International journal of critical illness and injury science · 2026
Plasma lactate and severity of illness for acute pulmonary embolism: A Bayesian probability model.
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Báez Amado Alejandro, Ibu Faith, Halasz Helena
Аннотация
BACKGROUND: Plasma lactate >2 mmol/L has been associated with increased mortality in acute pulmonary embolism (PE), even among hemodynamically stable patients. Despite strong evidence, lactate remains underutilized in risk stratification tools such as the simplified PE Severity Index (sPESI). To assess the incremental prognostic value of plasma lactate when integrated into a Bayesian probability model for Pulmonary Embolism.
METHODS: Pretest probabilities were defined by SI and sPESI. Sensitivity and specificity for plasma lactate >2 mmol/L were derived from prior meta-analyses and validated multicenter datasets. Likelihood ratios were applied within a Bayesian nomogram to estimate posttest probabilities. Diagnostic efficiency was evaluated using Bayesian Diagnostic Gain (BDG) and Bayesian Number Needed to Diagnose (BNND).
RESULTS: Thirty-day mortality for SI >1 was 24.1% versus 10.7% for low-risk SI and 10.7% versus 1.5% for sPESI. Lactate >2 mmol/L yielded 82.4% sensitivity (95% confidence interval [CI]: 56.8-95.3) and 73.5% specificity (95% CI: 71.8-74.4). Integrating lactate into the SI model produced an Absolute Diagnostic Gain (ADG) of 25.9% (BNND = 4), outperforming the sPESI model (ADG: 16.3%, BNND = 6).
CONCLUSIONS: Bayesian integration of plasma lactate with the SI enhances prognostic accuracy and produces superior diagnostic gains compared with sPESI-based models. This supports a probabilistic, physiology-driven approach to PE risk stratification.
METHODS: Pretest probabilities were defined by SI and sPESI. Sensitivity and specificity for plasma lactate >2 mmol/L were derived from prior meta-analyses and validated multicenter datasets. Likelihood ratios were applied within a Bayesian nomogram to estimate posttest probabilities. Diagnostic efficiency was evaluated using Bayesian Diagnostic Gain (BDG) and Bayesian Number Needed to Diagnose (BNND).
RESULTS: Thirty-day mortality for SI >1 was 24.1% versus 10.7% for low-risk SI and 10.7% versus 1.5% for sPESI. Lactate >2 mmol/L yielded 82.4% sensitivity (95% confidence interval [CI]: 56.8-95.3) and 73.5% specificity (95% CI: 71.8-74.4). Integrating lactate into the SI model produced an Absolute Diagnostic Gain (ADG) of 25.9% (BNND = 4), outperforming the sPESI model (ADG: 16.3%, BNND = 6).
CONCLUSIONS: Bayesian integration of plasma lactate with the SI enhances prognostic accuracy and produces superior diagnostic gains compared with sPESI-based models. This supports a probabilistic, physiology-driven approach to PE risk stratification.