可推广的手术时长预测模型：多中心开发与时间验证
Generalisable prediction model of surgical case duration: multicentre development and temporal validation
AI总结：
针对现有手术时长预测模型可推广性受限的问题，本研究开发了基于多中心数据的堆叠机器学习模型，经时间外部验证显示准确且校准良好，支持跨机构和时间迁移，可改进手术室调度。
摘要：
背景：准确预测手术时长是手术室（OR）调度的基础，但现有模型通常依赖特定机构或外科医生的输入，且很少经过外部验证，导致可推广性受限。方法：我们开展了一项回顾性多中心研究，使用日本两家综合医院常规收集的围手术期数据（开发集：2021年1月1日至2023年12月31日；时间测试集：2024年1月1日至12月31日）。纳入美国麻醉医师协会（ASA）身体状况分级1-4级的择期工作日手术。预先指定的术前预测因素包括手术背景（年份、月份、工作日、计划时长、全身麻醉指标、体位）和患者因素（性别、年龄、体重指数、过敏史、感染史、合并症、ASA分级）。缺失数据通过链式方程多重插补处理。四种学习器（弹性网络、广义相加模型、随机森林、梯度提升树）在内部-外部交叉验证（IECV；按机构-年份留一聚类）中进行调优，并通过堆叠泛化组合以预测对数转换后的时长。结果：我们分析了63206例手术（开发集45647例；时间测试集17559例）。提供了IECV的聚类特异性及汇总误差与校准结果，各机构和年份间性能一致。在2024年时间测试队列中，校准良好（截距0.423，95%置信区间0.372至0.474；斜率0.921，95%置信区间0.911至0.932）。结论：仅使用广泛可用的术前变量的堆叠机器学习模型在时间外部验证中实现了准确且校准良好的预测，支持跨机构和跨时间的可迁移性。此类通用工具或可在不依赖特殊输入的情况下改进OR调度。
英文摘要：
Background: Accurate prediction of surgical case duration underpins operating room (OR) scheduling, yet existing models often depend on site- or surgeon-specific inputs and rarely undergo external validation, limiting generalisability. Methods: We undertook a retrospective multicentre study using routinely collected perioperative data from two general hospitals in Japan (development: 1 January 2021-31 December 2023; temporal test: 1 January-31 December 2024). Elective weekday procedures with American Society of Anesthesiologists (ASA) Physical Status 1-4 were included. Pre-specified preoperative predictors comprised surgical context (year, month, weekday, scheduled duration, general anaesthesia indicator, body position) and patient factors (sex, age, body mass index, allergy, infection, comorbidity, ASA). Missing data were addressed by multiple imputation by chained equations. Four learners (elastic-net, generalised additive models, random forest, gradient-boosted trees) were tuned within internal-external cross-validation (IECV; leave-one-cluster-out by centre-year) and combined by stacked generalisation to predict log-transformed duration. Results: We analysed 63,206 procedures (development 45,647; temporal test 17,559). Cluster-specific and pooled errors and calibrations from IECV are provided with consistent performance across centres and years. In the 2024 temporal test cohort, calibration was good (intercept 0.423, 95%CI 0.372 to 0.474; slope 0.921, 95%CI 0.911 to 0.932). Conclusions: A stacked machine-learning model using only widely available preoperative variables achieved accurate, well-calibrated predictions in temporal external validation, supporting transportability across sites and over time. Such general-purpose tools may improve OR scheduling without relying on idiosyncratic inputs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
