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Pardo E, Le Cam E, Verdonk F. Artificial intelligence and nonoperating room anesthesia. Curr Opin Anaesthesiol 2024; 37:413-420. [PMID: 38934202 DOI: 10.1097/aco.0000000000001388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
Abstract
PURPOSE OF REVIEW The integration of artificial intelligence (AI) in nonoperating room anesthesia (NORA) represents a timely and significant advancement. As the demand for NORA services expands, the application of AI is poised to improve patient selection, perioperative care, and anesthesia delivery. This review examines AI's growing impact on NORA and how it can optimize our clinical practice in the near future. RECENT FINDINGS AI has already improved various aspects of anesthesia, including preoperative assessment, intraoperative management, and postoperative care. Studies highlight AI's role in patient risk stratification, real-time decision support, and predictive modeling for patient outcomes. Notably, AI applications can be used to target patients at risk of complications, alert clinicians to the upcoming occurrence of an intraoperative adverse event such as hypotension or hypoxemia, or predict their tolerance of anesthesia after the procedure. Despite these advances, challenges persist, including ethical considerations, algorithmic bias, data security, and the need for transparent decision-making processes within AI systems. SUMMARY The findings underscore the substantial benefits of AI in NORA, which include improved safety, efficiency, and personalized care. AI's predictive capabilities in assessing hypoxemia risk and other perioperative events, have demonstrated potential to exceed human prognostic accuracy. The implications of these findings advocate for a careful yet progressive adoption of AI in clinical practice, encouraging the development of robust ethical guidelines, continual professional training, and comprehensive data management strategies. Furthermore, AI's role in anesthesia underscores the need for multidisciplinary research to address the limitations and fully leverage AI's capabilities for patient-centered anesthesia care.
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Affiliation(s)
- Emmanuel Pardo
- Sorbonne University, GRC 29, AP-HP, DMU DREAM, Department of Anesthesiology and Critical Care, Saint-Antoine Hospital, Paris, France
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Cote MP, Lubowitz JH. Recommended Requirements and Essential Elements for Proper Reporting of the Use of Artificial Intelligence Machine Learning Tools in Biomedical Research and Scientific Publications. Arthroscopy 2024; 40:1033-1038. [PMID: 38300189 DOI: 10.1016/j.arthro.2023.12.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 12/30/2023] [Indexed: 02/02/2024]
Abstract
Essential elements required for proper use of artificial intelligence machine learning tools in biomedical research and scientific publications include (1) explanation justifying why a machine learning approach contributes to the purpose of the study; (2) description of the adequacy of the data (input) to produce the desired results (output); (3) details of the algorithmic (i.e., computational) approach including methods for organizing the data (preprocessing); the machine learning computational algorithm(s) assessed; on what data the models were trained; the presence of bias and efforts to mitigate these effects; and the methods for quantifying the variables (features) most influential in determining the results (e.g., Shapley values); (4) description of methods, and reporting of results, quantitating performance in terms of both model accuracy and model calibration (level of confidence in the model's predictions); (5) availability of the programming code (including a link to the code when available-ideally, the code should be available); (6) discussion of model internal validation (results applicable and sensitive to the population investigated and data on which the model was trained) and external validation (were the results investigated as to whether they are generalizable to different populations? If not, consideration of this limitation and discussion of plans for external validation, i.e., next steps). As biomedical research submissions using artificial intelligence technology increase, these requirements could facilitate purposeful use and comprehensive methodological reporting.
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Khoriati AA, Shahid Z, Fok M, Frank RM, Voss A, D'Hooghe P, Imam MA. Artificial intelligence and the orthopaedic surgeon: A review of the literature and potential applications for future practice: Current concepts. J ISAKOS 2024; 9:227-233. [PMID: 37949113 DOI: 10.1016/j.jisako.2023.10.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 10/28/2023] [Accepted: 10/30/2023] [Indexed: 11/12/2023]
Affiliation(s)
- Al-Achraf Khoriati
- Rowley Bristow Orthopaedic Centre, Ashford and St Peter's NHS Foundation Trust, Chertsey, KT106PZ, UK.
| | - Zuhaib Shahid
- Rowley Bristow Orthopaedic Centre, Ashford and St Peter's NHS Foundation Trust, Chertsey, KT106PZ, UK.
| | - Margaret Fok
- Department of Orthopaedics and Traumatology, Queen Mary Hospital, The University of Hong Kong, Pok Fu Lam Rd, High West, Hong Kong, China; Asia Pacific Orthopaedic Association, 57000, Malaysia.
| | - Rachel M Frank
- Department of Orthopaedic Surgery, Joint Preservation Program, University of Colorado School of Medicine, 12631 E 17th Ave, Mail Stop B202, Aurora, CO 80045, USA.
| | - Andreas Voss
- Sporthopaedicum Regensburg, Street, Hildegard-von-Bingen-Straße 1, 93053, Regensburg, Germany.
| | - Pieter D'Hooghe
- Aspetar Orthopedic and Sports Medicine Hospital, Aspire Zone, Sportscity Street 1, P.O. Box 29222, Doha, Qatar
| | - Mohamed A Imam
- Rowley Bristow Orthopaedic Centre, Ashford and St Peter's NHS Foundation Trust, Chertsey, KT106PZ, UK; Smart Health Centre, University of East London, University Way, London, E16 2RD, United Kingdom.
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Dijkstra H, van de Kuit A, de Groot T, Canta O, Groot OQ, Oosterhoff JH, Doornberg JN. Systematic review of machine-learning models in orthopaedic trauma. Bone Jt Open 2024; 5:9-19. [PMID: 38226447 PMCID: PMC10790183 DOI: 10.1302/2633-1462.51.bjo-2023-0095.r1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2024] Open
Abstract
Aims Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool. Methods A systematic search screening 3,252 studies identified 45 ML-based prediction models in orthopaedic trauma up to January 2023. The TRIPOD statement assessed transparent reporting and the PROBAST tool the risk of bias. Results A total of 40 studies reported on training and internal validation; four studies performed both development and external validation, and one study performed only external validation. The most commonly reported outcomes were mortality (33%, 15/45) and length of hospital stay (9%, 4/45), and the majority of prediction models were developed in the hip fracture population (60%, 27/45). The overall median completeness for the TRIPOD statement was 62% (interquartile range 30 to 81%). The overall risk of bias in the PROBAST tool was low in 24% (11/45), high in 69% (31/45), and unclear in 7% (3/45) of the studies. High risk of bias was mainly due to analysis domain concerns including small datasets with low number of outcomes, complete-case analysis in case of missing data, and no reporting of performance measures. Conclusion The results of this study showed that despite a myriad of potential clinically useful applications, a substantial part of ML studies in orthopaedic trauma lack transparent reporting, and are at high risk of bias. These problems must be resolved by following established guidelines to instil confidence in ML models among patients and clinicians. Otherwise, there will remain a sizeable gap between the development of ML prediction models and their clinical application in our day-to-day orthopaedic trauma practice.
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Affiliation(s)
- Hidde Dijkstra
- Department of Orthopaedic Surgery, University Medical Centre Groningen, Groningen, Netherlands
- University Center for Geriatric Medicine, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Anouk van de Kuit
- Department of Orthopaedic Surgery, University Medical Centre Groningen, Groningen, Netherlands
| | - Tom de Groot
- Department of Orthopaedic Surgery, University Medical Centre Groningen, Groningen, Netherlands
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Olga Canta
- Department of Orthopaedic Surgery, University Medical Centre Groningen, Groningen, Netherlands
| | - Olivier Q. Groot
- Department of Orthopaedic Surgery, University Medical Centre Utrecht, University of Utrecht, Utrecht, Netherlands
| | - Jacobien H. Oosterhoff
- Department of Engineering Systems & Services, Faculty Technology Policy and Management, Delft University of Technology, Delft, Netherlands
| | - Job N. Doornberg
- Department of Orthopaedic Surgery, University Medical Centre Groningen, Groningen, Netherlands
- Department of Orthopaedic Trauma Surgery, Flinders Medical Center, Flinders University, Adelaide, Australia
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Arina P, Kaczorek MR, Hofmaenner DA, Pisciotta W, Refinetti P, Singer M, Mazomenos EB, Whittle J. Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools. Anesthesiology 2024; 140:85-101. [PMID: 37944114 PMCID: PMC11146190 DOI: 10.1097/aln.0000000000004764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Indexed: 11/12/2023]
Abstract
BACKGROUND The utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential. The purpose of this systematic review is to assess the current state of machine learning in perioperative medicine, its utility in prediction of complications and prognostication, and limitations related to bias and validation. METHODS A multidisciplinary team of clinicians and engineers conducted a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol. Multiple databases were searched, including Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, PubMed, Medline, Embase, and Web of Science. The systematic review focused on study design, type of machine learning model used, validation techniques applied, and reported model performance on prediction of complications and prognostication. This review further classified outcomes and machine learning applications using an ad hoc classification system. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability of the studies. RESULTS A total of 103 studies were identified. The models reported in the literature were primarily based on single-center validations (75%), with only 13% being externally validated across multiple centers. Most of the mortality models demonstrated a limited ability to discriminate and classify effectively. The PROBAST assessment indicated a high risk of systematic errors in predicted outcomes and artificial intelligence or machine learning applications. CONCLUSIONS The findings indicate that the development of this field is still in its early stages. This systematic review indicates that application of machine learning in perioperative medicine is still at an early stage. While many studies suggest potential utility, several key challenges must be first overcome before their introduction into clinical practice. EDITOR’S PERSPECTIVE
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Affiliation(s)
- Pietro Arina
- Bloomsbury Institute of Intensive Care Medicine and Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom
| | - Maciej R. Kaczorek
- Wellcome/EPSRC Centre of Interventional and Surgical Sciences and Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Daniel A. Hofmaenner
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom; and Institute of Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Walter Pisciotta
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom
| | - Patricia Refinetti
- Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom
| | - Mervyn Singer
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom
| | - Evangelos B. Mazomenos
- Wellcome/EPSRC Centre of Interventional and Surgical Sciences and Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - John Whittle
- Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom
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Kunze KN, Williams RJ, Ranawat AS, Pearle AD, Kelly BT, Karlsson J, Martin RK, Pareek A. Artificial intelligence (AI) and large data registries: Understanding the advantages and limitations of contemporary data sets for use in AI research. Knee Surg Sports Traumatol Arthrosc 2024; 32:13-18. [PMID: 38226678 DOI: 10.1002/ksa.12018] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 11/27/2023] [Indexed: 01/17/2024]
Affiliation(s)
- Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, New York, New York, USA
| | - Riley J Williams
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, New York, New York, USA
| | - Anil S Ranawat
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, New York, New York, USA
| | - Andrew D Pearle
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, New York, New York, USA
| | - Bryan T Kelly
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, New York, New York, USA
| | - Jon Karlsson
- Department of Orthopaedics, Sahlgrenska University Hospital, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden
| | - R Kyle Martin
- Department of Orthopedic Surgery, University of Minnesota, Minneapolis, Minnesota, USA
| | - Ayoosh Pareek
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, New York, New York, USA
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Huyke-Hernández FA, Doxey SA, Only AJ, Sibley A, Mikhael N, Kweon CY, Cunningham BP. Autograft patellar bone-tendon-bone use does not increase operative time in anterior cruciate ligament reconstruction. J Orthop 2023; 45:6-12. [PMID: 37809348 PMCID: PMC10551805 DOI: 10.1016/j.jor.2023.09.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 09/20/2023] [Indexed: 10/10/2023] Open
Abstract
Background Anterior cruciate ligament reconstruction (ACLR) is a common procedure that has been shown to have relatively good outcomes amongst various graft types. Operative time in ACLR has been found to influence outcomes and cost. The purpose of this study was to evaluate the association of operative time in primary arthroscopically performed anterior cruciate ligament reconstruction (ACLR) and graft type while controlling for confounders that influence time. Methods All patients who received ACLR between 2018 and 2022 were included in this retrospective cohort study. Exclusion criteria consisted of age (≤16 years), revisions, concomitant ligament reconstruction or tendon repairs, or other simultaneously performed procedures that could potentially add substantial variation in operative time. The primary outcome was operative time. Graft types included allograft, bone-tendon-bone (BTB) autograft, hamstring tendon (HS) autograft and quadriceps tendon (QT) autograft. Results A total of 1813 primary ACLRs were included. The average operative time was 98.9 ± 33.0 min. Graft utilization varies considerably among surgeons. The most used graft type was BTB autograft (42.6%) followed by HS autograft (32.3%) and allograft (21.4%). Only 68 cases (3.8%) used a QT autograft. Seven of the 15 included surgeons primarily used BTB autograft. One surgeon predominately used QT autograft. No difference in operative time was observed among the autograft types (p = 0.342). Allograft ACLR was significantly faster by 27-33 min compared to using BTB autograft, HS autograft, or QT autograft (p < 0.001). Conclusion Operative time did not vary by type of autograft selected. Allograft ACLR was performed approximately 30 min faster than autograft ACLR. Further studies examining the effect on patient outcomes of reduced operative time and minimizing graft harvest morbidity in ACLR is important to more accurately determine the cost-effectiveness of allograft ACLR.
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Affiliation(s)
- Fernando A. Huyke-Hernández
- Department of Orthopaedic Surgery, Park Nicollet Methodist Hospital, St. Louis Park, MN, USA
- Department of Orthopaedic Surgery, TRIA Orthopaedic Institute, Bloomington, MN, USA
| | - Stephen A. Doxey
- Department of Orthopaedic Surgery, Park Nicollet Methodist Hospital, St. Louis Park, MN, USA
- Department of Orthopaedic Surgery, TRIA Orthopaedic Institute, Bloomington, MN, USA
| | - Arthur J. Only
- Department of Orthopaedic Surgery, University of Minnesota, Minneapolis, MN, USA
| | - Andrew Sibley
- Department of Orthopaedic Surgery, University of Minnesota, Minneapolis, MN, USA
| | - Nizar Mikhael
- Department of Orthopaedic Surgery, University of Minnesota, Minneapolis, MN, USA
| | | | - Brian P. Cunningham
- Department of Orthopaedic Surgery, Park Nicollet Methodist Hospital, St. Louis Park, MN, USA
- Department of Orthopaedic Surgery, TRIA Orthopaedic Institute, Bloomington, MN, USA
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Labott JR, Lu Y, Salmons HI, Camp CL, Wyles CC, Taunton MJ. Health and Socioeconomic Risk Factors for Unplanned Hospitalization Following Ambulatory Unicompartmental Knee Arthroplasty: Development of a Patient Selection Tool Using Machine Learning. J Arthroplasty 2023; 38:1982-1989. [PMID: 36709883 DOI: 10.1016/j.arth.2023.01.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 01/16/2023] [Accepted: 01/20/2023] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Identifying ambulatory surgical candidates at risk for adverse surgical outcomes can optimize outcomes. The purpose of this study was to develop and internally validate a machine learning (ML) algorithm to predict contributors to unexpected hospitalizations after ambulatory unicompartmental knee arthroplasty (UKA). METHODS A total of 2,521 patients undergoing UKA from 2006 to 2018 were retrospectively evaluated. Patients admitted overnight postoperatively were identified as those who had a length of stay ≥ 1 day were analyzed with four individual ML models (ie, random forest, extreme gradient boosting, adaptive boosting, and elastic net penalized logistic regression). An additional model was produced as a weighted ensemble of the four individual algorithms. Area under the receiver operating characteristics (AUROC) compared predictive capacity of these models to conventional logistic regression techniques. RESULTS Of the 2,521 patients identified, 103 (4.1%) required at least one overnight stay following ambulatory UKA. The ML ensemble model achieved the best performance based on discrimination assessed via internal validation (AUROC = 87.3), outperforming individual models and conventional logistic regression (AUROC = 81.9-85.7). The variables determined most important by the ensemble model were cumulative time in the operating room, utilization of general anesthesia, increasing age, and patient residency in more urban areas. The model was integrated into a web-based open-access application. CONCLUSION The ensemble gradient-boosted ML algorithm demonstrated the highest performance in identifying factors contributing to unexpected hospitalizations in patients receiving UKA. This tool allows physicians and healthcare systems to identify patients at a higher risk of needing inpatient care after UKA.
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Affiliation(s)
- Joshua R Labott
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota
| | - Yining Lu
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota; Department of Orthopedic Surgery, Orthopedic Surgery Artificial Intelligence Lab (OSAIL), Mayo Clinic, Rochester, Minnesota
| | - Harold I Salmons
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota
| | - Christopher L Camp
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota; Department of Orthopedic Surgery, Orthopedic Surgery Artificial Intelligence Lab (OSAIL), Mayo Clinic, Rochester, Minnesota
| | - Cody C Wyles
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota; Department of Orthopedic Surgery, Orthopedic Surgery Artificial Intelligence Lab (OSAIL), Mayo Clinic, Rochester, Minnesota
| | - Michael J Taunton
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota; Department of Orthopedic Surgery, Orthopedic Surgery Artificial Intelligence Lab (OSAIL), Mayo Clinic, Rochester, Minnesota
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Lopez CD, Gazgalis A, Peterson JR, Confino JE, Levine WN, Popkin CA, Lynch TS. Machine Learning Can Accurately Predict Overnight Stay, Readmission, and 30-Day Complications Following Anterior Cruciate Ligament Reconstruction. Arthroscopy 2023; 39:777-786.e5. [PMID: 35817375 DOI: 10.1016/j.arthro.2022.06.032] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 06/20/2022] [Accepted: 06/23/2022] [Indexed: 02/07/2023]
Abstract
PURPOSE This study aimed to develop machine learning (ML) models to predict hospital admission (overnight stay) as well as short-term complications and readmission rates following anterior cruciate ligament reconstruction (ACLR). Furthermore, we sought to compare the ML models with logistic regression models in predicting ACLR outcomes. METHODS The American College of Surgeons National Surgical Quality Improvement Program database was queried for patients who underwent elective ACLR from 2012 to 2018. Artificial neural network ML and logistic regression models were developed to predict overnight stay, 30-day postoperative complications, and ACL-related readmission, and model performance was compared using the area under the receiver operating characteristic curve. Regression analyses were used to identify variables that were significantly associated with the predicted outcomes. RESULTS A total of 21,636 elective ACLR cases met inclusion criteria. Variables associated with hospital admission included White race, obesity, hypertension, and American Society of Anesthesiologists classification 3 and greater, anesthesia other than general, prolonged operative time, and inpatient setting. The incidence of hospital admission (overnight stay) was 10.2%, 30-day complications was 1.3%, and 30-day readmission for ACLR-related causes was 0.9%. Compared with logistic regression models, artificial neural network models reported superior area under the receiver operating characteristic curve values in predicting overnight stay (0.835 vs 0.589), 30-day complications (0.742 vs 0.590), reoperation (0.842 vs 0.601), ACLR-related readmission (0.872 vs 0.606), deep-vein thrombosis (0.804 vs 0.608), and surgical-site infection (0.818 vs 0.596). CONCLUSIONS The ML models developed in this study demonstrate an application of ML in which data from a national surgical patient registry was used to predict hospital admission and 30-day postoperative complications after elective ACLR. ML models developed performed well, outperforming regression models in predicting hospital admission and short-term complications following elective ACLR. ML models performed best when predicting ACLR-related readmissions and reoperations, followed by overnight stay. LEVEL OF EVIDENCE IV, retrospective comparative prognostic trial.
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Affiliation(s)
- Cesar D Lopez
- New York-Presbyterian/Columbia University Irving Medical Center, New York, New York, U.S.A.
| | - Anastasia Gazgalis
- New York-Presbyterian/Columbia University Irving Medical Center, New York, New York, U.S.A
| | - Joel R Peterson
- New York-Presbyterian/Columbia University Irving Medical Center, New York, New York, U.S.A
| | - Jamie E Confino
- New York-Presbyterian/Columbia University Irving Medical Center, New York, New York, U.S.A
| | - William N Levine
- New York-Presbyterian/Columbia University Irving Medical Center, New York, New York, U.S.A
| | - Charles A Popkin
- New York-Presbyterian/Columbia University Irving Medical Center, New York, New York, U.S.A
| | - T Sean Lynch
- New York-Presbyterian/Columbia University Irving Medical Center, New York, New York, U.S.A
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Ramkumar PN, Williams RJ. Editorial Commentary: Machine Learning Is Just a Statistical Technique, Not a Mystical Methodology or Peer Review Panacea. Arthroscopy 2023; 39:787-789. [PMID: 36740298 DOI: 10.1016/j.arthro.2022.07.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 07/14/2022] [Accepted: 07/19/2022] [Indexed: 02/07/2023]
Abstract
Orthopaedic and sports medicine research surrounding artificial intelligence (AI) has dramatically risen over the last 4 years. Meaningful application and methodologic rigor in the scientific literature are critical to ensure appropriate use of AI. Common but critical errors for those engaging in AI-related research include failure to 1) ensure the question is important and previously unknown or unanswered; 2) establish that AI is necessary to answer the question; and 3) recognize model performance is more commonly a reflection of the data than the AI itself. We must take care to ensure we are not repackaging and internally validating registry data. Instead, we should be critically appraising our data-not the AI-based statistical technique. Without appropriate guardrails surrounding the use of artificial intelligence in Orthopaedic research, there is a risk of repackaging registry data and low-quality research in a recursive peer-reviewed loop.
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Li C, Alike Y, Hou J, Long Y, Zheng Z, Meng K, Yang R. Machine learning model successfully identifies important clinical features for predicting outpatients with rotator cuff tears. Knee Surg Sports Traumatol Arthrosc 2023:10.1007/s00167-022-07298-4. [PMID: 36629889 DOI: 10.1007/s00167-022-07298-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 12/20/2022] [Indexed: 01/12/2023]
Abstract
PURPOSE The aim of this study is to develop a machine learning model to identify important clinical features related to rotator cuff tears (RCTs) using explainable artificial intelligence (XAI) for efficiently predicting outpatients with RCTs. METHODS A retrospective review of a local clinical registry dataset was performed to include patients with shoulder pain and dysfunction who underwent questionnaires and physical examinations between 2019 and 2022. RCTs were diagnosed by shoulder arthroscopy. Six machine-learning algorithms (Stacking, Gradient Boosting Machine, Bagging, Random Forest, Extreme Gradient Boost (XGBoost), and Adaptive Boosting) were developed for the prediction. The performance of the models was assessed by the area under the receiver operating characteristic curve (AUC), Brier scores, and Decision curve. The interpretability of the predicted outcomes was evaluated using Shapley additive explanation (SHAP) values. RESULTS A total of 1684 patients who completed questionnaires and clinical tests were included, and 417 patients with RCTs underwent shoulder arthroscopy. In six machining learning algorithms for predicting RCTs, the accuracy, AUC values, and Brier scores were in the range of 0.81-0.86, 0.75-0.92, and 0.15-0.19, respectively. The XGBoost model showed superior performance with accuracy, AUC, and Brier scores of 0.85(95% confidence interval, 0.82-0.87), 0.92 (95% confidence interval,0.90-0.94), and 0.15 (95% confidence interval,0.14-0.16), respectively. The Shapley plot showed the impact of the clinical features on predicting RCTs. The most important variables were Jobe test, Bear hug test, and age for prediction, with mean SHAP values of 1.458, 0.950, and 0.790, respectively. CONCLUSION The machine learning model successfully identified important clinical variables for predicting patients with RCTs. In addition, the best algorithm was also integrated into a digital application to provide predictions in outpatient settings. This tool may assist patients in reducing their pain experience and providing prompt treatments. LEVEL OF EVIDENCE Level III.
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Affiliation(s)
- Cheng Li
- Department of Orthopedics, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, 107 Yan Jiang Road West, Guangzhou, 510120, Guangdong, China
| | - Yamuhanmode Alike
- Department of Orthopedics, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, 107 Yan Jiang Road West, Guangzhou, 510120, Guangdong, China
| | - Jingyi Hou
- Department of Orthopedics, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, 107 Yan Jiang Road West, Guangzhou, 510120, Guangdong, China
| | - Yi Long
- Department of Orthopedics, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, 107 Yan Jiang Road West, Guangzhou, 510120, Guangdong, China
| | - Zhenze Zheng
- Department of Orthopedics, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, 107 Yan Jiang Road West, Guangzhou, 510120, Guangdong, China
| | - Ke Meng
- Department of Orthopedics, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, 107 Yan Jiang Road West, Guangzhou, 510120, Guangdong, China
| | - Rui Yang
- Department of Orthopedics, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, 107 Yan Jiang Road West, Guangzhou, 510120, Guangdong, China.
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Factor S, Neuman Y, Vidra M, Shalom M, Lichtenstein A, Amar E, Rath E. Violation of expectations is correlated with satisfaction following hip arthroscopy. Knee Surg Sports Traumatol Arthrosc 2022; 31:2023-2029. [PMID: 36181523 DOI: 10.1007/s00167-022-07182-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 09/26/2022] [Indexed: 11/07/2022]
Abstract
PURPOSE The mechanism by which preoperative expectations may be associated with patient satisfaction and procedural outcomes following hip preservation surgery (HPS) is far from simple or linear. The purpose of this study is to better understand patient expectations regarding HPS and their relationship with patient-reported outcomes (PROs) and satisfaction using machine learning (ML) algorithms. METHODS Patients scheduled for hip arthroscopy completed the Hip Preservation Surgery Expectations Survey (HPSES) and the pre- and a minimum 2 year postoperative International Hip Outcome Tool (iHOT-33). Patient demographics, including age, gender, occupation, and body mass index (BMI), were also collected. At the latest follow-up, patients were evaluated for subjective satisfaction and postoperative complications. ML algorithms and standard statistics were used. RESULTS A total of 69 patients were included in this study (mean age 33.7 ± 13.1 years, 62.3% males). The mean follow-up period was 27 months. The mean HPSES score, patient satisfaction, preoperative, and postoperative iHOT-33 were 83.8 ± 16.5, 75.9 ± 26.9, 31.6 ± 15.8, and 73 ± 25.9, respectively. Fifty-nine patients (86%) reported that they would undergo the surgery again, with no significant difference with regards to expectations. A significant difference was found with regards to expectation violation (p < 0.001). Expectation violation scores were also found to be significantly correlated with satisfaction. CONCLUSION ML algorithms utilized in this study demonstrate that violation of expectations plays an important predictive role in postoperative outcomes and patient satisfaction and is associated with patients' willingness to undergo surgery again. LEVEL OF EVIDENCE IV.
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Affiliation(s)
- Shai Factor
- Orthopedic Division, Department of Orthopedic Surgery, Affiliated with the Sackler Faculty of Medicine, Tel Aviv Medical Center, 6 Weitzman St., 6423906, Tel Aviv, Israel.
| | - Yair Neuman
- Department of Cognitive and Brain Sciences and the Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, 84105, Beer-Sheva, Israel
| | - Matias Vidra
- Orthopedic Division, Department of Orthopedic Surgery, Affiliated with the Sackler Faculty of Medicine, Tel Aviv Medical Center, 6 Weitzman St., 6423906, Tel Aviv, Israel
| | - Moshe Shalom
- Orthopedic Division, Department of Orthopedic Surgery, Affiliated with the Sackler Faculty of Medicine, Tel Aviv Medical Center, 6 Weitzman St., 6423906, Tel Aviv, Israel
| | - Adi Lichtenstein
- Orthopedic Division, Department of Orthopedic Surgery, Affiliated with the Sackler Faculty of Medicine, Tel Aviv Medical Center, 6 Weitzman St., 6423906, Tel Aviv, Israel
| | - Eyal Amar
- Orthopedic Division, Department of Orthopedic Surgery, Affiliated with the Sackler Faculty of Medicine, Tel Aviv Medical Center, 6 Weitzman St., 6423906, Tel Aviv, Israel
| | - Ehud Rath
- Orthopedic Division, Department of Orthopedic Surgery, Affiliated with the Sackler Faculty of Medicine, Tel Aviv Medical Center, 6 Weitzman St., 6423906, Tel Aviv, Israel
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Ezuma CO, Lu Y, Pareek A, Wilbur R, Krych AJ, Forsythe B, Camp CL. A Machine Learning Algorithm Outperforms Traditional Multiple Regression to Predict Risk of Unplanned Overnight Stay Following Outpatient Medial Patellofemoral Ligament Reconstruction. Arthrosc Sports Med Rehabil 2022; 4:e1103-e1110. [PMID: 35747652 PMCID: PMC9210490 DOI: 10.1016/j.asmr.2022.03.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 03/28/2022] [Indexed: 12/21/2022] Open
Abstract
Purpose To determine whether conventional logistic regression or machine learning algorithms were more precise in identifying the risk factors for unplanned overnight admission after medial patellofemoral ligament (MPFL) reconstruction. Methods A retrospective review of the prospectively collected National Surgical Quality Improvement Program database was performed to identify patients who underwent outpatient MPFL reconstruction from 2006–2018. Patients admitted overnight were identified as those with length of stay of 1 or more days. Models were generated using random forest, extreme gradient boosting, adaptive boosting, or elastic net penalized logistic regression, and an additional model was produced as a weighted ensemble of the 4 final algorithms. The predictive capacity of these models was compared to that of logistic regression. Results Of the 1307 patients identified, 221 (16.9%) required at least one overnight stay after MPFL reconstruction. Multivariate logistic regression found the following variables to be predictors of inpatient admission: age (odds ratio [OR] = 1.03 [95% confidence interval {CI} 1.02-1.04]; P <.001), spinal anesthesia (OR = 3.42 [95% CI 1.98-6.08]; P < .001), American Society of Anesthesiologists (ASA) class 3/4 (OR = 1.96 [95% CI 1.25-3.06]; P < .001), history of chronic obstructive pulmonary disease (COPD) (OR = 6.44 [95% CI 1.58-26.17]; P = .02), and body mass index (BMI) (OR = 1.03 [95% CI 1.01-1.05]; P < .001). The ensemble model achieved the best performance based on discrimination assessed via internal validation (area under the curve = 0.722). The variables determined most important by the ensemble model were increasing BMI, increasing age, ASA class, anesthesia, smoking, hypertension, lateral release, and history of COPD. Conclusions An internally validated machine learning algorithm outperformed logistic regression modeling in predicting the need for unplanned overnight hospitalization after MPFL reconstruction. In this model, the most significant risk factors for admission were age, BMI, ASA class, smoking status, hypertension, lateral release, and history of COPD. This tool can be deployed to augment provider assessment to identify high-risk candidates and appropriately set postoperative expectations for patients. Clinical Relevance Identifying and mitigating patient risk factors to prevent adverse surgical outcomes and hospitalizations is one of our primary goals. There may be a key role for machine learning algorithms to help successfully and efficiently risk stratify patients to decrease costs, appropriately set postoperative expectations, and increase the quality of delivered care.
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Affiliation(s)
- Chimere O Ezuma
- School of Medicine, Vagelos Columbia College of Physicians and Surgeons, New York, New York
| | - Yining Lu
- Department of Orthopedic Surgery, Mayo Clinic, and Rochester, Minnesota
| | - Ayoosh Pareek
- Department of Orthopedic Surgery, Mayo Clinic, and Rochester, Minnesota
| | - Ryan Wilbur
- Department of Orthopedic Surgery, Mayo Clinic, and Rochester, Minnesota
| | - Aaron J Krych
- Department of Orthopedic Surgery, Mayo Clinic, and Rochester, Minnesota
| | - Brian Forsythe
- Midwest Orthopedics at Rush, Rush University Medical Center, Chicago, Illinois, U.S.A
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Awan MJ, Mohd Rahim MS, Salim N, Rehman A, Nobanee H. Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:2550120. [PMID: 35444781 PMCID: PMC9015864 DOI: 10.1155/2022/2550120] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 01/02/2022] [Accepted: 03/21/2022] [Indexed: 12/14/2022]
Abstract
In recent times, knee joint pains have become severe enough to make daily tasks difficult. Knee osteoarthritis is a type of arthritis and a leading cause of disability worldwide. The middle of the knee contains a vital portion, the anterior cruciate ligament (ACL). It is necessary to diagnose the ACL ruptured tears early to avoid surgery. The study aimed to perform a comparative analysis of machine learning models to identify the condition of three ACL tears. In contrast to previous studies, this study also considers imbalanced data distributions as machine learning techniques struggle to deal with this problem. The paper applied and analyzed four machine learning classification models, namely, random forest (RF), categorical boosting (Cat Boost), light gradient boosting machines (LGBM), and highly randomized classifier (ETC) on the balanced, structured dataset of ACL. After oversampling a hyperparameter adjustment, the above four models have achieved an average accuracy of 95.72%, 94.98%, 94.98%, and 98.26%. There are 2070 observations and eight features in the collection of three diagnosis ACL classes after oversampling. The area under curve value was approximately 0.998, respectively. Experiments were performed using twelve machine learning algorithms with imbalanced and balanced datasets. However, the accuracy of the imbalanced dataset has remained under 76% for all twelve models. After oversampling, the proposed model may contribute to the investigation of ACL tears on magnetic resonance imaging and other knee ligaments efficiently and automatically without involving radiologists.
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Affiliation(s)
- Mazhar Javed Awan
- School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor 81310, Malaysia
- Department of Software Engineering, University of Management and Technology, Lahore 54770, Pakistan
| | - Mohd Shafry Mohd Rahim
- School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor 81310, Malaysia
| | - Naomie Salim
- School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor 81310, Malaysia
| | - Amjad Rehman
- Artificial Intelligence and Data Analytics Laboratory, College of Computer and Information Sciences (CCIS), Prince Sultan University, Riyadh 11586, Saudi Arabia
| | - Haitham Nobanee
- College of Business, Abu Dhabi University, P.O. Box 59911, Abu Dhabi, UAE
- Oxford Centre for Islamic Studies, University of Oxford, Oxford OX1 2J, UK
- School of Histories Languages and Cultures, The University of Liverpool, Liverpool L69 3BX, UK
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15
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Martin RK, Ley C, Pareek A, Groll A, Tischer T, Seil R. Artificial intelligence and machine learning: an introduction for orthopaedic surgeons. Knee Surg Sports Traumatol Arthrosc 2022; 30:361-364. [PMID: 34528133 DOI: 10.1007/s00167-021-06741-2] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 09/02/2021] [Indexed: 01/15/2023]
Abstract
The application of artificial intelligence (AI) and machine learning to the field of orthopaedic surgery is rapidly increasing. While this represents an important step in the advancement of our specialty, the concept of AI is rich with statistical jargon and techniques unfamiliar to many clinicians. This knowledge gap may limit the impact and potential of these novel techniques. We aim to narrow this gap in a way that is accessible for all orthopaedic surgeons. With this manuscript, we introduce the concept of AI and machine learning and give examples of how it can impact clinical practice and patient care.Level of evidence VI.
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Affiliation(s)
- R Kyle Martin
- Department of Orthopedic Surgery, University of Minnesota, Minneapolis, MN, USA.
| | - Christophe Ley
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Ayoosh Pareek
- Department of Orthopaedic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Andreas Groll
- Department of Statistics, TU Dortmund University, Dortmund, Germany
| | - Thomas Tischer
- Department of Orthopaedic Surgery, University Medicine Rostock, Rostock, Germany
| | - Romain Seil
- Department of Orthopaedic Surgery, Centre Hospitalier Luxembourg and Luxembourg Institute of Health, Luxembourg, Luxembourg
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Bellini V, Valente M, Bertorelli G, Pifferi B, Craca M, Mordonini M, Lombardo G, Bottani E, Del Rio P, Bignami E. Machine learning in perioperative medicine: a systematic review. JOURNAL OF ANESTHESIA, ANALGESIA AND CRITICAL CARE 2022; 2:2. [PMCID: PMC8761048 DOI: 10.1186/s44158-022-00033-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Background Risk stratification plays a central role in anesthetic evaluation. The use of Big Data and machine learning (ML) offers considerable advantages for collection and evaluation of large amounts of complex health-care data. We conducted a systematic review to understand the role of ML in the development of predictive post-surgical outcome models and risk stratification. Methods Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, we selected the period of the research for studies from 1 January 2015 up to 30 March 2021. A systematic search in Scopus, CINAHL, the Cochrane Library, PubMed, and MeSH databases was performed; the strings of research included different combinations of keywords: “risk prediction,” “surgery,” “machine learning,” “intensive care unit (ICU),” and “anesthesia” “perioperative.” We identified 36 eligible studies. This study evaluates the quality of reporting of prediction models using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist. Results The most considered outcomes were mortality risk, systemic complications (pulmonary, cardiovascular, acute kidney injury (AKI), etc.), ICU admission, anesthesiologic risk and prolonged length of hospital stay. Not all the study completely followed the TRIPOD checklist, but the quality was overall acceptable with 75% of studies (Rev #2, comm #minor issue) showing an adherence rate to TRIPOD more than 60%. The most frequently used algorithms were gradient boosting (n = 13), random forest (n = 10), logistic regression (LR; n = 7), artificial neural networks (ANNs; n = 6), and support vector machines (SVM; n = 6). Models with best performance were random forest and gradient boosting, with AUC > 0.90. Conclusions The application of ML in medicine appears to have a great potential. From our analysis, depending on the input features considered and on the specific prediction task, ML algorithms seem effective in outcomes prediction more accurately than validated prognostic scores and traditional statistics. Thus, our review encourages the healthcare domain and artificial intelligence (AI) developers to adopt an interdisciplinary and systemic approach to evaluate the overall impact of AI on perioperative risk assessment and on further health care settings as well.
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Affiliation(s)
- Valentina Bellini
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126 Parma, Italy
| | - Marina Valente
- General Surgery Unit, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126 Parma, Italy
| | - Giorgia Bertorelli
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126 Parma, Italy
| | - Barbara Pifferi
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126 Parma, Italy
| | - Michelangelo Craca
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126 Parma, Italy
| | - Monica Mordonini
- Department of Engineering and Architecture, University of Parma, Viale G.P.Usberti 181/A, 43124 Parma, Italy
| | - Gianfranco Lombardo
- Department of Engineering and Architecture, University of Parma, Viale G.P.Usberti 181/A, 43124 Parma, Italy
| | - Eleonora Bottani
- Department of Engineering and Architecture, University of Parma, Viale G.P.Usberti 181/A, 43124 Parma, Italy
| | - Paolo Del Rio
- General Surgery Unit, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126 Parma, Italy
| | - Elena Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126 Parma, Italy
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Chen Y. Prediction and Analysis of Length of Stay Based on Nonlinear Weighted XGBoost Algorithm in Hospital. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:4714898. [PMID: 34900191 PMCID: PMC8654524 DOI: 10.1155/2021/4714898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 11/19/2021] [Accepted: 11/20/2021] [Indexed: 11/18/2022]
Abstract
An improved nonlinear weighted extreme gradient boosting (XGBoost) technique is developed to forecast length of stay for patients with imbalance data. The algorithm first chooses an effective technique for fitting the duration of stay and determining the distribution law and then optimizes the negative log likelihood loss function using a heuristic nonlinear weighting method based on sample percentage. Theoretical and practical results reveal that, when compared to existing algorithms, the XGBoost method based on nonlinear weighting may achieve higher classification accuracy and better prediction performance, which is beneficial in treating more patients with fewer hospital beds.
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Affiliation(s)
- Yong Chen
- The Affiliated Hospital of Chengde Medical College, Chengde, Hebei 067000, China
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18
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Wang KY, Ikwuezunma I, Puvanesarajah V, Babu J, Margalit A, Raad M, Jain A. Using Predictive Modeling and Supervised Machine Learning to Identify Patients at Risk for Venous Thromboembolism Following Posterior Lumbar Fusion. Global Spine J 2021; 13:1097-1103. [PMID: 34036817 DOI: 10.1177/21925682211019361] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
STUDY DESIGN Retrospective review. OBJECTIVE To use predictive modeling and machine learning to identify patients at risk for venous thromboembolism (VTE) following posterior lumbar fusion (PLF) for degenerative spinal pathology. METHODS Patients undergoing single-level PLF in the inpatient setting were identified in the National Surgical Quality Improvement Program database. Our outcome measure of VTE included all patients who experienced a pulmonary embolism and/or deep venous thrombosis within 30-days of surgery. Two different methodologies were used to identify VTE risk: 1) a novel predictive model derived from multivariable logistic regression of significant risk factors, and 2) a tree-based extreme gradient boosting (XGBoost) algorithm using preoperative variables. The methods were compared against legacy risk-stratification measures: ASA and Charlson Comorbidity Index (CCI) using area-under-the-curve (AUC) statistic. RESULTS 13, 500 patients who underwent single-level PLF met the study criteria. Of these, 0.95% had a VTE within 30-days of surgery. The 5 clinical variables found to be significant in the multivariable predictive model were: age > 65, obesity grade II or above, coronary artery disease, functional status, and prolonged operative time. The predictive model exhibited an AUC of 0.716, which was significantly higher than the AUCs of ASA and CCI (all, P < 0.001), and comparable to that of the XGBoost algorithm (P > 0.05). CONCLUSION Predictive analytics and machine learning can be leveraged to aid in identification of patients at risk of VTE following PLF. Surgeons and perioperative teams may find these tools useful to augment clinical decision making risk stratification tool.
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Affiliation(s)
- Kevin Y Wang
- Department of Orthopaedic Surgery, 1501The Johns Hopkins Hospital, Baltimore, MD, USA
| | - Ijezie Ikwuezunma
- Department of Orthopaedic Surgery, 1501The Johns Hopkins Hospital, Baltimore, MD, USA
| | - Varun Puvanesarajah
- Department of Orthopaedic Surgery, 1501The Johns Hopkins Hospital, Baltimore, MD, USA
| | - Jacob Babu
- Department of Orthopaedic Surgery, 1501The Johns Hopkins Hospital, Baltimore, MD, USA
| | - Adam Margalit
- Department of Orthopaedic Surgery, 1501The Johns Hopkins Hospital, Baltimore, MD, USA
| | - Micheal Raad
- Department of Orthopaedic Surgery, 1501The Johns Hopkins Hospital, Baltimore, MD, USA
| | - Amit Jain
- Department of Orthopaedic Surgery, 1501The Johns Hopkins Hospital, Baltimore, MD, USA
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Choi ES, Sim JA, Na YG, Seon JK, Shin HD. Machine-learning algorithm that can improve the diagnostic accuracy of septic arthritis of the knee. Knee Surg Sports Traumatol Arthrosc 2021; 29:3142-3148. [PMID: 33452576 PMCID: PMC8458173 DOI: 10.1007/s00167-020-06418-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 12/10/2020] [Indexed: 11/05/2022]
Abstract
PURPOSE Prompt diagnosis and treatment of septic arthritis of the knee is crucial. Nevertheless, the quality of evidence for the diagnosis of septic arthritis is low. In this study, the authors developed a machine learning-based diagnostic algorithm for septic arthritis of the native knee using clinical data in an emergency department and validated its diagnostic accuracy. METHODS Patients (n = 326) who underwent synovial fluid analysis at the emergency department for suspected septic arthritis of the knee were enrolled. Septic arthritis was diagnosed in 164 of the patients (50.3%) using modified Newman criteria. Clinical characteristics of septic and inflammatory arthritis were compared. Area under the receiver-operating characteristic (ROC) curve (AUC) statistics was applied to evaluate the efficacy of each variable for the diagnosis of septic arthritis. The dataset was divided into independent training and test sets (comprising 80% and 20%, respectively, of the data). Supervised machine-learning techniques (random forest and eXtreme Gradient Boosting: XGBoost) were applied to develop a diagnostic model using the training dataset. The test dataset was subsequently used to validate the developed model. The ROC curves of the machine-learning model and each variable were compared. RESULTS Synovial white blood cell (WBC) count was significantly higher in septic arthritis than in inflammatory arthritis in the multivariate analysis (P = 0.001). In the ROC comparison analysis, synovial WBC count yielded a significantly higher AUC than all other single variables (P = 0.002). The diagnostic model using the XGBoost algorithm yielded a higher AUC (0.831, 95% confidence interval 0.751-0.923) than synovial WBC count (0.740, 95% confidence interval 0.684-0.791; P = 0.033). The developed algorithm was deployed as a free access web-based application ( www.septicknee.com ). CONCLUSION The diagnosis of septic arthritis of the knee might be improved using a machine learning-based prediction model. LEVEL OF EVIDENCE Diagnostic study Level III (Case-control study).
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Affiliation(s)
- Eun-Seok Choi
- Department of Orthopaedic Surgery, Chungnam National University School of Medicine, Chungnam National University Hospital, 266 Munhwa-ro, Jung-gu, Daejeon, 35015, Republic of Korea.
| | - Jae Ang Sim
- grid.256155.00000 0004 0647 2973Department of Orthopaedic Surgery, Gachon University College of Medicine, Gil Medical Centre, Incheon, Republic of Korea
| | - Young Gon Na
- grid.489932.dDepartment of Orthopaedic Surgery, CM Hospital, Seoul, Republic of Korea
| | - Jong- Keun Seon
- grid.411597.f0000 0004 0647 2471Department of Orthopaedic Surgery, Chonnam National University School of Medicine, Chonnam National University Hospital, Gwangju, Republic of Korea
| | - Hyun Dae Shin
- grid.254230.20000 0001 0722 6377Department of Orthopaedic Surgery, Chungnam National University School of Medicine, Chungnam National University Hospital, 266 Munhwa-ro, Jung-gu, Daejeon, 35015 Republic of Korea
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