Predictive modeling of in-hospital mortality following elective surgery

The specific healthcare macroenvironment factors contributing to in-hospital mortality following elective surgery remain nuanced. We hypothesize an accurate global elective surgical mortality model can be created. FL AHCA and Hospital Compare (2016–2019) were queried for in-hospital mortality following elective surgeries. Stepwise logistic regression with 47 patient and hospital factors was followed by gradient boosting machine (GBM) modeling describing the relative influence on risk for in-hospital mortality. Deceased and surviving patients were matched (1:2) to perform univariate analysis and logistic regression of significant factors. A total of 511,897 admissions, 2,266 patient deaths and 162 Florida hospitals were included. GBM factors (AUC 0.94) included post-operative patient and hospital factors. In the final regression model, patient age older than 70 years of age and hospital 5-star rating were significant (OR 2.87, 0.47, respectively). Hospitals rated 5-stars were protective of mortality. In-patient mortality following elective surgery is influenced by patient and hospital level factors. Efforts should be made to mitigate these risks or enhance those that are protective. •In-hospital mortality following elective surgery remains a challenge.•FL AHCA State inpatient data was linked with CMS Hospital Compare Data to examine patient and hospital factors.•Linked data exhibits patient comorbidities and post op complications, hospital profiles and quality indicators.•Machine learning techniques exhibited relative influence of patient and hospital factors affecting in-hospital mortality.•Our analysis identified a 13 top contributory factors model that identifies patients at high risk for in-hospital mortality.•This investigation confirms certain patient and hospital level factors are important in influencing these risks.