Machine Learning Risk Prediction Model of 90-day Mortality After Gastrectomy for Cancer
Por:
Pera, M, Gibert, J, Gimeno, M, Garsot, E, Eizaguirre, E, Miro, M, Castro, S, Miranda, C, Reka, L, Leturio, S, Gonzalez-Duaigues, M, Codony, C, Gobbini, Y, Luna, A, Fernandez-Ananin, S, Sarriugarte, A, Olona, C, Rodriguez-Santiago, J, Osorio, J, Grande, L
Publicada:
1 nov 2022
Resumen:
Objective: To develop and validate a risk prediction model of 90-day mortality (90DM) using machine learning in a large multicenter cohort of patients undergoing gastric cancer resection with curative intent. Background: The 90DM rate after gastrectomy for cancer is a quality of care indicator in surgical oncology. There is a lack of well-validated instruments for personalized prognosis of gastric cancer. Methods: Consecutive patients with gastric adenocarcinoma who underwent potentially curative gastrectomy between 2014 and 2021 registered in the Spanish EURECCA Esophagogastric Cancer Registry database were included. The 90DM for all causes was the study outcome. Preoperative clinical characteristics were tested in four 90DM predictive models: Cross Validated Elastic regularized logistic regression method (cv-Enet), boosting linear regression (glmboost), random forest, and an ensemble model. Performance was evaluated using the area under the curve by 10-fold cross-validation. Results: A total of 3182 and 260 patients from 39 institutions in 6 regions were included in the development and validation cohorts, respectively. The 90DM rate was 5.6% and 6.2%, respectively. The random forest model showed the best discrimination capacity with a validated area under the curve of 0.844 [95% confidence interval (CI): 0.841-0.848] as compared with cv-Enet (0.796, 95% CI: 0.784-0.808), glmboost (0.797, 95% CI: 0.785-0.809), and ensemble model (0.847, 95% CI: 0.836-0.858) in the development cohort. Similar discriminative capacity was observed in the validation cohort. Conclusions: A robust clinical model for predicting the risk of 90DM after surgery of gastric cancer was developed. Its use may aid patients and surgeons in making informed decisions.
Filiaciones:
Pera, M:
Univ Autonoma Barcelona, Sect Gastrointestinal Surg, Hosp del Mar, Dept Surg,Hosp del Mar Med Res Inst IMIM, Barcelona, Spain
Gibert, J:
Hosp del Mar Med Res Inst IMIM, Hosp Univ del Mar, Dept Pathol, Canc Res Program, Barcelona, Spain
Gimeno, M:
Univ Autonoma Barcelona, Sect Gastrointestinal Surg, Hosp del Mar, Dept Surg,Hosp del Mar Med Res Inst IMIM, Barcelona, Spain
Garsot, E:
Univ Autonoma Barcelona, Hosp Univ Germans Trias I Pujol, Dept Surg, Barcelona, Spain
Eizaguirre, E:
Hosp Univ Donostia, Dept Surg, Donostia San Sebastian, Spain
Miro, M:
Hosp Univ Bellvitge, Dept Surg, Barcelona, Spain
Castro, S:
Univ Autonoma Barcelona, Hosp Univ Vall dHebron, Dept Surg, Barcelona, Spain
Miranda, C:
Hosp Univ Navarra, Dept Surg, Pamplona, Spain
Reka, L:
Hosp Univ Araba, Dept Surg, Vitoria, Spain
Leturio, S:
Hosp Univ Basurto, Dept Surg, Bilbao, Spain
Gonzalez-Duaigues, M:
Hosp Arnau Vilanova, Dept Surg, Lleida, Spain
Codony, C:
Hosp Univ Josep Trueta, Dept Surg, Girona, Spain
Gobbini, Y:
Hosp St Joan Despi Moises Broggi, Dept Surg, Barcelona, Spain
Luna, A:
Hosp Univ Parc Tauli Sabadell, Dept Surg, Barcelona, Spain
Fernandez-Ananin, S:
Univ Autonoma Barcelona, Dept Surg, Hosp Santa Creu & St Pau, Barcelona, Spain
Sarriugarte, A:
Univ Basque Country, OSI EE Cruces, IIS Biocruces, Dept Surg, Bizkaia, Spain
Olona, C:
Hosp Univ Tarragona Joan XXIII, Dept Surg, Tarragona, Spain
Rodriguez-Santiago, J:
Hosp Univ Mutua Terrassa, Dept Surg, Barcelona, Spain
Osorio, J:
Hosp Univ Bellvitge, Dept Surg, Barcelona, Spain
Grande, L:
Univ Autonoma Barcelona, Sect Gastrointestinal Surg, Hosp del Mar, Dept Surg,Hosp del Mar Med Res Inst IMIM, Barcelona, Spain
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