The Cumulative Perioperative Model: Predicting 30-Day Mortality in Abdominal Surgery Cancer Patients

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Author Info

Christopher M Jermaine Joseph Ruiz Joseph L Nates Risa B Myers

Corresponding Author

Joseph L Nates
Department of Critical Care, Division of Anesthesiology and Critical Care, University of Texas MD Anderson Cancer Center, Texas, USA

A B S T R A C T

Objectives: 1) To develop a cumulative perioperative model (CPM) using the hospital clinical course of abdominal surgery cancer patients that predicts 30 and 90-day mortality risk; 2) To compare the predictive ability of this model to ten existing other models. Materials and Methods: We constructed a multivariate logistic regression model of 30 (90)-day mortality, which occurred in 106 (290) of the cases, using 13,877 major abdominal surgical cases performed at the University of Texas MD Anderson Cancer Center from January 2007 to March 2014. The model includes race, starting location (home, inpatient ward, intensive care unit or emergency center), Charlson Comorbidity Index, emergency status, ASA-PS classification, procedure, surgical Apgar score, destination after surgery (hospital ward location) and delayed intensive care unit admit within six days. We computed and compared the model mortality prediction ability (C-statistic) as we accumulated features over time. Results: We were able to predict 30 (90)-day mortality with C-statistics from 0.70 (0.71) initially to 0.87 (0.84) within six days postoperatively. Conclusion: We achieved a high level of model discrimination. The CPM enables a continuous cumulative assessment of the patient’s mortality risk, which could then be used as a decision support aid regarding patient care and treatment, potentially resulting in improved outcomes, decreased costs and more informed decisions.

Article Info

Article Type

Research Article

Publication history

Received: Mon 17, Feb 2020
Accepted: Mon 02, Mar 2020
Published: Tue 10, Mar 2020

Copyright

© 2023 Joseph L Nates . This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Hosting by Science Repository.
DOI: 10.31487/j.JSO.2020.01.10