Economic impact of a machine learning-based strategy for preparation of blood products in brain tumor surgery

• Published in: PloS one Vol. 17; no. 7; p. e0270916
• Main Authors: Tunthanathip, Thara, Sae-heng, Sakchai, Oearsakul, Thakul, Kaewborisutsakul, Anukoon, Taweesomboonyat, Chin
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 01-07-2022; Public Library of Science (PLoS)
• Subjects: Applications programs; Biology and Life Sciences; Blood; Blood products; Blood transfusions; Brain; Brain cancer; Brain tumors; Care and treatment; Computer and Information Sciences; Coronaviruses; COVID-19; Decision making; Economic aspects; Economic impact; Histocompatibility testing; Impact analysis; Learning algorithms; Machine learning; Medicine and Health Sciences; Methods; Optimization; Physicians; Social Sciences; Surgery; Transfusion; Tumors; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0270916

Background Globally, blood donation has been disturbed due to the pandemic. Consequently, the optimization of preoperative blood preparation should be a point of concern. Machine learning (ML) is one of the modern approaches that have been applied by physicians to help decision-making. The main objective of this study was to identify the cost differences of the ML-based strategy compared with other strategies in preoperative blood products preparation. A secondary objective was to compare the effectiveness indexes of blood products preparation among strategies. Methods The study utilized a retrospective cohort design conducted on brain tumor patients who had undergone surgery between January 2014 and December 2021. Overall data were divided into two cohorts. The first cohort was used for the development and deployment of the ML-based web application, while validation, comparison of the effectiveness indexes, and economic evaluation were performed using the second cohort. Therefore, the effectiveness indexes of blood preparation and cost difference were compared among the ML-based strategy, clinical trial-based strategy, and routine-based strategy. Results Over a 2-year period, the crossmatch to transfusion (C/T) ratio, transfusion probability (Tp), and transfusion index (Ti) of the ML-based strategy were 1.10, 57.0%, and 1.62, respectively, while the routine-based strategy had a C/T ratio of 4.67%, Tp of 27.9%%, and Ti of 0.79. The overall costs of blood products preparation among the ML-based strategy, clinical trial-based strategy, and routine-based strategy were 30, 061.56$, 57,313.92$, and 136,292.94$, respectively. From the cost difference between the ML-based strategy and routine-based strategy, we observed cost savings of 92,519.97$ (67.88%) for the 2-year period. Conclusion The ML-based strategy is one of the most effective strategies to balance the unnecessary workloads at blood banks and reduce the cost of unnecessary blood products preparation from low C/T ratio as well as high Tp and Ti. Further studies should be performed to confirm the generalizability and applicability of the ML-based strategy.