From the Division of Trauma, Emergency Surgery, and Surgical Critical Care (M.E.M., A.G., L.R.M., L.N., M.E.H., K.B., A.D.-G., R.S., G.V., H.M.A.K.), Massachusetts General Hospital, Boston; and Massachusetts Institute of Technology (D.B.), Cambridge, Massachusetts.
J Trauma Acute Care Surg. 2023 Oct 1;95(4):565-572. doi: 10.1097/TA.0000000000004030. Epub 2023 Jun 14.
Artificial intelligence (AI) risk prediction algorithms such as the smartphone-available Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) for emergency general surgery (EGS) are superior to traditional risk calculators because they account for complex nonlinear interactions between variables, but how they compare to surgeons' gestalt remains unknown. Herein, we sought to: (1) compare POTTER to surgeons' surgical risk estimation and (2) assess how POTTER influences surgeons' risk estimation.
A total of 150 patients who underwent EGS at a large quaternary care center between May 2018 and May 2019 were prospectively followed up for 30-day postoperative outcomes (mortality, septic shock, ventilator dependence, bleeding requiring transfusion, pneumonia), and clinical cases were systematically created representing their initial presentation. POTTER's outcome predictions for each case were also recorded. Thirty acute care surgeons with diverse practice settings and levels of experience were then randomized into two groups: 15 surgeons (SURG) were asked to predict the outcomes without access to POTTER's predictions while the remaining 15 (SURG-POTTER) were asked to predict the same outcomes after interacting with POTTER. Comparing to actual patient outcomes, the area under the curve (AUC) methodology was used to assess the predictive performance of (1) POTTER versus SURG, and (2) SURG versus SURG-POTTER.
POTTER outperformed SURG in predicting all outcomes (mortality-AUC: 0.880 vs. 0.841; ventilator dependence-AUC: 0.928 vs. 0.833; bleeding-AUC: 0.832 vs. 0.735; pneumonia-AUC: 0.837 vs. 0.753) except septic shock (AUC: 0.816 vs. 0.820). SURG-POTTER outperformed SURG in predicting mortality (AUC: 0.870 vs. 0.841), bleeding (AUC: 0.811 vs. 0.735), pneumonia (AUC: 0.803 vs. 0.753) but not septic shock (AUC: 0.712 vs. 0.820) or ventilator dependence (AUC: 0.834 vs. 0.833).
The AI risk calculator POTTER outperformed surgeons' gestalt in predicting the postoperative mortality and outcomes of EGS patients, and when used, improved the individual surgeons' risk prediction. Artificial intelligence algorithms, such as POTTER, could prove useful as a bedside adjunct to surgeons when preoperatively counseling patients.
Prognostic and Epidemiological; Level II.
智能手机可用的预测紧急外科手术风险的最优树（POTTER）等人工智能（AI）风险预测算法在预测紧急普通外科（EGS）手术风险方面优于传统风险计算器，因为它们可以解释变量之间复杂的非线性相互作用，但它们与外科医生的整体判断相比如何仍然未知。在此，我们试图：（1）比较 POTTER 与外科医生的手术风险评估；（2）评估 POTTER 如何影响外科医生的风险评估。
在 2018 年 5 月至 2019 年 5 月期间，在一家大型四级保健中心接受 EGS 的 150 名患者前瞻性随访 30 天术后结局（死亡率、脓毒性休克、呼吸机依赖、需要输血的出血、肺炎），并系统地创建了代表其初始表现的临床病例。还记录了 POTTER 对每个病例的结果预测。然后，将 30 名具有不同实践环境和经验水平的急性护理外科医生随机分为两组：15 名外科医生（SURG）被要求在没有 POTTER 预测结果的情况下预测结果，而其余 15 名外科医生（SURG-POTTER）则被要求在与 POTTER 交互后预测相同的结果。使用曲线下面积（AUC）方法评估（1）POTTER 与 SURG 和（2）SURG 与 SURG-POTTER 的预测性能。
POTTER 在预测所有结局方面均优于 SURG（死亡率-AUC：0.880 比 0.841；呼吸机依赖-AUC：0.928 比 0.833；出血-AUC：0.832 比 0.735；肺炎-AUC：0.837 比 0.753），但脓毒性休克除外（AUC：0.816 比 0.820）。SURG-POTTER 在预测死亡率（AUC：0.870 比 0.841）、出血（AUC：0.811 比 0.735）、肺炎（AUC：0.803 比 0.753）方面优于 SURG，但脓毒性休克（AUC：0.712 比 0.820）或呼吸机依赖（AUC：0.834 比 0.833）方面无差异。
人工智能风险计算器 POTTER 在预测 EGS 患者的术后死亡率和结局方面优于外科医生的整体判断，并且在使用时提高了个体外科医生的风险预测能力。人工智能算法，如 POTTER，在术前为患者提供咨询时，可以作为外科医生的床边辅助工具证明是有用的。
预后和流行病学；二级。
