Background Massive hemorrhage is the main cause of preventable death after trauma. This study aimed to establish prediction models for early diagnosis of massive hemorrhage in trauma. Methods Using the trauma database of Chinese PLA General Hospital, two logistic regression (LR) models were fit to predict the risk of massive hemorrhage in trauma. Sixty-two potential predictive variables, including clinical symptoms, vital signs, laboratory tests, and imaging results, were included in this study. Variable selection was done using the least absolute shrinkage and selection operator (LASSO) method. The first model was constructed based on LASSO feature selection results. The second model was constructed based on the first vital sign recordings of trauma patients after admission. Finally, a web calculator was developed for clinical use. Results A total of 2353 patients were included in this study. There were 377 (16.02%) patients with massive hemorrhage. The selected predictive variables were heart rate (OR: 1.01; 95% CI: 1.01–1.02; P<0.001), pulse pressure (OR: 0.99; 95% CI: 0.98–0.99; P = 0.004), base excess (OR: 0.90; 95% CI: 0.87–0.93; P<0.001), hemoglobin (OR: 0.95; 95% CI: 0.95–0.96; P<0.001), displaced pelvic fracture (OR: 2.13; 95% CI: 1.48–3.06; P<0.001), and a positive computed tomography scan or positive focused assessment with sonography for trauma (OR: 1.62; 95% CI: 1.21–2.18; P = 0.001). Model 1, which was developed based on LASSO feature selection results and LR, displayed excellent discrimination (AUC: 0.894; 95% CI: 0.875–0.912), good calibration (P = 0.405), and clinical utility. In addition, the predictive power of model 1 was better than that of model 2 (AUC: 0.718; 95% CI: 0.679–0.757). Model 1 was deployed as a public web tool (http://82.156.217.249:8080/). Conclusions Our study developed and validated prediction models to assist medical staff in the early diagnosis of massive hemorrhage in trauma. An open web calculator was developed to facilitate the practical application of the research results.
Background: This study aimed to develop and to validate dynamic predictive models for trauma-associated severe hemorrhage based on vital signs to register early warning and dynamic prediction of severe hemorrhage in trauma patients.Methods: The MIMIC-IV cohort was collected retrospectively. The inclusion criteria were trauma patients aged ≥16 years with complete clinical data. Heart rate, respiratory rate, systolic blood pressure, diastolic blood pressure, and peripheral oxygen saturation were extracted as predictive variables. Based on logistic regression, support vector machine, random forest, adaptive boosting, gated recurrent unit, and gated recurrent unit-d, predictive models for trauma-associated severe hemorrhage were developed and validated to dynamically predict whether severe hemorrhage will occur in trauma patients in the next 1 h/2 h/3 h. This study was based on the Trauma database of the General Hospital of the People's Liberation Army for external validation. The models were developed and validated using Python 3.8.5 software. SPSS.21 software was used for statistical analysis.Results: Of the 7522 trauma patients in the MIMIC-IV cohort, 283 (3.76%) had a severe hemorrhage. The area under the curve of the gated recurrent unit-d model was the best in the 1 h (0.946±0.029), 2 h (0.940±0.032), and 3 h groups (0.943±0.034), and there was no significant difference among the three groups. In the Trauma cohort, the area under the curve of the gated recurrent unit-d model also achieved the best performance in the 1 h (0.779±0.013), 2 h (0.780±0.008), and 3 h groups (0.778±0.009), and there was no significant difference among the three groups. When comparing the gated recurrent unit-d model with the traditional scoring systems, the gated recurrent unit-d model still has advantages. Moreover, we have developed a web-based predictive system to help clinicians use our models.Conclusions: This study developed and validated dynamic predictive models for trauma-associated severe hemorrhage based on vital signs to assist pre-hospital or in-hospital emergency personnel to make decisions, and the gated recurrent unit-d model performed best.Trial registration: The MIMIC-IV database was previously de-identified and reviewed by the institutional review board (IRB) of its host organization and determined to be exempted from subsequent IRB. We obtained the administrative permissions to use the database (Certification Number: 27959316) for our research, after completing the National Institutes of Health web-based training course: Protecting Human Research Participants. We were reviewed and approved by the Ethics Committee of Chinese PLA General Hospital to use the Trauma database. The ethical batch number is S2021-466-01. Moreover, the informed consent of subjects was waived by the Ethics Committee of Chinese PLA General Hospital.
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