Complications are common following major surgery and are associated with increased use of healthcare resources, disability and mortality. Continued reliance on mortality estimates risks harming patients and health systems, but existing tools for predicting complications are unwieldy and inaccurate. We aimed to systematically construct an accurate pre-operative model for predicting major postoperative complications; compare its performance against existing tools; and identify sources of inaccuracy in predictive models more generally. Complete patient records from the UK Peri-operative Quality Improvement Programme dataset were analysed. Major complications were defined as Clavien–Dindo grade ≥ 2 for novel models. In a 75% train:25% test split cohort, we developed a pipeline of increasingly complex models, prioritising pre-operative predictors using Least Absolute Shrinkage and Selection Operators (LASSO). We defined the best model in the training cohort by the lowest Akaike's information criterion, balancing accuracy and simplicity. Of the 24,983 included cases, 6389 (25.6%) patients developed major complications. Potentially modifiable risk factors (pain, reduced mobility and smoking) were retained. The best-performing model was highly complex, specifying individual hospital complication rates and 11 patient covariates. This novel model showed substantially superior performance over generic and specific prediction models and scores. We have developed a novel complications model with good internal accuracy, re-prioritised predictor variables and identified hospital-level variation as an important, but overlooked, source of inaccuracy in existing tools. The complexity of the best-performing model does, however, highlight the need for a step-change in clinical risk prediction to automate the delivery of informative risk estimates in clinical systems.

中文翻译：

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在围手术期质量改进项目 (PQIP) 数据集中系统开发和验证主要术后并发症的预测模型

并发症在大手术后很常见，并且与医疗资源的使用增加、残疾和死亡率有关。继续依赖死亡率估计可能会损害患者和卫生系统，但现有的并发症预测工具既笨重又不准确。我们的目标是系统地构建准确的术前模型来预测术后主要并发症；将其性能与现有工具进行比较；并更广泛地识别预测模型中不准确的来源。对英国围手术期质量改进计划数据集中的完整患者记录进行了分析。对于新型模型，主要并发症被定义为 Clavien-Dindo 等级≥2。在 75% 训练：25% 测试分割队列中，我们开发了一系列日益复杂的模型，使用最小绝对收缩和选择算子 (LASSO) 优先考虑术前预测变量。我们通过最低的 Akaike 信息标准定义了训练队列中的最佳模型，平衡了准确性和简单性。在纳入的 24,983 例病例中，6389 例（25.6%）患者出现严重并发症。保留潜在可改变的危险因素（疼痛、活动能力下降和吸烟）。表现最好的模型非常复杂，指定了各个医院的并发症发生率和 11 个患者协变量。这种新颖的模型显示出比通用和特定的预测模型和分数显着优越的性能。我们开发了一种新颖的并发症模型，具有良好的内部准确性，重新确定了预测变量的优先级，并将医院水平的变化确定为现有工具中重要但被忽视的不准确性来源。然而，性能最佳模型的复杂性确实凸显了临床风险预测需要发生重大变化，以在临床系统中自动提供信息丰富的风险估计。