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

Omar Cantu-Martinez; Jose Manuel Martinez Manzano; Sahana Tito; Alexander Prendergast; Simone A Jarrett; Brenda Chiang; Ammaar Wattoo; Zurab Azmaiparashvili; Kevin Bryan Lo; Sadia Benzaquen; Glenn Eiger

doi:10.1016/j.rmed.2023.107295

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

Respir Med. 2023 Aug-Sep:215:107295. doi: 10.1016/j.rmed.2023.107295. Epub 2023 May 24.

Affiliations

¹ Department of Medicine, Einstein Medical Center Philadelphia 5501 Old York Road, Philadelphia, PA, 19141, USA. Electronic address: omar.cantumartinez@jefferson.edu.
² Department of Medicine, Einstein Medical Center Philadelphia 5501 Old York Road, Philadelphia, PA, 19141, USA.
³ Department of Medicine, Einstein Medical Center Philadelphia 5501 Old York Road, Philadelphia, PA, 19141, USA; Sidney Kimmel College of Medicine, Thomas Jefferson University, PA 5501 Old York Road, Philadelphia, PA, 19141, USA.
⁴ Department of Medicine, Einstein Medical Center Philadelphia 5501 Old York Road, Philadelphia, PA, 19141, USA; Sidney Kimmel College of Medicine, Thomas Jefferson University, PA 5501 Old York Road, Philadelphia, PA, 19141, USA; Department of Pulmonary and Critical Care Medicine, Einstein Medical Center, 5501 Old York Road, Philadelphia, PA, 19141, USA.

PMID: 37236407
DOI: 10.1016/j.rmed.2023.107295

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: Acute kidney injury; Leukocytosis; Outcomes; Pulmonary embolism; Tachypnea; Troponin.

MeSH terms

Acute Disease
Creatinine
Female
Humans
Male
Middle Aged
Prognosis
Pulmonary Embolism* / diagnosis
Pulmonary Embolism* / epidemiology
Pulmonary Embolism* / therapy
Retrospective Studies
Risk Assessment / methods
Risk Factors
Troponin

Substances

Creatinine
Troponin