Department of Anaesthesiology and Perioperative Medicine, Alfred Health, Melbourne, Victoria, Australia.
Department of Anaesthesiology and Perioperative Medicine, Monash University, Melbourne, Victoria, Australia.
ANZ J Surg. 2022 Nov;92(11):2873-2880. doi: 10.1111/ans.17946. Epub 2022 Aug 18.
We previously conducted a systematic review to identify surgical mortality risk prediction tools suitable for adapting in the Australian context and identified the Surgical Outcome Risk Tool (SORT) as an ideal model. The primary aim was to investigate the external validity of SORT for predicting in-hospital mortality in a large Australian private health insurance dataset.
A cohort study using a prospectively collected Australian private health insurance dataset containing over 2 million deidentified records. External validation was conducted by applying the predictive equation for SORT to the complete case analysis dataset. Model re-estimation (recalibration) was performed by logistic regression.
The complete case analysis dataset contained 161 277 records. In-hospital mortality was 0.2% (308/161277). The mean estimated risk given by SORT was 0.2% and the median (IQR) was 0.01% (0.003%-0.08%). Discrimination was high (c-statistic 0.96) and calibration was accurate over the range 0%-10%, beyond which mortality was over-predicted but confidence intervals included or closely approached the perfect prediction line. Re-estimation of the equation did not improve over-prediction. Model diagnostics suggested the presence of outliers or highly influential values.
The low perioperative mortality rate suggests the dataset was not representative of the overall Australian surgical population, primarily due to selection bias and classification bias. Our results suggest SORT may significantly under-predict 30-day mortality in this dataset. Given potential differences in perioperative mortality, private health insurance status and hospital setting should be considered as covariables when a locally validated national surgical mortality risk prediction model is developed.
我们之前进行了一项系统评价，以确定适合澳大利亚国情的外科手术死亡率风险预测工具，并将外科手术结局风险工具（SORT）确定为理想模型。主要目的是研究 SORT 在预测澳大利亚大型私人医疗保险数据集中院内死亡率的外部有效性。
一项使用前瞻性收集的澳大利亚私人医疗保险数据集的队列研究，该数据集包含超过 200 万份匿名记录。通过将 SORT 的预测方程应用于完整病例分析数据集，进行外部验证。通过逻辑回归进行模型重新估算（再校准）。
完整病例分析数据集包含 161277 条记录。院内死亡率为 0.2%（308/161277）。SORT 给出的平均估计风险为 0.2%，中位数（IQR）为 0.01%（0.003%-0.08%）。区分度高（c 统计量为 0.96），校准准确，范围为 0%-10%，超过此范围死亡率被高估，但置信区间包含或接近完美预测线。重新估算方程并未改善高估。模型诊断表明存在离群值或高度影响值。
围手术期死亡率低表明该数据集不能代表澳大利亚整个外科人群，主要是由于选择偏倚和分类偏倚。我们的结果表明，在该数据集中，SORT 可能会显著低估 30 天死亡率。鉴于围手术期死亡率、私人医疗保险状况和医院环境可能存在差异，在开发经过验证的全国外科手术死亡率风险预测模型时，应将这些因素视为协变量。
