-65 model (2 vs. 3). A comparison of diagnostic gains revealed that neither the positive likelihood ratio (P = 0.62) nor the negative likelihood ratio (P = 0.95) differed significantly between the IVC-COV2 NEWS model and the CURB-65 model.

Conclusion: According to the proposed mathematical model, the combination of the IVC-COV2 intermediate score and NEWS or CURB-65 score yields superior results and a greater predictive value for the severity of illness. To the best of our knowledge, this is the first population-based/mathematical model developed for use in COVID-19 critical care decision-making.

Prediction value of procalcitonin combining CURB-65 for 90-day mortality in community-acquired pneumonia

Background: Due to its high mortality rate, immediate and reliable severity assessment and accurate prediction of prognosis at hospital admission is critical for the management of community-acquired pneumonia (CAP) patients.Methods: Consecutive patients with primary diagnosis of CAP and hospitalized at our hospital from January 2013 to December 2015 were screened for this retrospective study. Demographic information, clinical and laboratory examination, severity model scoring, and 90-day outcomes were studied. Area under the curve (AUC) of receiver operating characteristic curve (ROC) was analyzed to compare the predictive value of different prognostic scoring methods.Results: 2099 CAP patients with a median age of 60 (IQR 44.0-73.0) years-old were included in this study. Median length of stay was 10 days (IQR 8.0-13.0). The all-cause 90-day mortality was found in 2.19% (46/2099) of all patients. PCT was identified as an independent predictor for the prognosis of CAP patients. CURB-65 in combination with PCT outperformed other predictive methods in 90-day mortality with the optimal AUC of 0.900 and Youden's Index of 0.706.Conclusions: PCT is a good marker for the assessment of severity and 90-day mortality of CAP patients. The combination of PCT and CURB-65 was more accurate than other prognostic models in predicting 90-day mortality.