ArticlePloS one2022
Economic impact of a machine learning-based strategy for preparation of blood products in brain tumor surgery.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- A nomogram for the prediction of traumatic intracranial abnormalities in the elderly: Development and validation.Chinese journal of traumatology = Zhonghua chuang shang za zhi · 2026Article
- Impact of Preoperative Hair Removal on Self-Esteem after Brain Tumor Surgery.Asian journal of neurosurgery · 2026Article
- Prognosis of subarachnoid hemorrhage determined by intracranial pressure thresholds.Journal of cerebrovascular and endovascular neurosurgery · 2025Article
- Dynamic nomogram for predicting long-term survival in patients with brain abscess.Chinese neurosurgical journal · 2025Article
- Deep learning-based model for detection of intracranial waveforms with poor brain compliance in southern Thailand.Acute and critical care · 2025Article
- Cost-effectiveness of intracranial pressure monitoring in severe traumatic brain injury in Southern Thailand.Acute and critical care · 2025Article
- Machine learning in transfusion medicine: A scoping review.Transfusion · 2024Article
- Machine learning-based classification of pineal germinoma from magnetic resonance imaging.World neurosurgery: X · 2023Article
- Comparison of intracranial pressure prediction in hydrocephalus patients among linear, non-linear, and machine learning regression models in Thailand.Acute and critical care · 2023Article
- Development and internal validation of a nomogram to predict massive blood transfusions in neurosurgical operations.Journal of neurosciences in rural practiceArticle
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5 authors.
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Abstract
backgroundGlobally, 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.
methodsThe 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.
resultsOver 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.
conclusionThe 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.
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