Abstract

OBJECTIVES

The ability to accurately estimate the risk of peri-operative mortality after lung resection is important. There are concerns about the performance and validity of existing models developed for this purpose, especially when predicting mortality within 90 days of surgery. The aim of this study was therefore to develop a clinical prediction model for mortality within 90 days of undergoing lung resection.

METHODS

A retrospective database of patients undergoing lung resection in two UK centres between 2012 and 2018 was used to develop a multivariable logistic risk prediction model, with bootstrap sampling used for internal validation. Apparent and adjusted measures of discrimination (area under receiving operator characteristic curve) and calibration (calibration-in-the-large and calibration slope) were assessed as measures of model performance.

RESULTS

Data were available for 6600 lung resections for model development. Predictors included in the final model were age, sex, performance status, percentage predicted diffusion capacity of the lung for carbon monoxide, anaemia, serum creatinine, pre-operative arrhythmia, right-sided resection, number of resected bronchopulmonary segments, open approach and malignant diagnosis. Good model performance was demonstrated, with adjusted area under receiving operator characteristic curve, calibration-in-the-large and calibration slope values (95% confidence intervals) of 0.741 (0.700, 0.782), 0.006 (−0.143, 0.156) and 0.870 (0.679, 1.060), respectively.

CONCLUSIONS

The RESECT-90 model demonstrates good statistical performance for the prediction of 90-day mortality after lung resection. A project to facilitate large-scale external validation of the model to ensure that the model retains accuracy and is transferable to other centres in different geographical locations is currently underway.

中文翻译：

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肺切除术后 90 天死亡率临床预测模型的开发和内部验证：RESECT-90 评分

摘要

目标

准确估计肺切除术后围手术期死亡风险的能力很重要。人们担心为此目的开发的现有模型的性能和有效性，特别是在预测手术后 90 天内的死亡率时。因此，本研究的目的是开发一种用于肺切除术后 90 天内死亡率的临床预测模型。

方法

使用 2012 年至 2018 年间在两个英国中心接受肺切除术的患者的回顾性数据库来开发多变量逻辑风险预测模型，并使用自举抽样进行内部验证。区分表观和调整的措施（接收操作员特征曲线下的面积）和校准（大校准和校准斜率）被评估为模型性能的度量。

结果

6600 例肺切除的数据可用于模型开发。最终模型中包括的预测因素是年龄、性别、体能状态、预测的肺一氧化碳扩散能力百分比、贫血、血清肌酐、术前心律失常、右侧切除、切除的支气管肺段数量、开放方法和恶性诊断。证明了良好的模型性能，接收操作员特征曲线下的调整面积、大校准和校准斜率值（95% 置信区间）分别为 0.741（0.700、0.782）、0.006（-0.143、0.156）和 0.870（ 0.679，1.060），分别。

结论

RESECT-90 模型在预测肺切除术后 90 天死亡率方面表现出良好的统计性能。目前正在进行一个促进模型的大规模外部验证的项目，以确保模型保持准确性并可以转移到不同地理位置的其他中心。