Evidence map›Paper›PMID 39841336›Full record

ArticleAnnals of surgical oncology2025

Predicting Postoperative Infection After Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy with Splenectomy.

Nolan M Winicki, Shannon N Radomski, Yusuf Ciftci, Fabian M Johnston, Jonathan B Greer

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Article in Annals of surgical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Nolan M Winicki *Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Shannon N Radomski *Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Yusuf CiftciDepartment of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Fabian M JohnstonDepartment of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Jonathan B GreerDepartment of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA. jgreer13@jhmi.edu.ORCID http://orcid.org/0000-0001-8968-9710

Funding

NCI NIH HHS 5T32CA126607-12
6 · The paper itself

Abstract

backgroundHematologic 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.

methodsThe 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.

resultsFrom 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).

conclusionA 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.

Indexed as

Cytoreduction Surgical ProceduresHyperthermic Intraperitoneal ChemotherapyInfectionsMachine LearningPeritoneal NeoplasmsPostoperative ComplicationsSplenectomyAgedAppendiceal NeoplasmsCombined Modality TherapyFemaleFollow-Up StudiesHumansMaleMiddle AgedPrognosisCRS/HIPECMachine learningPost-operative infectionSplenectomy

Identifiers

PMID39841336

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.