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- Title
Development and Validation of a Prediction Model for Need for Massive Transfusion During Surgery Using Intraoperative Hemodynamic Monitoring Data.
- Authors
Lee, Seung Mi; Lee, Garam; Kim, Tae Kyong; Le, Trang; Hao, Jie; Jung, Young Mi; Park, Chan-Wook; Park, Joong Shin; Jun, Jong Kwan; Lee, Hyung-Chul; Kim, Dokyoon
- Abstract
This prognostic study assesses a real-time prediction model for massive transfusion during surgery based on the incorporation of preoperative data and intraoperative hemodynamic monitoring data. Key Points: Question: Can intraoperative hemodynamic monitoring data be used in real time to develop a prediction model for the need for massive transfusion during surgery? Findings: In this prognostic study of 17 986 patients, the real-time prediction model for massive transfusion using preoperative and intraoperative parameters was associated with significantly improved performance (area under the receiver operating characteristic curve = 0.972) over the benchmark model (area under the curve = 0.824) that used only preoperative variables. Patients with the highest massive transfusion index (ie, >90th percentile) had a 47.5-fold increased risk for massive transfusion compared with those with a lower massive transfusion index (ie, <80th percentile). Meaning: These findings suggest that early identification of the need for massive transfusion may allow timely intervention for high-risk patients during surgery. Importance: Massive transfusion is essential to prevent complications during uncontrolled intraoperative hemorrhage. As massive transfusion requires time for blood product preparation and additional medical personnel for a team-based approach, early prediction of massive transfusion is crucial for appropriate management. Objective: To evaluate a real-time prediction model for massive transfusion during surgery based on the incorporation of preoperative data and intraoperative hemodynamic monitoring data. Design, Setting, and Participants: This prognostic study used data sets from patients who underwent surgery with invasive blood pressure monitoring at Seoul National University Hospital (SNUH) from 2016 to 2019 and Boramae Medical Center (BMC) from 2020 to 2021. SNUH represented the development and internal validation data sets (n = 17 986 patients), and BMC represented the external validation data sets (n = 494 patients). Data were analyzed from November 2020 to December 2021. Exposures: A deep learning–based real-time prediction model for massive transfusion. Main Outcomes and Measures: Massive transfusion was defined as a transfusion of 3 or more units of red blood cells over an hour. A preoperative prediction model for massive transfusion was developed using preoperative variables. Subsequently, a real-time prediction model using preoperative and intraoperative parameters was constructed to predict massive transfusion 10 minutes in advance. A prediction model, the massive transfusion index, calculated the risk of massive transfusion in real time. Results: Among 17 986 patients at SNUH (mean [SD] age, 58.65 [14.81] years; 9036 [50.2%] female), 416 patients (2.3%) underwent massive transfusion during the operation (mean [SD] duration of operation, 170.99 [105.03] minutes). The real-time prediction model constructed with the use of preoperative and intraoperative parameters significantly outperformed the preoperative prediction model (area under the receiver characteristic curve [AUROC], 0.972; 95% CI, 0.968-0.976 vs AUROC, 0.824; 95% CI, 0.813-0.834 in the SNUH internal validation data set; P <.001). Patients with the highest massive transfusion index (ie, >90th percentile) had a 47.5-fold increased risk for a massive transfusion compared with those with a lower massive transfusion index (ie, <80th percentile). The real-time prediction model also showed excellent performance in the external validation data set (AUROC of 0.943 [95% CI, 0.919-0.961] in BMC). Conclusions and Relevance: The findings of this prognostic study suggest that the real-time prediction model for massive transfusion showed high accuracy of prediction performance, enabling early intervention for high-risk patients. It suggests strong confidence in artificial intelligence-assisted clinical decision support systems in the operating field.
- Subjects
CONFIDENCE intervals; BLOOD transfusion; RESEARCH methodology; DESCRIPTIVE statistics; RESEARCH funding; PREDICTION models; HEMODYNAMICS; RECEIVER operating characteristic curves; EVALUATION
- Publication
JAMA Network Open, 2022, Vol 5, Issue 12, pe2246637
- ISSN
2574-3805
- Publication type
Article
- DOI
10.1001/jamanetworkopen.2022.46637