Background

Although most conventional risk prediction models have been based on preoperative information, intra- and post-operative events may be more relevant to mortality after surgery. We sought to develop a mortality risk calculator based on real time characteristics associated with hepatectomy.

Methods

Patients who underwent hepatectomy between 2014 and 2017 were identified in the ACS-NSQIP dataset. Three prediction models (pre-, intra-, post-operative) were developed and validated using perioperative data.

Results

Among 14,720 patients, 197 (1.3%) experienced 30-day mortality. The predictive ability of the real-time mortality risk calculator was very good based on only preoperative factors (AUC; training cohort: 0.813, validation cohort: 0.731). Incorporating intra-operative variables into the model increased the AUC (training: 0.838, validation: 0.777), while the post-operative model achieved an AUC of 0.922 in the training and 0.885 in the validation cohorts, respectively. While patients with low preoperative risk had only very small fluctuations in the estimated 30-day mortality risk during the intraoperative (Δ0.4%) and postoperative (Δ0.6%) phases, patients who were already deemed high risk preoperatively had additional increased mortality risk based on factors that occurred in the intraoperative (Δ5.4%) and postoperative (Δ9.3%) periods.

Conclusion

A real-time mortality risk calculator may better help clinicians identify patients at risk of death at the different stages of the surgical episode.

中文翻译：

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肝切除术前、中、后实时死亡风险计算器的开发和验证：对 ACS NSQIP 数据库的分析。

背景

尽管大多数传统的风险预测模型都基于术前信息，但术中和术后事件可能与术后死亡率更相关。我们试图开发一种基于与肝切除术相关的实时特征的死亡风险计算器。

方法

在 ACS-NSQIP 数据集中确定了 2014 年至 2017 年期间接受肝切除术的患者。使用围手术期数据开发和验证了三种预测模型（术前、术中、术后）。

结果

在 14,720 名患者中，197 名（1.3%）经历了 30 天的死亡率。仅基于术前因素（AUC；训练队列：0.813，验证队列：0.731），实时死亡风险计算器的预测能力非常好。将术中变量纳入模型增加了 AUC（训练：0.838，验证：0.777），而术后模型在训练和验证队列中分别达到 0.922 和 0.885。虽然术前风险低的患者在术中 (Δ0.4%) 和术后 (Δ0.6%) 阶段估计的 30 天死亡风险波动很小，但术前已被认为是高风险的患者死亡率进一步增加风险基于术中 (Δ5.4%) 和术后 (Δ9.3%) 期间发生的因素。

结论

实时死亡风险计算器可以更好地帮助临床医生识别在手术发作的不同阶段有死亡风险的患者。