Winicki Nolan M, Radomski Shannon N, Ciftci Yusuf, Johnston Fabian M, Greer Jonathan B
Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Division of Gastrointestinal Surgical Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Ann Surg Oncol. 2025 Apr;32(4):2903-2911. doi: 10.1245/s10434-024-16728-1. Epub 2025 Jan 22.
Hematologic changes after splenectomy and hyperthermic intraperitoneal chemotherapy (HIPEC) can complicate postoperative assessment of infection. This study aimed to develop a machine-learning model to predict postoperative infection after cytoreductive surgery (CRS) and HIPEC with splenectomy.
The study enrolled patients in the national TriNetX database and at the Johns Hopkins Hospital (JHH) who underwent splenectomy during CRS/HIPEC from 2010 to 2024. Demographics, comorbidities, vital signs, daily laboratory values, and documented infections were collected. The patients were divided into infected and non-infected cohorts within 14 days postoperatively. Extreme gradient boost (XGBoost) machine-learning was used to predict postoperative infection. An initial model was generated using the TriNetX dataset and externally validated in the JHH cohort.
From TriNetX, 1016 patients were included: 802 in the non-infected group (79%) and 214 (21%) in the postoperative infection group. The mean age was 61 ± 13 years, and 597 (56%) of the patientswere female. Most of the patients underwent CRS/HIPEC with splenectomy for appendiceal cancer (n = 590, 56%), followed by colorectal malignancy (n = 299, 29%). The remainder (n = 127, 15%) underwent CRS/HIPEC with splenectomy for gastric, pancreatic, ovarian, and small bowel malignancies or peritoneal mesothelioma. In detecting any infection, XGBoost exhibited excellent prediction accuracy (area under the receiver operating characteristic curve [AUC], 0.910 ± 0.073; F1 score, 0.915 ± 0.040) and retained high accuracy upon external validation with 96 demographically similar JHH patients (AUC, 0.823 ± 0.08; F1 score, 0.864 ± 0.03).
A novel machine-learning algorithm was developed to predict postoperative infection after CRS/HIPEC with splenectomy that could aid in the early diagnosis and initiation of treatment.
脾切除术后及热灌注化疗（HIPEC）后的血液学变化可能使术后感染评估复杂化。本研究旨在开发一种机器学习模型，以预测减瘤手术（CRS）联合脾切除及HIPEC术后的感染情况。
本研究纳入了国家TriNetX数据库及约翰霍普金斯医院（JHH）中2010年至2024年期间在CRS/HIPEC手术中接受脾切除术的患者。收集了患者的人口统计学资料、合并症、生命体征、每日实验室检查值及记录的感染情况。患者在术后14天内被分为感染组和非感染组。采用极端梯度提升（XGBoost）机器学习方法预测术后感染。使用TriNetX数据集生成初始模型，并在JHH队列中进行外部验证。
从TriNetX数据库中纳入了1016例患者：非感染组802例（79%），术后感染组214例（21%）。平均年龄为61±13岁，597例（56%）患者为女性。大多数患者因阑尾癌接受CRS/HIPEC联合脾切除术（n = 590，56%），其次是结直肠癌（n = 299，29%）。其余患者（n = 127，15%）因胃、胰腺、卵巢、小肠恶性肿瘤或腹膜间皮瘤接受CRS/HIPEC联合脾切除术。在检测任何感染时，XGBoost表现出出色 的预测准确性（受试者工作特征曲线下面积[AUC]，0.910±0.073；F1评分，0.915±0.040），并在对96例人口统计学特征相似的JHH患者进行外部验证时保持了较高的准确性（AUC，0.823±0.08；F1评分，0.864±0.03）。
开发了一种新的机器学习算法来预测CRS/HIPEC联合脾切除术后的感染情况，这有助于早期诊断和开始治疗。
