Predicting in-hospital mortality in pneumonia-associated septic shock patients using a classification and regression tree: a nested cohort study.
Jaime L SpeiserConstantine J KarvellasGeoffery ShumilakWendy I SliglYazdan MirzanejadDave GurkaAseem KumarAnand Kumarnull nullPublished in: Journal of intensive care (2018)
Overall mortality (51%) in patients with pneumonia complicated by septic shock is high. Increased time to administration of antimicrobial therapy, Acute Physiology and Chronic Health Evaluation II Score, serum lactate, and age were associated with increased in-hospital mortality. Classification and regression tree methodology offers a simple prognostic model with good performance in predicting in-hospital mortality.
Keyphrases
- septic shock
- end stage renal disease
- machine learning
- respiratory failure
- deep learning
- ejection fraction
- healthcare
- newly diagnosed
- chronic kidney disease
- peritoneal dialysis
- liver failure
- prognostic factors
- stem cells
- risk factors
- cardiovascular disease
- coronary artery disease
- patient reported outcomes
- mesenchymal stem cells
- social media
- aortic dissection
- community acquired pneumonia