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. Prospective imputation allows early prognostication in patients where these variables are not yet measured, accounting for the additional uncertainty this induces. Our model showed good discrimination and calibration (95% confidence intervals: Brier score 0.071–0.078, C statistic 0.859–0.873, calibration error 0.031–0.059) on unseen data from 37 hospitals, consistently improving upon the current gold-standard model. The dispersion of the predicted risks varied significantly between patients and increased where prospective imputation occurred. We present a case study that illustrates the potential impact of uncertainty quantification on clinical decision making. Our model improves mortality risk prediction in emergency laparotomy and has the potential to inform decision-makers and assist discussions with patients and their families. Our analysis code was robustly developed and is publicly available for easy replication of our study and adaptation to predicting other outcomes.

临床预测模型通常对风险进行点估计。然而，在模型开发或预测时，关键变量的值经常丢失，这意味着点估计掩盖了显着的不确定性，并可能导致过度自信的决策。我们提出了紧急剖腹手术中的死亡风险模型，该模型呈现了预测风险的分布，通过直观的可视化强调了死亡风险的不确定性。我们使用 2013 年至 2019 年英格兰和威尔士 127134 例紧急剖腹手术患者的数据开发并验证了我们的模型。我们使用多重插补捕获了因缺失数据而产生的不确定性，从而可以对经常缺失的变量进行前瞻性、特定于患者的插补。前瞻性插补可以在尚未测量这些变量的情况下对患者进行早期预测，从而解释由此引起的额外不确定性。我们的模型对来自 37 家医院的未见数据表现出良好的区分和校准（95% 置信区间：Brier 评分 0.071–0.078，C 统计量 0.859–0.873，校准误差 0.031–0.059），不断改进当前的黄金标准模型。预测风险的离散度在患者之间存在显着差异，并且在发生前瞻性插补时会增加。我们提出了一个案例研究，说明不确定性量化对临床决策的潜在影响。我们的模型改进了紧急剖腹手术的死亡风险预测，并有可能为决策者提供信息并协助与患者及其家人进行讨论。我们的分析代码经过精心开发，可公开获取，以便轻松复制我们的研究并适应预测其他结果。