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Background: Community-acquired pneumonia (CAP) is one of the leading causes of morbidity and mortality in the USA. Our objective was to assess the predictive value on critical illness and disposition of a sequential Bayesian Model that integrates Lactate and procalcitonin (PCT) for pneumonia., Methods: Sensitivity and specificity of lactate and PCT attained from pooled meta-analysis data. Likelihood ratios calculated and inserted in Bayesian/ Fagan nomogram to calculate posttest probabilities. Bayesian Diagnostic Gains (BDG) were analyzed comparing pre and post-test probability. To assess the value of integrating both PCT and Lactate in Severity of Illness Prediction we built a model that combined CURB65 with PCT as the Pre-Test markers and later integrated the Lactate Likelihood Ratio Values to generate a combined CURB 65 + Procalcitonin + Lactate Sequential value., Results: The BDG model integrated a CUBR65 Scores combined with Procalcitonin (LR+ and LR-) for Pre-Test Probability Intermediate and High with Lactate Positive Likelihood Ratios. This generated for the PCT LR+ Post-test Probability (POSITIVE TEST) Posterior probability: 93% (95% CI [91,96%]) and Post Test Probability (NEGATIVE TEST) of: 17% (95% CI [15-20%]) for the Intermediate subgroup and 97% for the high risk sub-group POSITIVE TEST: Post-Test probability:97% (95% CI [95,98%]) NEGATIVE TEST: Post-test probability: 33% (95% CI [31,36%]) . ANOVA analysis for CURB 65 (alone) vs CURB 65 and PCT (LR+) vs CURB 65 and PCT (LR+) and Lactate showed a statistically significant difference (P value = 0.013)., Conclusions: The sequential combination of CURB 65 plus PCT with Lactate yielded statistically significant results, demonstrating a greater predictive value for severity of illness thus ICU level care.

The objective of this study was to develop a Bayesian clinical decision support mathematical model that can assist in assessing a diagnostic utility integrating the aortic dissection detection risk score (ADD-RS) combined with the diagnostic quality of D-dimer testing., Methods: Our method uses the Bayes nomogram. Pretest probability scoring for the ADD-RS was obtained using their derived precalculated effects models. Sensitivity, specificity, and positive and negative likelihood ratios (LRs) for D-dimer testing were obtained by meta-analysis. Posttest probability was obtained from Bayesian statistical modeling integrating low, intermediate, and high pretest for the ADD-RS and LRs for D-dimer testing. Relative (RDG) and absolute (AADG) diagnostic gains were calculated., Results: Pool meta-analysis of D-dimer data demonstrated a sensitivity of 0.97 (95% confidence interval [CI], 0.94-0.99), specificity of 0.56 (95% CI, 0.51-0.60), negative LR of 0.06 (95% CI, 0.03-0.12), and positive LR of 2.43 (95% CI, 1.89-3.12). Bayesian modeling for negative LRs demonstrated posttest probabilities scores of 0.24% for low risk (AADG = 4.06% and RDG=94.42%), 3.4% for intermediate risk (AADG = 33.1% and RDG=90.68%), and 7.9% for high risk (AADG = 51.3% and RDG=86.65%)., Conclusion: The integration of the ADD-RS and D-dimer testing in a decision support scheme suggested rule-out diagnostic value and gains, mostly evidenced in the AADD-RS low and intermediate pretest probability categories. We propose further evaluating the use of this decision support scheme in a prospective model and as a potential triage tool for aortic dissection., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Unlabelled: Previous research demonstrated that shock index and respiratory rate are highly predictive of intensive care unit admissions., Objective: The objective of the study is to evaluate the integration of the prehospital sepsis project score (PSP-S) and point-of-care lactate in assisting prediction of severity of illness using Bayesian statistical modeling., Methods: The PSP-S incorporates fever (38°C [100.4°F]) allotted with 1 point, shock index greater than or equal to 0.7 given 2 points, and a respiratory rate greater than or equal to 22 breaths per minute given 1 point for a total maximum score of 4 points. The patient population was stratified based on the PSP-S: 1 point is low risk, 2 points is moderate risk, and 3 to 4 points is high risk. Percentage risk was obtained based on intensive care unit admissions and used as pretest probability. Prehospital lactate pooled data were obtained and used to calculate likelihood ratio (LR). Percentage risk used as pretest probability and LRs for prehospital lactate were charted into the Bayesian nomogram to obtain posttest probabilities. Absolute diagnostic gain (ADG) and relative diagnostic gains (RDG) were then calculated., Results: Pooled data for prehospital point of care lactate demonstrated a positive LR of 1.6 and negative LR of 0.44. Posttest probability for low risk was 16% with an ADG of 6% and RDG of 160%. Moderate risk population yielded a posttest probability of 47%, ADG of 12.5%, and RDG of 136.2%. High-risk population resulted in a posttest probability of 72%, ADG of 12%, and RDG of 120%., Conclusion: We found that PSP-S can be clinically complemented with the use of point-of-care lactate., (Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.)