Clinical features and risk factors of adverse clinical outcomes in central pulmonary embolism using machine learning...

In prior studies, central pulmonary embolism (PE) was associated with high clot burden and was considered an independent predictor for thrombolysis. Further information about predictors of adverse outcomes in these patients is needed for better risk stratification. The objective is to describe independent predictors of adverse clinical outcomes in patients with central PE.

Large retrospective, observational, and single-center study of hospitalized patients with central PE. Data were gathered on demographics, comorbidities, clinical features on admission, imaging, treatments, and outcomes. Multivariable standard and Least Absolute Shrinkage and Selection Operator (LASSO) machine learning logistic regressions and sensitivity analyses were used to analyze factors associated with a composite of adverse clinical outcomes, including vasopressor use, mechanical ventilation, and inpatient mortality.

A total of 654 patients had central PE. The mean age was 63.1 years, 59% were women, and 82% were African American. The composite adverse outcome was observed in 18% (n = 115) of patients. Serum creatinine elevation (odds ratio [OR] = 1.37, 95% CI = 1.20-1.57; p = 0.0001), white blood cell (WBC) count elevation (OR = 1.10, 95% CI = 1.05-1.15; p < 0.001), higher simplified pulmonary embolism severity index (sPESI) score (OR = 1.47, 95% CI = 1.18-1.84; p = 0.001), serum troponin elevation (OR = 1.26, 95% CI 1.02-1.56; p = 0.03), and respiratory rate increase (OR = 1.03, 95% CI = 1.0-1.05; p = 0.02) were independent predictors of adverse clinical outcomes.

Among patients with central PE, higher sPESI score, WBC count elevation, serum creatinine elevation, serum troponin elevation, and respiratory rate increase were independent predictors of adverse clinical outcomes. Right ventricular dysfunction on imaging and saddle PE location did not predict adverse outcomes.