Development and validation of a machine learning-based model to assess probability of systemic inflammatory response syndrome in patients with severe multiple traumas

• Published in: BMC medical informatics and decision making Vol. 24; no. 1; pp. 235 - 14
• Main Authors: Prokazyuk, Alexander, Tlemissov, Aidos, Zhanaspayev, Marat, Aubakirova, Sabina, Mussabekov, Arman
• Format: Journal Article
• Language: English
• Published: London BioMed Central 27.08.2024; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Adult; Blood; Blood coagulation; Body temperature; Business metrics; Care and treatment; Causes of; Decision making; Decision trees; Emergency medical care; Female; Health Informatics; Humans; Inflammation; Inflammatory response; Information Systems and Communication Service; Injuries; Laboratories; Learning algorithms; Machine Learning; Male; Management of Computing and Information Systems; Medicine; Medicine & Public Health; Middle Aged; Mortality; Multiple Trauma; Neural networks; Patient outcomes; Patients; Physiology; Polytrauma; Probability learning; Regression analysis; Retrospective Studies; Risk; Sepsis; SIRS; Statistical analysis; Surgery; Systemic Inflammatory Response Syndrome; Trauma; Wounds and injuries; Kazakhstan; SIRS; Polytrauma; Decision making; Machine learning
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-024-02640-x

Background Systemic inflammatory response syndrome (SIRS) is a predictor of serious infectious complications, organ failure, and death in patients with severe polytrauma and is one of the reasons for delaying early total surgical treatment. To determine the risk of SIRS within 24 h after hospitalization, we developed six machine learning models. Materials and methods Using retrospective data about the patient, the nature of the injury, the results of general and standard biochemical blood tests, and coagulation tests, six models were developed: decision tree, random forest, logistic regression, support vector and gradient boosting classifiers, logistic regressor, and neural network. The effectiveness of the models was assessed through internal and external validation. Results Among the 439 selected patients with severe polytrauma in 230 (52.4%), SIRS was diagnosed within the first 24 h of hospitalization. The SIRS group was more strongly associated with class II bleeding (39.5% vs. 60.5%; OR 1.81 [95% CI: 1.23–2.65]; P  = 0.0023), long-term vasopressor use (68.4% vs. 31.6%; OR 5.51 [95% CI: 2.37–5.23]; P  < 0.0001), risk of acute coagulopathy (67.8% vs. 32.2%; OR 2.4 [95% CI: 1.55–3.77]; P  < 0.0001), and greater risk of pneumonia (59.5% vs. 40.5%; OR 1.74 [95% CI: 1.19–2.54]; P  = 0.0042), longer ICU length of stay (5 ± 6.3 vs. 2.7 ± 4.3 days; P  < 0.0001) and mortality rate (64.5% vs. 35.5%; OR 10.87 [95% CI: 6.3–19.89]; P  = 0.0391). Of all the models, the random forest classifier showed the best predictive ability in the internal (AUROC 0.89; 95% CI: 0.83–0.96) and external validation (AUROC 0.83; 95% CI: 0.75–0.91) datasets. Conclusions The developed model made it possible to accurately predict the risk of developing SIRS in the early period after injury, allowing clinical specialists to predict patient management tactics and calculate medication and staffing needs for the patient. Level of evidence Level 3. Trial registration The study was retrospectively registered in the ClinicalTrials.gov database of the National Library of Medicine (NCT06323096).