title: Statistical methods for predicting mortality in patients diagnosed with acute pulmonary embolism.
abstract: OBJECTIVE
  Risk stratification in pulmonary embolism (PE) guides patient management. The purpose of this study was to develop and test novel mortality risk prediction models for subjects with acute PE diagnosed using computed tomographic pulmonary angiography in a large cohort with comprehensive clinical data.
  
  
  METHODS
  Retrospective analyses of 1596 consecutive subjects diagnosed with acute PE from a single, large, urban teaching hospital included two modern statistical methods to predict survival in patients with acute PE. Landmark analysis was used for 90-day mortality. Adaptive least absolute shrinkage and selection operator (aLASSO), a penalization method, was used to select variables important for prediction and to estimate model coefficients. Receiver-operating characteristic analysis was used to evaluate the resulting prediction rules.
  
  
  RESULTS
  Using 30-day all-cause mortality outcome, three of the 16 clinical risk factors (the presence of a known malignancy, coronary artery disease, and increased age) were associated with high risk, while subjects treated with anticoagulation had lower risk. For 90-day landmark mortality, subjects with recent operations had a lower risk for death. Both prediction rules developed using aLASSO performed well compared to standard logistic regression.
  
  
  CONCLUSIONS
  The aLASSO regression approach combined with landmark analysis provides a novel tool for large patient populations and can be applied for clinical risk stratification among subjects diagnosed with acute PE. After positive results on computed tomographic pulmonary angiography, the presence of a known malignancy, coronary artery disease, and advanced age increase 30-day mortality. Additional risk stratification can be simplified with these methods, and future work will place imaging-based prediction of mortality in perspective with other clinical data.
authors.affiliation: Department of Biostatistics, Harvard University, Cambridge, MA, USA.
authors.forename: Layla
authors.initials: L
authors.lastname: Parast
authors.forename: Bryan
authors.initials: B
authors.lastname: Cai
authors.forename: Arash
authors.initials: A
authors.lastname: Bedayat
authors.forename: Kanako K
authors.initials: KK
authors.lastname: Kumamaru
authors.forename: Elizabeth
authors.initials: E
authors.lastname: George
authors.forename: Karin E
authors.initials: KE
authors.lastname: Dill
authors.forename: Frank J
authors.initials: FJ
authors.lastname: Rybicki
mesh_terms: D000208:Acute Disease; D003627:Data Interpretation, Statistical; D006801:Humans; D016015:Logistic Models; D008168:Lung / Q000098:blood supply*; D011655:Pulmonary Embolism / Q000000981:diagnostic imaging* / Q000401:mortality*; D012372:ROC Curve; D018570:Risk Assessment; D014057:Tomography, X-Ray Computed; D018497:Ventricular Dysfunction, Right / Q000150:complications