Anirudh K Gowd1, Avinesh Agarwalla2, Nirav H Amin3, Anthony A Romeo4, Gregory P Nicholson5, Nikhil N Verma5, Joseph N Liu6. 1. Wake Forest University Baptist Medical Center, Winston-Salem, NC, USA. Electronic address: anirudhkgowd@gmail.com. 2. Westchester Medical Center, Valhalla, NY, USA. 3. Veterans Affairs Loma Linda, Loma Linda, CA, USA. 4. The Rothman Institute, New York, NY, USA. 5. Rush University Medical Center, Chicago, IL, USA. 6. Loma Linda University Medical Center, Loma Linda, CA, USA.
Abstract
BACKGROUND: We aimed to demonstrate that supervised machine learning (ML) models can better predict postoperative complications after total shoulder arthroplasty (TSA) than comorbidity indices. METHODS: The American College of Surgeons-National Surgical Quality Improvement Program database was queried from 2005-2017 for TSA cases. Training and validation sets were created by randomly assigning 80% and 20% of the data set. Included variables were age, body mass index (BMI), operative time, smoking status, comorbidities, diagnosis, and preoperative hematocrit and albumin. Complications included any adverse event, transfusion, extended length of stay (>3 days), surgical site infection, return to the operating room, deep vein thrombosis or pulmonary embolism, and readmission. Each SML algorithm was compared with one another and to a baseline model using American Society of Anesthesiologists (ASA) classification. Model strength was evaluated by calculating the area under the receiver operating characteristic curve (AUC) and the positive predictive value (PPV) of complications. RESULTS: We identified a total of 17,119 TSA cases. Mean age, BMI, and length of stay were 69.5 ± 9.6 years, 31.1 ± 6.8, and 2.0 ± 2.2 days. Percentage hematocrit, BMI, and operative time were of highest importance in outcome prediction. SML algorithms outperformed ASA classification models for predicting any adverse event (71.0% vs. 63.0%), transfusion (77.0% vs. 64.0%), extended length of stay (68.0% vs. 60.0%), surgical site infection (65.0% vs. 58.0%), return to the operating room (59.0% vs. 54.0%), and readmission (64.0% vs. 58.0%). SML algorithms demonstrated the greatest PPV for any adverse event (62.5%), extended length of stay (61.4%), transfusion (52.2%), and readmission (10.1%). ASA classification had a 0.0% PPV for complications. CONCLUSION: With continued validation, intelligent models could calculate patient-specific risk for complications to adjust perioperative care and site of surgery.
BACKGROUND: We aimed to demonstrate that supervised machine learning (ML) models can better predict postoperative complications after total shoulder arthroplasty (TSA) than comorbidity indices. METHODS: The American College of Surgeons-National Surgical Quality Improvement Program database was queried from 2005-2017 for TSA cases. Training and validation sets were created by randomly assigning 80% and 20% of the data set. Included variables were age, body mass index (BMI), operative time, smoking status, comorbidities, diagnosis, and preoperative hematocrit and albumin. Complications included any adverse event, transfusion, extended length of stay (>3 days), surgical site infection, return to the operating room, deep vein thrombosis or pulmonary embolism, and readmission. Each SML algorithm was compared with one another and to a baseline model using American Society of Anesthesiologists (ASA) classification. Model strength was evaluated by calculating the area under the receiver operating characteristic curve (AUC) and the positive predictive value (PPV) of complications. RESULTS: We identified a total of 17,119 TSA cases. Mean age, BMI, and length of stay were 69.5 ± 9.6 years, 31.1 ± 6.8, and 2.0 ± 2.2 days. Percentage hematocrit, BMI, and operative time were of highest importance in outcome prediction. SML algorithms outperformed ASA classification models for predicting any adverse event (71.0% vs. 63.0%), transfusion (77.0% vs. 64.0%), extended length of stay (68.0% vs. 60.0%), surgical site infection (65.0% vs. 58.0%), return to the operating room (59.0% vs. 54.0%), and readmission (64.0% vs. 58.0%). SML algorithms demonstrated the greatest PPV for any adverse event (62.5%), extended length of stay (61.4%), transfusion (52.2%), and readmission (10.1%). ASA classification had a 0.0% PPV for complications. CONCLUSION: With continued validation, intelligent models could calculate patient-specific risk for complications to adjust perioperative care and site of surgery.
Authors: Alexander L Hornung; Christopher M Hornung; G Michael Mallow; J Nicolás Barajas; Augustus Rush; Arash J Sayari; Fabio Galbusera; Hans-Joachim Wilke; Matthew Colman; Frank M Phillips; Howard S An; Dino Samartzis Journal: Eur Spine J Date: 2022-03-27 Impact factor: 2.721
Authors: Sai K Devana; Akash A Shah; Changhee Lee; Andrew R Jensen; Edward Cheung; Mihaela van der Schaar; Nelson F SooHoo Journal: J Shoulder Elb Arthroplast Date: 2022-04-19
Authors: Vikas Kumar; Christopher Roche; Steven Overman; Ryan Simovitch; Pierre-Henri Flurin; Thomas Wright; Joseph Zuckerman; Howard Routman; Ankur Teredesai Journal: Clin Orthop Relat Res Date: 2020-10 Impact factor: 4.755
Authors: Sai K Devana; Akash A Shah; Changhee Lee; Varun Gudapati; Andrew R Jensen; Edward Cheung; Carlos Solorzano; Mihaela van der Schaar; Nelson F SooHoo Journal: J Shoulder Elb Arthroplast Date: 2021-10-28