Highlighting uncertainty in clinical risk prediction using a model of emergency laparotomy mortality risk.

ABSTRACT: Clinical prediction models typically make point estimates of risk. However, values of key variables are often missing during model development or at prediction time, meaning that the point estimates mask significant uncertainty and can lead to over-confident decision making. We present a model of mortality risk in emergency laparotomy which instead presents a distribution of predicted risks, highlighting the uncertainty over the risk of death with an intuitive visualisation. We developed and validated our model using data from 127134 emergency laparotomies from patients in England and Wales during 2013-2019. We captured the uncertainty arising from missing data using multiple imputation, allowing prospective, patient-specific imputation for variables that were frequently missing. Prospecti

SUBMITTER: Mathiszig-Lee JF 

PROVIDER: S-EPMC9177766 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature