A machine learning-based prediction model for in-hospital mortality among critically ill patients with hip fracture: An internal and external validated study |
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| Affiliation: | 1. Department of Orthopedic Surgery, Hainan Hospital of Chinese PLA General Hospital, 80 Jianglin Road, Sanya 572022, China;2. Chinese PLA Medical School, 28 Fuxing Road, Beijing 100853, China;3. Department of Orthopedic Surgery, National Clinical Research Center for Orthopedics, Sports Medicine & Rehabilitation, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China;4. Xiangya School of Medicine, Central South University, 172 Tongzipo Road, Changsha 410013, China;5. Department of Orthopaedic Surgery, Shanghai Sixth People''s Hospital Affiliated to Shanghai Jiao Tong University, 600 Yishan Road, Shanghai, 200233, China;6. Department of Orthopedic Surgery, the First Affiliated Hospital of Huzhou University, 158 Guangchang Back Road, Huzhou 313000, China;7. Department of Orthopedics Surgery, the First People Hospital of Huzhou, 158 Guangchang Back Road, Huzhou 313000, China;8. Department of Orthopedic Surgery, the Second Xiangya Hospital of Central South University, 139 Renmin Middle Road, Changsha 410011, China;1. Major Trauma Clinical Psychology Service, Leeds General Infirmary, Leeds, LS1 3EX, United Kingdom;2. Deaprtment of Orthopaedic and Trauma Surgery, Leeds General Infirmary, Leeds, LS1 3EX, United Kingdom;1. Department of Orthopedic Surgery, The Steadman Clinic, Vail, CO, United States;2. Department of Orthopaedic Surgery, University of Washington, Seattle, WA, United States;3. Department of Orthopaedic Surgery, Washington University School of Medicine, St. Louis, MO, United States;4. Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, United States;1. Department of Emergency Medicine, Aga Khan University Hospital, Karachi, Pakistan;2. Medical College, Aga Khan University, Karachi, Pakistan;1. Uniformed Services University of the Health Sciences, 4301 Jones Bridge Road, Bethesda, MD 20814, United States;2. Department of Surgery, Brooke Army Medical Center, 3551 Roger Brooke Drive, JBSA Ft. Sam Houston, San Antonio, TX 78234, United States;3. Department of Medicine, Brooke Army Medical Center, 3551 Roger Brooke Drive, JBSA Ft. Sam Houston, San Antonio, TX 78234, United States;1. NSU NOVA Southeastern University School of Allopathic Medicine, Fort Lauderdale, FL, USA;2. Department of Surgery, Division of Trauma and Surgical Critical Care, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA;3. University of San Francisco, San Francisco, CA, USA;4. US Army Institute of Surgical Research and the DoD Joint Trauma System, San Antonio, TX, USA;5. Department of Surgery, Division of Trauma and Surgical Critical Care, Orlando Regional Medical Center, Orlando, FL, USA, 86 W Underwood St., Orlando, FL 32806, USA;6. Department of Surgical Education, Orlando Regional Medical Center, Orlando, FL, USA |
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| Abstract: | ![]()
IntroductionFew studies have investigated the in-hospital mortality among critically ill patients with hip fracture. This study aimed to develop and validate a model to estimate the risk of in-hospital mortality among critically ill patients with hip fracture.MethodsFor this study, data from the Medical Information Mart for Intensive Care III (MIMIC-III) Database and electronic Intensive Care Unit (eICU) Collaborative Research Database were evaluated. Enrolled patients (n=391) in the MIMIC-III database were divided into a training (2/3, n=260) and a validation (1/3, n=131) group at random. Using machine learning algorithms such as random forest, gradient boosting machine, decision tree, and eXGBoosting machine approach, the training group was utilized to train and optimize models. The validation group was used to internally validate models and the optimal model could be obtained in terms of discrimination (area under the receiver operating characteristic curve, AUROC) and calibration (calibration curve). External validation was done in the eICU Collaborative Research Database (n=165). To encourage practical use of the model, a web-based calculator was developed according to the eXGBoosting machine approach.ResultsThe in-hospital death rate was 13.81% (54/391) in the MIMIC-III database and 10.91% (18/165) in the eICU Collaborative Research Database. Age, gender, anemia, mechanical ventilation, cardiac arrest, and chronic airway obstruction were the six model parameters which were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) method combined with 10-fold cross-validation. The model established using the eXGBoosting machine approach showed the highest area under curve (AUC) value (0.797, 95% CI: 0.696-0.898) and the best calibrating ability, with a calibration slope of 0.999 and intercept of -0.019. External validation also revealed favorable discrimination (AUC: 0.715, 95% CI: 0.566-0.864; accuracy: 0.788) and calibration (calibration slope: 0.805) in the eICU Collaborative Research Database. The web-based calculator could be available at https://doctorwangsj-webcalculator-main-yw69yd.streamlitapp.com/.ConclusionThe model has the potential to be a pragmatic risk prediction tool that is able to identify hip fracture patients who are at a high risk of in-hospital mortality in ICU settings, guide patient risk counseling, and simplify prognosis bench-marking by controlling for baseline risk. |
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