Annals of surgical oncology · 2025
Predicting Postoperative Infection After Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy with Splenectomy.
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Winicki Nolan M, Radomski Shannon N, Ciftci Yusuf, Johnston Fabian M, Greer Jonathan B
Аннотация
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSION: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSION: 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.