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.

中文翻译：

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针对肺炎的疾病预测因子和重症监护病房严重程度的贝叶斯决策支持顺序模型。

背景技术社区获得性肺炎（CAP）是美国发病率和死亡率的主要原因之一。我们的目标是评估危重病的预测价值，并采用整合乳酸盐和降钙素（PCT）治疗肺炎的序贯贝叶斯模型的处置价值。方法从汇总的荟萃分析数据中获得乳酸和PCT的敏感性和特异性。计算似然比，并将其插入贝叶斯/法根诺模图中以计算后测概率。分析贝叶斯诊断增益（BDG），比较测试前和测试后的概率。为了评估在疾病严重程度预测中整合PCT和乳酸的价值，我们建立了一个模型，该模型将CURB65与PCT作为预测试标记，然后整合了乳酸可能性比值，以生成CURB 65 +降钙素原+乳酸序列值的合并值。结果BDG模型将CUBR65评分与降钙素原（LR +和LR-）结合在一起，可得出中等或较高的预测概率，且乳酸阳性可能性高。这是针对PCT LR +测试后概率（正测试）产生的后验概率：93％（95％CI [91,96％]），测试后概率（负测试）为：17％（95％CI [15-20]百分比]）（中级小组）和97％（高风险小组）阳性测试：测试后概率：97％（95％CI [95,98％]）阴性测试：测试后概率：33％（95 ％CI [31,36％]）。CURB 65（单独）与CURB 65和PCT（LR +）与CURB 65和PCT（LR +）和乳酸盐的ANOVA分析显示出统计学上的显着差异（P值= 0.013）。结论CURB 65加上PCT与乳酸盐的顺序组合产生了统计学上显着的结果，表明对ICU级护理的疾病严重程度具有更大的预测价值。