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Chirongoma T, Cabrera A, Bouterse A, Chung D, Patton D, Essilfie A. Predicting Prolonged Length of Hospital Stay and Identifying Risk Factors Following Total Ankle Arthroplasty: A Supervised Machine Learning Methodology. J Foot Ankle Surg 2024:S1067-2516(24)00096-6. [PMID: 38789076 DOI: 10.1053/j.jfas.2024.05.005] [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: 12/23/2023] [Revised: 05/07/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024]
Abstract
Ankle osteoarthritis (OA) is a debilitating condition that arises as a result of trauma or injury to the ankle and often progresses to chronic pain and loss of function that may require surgical intervention. Total ankle arthroplasty (TAA) has emerged as a means of operative treatment for end-stage ankle OA. Increased hospital length of stay (LOS) is a common adverse postoperative outcome that increases both the complications and cost of care associated with arthroplasty procedures. The purpose of this study was to employ four machine learning (ML) algorithms to predict LOS in patients undergoing TAA using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database. The ACS-NSQIP database was queried to identify adult patients undergoing elective TAA from 2008 to 2018. Four supervised ML classification algorithms were utilized and tasked with predicting increased hospital length of stay (LOS). Among these variables, female sex, ASA Class III, preoperative sodium, preoperative hematocrit, diabetes, preoperative creatinine, other arthritis, BMI, preoperative WBC, and Hispanic ethnicity carried the highest importance across predictions generated by 4 independent ML algorithms. Predictions generated by these algorithms were made with an average AUC of 0.7257, as well as an average accuracy of 73.98% and an average sensitivity and specificity of 48.47% and 79.38%, respectively. These findings may be useful for guiding decision-making within the perioperative period and may serve to identify patients at increased risk for a prolonged LOS.
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Affiliation(s)
| | - Andrew Cabrera
- School of Medicine, Loma Linda University, Loma Linda, CA
| | | | - David Chung
- Department of Orthopedics, Loma Linda University, Loma Linda, CA
| | | | - Anthony Essilfie
- Department of Orthopedics, Loma Linda University, Loma Linda, CA.
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Kianian R, Carter M, Finkelshtein I, Eleswarapu SV, Kachroo N. Application of Artificial Intelligence to Patient-Targeted Health Information on Kidney Stone Disease. J Ren Nutr 2024; 34:170-176. [PMID: 37839591 DOI: 10.1053/j.jrn.2023.10.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 09/25/2023] [Accepted: 10/08/2023] [Indexed: 10/17/2023] Open
Abstract
OBJECTIVE The American Medical Association recommends health information to be written at a 6th grade level reading level. Our aim was to determine whether Artificial Intelligence can outperform the existing health information on kidney stone prevention and treatment. METHODS The top 50 search results for "Kidney Stone Prevention" and "Kidney Stone Treatment" on Google, Bing, and Yahoo were selected. Duplicate webpages, advertisements, pages intended for health professionals such as science articles, links to videos, paid subscription pages, and links nonrelated to kidney stone prevention and/or treatment were excluded. Included pages were categorized into academic, hospital-affiliated, commercial, nonprofit foundations, and other. Quality and readability of webpages were evaluated using validated tools, and the reading level was descriptively compared with ChatGPT generated health information on kidney stone prevention and treatment. RESULTS 50 webpages on kidney stone prevention and 49 on stone treatment were included in this study. The reading level was determined to equate to that of a 10th to 12th grade student. Quality was measured as "fair" with no pages scoring "excellent" and only 20% receiving a "good" quality. There was no significant difference between pages from academic, hospital-affiliated, commercial, and nonprofit foundation publications. The text generated by ChatGPT was considerably easier to understand with readability levels measured as low as 5th grade. CONCLUSIONS The language used in existing information on kidney stone disease is of subpar quality and too complex to understand. Machine learning tools could aid in generating information that is comprehensible by the public.
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Affiliation(s)
- Reza Kianian
- Department of Urology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Matthew Carter
- Department of Urology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Ilana Finkelshtein
- Department of Urology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Sriram V Eleswarapu
- Department of Urology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Naveen Kachroo
- Department of Urology, Vattikuti Urology Institute, Henry Ford Hospital, Detroit, Michigan.
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Huffman N, Pasqualini I, Khan ST, Klika AK, Deren ME, Jin Y, Kunze KN, Piuzzi NS. Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence: A Critical Analysis Review. JBJS Rev 2024; 12:01874474-202403000-00006. [PMID: 38466797 DOI: 10.2106/jbjs.rvw.23.00232] [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: 03/13/2024]
Abstract
» The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.» Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.» Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.» AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.» Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.
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Affiliation(s)
- Nickelas Huffman
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | | | - Shujaa T Khan
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Alison K Klika
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Matthew E Deren
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Yuxuan Jin
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York
| | - Nicolas S Piuzzi
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
- Department of Biomedical Engineering, Cleveland Clinic Foundation, Cleveland, Ohio
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Chiasakul T, Lam BD, McNichol M, Robertson W, Rosovsky RP, Lake L, Vlachos IS, Adamski A, Reyes N, Abe K, Zwicker JI, Patell R. Artificial intelligence in the prediction of venous thromboembolism: A systematic review and pooled analysis. Eur J Haematol 2023; 111:951-962. [PMID: 37794526 PMCID: PMC10900245 DOI: 10.1111/ejh.14110] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/16/2023] [Accepted: 09/18/2023] [Indexed: 10/06/2023]
Abstract
BACKGROUND Accurate diagnostic and prognostic predictions of venous thromboembolism (VTE) are crucial for VTE management. Artificial intelligence (AI) enables autonomous identification of the most predictive patterns from large complex data. Although evidence regarding its performance in VTE prediction is emerging, a comprehensive analysis of performance is lacking. AIMS To systematically review the performance of AI in the diagnosis and prediction of VTE and compare it to clinical risk assessment models (RAMs) or logistic regression models. METHODS A systematic literature search was performed using PubMed, MEDLINE, EMBASE, and Web of Science from inception to April 20, 2021. Search terms included "artificial intelligence" and "venous thromboembolism." Eligible criteria were original studies evaluating AI in the prediction of VTE in adults and reporting one of the following outcomes: sensitivity, specificity, positive predictive value, negative predictive value, or area under receiver operating curve (AUC). Risks of bias were assessed using the PROBAST tool. Unpaired t-test was performed to compare the mean AUC from AI versus conventional methods (RAMs or logistic regression models). RESULTS A total of 20 studies were included. Number of participants ranged from 31 to 111 888. The AI-based models included artificial neural network (six studies), support vector machines (four studies), Bayesian methods (one study), super learner ensemble (one study), genetic programming (one study), unspecified machine learning models (two studies), and multiple machine learning models (five studies). Twelve studies (60%) had both training and testing cohorts. Among 14 studies (70%) where AUCs were reported, the mean AUC for AI versus conventional methods were 0.79 (95% CI: 0.74-0.85) versus 0.61 (95% CI: 0.54-0.68), respectively (p < .001). However, the good to excellent discriminative performance of AI methods is unlikely to be replicated when used in clinical practice, because most studies had high risk of bias due to missing data handling and outcome determination. CONCLUSION The use of AI appears to improve the accuracy of diagnostic and prognostic prediction of VTE over conventional risk models; however, there was a high risk of bias observed across studies. Future studies should focus on transparent reporting, external validation, and clinical application of these models.
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Affiliation(s)
- Thita Chiasakul
- Division of Hematology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
- Division of Hemostasis and Thrombosis, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
- Division of Hematology, Faculty of Medicine, Department of Medicine, Center of Excellence in Translational Hematology, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Barbara D Lam
- Division of Hematology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
- Division of Hemostasis and Thrombosis, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Megan McNichol
- Division of Knowledge Services, Department of Information Services (M.M.), Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - William Robertson
- National Blood Clot Alliance, Philadelphia, Pennsylvania, USA
- Department of Emergency Healthcare, College of Health Professions, Weber State University, Ogden, Utah, USA
| | - Rachel P Rosovsky
- Division of Hematology/Oncology, Department of Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Leslie Lake
- National Blood Clot Alliance, Philadelphia, Pennsylvania, USA
| | - Ioannis S Vlachos
- Department of Pathology, Cancer Research Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Alys Adamski
- Division of Blood Disorders, National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Nimia Reyes
- Division of Blood Disorders, National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Karon Abe
- Division of Blood Disorders, National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Jeffrey I Zwicker
- Division of Hematology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
- Division of Hemostasis and Thrombosis, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
- Department of Medicine, Hematology Service, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Rushad Patell
- Division of Hematology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
- Division of Hemostasis and Thrombosis, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
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Michelsen C, Jørgensen CC, Heltberg M, Jensen MH, Lucchetti A, Petersen PB, Petersen T, Kehlet H, Madsen F, Hansen TB, Gromov K, Jakobsen T, Varnum C, Overgaard S, Rathsach M, Hansen L. Machine-learning vs. logistic regression for preoperative prediction of medical morbidity after fast-track hip and knee arthroplasty-a comparative study. BMC Anesthesiol 2023; 23:391. [PMID: 38030979 PMCID: PMC10685559 DOI: 10.1186/s12871-023-02354-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 11/21/2023] [Indexed: 12/01/2023] Open
Abstract
BACKGROUND Machine-learning models may improve prediction of length of stay (LOS) and morbidity after surgery. However, few studies include fast-track programs, and most rely on administrative coding with limited follow-up and information on perioperative care. This study investigates potential benefits of a machine-learning model for prediction of postoperative morbidity in fast-track total hip (THA) and knee arthroplasty (TKA). METHODS Cohort study in consecutive unselected primary THA/TKA between 2014-2017 from seven Danish centers with established fast-track protocols. Preoperative comorbidity and prescribed medication were recorded prospectively and information on length of stay and readmissions was obtained through the Danish National Patient Registry and medical records. We used a machine-learning model (Boosted Decision Trees) based on boosted decision trees with 33 preoperative variables for predicting "medical" morbidity leading to LOS > 4 days or 90-days readmissions and compared to a logistical regression model based on the same variables. We also evaluated two parsimonious models, using the ten most important variables in the full machine-learning and logistic regression models. Data collected between 2014-2016 (n:18,013) was used for model training and data from 2017 (n:3913) was used for testing. Model performances were analyzed using precision, area under receiver operating (AUROC) and precision recall curves (AUPRC), as well as the Mathews Correlation Coefficient. Variable importance was analyzed using Shapley Additive Explanations values. RESULTS Using a threshold of 20% "risk-patients" (n:782), precision, AUROC and AUPRC were 13.6%, 76.3% and 15.5% vs. 12.4%, 74.7% and 15.6% for the machine-learning and logistic regression model, respectively. The parsimonious machine-learning model performed better than the full logistic regression model. Of the top ten variables, eight were shared between the machine-learning and logistic regression models, but with a considerable age-related variation in importance of specific types of medication. CONCLUSION A machine-learning model using preoperative characteristics and prescriptions slightly improved identification of patients in high-risk of "medical" complications after fast-track THA and TKA compared to a logistic regression model. Such algorithms could help find a manageable population of patients who may benefit most from intensified perioperative care.
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Affiliation(s)
- Christian Michelsen
- The Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100, Copenhagen, Denmark
| | - Christoffer C Jørgensen
- Department of Anesthesia and Intensive Care, Hospital of Northern Zealand, Dyrehavevej 29 3400, Hillerød, Denmark.
- The Centre for Fast-Track Hip and Knee Replacement, 7621, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.
| | - Mathias Heltberg
- The Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100, Copenhagen, Denmark
| | - Mogens H Jensen
- The Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100, Copenhagen, Denmark
| | - Alessandra Lucchetti
- The Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100, Copenhagen, Denmark
| | - Pelle B Petersen
- Department of Anesthesia and Intensive Care, Hospital of Northern Zealand, Dyrehavevej 29 3400, Hillerød, Denmark
- The Centre for Fast-Track Hip and Knee Replacement, 7621, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Troels Petersen
- The Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100, Copenhagen, Denmark
| | - Henrik Kehlet
- The Centre for Fast-Track Hip and Knee Replacement, 7621, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
- Section of Surgical Pathophysiology, 7621, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
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6
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Ito S, Nakashima H, Yoshii T, Egawa S, Sakai K, Kusano K, Tsutui S, Hirai T, Matsukura Y, Wada K, Katsumi K, Koda M, Kimura A, Furuya T, Maki S, Nagoshi N, Nishida N, Nagamoto Y, Oshima Y, Ando K, Takahata M, Mori K, Nakajima H, Murata K, Miyagi M, Kaito T, Yamada K, Banno T, Kato S, Ohba T, Inami S, Fujibayashi S, Katoh H, Kanno H, Oda M, Mori K, Taneichi H, Kawaguchi Y, Takeshita K, Matsumoto M, Yamazaki M, Okawa A, Imagama S. Deep learning-based prediction model for postoperative complications of cervical posterior longitudinal ligament ossification. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2023; 32:3797-3806. [PMID: 36740608 DOI: 10.1007/s00586-023-07562-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 01/06/2023] [Accepted: 01/24/2023] [Indexed: 02/07/2023]
Abstract
PURPOSE Postoperative complication prediction helps surgeons to inform and manage patient expectations. Deep learning, a model that finds patterns in large samples of data, outperform traditional statistical methods in making predictions. This study aimed to create a deep learning-based model (DLM) to predict postoperative complications in patients with cervical ossification of the posterior longitudinal ligament (OPLL). METHODS This prospective multicenter study was conducted by the 28 institutions, and 478 patients were included in the analysis. Deep learning was used to create two predictive models of the overall postoperative complications and neurological complications, one of the major complications. These models were constructed by learning the patient's preoperative background, clinical symptoms, surgical procedures, and imaging findings. These logistic regression models were also created, and these accuracies were compared with those of the DLM. RESULTS Overall complications were observed in 127 cases (26.6%). The accuracy of the DLM was 74.6 ± 3.7% for predicting the overall occurrence of complications, which was comparable to that of the logistic regression (74.1%). Neurological complications were observed in 48 cases (10.0%), and the accuracy of the DLM was 91.7 ± 3.5%, which was higher than that of the logistic regression (90.1%). CONCLUSION A new algorithm using deep learning was able to predict complications after cervical OPLL surgery. This model was well calibrated, with prediction accuracy comparable to that of regression models. The accuracy remained high even for predicting only neurological complications, for which the case number is limited compared to conventional statistical methods.
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Affiliation(s)
- Sadayuki Ito
- Department of Orthopaedic Surgery, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa Ward, Nagoya, Aichi, 466-8550, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Hiroaki Nakashima
- Department of Orthopaedic Surgery, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa Ward, Nagoya, Aichi, 466-8550, Japan.
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan.
| | - Toshitaka Yoshii
- Department of Orthopaedic Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo Ward, Tokyo, 113-8519, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Satoru Egawa
- Department of Orthopaedic Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo Ward, Tokyo, 113-8519, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Kenichiro Sakai
- Department of Orthopaedic Surgery, Saiseikai Kawaguchi General Hospital, 5-11-5 Nishikawaguchi, Kawaguchishi, Saitama, 332-8558, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Kazuo Kusano
- Department of Orthopaedic Surgery, Kudanzaka Hospital, 1-6-12 Kudanminami, Chiyodaku, 102-0074, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Shinji Tsutui
- Department of Orthopaedic Surgery, Wakayama Medical University, 811-1 KImiidera, Wakayama-city, Wakayama, 641-8510, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Takashi Hirai
- Department of Orthopaedic Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo Ward, Tokyo, 113-8519, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Yu Matsukura
- Department of Orthopaedic Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo Ward, Tokyo, 113-8519, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Kanichiro Wada
- Department of Orthopaedic Surgery, Hirosaki University Graduate School of Medicine, 5 Zaifucho, Hirosaki, Aomori, 036-8562, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Keiichi Katsumi
- Department of Orthopaedic Surgery, Niigata University Medicine and Dental General Hospital, 1-754 Asahimachidori, Chuo Ward, Niigata, Niigata, 951-8520, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Masao Koda
- Department of Orthopaedic Surgery, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Atsushi Kimura
- Department of Orthopaedic, Jichi Medical University, 3311-1 Yakushiji, Shimotsuke, Tochigi, 329-0498, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Takeo Furuya
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo Ward, Chiba, Chiba, 260-8670, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Satoshi Maki
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo Ward, Chiba, Chiba, 260-8670, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Narihito Nagoshi
- Department of Orthopaedic Surgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku Ward, Tokyo, 160-8582, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Norihiro Nishida
- Department of Orthopaedic Surgery, Yamaguchi University School of Medicine, 111 Minami Kogushi, Ube, Yamaguchi, 755-8505, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Yukitaka Nagamoto
- Department of Orthopaedic Surgery, Osaka Rosai Hospital, 1179-3 Nagasonecho, Sakaishi, Osaka, 591-8025, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Yasushi Oshima
- Department of Orthopaedic Surgery, Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Kei Ando
- Department of Orthopaedic Surgery, Japanese Red Cross Aichi Medical Center Nagoya Daini Hospital, Myokencho 2-9, Showa Ward, Nagoya, Aichi, 466-8650, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Masahiko Takahata
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Sapporo, 060-8638, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Kanji Mori
- Department of Orthopaedic Surgery, Shiga University of Medical Science, Tsukinowa-cho, Seta, Otsu, Shiga, 520-2192, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Hideaki Nakajima
- Department of Orthopaedics and Rehabilitation Medicine, Faculty of Medical Sciences, University of Fukui, 23-3 Matsuoka Shimoaizuki, Eiheiji-cho, Yoshida-gun, Fukui, 910-1193, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Kazuma Murata
- Department of Orthopaedic Surgery, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Masayuki Miyagi
- Department of Orthopedic Surgery, School of Medicine, Kitasato University, 1-15-1 Kitazato, Minami-ku, Sagamiharashi, Kanagawa, 252-0375, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Takashi Kaito
- Department of Orthopaedic Surgery, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita-shi, Osaka, 565-0871, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Kei Yamada
- Department of Orthopaedic Surgery, Kurume University School of Medicine, 67 Asahi-machi, Kurume-shi, Fukuoka, 830-0011, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Tomohiro Banno
- Department of Orthopaedic Surgery, Hamamatsu University School of Medicine, 1-20-1 Handayama, Hamamatsu, Shizuoka, 431-3125, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Satoshi Kato
- Department of Orthopaedic Surgery, Graduate School of Medical Sciences, Kanazawa University, 13-1 Takara-machi, Kanazawa, 920-8641, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Tetsuro Ohba
- Department of Orthopaedic Surgery, University of Yamanashi, 1110 Shimokato, Chuo Ward, Yamanashi, 409-3898, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Satoshi Inami
- Department of Orthopaedic Surgery, Dokkyo Medical University School of Medicine, 880 Kitakobayashi, Mibu-machi, Shimotsuga-gun, Tochigi, 321-0293, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Shunsuke Fujibayashi
- Department of Orthopaedic Surgery, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Hiroyuki Katoh
- Department of Orthopaedic Surgery, Surgical Science, Tokai University School of Medicine, 143 Shimokasuya, Isehara, Kanagawa, 259-1193, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Haruo Kanno
- Department of Orthopaedic Surgery, Tohoku University School of Medicine, 1-1 Seiryomachi, Aoba Ward, Sendai, Miyagi, 980-8574, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Masahiro Oda
- Information Strategy Office, Information and Communications, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, 464-8601, Japan
| | - Kensaku Mori
- Information Strategy Office, Information and Communications, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, 464-8601, Japan
- Department of Intelligent Systems, Nagoya University Graduate School of Informatics, Furo-cho, Chikusa-ku, Nagoya, Aichi, 464-8601, Japan
- Research Center for Medical Bigdata, National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, 101-8430, Japan
| | - Hiroshi Taneichi
- Department of Orthopaedic Surgery, Dokkyo Medical University School of Medicine, 880 Kitakobayashi, Mibu-machi, Shimotsuga-gun, Tochigi, 321-0293, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Yoshiharu Kawaguchi
- Department of Orthopaedic Surgery, Faculty of Medicine, University of Toyama, 2630 Sugitani, Toyama, Toyama, 930-0194, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Katsushi Takeshita
- Department of Orthopaedic, Jichi Medical University, 3311-1 Yakushiji, Shimotsuke, Tochigi, 329-0498, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Morio Matsumoto
- Department of Orthopaedic Surgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku Ward, Tokyo, 160-8582, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Masashi Yamazaki
- Department of Orthopaedic Surgery, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Atsushi Okawa
- Department of Orthopaedic Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo Ward, Tokyo, 113-8519, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
| | - Shiro Imagama
- Department of Orthopaedic Surgery, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa Ward, Nagoya, Aichi, 466-8550, Japan
- Japanese Multicenter Research Organization for Ossification of the Spinal Ligament, Tokyo, Japan
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7
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Karlin EA, Lin CC, Meftah M, Slover JD, Schwarzkopf R. The Impact of Machine Learning on Total Joint Arthroplasty Patient Outcomes: A Systemic Review. J Arthroplasty 2023; 38:2085-2095. [PMID: 36441039 DOI: 10.1016/j.arth.2022.10.039] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 10/19/2022] [Accepted: 10/24/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Supervised machine learning techniques have been increasingly applied to predict patient outcomes after hip and knee arthroplasty procedures. The purpose of this study was to systematically review the applications of supervised machine learning techniques to predict patient outcomes after primary total hip and knee arthroplasty. METHODS A comprehensive literature search using the electronic databases MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and Cochrane Database of Systematic Reviews was conducted in July of 2021. The inclusion criteria were studies that utilized supervised machine learning techniques to predict patient outcomes after primary total hip or knee arthroplasty. RESULTS Search criteria yielded n = 30 relevant studies. Topics of study included patient complications (n = 6), readmissions (n = 1), revision (n = 2), patient-reported outcome measures (n = 4), patient satisfaction (n = 4), inpatient status and length of stay (LOS) (n = 9), opioid usage (n = 3), and patient function (n = 1). Studies involved TKA (n = 12), THA (n = 11), or a combination (n = 7). Less than 35% of predictive outcomes had an area under the receiver operating characteristic curve (AUC) in the excellent or outstanding range. Additionally, only 9 of the studies found improvement over logistic regression, and only 9 studies were externally validated. CONCLUSION Supervised machine learning algorithms are powerful tools that have been increasingly applied to predict patient outcomes after total hip and knee arthroplasty. However, these algorithms should be evaluated in the context of prognostic accuracy, comparison to traditional statistical techniques for outcome prediction, and application to populations outside the training set. While machine learning algorithms have been received with considerable interest, they should be critically assessed and validated prior to clinical adoption.
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Affiliation(s)
- Elan A Karlin
- MedStar Georgetown University Hospital, Washington, District of Columbia
| | - Charles C Lin
- Department of Orthopedic Surgery, NYU Langone Health, New York, New York
| | - Morteza Meftah
- Department of Orthopedic Surgery, NYU Langone Health, New York, New York
| | - James D Slover
- Department of Orthopedic Surgery, NYU Langone Health, New York, New York
| | - Ran Schwarzkopf
- Department of Orthopedic Surgery, NYU Langone Health, New York, New York
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8
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Imrie F, Cebere B, McKinney EF, van der Schaar M. AutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning. PLOS DIGITAL HEALTH 2023; 2:e0000276. [PMID: 37347752 DOI: 10.1371/journal.pdig.0000276] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 05/17/2023] [Indexed: 06/24/2023]
Abstract
Diagnostic and prognostic models are increasingly important in medicine and inform many clinical decisions. Recently, machine learning approaches have shown improvement over conventional modeling techniques by better capturing complex interactions between patient covariates in a data-driven manner. However, the use of machine learning introduces technical and practical challenges that have thus far restricted widespread adoption of such techniques in clinical settings. To address these challenges and empower healthcare professionals, we present an open-source machine learning framework, AutoPrognosis 2.0, to facilitate the development of diagnostic and prognostic models. AutoPrognosis leverages state-of-the-art advances in automated machine learning to develop optimized machine learning pipelines, incorporates model explainability tools, and enables deployment of clinical demonstrators, without requiring significant technical expertise. To demonstrate AutoPrognosis 2.0, we provide an illustrative application where we construct a prognostic risk score for diabetes using the UK Biobank, a prospective study of 502,467 individuals. The models produced by our automated framework achieve greater discrimination for diabetes than expert clinical risk scores. We have implemented our risk score as a web-based decision support tool, which can be publicly accessed by patients and clinicians. By open-sourcing our framework as a tool for the community, we aim to provide clinicians and other medical practitioners with an accessible resource to develop new risk scores, personalized diagnostics, and prognostics using machine learning techniques. Software: https://github.com/vanderschaarlab/AutoPrognosis.
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Affiliation(s)
- Fergus Imrie
- Department of Electrical and Computer Engineering, University of California, Los Angeles, California, United States of America
| | - Bogdan Cebere
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
| | - Eoin F McKinney
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Mihaela van der Schaar
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
- The Alan Turing Institute, London, United Kingdom
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9
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Shah AA, Devana SK, Lee C, Olson TE, Upfill-Brown A, Sheppard WL, Lord EL, Shamie AN, van der Schaar M, SooHoo NF, Park DY. Development and External Validation of a Risk Calculator for Prediction of Major Complications and Readmission After Anterior Cervical Discectomy and Fusion. Spine (Phila Pa 1976) 2023; 48:460-467. [PMID: 36730869 PMCID: PMC10023283 DOI: 10.1097/brs.0000000000004531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 10/22/2022] [Indexed: 02/04/2023]
Abstract
STUDY DESIGN A retrospective, case-control study. OBJECTIVE We aim to build a risk calculator predicting major perioperative complications after anterior cervical fusion. In addition, we aim to externally validate this calculator with an institutional cohort of patients who underwent anterior cervical discectomy and fusion (ACDF). SUMMARY OF BACKGROUND DATA The average age and proportion of patients with at least one comorbidity undergoing ACDF have increased in recent years. Given the increased morbidity and cost associated with perioperative complications and unplanned readmission, accurate risk stratification of patients undergoing ACDF is of great clinical utility. METHODS This is a retrospective cohort study of adults who underwent anterior cervical fusion at any nonfederal California hospital between 2015 and 2017. The primary outcome was major perioperative complication or 30-day readmission. We built standard and ensemble machine learning models for risk prediction, assessing discrimination, and calibration. The best-performing model was validated on an external cohort comprised of consecutive adult patients who underwent ACDF at our institution between 2013 and 2020. RESULTS A total of 23,184 patients were included in this study; there were 1886 cases of major complication or readmissions. The ensemble model was well calibrated and demonstrated an area under the receiver operating characteristic curve of 0.728. The variables most important for the ensemble model include male sex, medical comorbidities, history of complications, and teaching hospital status. The ensemble model was evaluated on the validation cohort (n=260) with an area under the receiver operating characteristic curve of 0.802. The ensemble algorithm was used to build a web-based risk calculator. CONCLUSION We report derivation and external validation of an ensemble algorithm for prediction of major perioperative complications and 30-day readmission after anterior cervical fusion. This model has excellent discrimination and is well calibrated when tested on a contemporaneous external cohort of ACDF cases.
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Affiliation(s)
- Akash A. Shah
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Sai K. Devana
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Changhee Lee
- Department of Artificial Intelligence, Chung-Ang University, Seoul, South Korea
| | - Thomas E. Olson
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Alexander Upfill-Brown
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - William L. Sheppard
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Elizabeth L. Lord
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Arya N. Shamie
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Mihaela van der Schaar
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
| | - Nelson F. SooHoo
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Don Y. Park
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA
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Tsuda Y. CORR Insights®: What Proportion of Patients With Musculoskeletal Tumors Demonstrate Thromboelastographic Markers of Hypercoagulability? A Pilot Study. Clin Orthop Relat Res 2023; 481:562-563. [PMID: 35997645 PMCID: PMC9928834 DOI: 10.1097/corr.0000000000002372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 08/02/2022] [Accepted: 08/02/2022] [Indexed: 01/31/2023]
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11
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Leveraging electronic data to expand infection detection beyond traditional settings and definitions (Part II/III). ANTIMICROBIAL STEWARDSHIP & HEALTHCARE EPIDEMIOLOGY : ASHE 2023; 3:e27. [PMID: 36865709 PMCID: PMC9972537 DOI: 10.1017/ash.2022.342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/01/2022] [Accepted: 11/01/2022] [Indexed: 02/12/2023]
Abstract
The rich and complex electronic health record presents promise for expanding infection detection beyond currently covered settings of care. Here, we review the "how to" of leveraging electronic data sources to expand surveillance to settings of care and infections that have not been the traditional purview of the National Healthcare Safety Network (NHSN), including a discussion of creation of objective and reproducible infection surveillance definitions. In pursuit of a 'fully automated' system, we also examine the promises and pitfalls of leveraging unstructured, free-text data to support infection prevention activities and emerging technological advances that will likely affect the practice of automated infection surveillance. Finally, barriers to achieving a completely 'automated' infection detection system and challenges with intra- and interfacility reliability and missing data are discussed.
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12
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Artificial intelligence and machine learning as a viable solution for hip implant failure diagnosis-Review of literature and in vitro case study. Med Biol Eng Comput 2023; 61:1239-1255. [PMID: 36701013 DOI: 10.1007/s11517-023-02779-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 01/09/2023] [Indexed: 01/27/2023]
Abstract
The digital health industry is experiencing fast-paced research which can provide digital care programs and technologies to enhance the competence of healthcare delivery. Orthopedic literature also confirms the applicability of artificial intelligence (AI) and machine learning (ML) models to medical diagnosis and clinical decision-making. However, implant monitoring after primary surgery often happens with a wellness visit or when a patient complains about it. Neglecting implant design and other technical errors in this scenario, unmonitored circumstances, and lack of post-surgery monitoring may ultimately lead to the implant system's failure and leave us with the only option of high-risk revision surgery. Preventive maintenance seems to be a good choice to identify the onset of an irreversible prosthesis failure. Considering all these aspects for hip implant monitoring, this paper explores existing studies linking ML models and intelligent systems for hip implant diagnosis. This paper explores the feasibility of an alternative continuous monitoring technique for post-surgery implant monitoring backed by an in vitro ML case study. Tribocorrosion and acoustic emission (AE) data are considered based on their efficacy in determining irreversible alteration of implant material to prevent total failures. This study also facilitates the relevance of developing an artificially intelligent implant monitoring methodology that can function with daily patient activities and how it can influence the digital orthopedic diagnosis. AI-based non-invasive hip implant monitoring system enabling point-of-care testing.
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13
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Lee W, Schwartz N, Bansal A, Khor S, Hammarlund N, Basu A, Devine B. A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 2-Data From Nonwearables. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2022; 25:2053-2061. [PMID: 35989154 DOI: 10.1016/j.jval.2022.07.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 06/10/2022] [Accepted: 07/10/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVES Despite the increasing interest in applying machine learning (ML) methods in health economics and outcomes research (HEOR), stakeholders face uncertainties in when and how ML can be used. We reviewed the recent applications of ML in HEOR. METHODS We searched PubMed for studies published between January 2020 and March 2021 and randomly chose 20% of the identified studies for the sake of manageability. Studies that were in HEOR and applied an ML technique were included. Studies related to wearable devices were excluded. We abstracted information on the ML applications, data types, and ML methods and analyzed it using descriptive statistics. RESULTS We retrieved 805 articles, of which 161 (20%) were randomly chosen. Ninety-two of the random sample met the eligibility criteria. We found that ML was primarily used for predicting future events (86%) rather than current events (14%). The most common response variables were clinical events or disease incidence (42%) and treatment outcomes (22%). ML was less used to predict economic outcomes such as health resource utilization (16%) or costs (3%). Although electronic medical records (35%) were frequently used for model development, claims data were used less frequently (9%). Tree-based methods (eg, random forests and boosting) were the most commonly used ML methods (31%). CONCLUSIONS The use of ML techniques in HEOR is growing rapidly, but there remain opportunities to apply them to predict economic outcomes, especially using claims databases, which could inform the development of cost-effectiveness models.
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Affiliation(s)
- Woojung Lee
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA.
| | - Naomi Schwartz
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Aasthaa Bansal
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Sara Khor
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Noah Hammarlund
- Department of Health Services Research, Management & Policy, University of Florida, Gainesville, FL, USA
| | - Anirban Basu
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Beth Devine
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
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14
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Polisetty TS, Jain S, Pang M, Karnuta JM, Vigdorchik JM, Nawabi DH, Wyles CC, Ramkumar PN. Concerns surrounding application of artificial intelligence in hip and knee arthroplasty. Bone Joint J 2022; 104-B:1292-1303. [DOI: 10.1302/0301-620x.104b12.bjj-2022-0922.r1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered. Cite this article: Bone Joint J 2022;104-B(12):1292–1303.
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Affiliation(s)
- Teja S. Polisetty
- Department of Orthopaedic Surgery, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Samagra Jain
- Department of Orthopaedic Surgery, Baylor College of Medicine, Houston, Texas, USA
| | - Michael Pang
- Department of Orthopaedic Surgery, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Jaret M. Karnuta
- Department of Orthopaedic Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Danyal H. Nawabi
- Sports Medicine Institute, Hospital for Special Surgery, New York, New York, USA
| | - Cody C. Wyles
- Department of Orthopaedic Surgery, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Prem N. Ramkumar
- Department of Orthopaedic Surgery, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Sports Medicine Institute, Hospital for Special Surgery, New York, New York, USA
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15
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Carrigan A, Roberts N, Clay-Williams R, Hibbert PD, Pomare C, Mahmoud Z, Maka K, Mitchell R, Zurynski Y, Long JC, Rapport F, Arnolda G, Loy G, Braithwaite J. Innovative models of care for the health facility of the future: a protocol for a mixed-methods study to elicit consumer and provider views. BMJ Open 2022; 12:e059330. [PMID: 36385023 PMCID: PMC9670088 DOI: 10.1136/bmjopen-2021-059330] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
INTRODUCTION To address the challenges of rapidly changing healthcare, governments and health services are increasingly emphasising healthcare delivery models that are flexible, person centred, cost-effective and integrate hospital services more closely with primary healthcare and social services. In addition, such models increasingly embed consumer codesign, integration of services, and leverage digital technologies such as telehealth and sophisticated medical records systems. OBJECTIVES This paper provides a study protocol to describe a method to elicit consumer and healthcare provider needs and expectations for the development of innovative care models. METHODS AND ANALYSIS A literature review identified six key models of care, supported by a common theme of consumer-focused care, along with the international evidence supporting the efficacy of these models. A mixed-methods study of the needs and expectations of consumer members and health providers who reside or work in the area of a new hospital catchment will be undertaken. They will complete a community-specific and provider-specific, short demographic questionnaire (delivered during the recruitment process) and be assigned to facilitator-coordinated online workshops comprising small focus groups. Follow-up interviews will be offered. Culturally and linguistically diverse members and Aboriginal and Torres Strait Islander Elders and their communities will also be consulted. Data will be analysed thematically (qualitative) and statistically (quantitative), and findings synthesised using a triangulated approach. ETHICS AND DISSEMINATION The results will be actively disseminated through peer-reviewed journals, conference presentations and in a report to stakeholders. This study was reviewed and approved by the relevant Ethics Committee in New South Wales, Australia.
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Affiliation(s)
- Ann Carrigan
- Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
| | - Natalie Roberts
- Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
| | - Robyn Clay-Williams
- Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
| | - Peter D Hibbert
- Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
- IMPACT in Health, Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia
| | - Chiara Pomare
- Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
| | - Zeyad Mahmoud
- Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
| | - Katherine Maka
- Westmead Hospital, Western Sydney Local Health District, Wentworthville, New South Wales, Australia
| | - Rebecca Mitchell
- Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
| | - Yvonne Zurynski
- Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
- Partnership Centre for Health System Sustainabilty, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW, Australia
| | - Janet C Long
- Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
| | - Frances Rapport
- Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
| | - Gaston Arnolda
- Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
| | - Graeme Loy
- Westmead Hospital, Western Sydney Local Health District, Wentworthville, New South Wales, Australia
| | - Jeffrey Braithwaite
- Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
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16
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Prediction model for an early revision for dislocation after primary total hip arthroplasty. PLoS One 2022; 17:e0274384. [PMID: 36084121 PMCID: PMC9462822 DOI: 10.1371/journal.pone.0274384] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 08/25/2022] [Indexed: 12/05/2022] Open
Abstract
Dislocation is one of the most common complications after primary total hip arthroplasty (THA). Several patient-related risk factors for dislocation have been reported in the previous literature, but only few prediction models for dislocation have been made. Our aim was to build a prediction model for an early (within the first 2 years) revision for dislocation after primary THA using two different statistical methods. The study data constituted of 37 pre- or perioperative variables and postoperative follow-up data of 16 454 primary THAs performed at our institution in 2008–2021. Model I was a traditional logistic regression model and Model II was based on the elastic net method that utilizes machine learning. The models’ overall performance was measured using the pseudo R2 values. The discrimination of the models was measured using C-index in Model I and Area Under the Curve (AUC) in Model II. Calibration curves were made for both models. At 2 years postoperatively, 95 hips (0.6% prevalence) had been revised for dislocation. The pseudo R2 values were 0.04 in Model I and 0.02 in Model II indicating low predictive capability in both models. The C-index in Model I was 0.67 and the AUC in Model II was 0.73 indicating modest discrimination. The prediction of an early revision for dislocation after primary THA is difficult even in a large cohort of patients with detailed data available because of the reasonably low prevalence and multifactorial nature of dislocation. Therefore, the risk of dislocation should be kept in mind in every primary THA, whether the patient has predisposing factors for dislocation or not. Further, when conducting a prediction model, sophisticated methods that utilize machine learning may not necessarily offer significant advantage over traditional statistical methods in clinical setup.
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Shah AA, Devana SK, Lee C, Bugarin A, Lord EL, Shamie AN, Park DY, van der Schaar M, SooHoo NF. Machine learning-driven identification of novel patient factors for prediction of major complications after posterior cervical spinal fusion. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2022; 31:1952-1959. [PMID: 34392418 PMCID: PMC8844303 DOI: 10.1007/s00586-021-06961-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 06/23/2021] [Accepted: 08/08/2021] [Indexed: 01/20/2023]
Abstract
PURPOSE Posterior cervical fusion is associated with increased rates of complications and readmission when compared to anterior fusion. Machine learning (ML) models for risk stratification of patients undergoing posterior cervical fusion remain limited. We aim to develop a novel ensemble ML algorithm for prediction of major perioperative complications and readmission after posterior cervical fusion and identify factors important to model performance. METHODS This is a retrospective cohort study of adults who underwent posterior cervical fusion at non-federal California hospitals between 2015 and 2017. The primary outcome was readmission or major complication. We developed an ensemble model predicting complication risk using an automated ML framework. We compared performance with standard ML models and logistic regression (LR), ranking contribution of included variables to model performance. RESULTS Of the included 6822 patients, 18.8% suffered a major complication or readmission. The ensemble model demonstrated slightly superior predictive performance compared to LR and standard ML models. The most important features to performance include sex, malignancy, pneumonia, stroke, and teaching hospital status. Seven of the ten most important features for the ensemble model were markedly less important for LR. CONCLUSION We report an ensemble ML model for prediction of major complications and readmission after posterior cervical fusion with a modest risk prediction advantage compared to LR and benchmark ML models. Notably, the features most important to the ensemble are markedly different from those for LR, suggesting that advanced ML methods may identify novel prognostic factors for adverse outcomes after posterior cervical fusion.
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Affiliation(s)
- Akash A Shah
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, 90095, USA.
| | - Sai K Devana
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, 90095, USA
| | - Changhee Lee
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Amador Bugarin
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, 90095, USA
| | - Elizabeth L Lord
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, 90095, USA
| | - Arya N Shamie
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, 90095, USA
| | - Don Y Park
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, 90095, USA
| | - Mihaela van der Schaar
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Nelson F SooHoo
- Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, 90095, USA
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Artificial intelligence for distinguishment of hammering sound in total hip arthroplasty. Sci Rep 2022; 12:9826. [PMID: 35701656 PMCID: PMC9198079 DOI: 10.1038/s41598-022-14006-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 05/31/2022] [Indexed: 11/30/2022] Open
Abstract
Recent studies have focused on hammering sound analysis during insertion of the cementless stem to decrease complications in total hip arthroplasty. However, the nature of the hammering sound is complex to analyse and varies widely owing to numerous possible variables. Therefore, we performed a preliminary feasibility study that aimed to clarify the accuracy of a prediction model using a machine learning algorithm to identify the final rasping hammering sound recorded during surgery. The hammering sound data of 29 primary THA without complication were assessed. The following definitions were adopted. Undersized rasping: all undersized stem rasping before the rasping of the final stem size, Final size rasping: rasping of the final stem size, Positive example: hammering sound during final size rasping, Negative example A: hammering sound during minimum size stem rasping, Negative example B: hammering sound during all undersized rasping. Three datasets for binary classification were set. Finally, binary classification was analysed in six models for the three datasets. The median values of the ROC-AUC in models A–F among each dataset were dataset a: 0.79, 0.76, 0.83, 0.90, 0.91, and 0.90, dataset B: 0.61, 0.53, 0.67, 0.69, 0.71, and 0.72, dataset C: 0.60, 0.48, 0.57, 0.63, 0.67, and 0.63, respectively. Our study demonstrated that artificial intelligence using machine learning was able to distinguish the final rasping hammering sound from the previous hammering sound with a relatively high degree of accuracy. Future studies are warranted to establish a prediction model using hammering sound analysis with machine learning to prevent complications in THA.
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19
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Devana SK, Shah AA, Lee C, Jensen AR, Cheung E, van der Schaar M, SooHoo NF. Development of a Machine Learning Algorithm for Prediction of Complications and Unplanned Readmission Following Primary Anatomic Total Shoulder Replacements. J Shoulder Elb Arthroplast 2022; 6:24715492221075444. [PMID: 35669619 PMCID: PMC9163721 DOI: 10.1177/24715492221075444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 12/23/2021] [Accepted: 01/05/2022] [Indexed: 11/16/2022] Open
Abstract
Background The demand and incidence of anatomic total shoulder arthroplasty (aTSA) procedures is projected to increase substantially over the next decade. There is a paucity of accurate risk prediction models which would be of great utility in minimizing morbidity and costs associated with major post-operative complications. Machine learning is a powerful predictive modeling tool and has become increasingly popular, especially in orthopedics. We aimed to build a ML model for prediction of major complications and readmission following primary aTSA. Methods A large California administrative database was retrospectively reviewed for all adults undergoing primary aTSA between 2015 to 2017. The primary outcome was any major complication or readmission following aTSA. A wide scope of standard ML benchmarks, including Logistic regression (LR), XGBoost, Gradient boosting, AdaBoost and Random Forest were employed to determine their power to predict outcomes. Additionally, important patient features to the prediction models were indentified. Results There were a total of 10,302 aTSAs with 598 (5.8%) having at least one major post-operative complication or readmission. XGBoost had the highest discriminative power (area under receiver operating curve AUROC of 0.689) of the 5 ML benchmarks with an area under precision recall curve AURPC of 0.207. History of implant complication, severe chronic kidney disease, teaching hospital status, coronary artery disease and male sex were the most important features for the performance of XGBoost. In addition, XGBoost identified teaching hospital status and male sex as markedly more important predictors of outcomes compared to LR models. Conclusion We report a well calibrated XGBoost ML algorithm for predicting major complications and 30-day readmission following aTSA. History of prior implant complication was the most important patient feature for XGBoost performance, a novel patient feature that surgeons should consider when counseling patients.
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Affiliation(s)
- Sai K Devana
- David Geffen School of Medicine UCLA, Los Angeles, CA
| | - Akash A Shah
- David Geffen School of Medicine UCLA, Los Angeles, CA
| | | | | | - Edward Cheung
- David Geffen School of Medicine UCLA, Los Angeles, CA
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Lazic I, Hinterwimmer F, Langer S, Pohlig F, Suren C, Seidl F, Rückert D, Burgkart R, von Eisenhart-Rothe R. Prediction of Complications and Surgery Duration in Primary Total Hip Arthroplasty Using Machine Learning: The Necessity of Modified Algorithms and Specific Data. J Clin Med 2022; 11:jcm11082147. [PMID: 35456239 PMCID: PMC9032696 DOI: 10.3390/jcm11082147] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 04/04/2022] [Accepted: 04/07/2022] [Indexed: 01/18/2023] Open
Abstract
Background: Machine Learning (ML) in arthroplasty is becoming more popular, as it is perfectly suited for prediction models. However, results have been heterogeneous so far. We hypothesize that an accurate ML model for outcome prediction in THA must be able to compute arthroplasty-specific data. In this study, we evaluate a ML approach applying data from two German arthroplasty-specific registries to predict adverse outcomes after THA, after careful evaluations of ML algorithms, outcome and input variables by an interdisciplinary team of data scientists and surgeons. Methods: Data of 1217 cases of primary THA from a single center were derived from two German arthroplasty-specific registries between 2016 to 2019. The XGBoost algorithm was adjusted and applied. Accuracy, sensitivity, specificity and AUC were calculated. Results: For the prediction of complications, the ML algorithm achieved an accuracy of 80.3%, a sensitivity of 31.0%, a specificity of 89.4% and an AUC of 64.1%. For the prediction of surgery duration, the ML algorithm yielded an accuracy of 81.7%, a sensitivity of 58.2%, a specificity of 91.6% and an AUC of 89.1%. The feature importance indicated non-linear outcomes for age, height, weight and surgeon. No relevant linear correlations were found. Conclusion: The attunement of input and output data as well as the modifications of the ML algorithm permitted the development of a feasible ML model for the prediction of complications and surgery duration.
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Affiliation(s)
- Igor Lazic
- Department of Orthopaedics and Sports Orthopaedics, Klinikum Rechts der Isar, Technical University of Munich, 80333 Munich, Germany; (F.H.); (S.L.); (F.P.); (C.S.); (R.B.); (R.v.E.-R.)
- Correspondence:
| | - Florian Hinterwimmer
- Department of Orthopaedics and Sports Orthopaedics, Klinikum Rechts der Isar, Technical University of Munich, 80333 Munich, Germany; (F.H.); (S.L.); (F.P.); (C.S.); (R.B.); (R.v.E.-R.)
- Institute for AI and Informatics in Medicine, Technical University of Munich, 80333 Munich, Germany;
| | - Severin Langer
- Department of Orthopaedics and Sports Orthopaedics, Klinikum Rechts der Isar, Technical University of Munich, 80333 Munich, Germany; (F.H.); (S.L.); (F.P.); (C.S.); (R.B.); (R.v.E.-R.)
| | - Florian Pohlig
- Department of Orthopaedics and Sports Orthopaedics, Klinikum Rechts der Isar, Technical University of Munich, 80333 Munich, Germany; (F.H.); (S.L.); (F.P.); (C.S.); (R.B.); (R.v.E.-R.)
| | - Christian Suren
- Department of Orthopaedics and Sports Orthopaedics, Klinikum Rechts der Isar, Technical University of Munich, 80333 Munich, Germany; (F.H.); (S.L.); (F.P.); (C.S.); (R.B.); (R.v.E.-R.)
| | - Fritz Seidl
- Department of Trauma Surgery, Klinikum Rechts der Isar, Technical University of Munich, 80333 Munich, Germany;
| | - Daniel Rückert
- Institute for AI and Informatics in Medicine, Technical University of Munich, 80333 Munich, Germany;
| | - Rainer Burgkart
- Department of Orthopaedics and Sports Orthopaedics, Klinikum Rechts der Isar, Technical University of Munich, 80333 Munich, Germany; (F.H.); (S.L.); (F.P.); (C.S.); (R.B.); (R.v.E.-R.)
| | - Rüdiger von Eisenhart-Rothe
- Department of Orthopaedics and Sports Orthopaedics, Klinikum Rechts der Isar, Technical University of Munich, 80333 Munich, Germany; (F.H.); (S.L.); (F.P.); (C.S.); (R.B.); (R.v.E.-R.)
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21
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Kurmis AP, Ianunzio JR. Artificial intelligence in orthopedic surgery: evolution, current state and future directions. ARTHROPLASTY 2022; 4:9. [PMID: 35232490 PMCID: PMC8889658 DOI: 10.1186/s42836-022-00112-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 12/31/2021] [Indexed: 12/14/2022] Open
Abstract
Technological advances continue to evolve at a breath-taking pace. Computer-navigation, robot-assistance and three-dimensional digital planning have become commonplace in many parts of the world. With near exponential advances in computer processing capacity, and the advent, progressive understanding and refinement of software algorithms, medicine and orthopaedic surgery have begun to delve into artificial intelligence (AI) systems. While for some, such applications still seem in the realm of science fiction, these technologies are already in selective clinical use and are likely to soon see wider uptake. The purpose of this structured review was to provide an understandable summary to non-academic orthopaedic surgeons, exploring key definitions and basic development principles of AI technology as it currently stands. To ensure content validity and representativeness, a structured, systematic review was performed following the accepted PRISMA principles. The paper concludes with a forward-look into heralded and potential applications of AI technology in orthopedic surgery.While not intended to be a detailed technical description of the complex processing that underpins AI applications, this work will take a small step forward in demystifying some of the commonly-held misconceptions regarding AI and its potential benefits to patients and surgeons. With evidence-supported broader awareness, we aim to foster an open-mindedness among clinicians toward such technologies in the future.
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Affiliation(s)
- Andrew P Kurmis
- Discipline of Medical Specialties, University of Adelaide, Adelaide, SA, Australia. .,Department of Orthopaedic Surgery, Lyell McEwin Hospital, Vale, Elizabeth, SA, Australia.
| | - Jamie R Ianunzio
- Department of Orthopaedic Surgery, Lyell McEwin Hospital, Vale, Elizabeth, SA, Australia.,School of Medicine, University of Adelaide, Adelaide, SA, Australia
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22
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Yeo I, Klemt C, Robinson MG, Esposito JG, Uzosike AC, Kwon YM. The Use of Artificial Neural Networks for the Prediction of Surgical Site Infection Following TKA. J Knee Surg 2022; 36:637-643. [PMID: 35016246 DOI: 10.1055/s-0041-1741396] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
This is a retrospective study. Surgical site infection (SSI) is associated with adverse postoperative outcomes following total knee arthroplasty (TKA). However, accurately predicting SSI remains a clinical challenge due to the multitude of patient and surgical factors associated with SSI. This study aimed to develop and validate machine learning models for the prediction of SSI following primary TKA. This is a retrospective study for patients who underwent primary TKA. Chart review was performed to identify patients with superficial or deep SSIs, defined in concordance with the criteria of the Musculoskeletal Infection Society. All patients had a minimum follow-up of 2 years (range: 2.1-4.7 years). Five machine learning algorithms were developed to predict this outcome, and model assessment was performed by discrimination, calibration, and decision curve analysis. A total of 10,021 consecutive primary TKA patients was included in this study. At an average follow-up of 2.8 ± 1.1 years, SSIs were reported in 404 (4.0%) TKA patients, including 223 superficial SSIs and 181 deep SSIs. The neural network model achieved the best performance across discrimination (area under the receiver operating characteristic curve = 0.84), calibration, and decision curve analysis. The strongest predictors of the occurrence of SSI following primary TKA, in order, were Charlson comorbidity index, obesity (BMI >30 kg/m2), and smoking. The neural network model presented in this study represents an accurate method to predict patient-specific superficial and deep SSIs following primary TKA, which may be employed to assist in clinical decision-making to optimize outcomes in at-risk patients.
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Affiliation(s)
- Ingwon Yeo
- Department of Orthopedic Surgery, Bioengineering Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Christian Klemt
- Department of Orthopedic Surgery, Bioengineering Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Matthew Gerald Robinson
- Department of Orthopedic Surgery, Bioengineering Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - John G Esposito
- Department of Orthopedic Surgery, Bioengineering Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Akachimere Cosmas Uzosike
- Department of Orthopedic Surgery, Bioengineering Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Young-Min Kwon
- Department of Orthopedic Surgery, Bioengineering Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
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Jakuscheit A, Schaefer N, Roedig J, Luedemann M, von Hertzberg-Boelch SP, Weissenberger M, Schmidt K, Holzapfel BM, Rudert M. Modifiable Individual Risks of Perioperative Blood Transfusions and Acute Postoperative Complications in Total Hip and Knee Arthroplasty. J Pers Med 2021; 11:jpm11111223. [PMID: 34834575 PMCID: PMC8622846 DOI: 10.3390/jpm11111223] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 11/08/2021] [Accepted: 11/15/2021] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND The primary aim of this study was to identify modifiable patient-related predictors of blood transfusions and perioperative complications in total hip and knee arthroplasty. Individual predictor-adjusted risks can be used to define preoperative treatment thresholds. METHODS We performed this retrospective monocentric study in orthopaedic patients who underwent primary total knee or hip arthroplasty. Multivariate logistic regression models were used to assess the predictive value of patient-related characteristics. Predictor-adjusted individual risks of blood transfusions and the occurrence of any perioperative adverse event were calculated for potentially modifiable risk factors. RESULTS 3754 patients were included in this study. The overall blood transfusion and complication rates were 4.8% and 6.4%, respectively. Haemoglobin concentration (Hb, p < 0.001), low body mass index (BMI, p < 0.001) and estimated glomerular filtration rate (eGFR, p = 0.004) were the strongest potentially modifiable predictors of a blood transfusion. EGFR (p = 0.001) was the strongest potentially modifiable predictor of a complication. Predictor-adjusted risks of blood transfusions and acute postoperative complications were calculated for Hb and eGFR. Hb = 12.5 g/dL, BMI = 17.6 kg/m2, and eGFR = 54 min/mL were associated, respectively, with a 10% risk of a blood transfusion, eGFR = 59 mL/min was associated with a 10% risk of a complication. CONCLUSION The individual risks for blood transfusions and acute postoperative complications are strongly increased in patients with a low preoperative Hb, low BMI or low eGFR. We recommend aiming at a preoperative Hb ≥ 13g/dL, an eGFR ≥ 60 mL/min and to avoid a low BMI. Future studies must show if a preoperative increase of eGFR and BMI is feasible and truly beneficial.
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Affiliation(s)
- Axel Jakuscheit
- Department of Orthopaedic Surgery, University of Wuerzburg, Koenig-Ludwig-Haus, Brettreichstr. 11, 97070 Wuerzburg, Germany; (N.S.); (J.R.); (M.L.); (S.P.v.H.-B.); (M.W.); (M.R.)
- Correspondence:
| | - Nina Schaefer
- Department of Orthopaedic Surgery, University of Wuerzburg, Koenig-Ludwig-Haus, Brettreichstr. 11, 97070 Wuerzburg, Germany; (N.S.); (J.R.); (M.L.); (S.P.v.H.-B.); (M.W.); (M.R.)
| | - Johannes Roedig
- Department of Orthopaedic Surgery, University of Wuerzburg, Koenig-Ludwig-Haus, Brettreichstr. 11, 97070 Wuerzburg, Germany; (N.S.); (J.R.); (M.L.); (S.P.v.H.-B.); (M.W.); (M.R.)
| | - Martin Luedemann
- Department of Orthopaedic Surgery, University of Wuerzburg, Koenig-Ludwig-Haus, Brettreichstr. 11, 97070 Wuerzburg, Germany; (N.S.); (J.R.); (M.L.); (S.P.v.H.-B.); (M.W.); (M.R.)
| | - Sebastian Philipp von Hertzberg-Boelch
- Department of Orthopaedic Surgery, University of Wuerzburg, Koenig-Ludwig-Haus, Brettreichstr. 11, 97070 Wuerzburg, Germany; (N.S.); (J.R.); (M.L.); (S.P.v.H.-B.); (M.W.); (M.R.)
| | - Manuel Weissenberger
- Department of Orthopaedic Surgery, University of Wuerzburg, Koenig-Ludwig-Haus, Brettreichstr. 11, 97070 Wuerzburg, Germany; (N.S.); (J.R.); (M.L.); (S.P.v.H.-B.); (M.W.); (M.R.)
| | - Karsten Schmidt
- Department of Trauma, Hand, Plastic and Reconstructive Surgery, University Hospital Würzburg, Josef-Schneider-Str. 2, 97080 Wuerzburg, Germany;
| | - Boris Michael Holzapfel
- Department of Orthopedics and Trauma Surgery, Musculoskeletal University Center Munich, Marchionistr. 15, 81377 Munich, Germany;
| | - Maximilian Rudert
- Department of Orthopaedic Surgery, University of Wuerzburg, Koenig-Ludwig-Haus, Brettreichstr. 11, 97070 Wuerzburg, Germany; (N.S.); (J.R.); (M.L.); (S.P.v.H.-B.); (M.W.); (M.R.)
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Purnomo G, Yeo SJ, Liow MHL. Artificial intelligence in arthroplasty. ARTHROPLASTY 2021; 3:37. [PMID: 35236494 PMCID: PMC8796516 DOI: 10.1186/s42836-021-00095-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 08/31/2021] [Indexed: 01/10/2023] Open
Abstract
Artificial intelligence (AI) is altering the world of medicine. Given the rapid advances in technology, computers are now able to learn and improve, imitating humanoid cognitive function. AI applications currently exist in various medical specialties, some of which are already in clinical use. This review presents the potential uses and limitations of AI in arthroplasty to provide a better understanding of the existing technology and future direction of this field.Recent literature demonstrates that the utilization of AI in the field of arthroplasty has the potential to improve patient care through better diagnosis, screening, planning, monitoring, and prediction. The implementation of AI technology will enable arthroplasty surgeons to provide patient-specific management in clinical decision making, preoperative health optimization, resource allocation, decision support, and early intervention. While this technology presents a variety of exciting opportunities, it also has several limitations and challenges that need to be overcome to ensure its safety and effectiveness.
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Affiliation(s)
- Glen Purnomo
- St. Vincentius a Paulo Catholic Hospital, Surabaya, Indonesia.
- Adult Reconstruction Service, Department of Orthopaedic Surgery, Singapore General Hospital, Singapore, Singapore.
| | - Seng-Jin Yeo
- Adult Reconstruction Service, Department of Orthopaedic Surgery, Singapore General Hospital, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Ming Han Lincoln Liow
- Adult Reconstruction Service, Department of Orthopaedic Surgery, Singapore General Hospital, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
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