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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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Comulada WS, Rotheram-Borus MJ, Arnold EM, Norwood P, Lee SJ, Ocasio MA, Flynn R, Nielsen K, Bolan R, Klausner J, Swendeman D. Using Machine Learning to Identify Predictors of Sexually Transmitted Infections Over Time Among Young People Living With or at Risk for HIV Who Participated in ATN Protocols 147, 148, and 149. Sex Transm Dis 2023; 50:739-745. [PMID: 37643402 PMCID: PMC10592122 DOI: 10.1097/olq.0000000000001854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
BACKGROUND Sexually transmitted infections (STIs) among youth aged 12 to 24 years have doubled in the last 13 years, accounting for 50% of STIs nationally. We need to identify predictors of STI among youth in urban HIV epicenters. METHODS Sexual and gender minority (gay, bisexual, transgender, gender-diverse) and other youth with multiple life stressors (homelessness, incarceration, substance use, mental health disorders) were recruited from 13 sites in Los Angeles and New Orleans (N = 1482). Self-reports and rapid diagnostic tests for STI, HIV, and drug use were conducted at 4-month intervals for up to 24 months. Machine learning was used to identify predictors of time until new STI (including a new HIV diagnosis). RESULTS At recruitment, 23.9% of youth had a current or past STI. Over 24 months, 19.3% tested positive for a new STI. Heterosexual males had the lowest STI rate (12%); African American youth were 23% more likely to acquire an STI compared with peers of other ethnicities. Time to STI was best predicted by attending group sex venues or parties, moderate but not high dating app use, and past STI and HIV seropositive status. CONCLUSIONS Sexually transmitted infections are concentrated among a subset of young people at highest risk. The best predictors of youth's risk are their sexual environments and networks. Machine learning will allow the next generation of research on predictive patterns of risk to be more robust.
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Affiliation(s)
- W. Scott Comulada
- Department of Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Mary Jane Rotheram-Borus
- Department of Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | | | - Peter Norwood
- Department of Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Sung-Jae Lee
- Department of Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Manuel A. Ocasio
- Department of Pediatrics, School of Medicine, Tulane University, New Orleans, LA
| | - Risa Flynn
- Los Angeles LGBT Center, Los Angeles, CA
| | - Karin Nielsen
- Department of Pediatrics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | | | - Jeffrey Klausner
- Department of Infectious Diseases, Keck School of Medicine, University of Southern CA
| | - Dallas Swendeman
- Department of Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Adolescent Medicine Trials Network (ATN) CARES Team
- Department of Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
- Department of Pediatrics, School of Medicine, Tulane University, New Orleans, LA
- Department of Pediatrics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
- Nova Southeastern University, Fort Lauderdale, FL
- School of Public Health, Portland State University, Portland, OR
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA
- Friends Research Institute Inc, Los Angeles, CA
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Tian CW, Chen XX, Shi L, Zhu HY, Dai GC, Chen H, Rui YF. Machine learning applications for the prediction of extended length of stay in geriatric hip fracture patients. World J Orthop 2023; 14:741-754. [PMID: 37970626 PMCID: PMC10642403 DOI: 10.5312/wjo.v14.i10.741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 09/08/2023] [Accepted: 09/28/2023] [Indexed: 10/16/2023] Open
Abstract
BACKGROUND Geriatric hip fractures are one of the most common fractures in elderly individuals, and prolonged hospital stays increase the risk of death and complications. Machine learning (ML) has become prevalent in clinical data processing and predictive models. This study aims to develop ML models for predicting extended length of stay (eLOS) among geriatric patients with hip fractures and to identify the associated risk factors. AIM To develop ML models for predicting the eLOS among geriatric patients with hip fractures, identify associated risk factors, and compare the performance of each model. METHODS A retrospective study was conducted at a single orthopaedic trauma centre, enrolling all patients who underwent hip fracture surgery between January 2018 and December 2022. The study collected various patient characteristics, encompassing demographic data, general health status, injury-related data, laboratory examinations, surgery-related data, and length of stay. Features that exhibited significant differences in univariate analysis were integrated into the ML model establishment and subsequently cross-verified. The study compared the performance of the ML models and determined the risk factors for eLOS. RESULTS The study included 763 patients, with 380 experiencing eLOS. Among the models, the decision tree, random forest, and extreme Gradient Boosting models demonstrated the most robust performance. Notably, the artificial neural network model also exhibited impressive results. After cross-validation, the support vector machine and logistic regression models demonstrated superior performance. Predictors for eLOS included delayed surgery, D-dimer level, American Society of Anaesthesiologists (ASA) classification, type of surgery, and sex. CONCLUSION ML proved to be highly accurate in predicting the eLOS for geriatric patients with hip fractures. The identified key risk factors were delayed surgery, D-dimer level, ASA classification, type of surgery, and sex. This valuable information can aid clinicians in allocating resources more efficiently to meet patient demand effectively.
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Affiliation(s)
- Chu-Wei Tian
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Xiang-Xu Chen
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Liu Shi
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Huan-Yi Zhu
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Guang-Chun Dai
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Hui Chen
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Yun-Feng Rui
- Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
- Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
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Alinia S, Asghari-Jafarabadi M, Mahmoudi L, Norouzi S, Safari M, Roshanaei G. Survival prediction and prognostic factors in colorectal cancer after curative surgery: insights from cox regression and neural networks. Sci Rep 2023; 13:15675. [PMID: 37735621 PMCID: PMC10514146 DOI: 10.1038/s41598-023-42926-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 09/16/2023] [Indexed: 09/23/2023] Open
Abstract
Medical research frequently relies on Cox regression to analyze the survival distribution of cancer patients. Nonetheless, in specific scenarios, neural networks hold the potential to serve as a robust alternative. In this study, we aim to scrutinize the effectiveness of Cox regression and neural network models in assessing the survival outcomes of patients who have undergone treatment for colorectal cancer. We conducted a retrospective study on 284 colorectal cancer patients who underwent surgery at Imam Khomeini clinic in Hamadan between 2001 and 2017. The data was used to train both Cox regression and neural network models, and their predictive accuracy was compared using diagnostic measures such as sensitivity, specificity, positive predictive value, accuracy, negative predictive value, and area under the receiver operating characteristic curve. The analyses were performed using STATA 17 and R4.0.4 software. The study revealed that the best neural network model had a sensitivity of 74.5% (95% CI 61.0-85.0), specificity of 83.3% (65.3-94.4), positive predictive value of 89.1% (76.4-96.4), negative predictive value of 64.1% (47.2-78.8), AUC of 0.79 (0.70-0.88), and accuracy of 0.776 for death prediction. For recurrence, the best neural network model had a sensitivity of 88.1% (74.4-96.0%), specificity of 83.7% (69.3-93.2%), positive predictive value of 84.1% (69.9-93.4%), negative predictive value of 87.8% (73.8-95.9%), AUC of 0.86 (0.78-0.93), and accuracy of 0.859. The Cox model had comparable results, with a sensitivity of 73.6% (64.8-81.2) and 85.5% (78.3-91.0), specificity of 89.6% (83.8-93.8) and 98.0% (94.4-99.6), positive predictive value of 84.0% (75.6-90.4) and 97.4% (92.6-99.5), negative predictive value of 82.0% (75.6-90.4) and 88.8% (0.83-93.1), AUC of 0.82 (0.77-0.86) and 0.92 (0.89-0.95), and accuracy of 0.88 and 0.92 for death and recurrence prediction, respectively. In conclusion, the study found that both Cox regression and neural network models are effective in predicting early recurrence and death in patients with colorectal cancer after curative surgery. The neural network model showed slightly better sensitivity and negative predictive value for death, while the Cox model had better specificity and positive predictive value for recurrence. Overall, both models demonstrated high accuracy and AUC, indicating their usefulness in predicting these outcomes.
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Affiliation(s)
- Shayeste Alinia
- Department of Statistics and Epidemiology, School of Medicine, Zanjan University of Medical Sciences, Mahdavi Blvd, Zanjan, 4513956111, Iran
| | - Mohammad Asghari-Jafarabadi
- Faculty of Health, Road Traffic Injury Research Center, Tabriz University of Medical Sciences, Golgasht St. Attar E Neshabouri St., Tabriz, 5166614711, Iran.
- Cabrini Research, Cabrini Health, Malvern, VIC, 3144, Australia.
- Faculty of Medicine, Nursing and Health Sciences, School of Public Health and Preventative Medicine, Monash University, Melbourne, VIC, 3004, Australia.
- Department of Psychiatry, Faculty of Medicine, Nursing and Health Sciences, School of Clinical Sciences, Monash University, Clayton, VIC, 3168, Australia.
| | - Leila Mahmoudi
- Department of Statistics and Epidemiology, School of Medicine, Zanjan University of Medical Sciences, Mahdavi Blvd, Zanjan, 4513956111, Iran.
| | - Solmaz Norouzi
- Department of Statistics and Epidemiology, School of Medicine, Zanjan University of Medical Sciences, Mahdavi Blvd, Zanjan, 4513956111, Iran
| | - Maliheh Safari
- Department of Biostatistics, School of Medicine, Arak University of Medical Sciences, Arak, Iran
| | - Ghodratollah Roshanaei
- Department of Biostatistics, Modeling of Non-Communicable Diseases Research Center, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
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Dala Ali AHH, Harun SN, Othman N, Ibrahim B, Abdulbagi OE, Abdullah I, Ariffin IA. Determinants of Inadequate Empiric Antimicrobial Therapy in ICU Sepsis Patients in Al-Madinah Al-Munawwarah, Saudi Arabia: A Comparison of Artificial Neural Network and Regression Analysis. Antibiotics (Basel) 2023; 12:1305. [PMID: 37627725 PMCID: PMC10451895 DOI: 10.3390/antibiotics12081305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 06/15/2023] [Accepted: 06/20/2023] [Indexed: 08/27/2023] Open
Abstract
In the management of sepsis, providing adequate empiric antimicrobial therapy is one of the most important pillars of sepsis management. Therefore, it is important to evaluate the adequacy of empiric antimicrobial therapy (EAMT) in sepsis patients admitted to intensive care units (ICU) and to identify the determinants of inadequate EAMT. The aim of this study was to evaluate the adequacy of empiric antimicrobial therapy in patients admitted to the ICU with sepsis or septic shock, and the determinants of inadequate EAMT. The data of patients admitted to the ICU units due to sepsis or septic shock in two tertiary healthcare facilities in Al-Madinah Al-Munawwarah were retrospectively reviewed. The current study used logistic regression analysis and artificial neural network (ANN) analysis to identify determinants of inadequate empiric antimicrobial therapy, and evaluated the performance of these two approaches in predicting the inadequacy of EAMT. The findings of this study showed that fifty-three per cent of patients received inadequate EAMT. Determinants for inadequate EAMT were APACHE II score, multidrug-resistance organism (MDRO) infections, surgical history (lower limb amputation), and comorbidity (coronary artery disease). ANN performed as well as or better than logistic regression in predicating inadequate EAMT, as the receiver operating characteristic area under the curve (ROC-AUC) of the ANN model was higher when compared with the logistic regression model (LRM): 0.895 vs. 0.854. In addition, the ANN model performed better than LRM in predicting inadequate EAMT in terms of classification accuracy. In addition, ANN analysis revealed that the most important determinants of EAMT adequacy were the APACHE II score and MDRO. In conclusion, more than half of the patients received inadequate EAMT. Determinants of inadequate EAMT were APACHE II score, MDRO infections, comorbidity, and surgical history. This provides valuable inputs to improve the prescription of empiric antimicrobials in Saudi Arabia going forward. In addition, our study demonstrated the potential utility of applying artificial neural network analysis in the prediction of outcomes in healthcare research.
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Affiliation(s)
- Ahmad Habeeb Hattab Dala Ali
- Discipline of Clinical Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia (USM), Penang 11800, Malaysia
- Department of Pharmacy Practice, College of Pharmacy, AlMaarefa University, Dariyah, Riyadh 13713, Saudi Arabia
| | - Sabariah Noor Harun
- Discipline of Clinical Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia (USM), Penang 11800, Malaysia
| | - Noordin Othman
- Department of Clinical and Hospital Pharmacy, College of Pharmacy, Taibah University, Al-Madinah Al-Munawwarah 42353, Saudi Arabia
- School of Pharmacy, Management and Science University, University Drive, Off Persiaran Olahraga, Shah Alam 40100, Malaysia
| | - Baharudin Ibrahim
- Faculty of Pharmacy, University of Malaya, Wilayah Persekutuan Kuala Lumpur 50603, Malaysia
| | | | - Ibrahim Abdullah
- School of Pharmacy, Management and Science University, University Drive, Off Persiaran Olahraga, Shah Alam 40100, Malaysia
| | - Indang Ariati Ariffin
- Research Management Centre, Management and Science University, University Drive, Off Persiaran Olahraga, Section 13, Shah Alam 40100, Malaysia
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Ghanem M, Ghaith AK, Zamanian C, Bon-Nieves A, Bhandarkar A, Bydon M, Quiñones-Hinojosa A. Deep Learning Approaches for Glioblastoma Prognosis in Resource-Limited Settings: A Study Using Basic Patient Demographic, Clinical, and Surgical Inputs. World Neurosurg 2023; 175:e1089-e1109. [PMID: 37088416 DOI: 10.1016/j.wneu.2023.04.072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 04/15/2023] [Accepted: 04/17/2023] [Indexed: 04/25/2023]
Abstract
BACKGROUND Glioblastoma (GBM) is the most common brain tumor in the United States, with an annual incidence rate of 3.21 per 100,000. It is the most aggressive type of diffuse glioma and has a median survival of months after treatment. This study aims to assess the accuracy of different novel deep learning models trained on a set of simple clinical, demographic, and surgical variables to assist in clinical practice, even in areas with constrained health care infrastructure. METHODS Our study included 37,095 patients with GBM from the SEER (Surveillance Epidemiology and End Results) database. All predictors were based on demographic, clinicopathologic, and treatment information of the cases. Our outcomes of interest were months of survival and vital status. Concordance index (C-index) and integrated Brier scores (IBS) were used to evaluate the performance of the models. RESULTS The patient characteristics and the statistical analyses were consistent with the epidemiologic literature. The models C-index and IBS ranged from 0.6743 to 0.6918 and from 0.0934 to 0.1034, respectively. Probabilistic matrix factorization (0.6918), multitask logistic regression (0.6916), and logistic hazard (0.6916) had the highest C-index scores. The models with the lowest IBS were the probabilistic matrix factorization (0.0934), multitask logistic regression (0.0935), and logistic hazard (0.0936). These models had an accuracy (1-IBS) of 90.66%; 90.65%, and 90.64%, respectively. The deep learning algorithms were deployed on an interactive Web-based tool for practical use available via https://glioblastoma-survanalysis.herokuapp.com/. CONCLUSIONS Novel deep learning algorithms can better predict GBM prognosis than do baseline methods and can lead to more personalized patient care regardless of extensive electronic health record availability.
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Affiliation(s)
- Marc Ghanem
- Gilbert and Rose-Marie Chagoury School of Medicine, Lebanese American University, Beirut, Lebanon
| | - Abdul Karim Ghaith
- Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, Minnesota, USA; Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, USA
| | - Cameron Zamanian
- Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, Minnesota, USA; Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, USA
| | - Antonio Bon-Nieves
- Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, Minnesota, USA; Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, USA
| | - Archis Bhandarkar
- Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, Minnesota, USA; Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, USA
| | - Mohamad Bydon
- Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, Minnesota, USA; Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, USA.
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Lex JR, Di Michele J, Koucheki R, Pincus D, Whyne C, Ravi B. Artificial Intelligence for Hip Fracture Detection and Outcome Prediction: A Systematic Review and Meta-analysis. JAMA Netw Open 2023; 6:e233391. [PMID: 36930153 PMCID: PMC10024206 DOI: 10.1001/jamanetworkopen.2023.3391] [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] [Indexed: 03/18/2023] Open
Abstract
IMPORTANCE Artificial intelligence (AI) enables powerful models for establishment of clinical diagnostic and prognostic tools for hip fractures; however the performance and potential impact of these newly developed algorithms are currently unknown. OBJECTIVE To evaluate the performance of AI algorithms designed to diagnose hip fractures on radiographs and predict postoperative clinical outcomes following hip fracture surgery relative to current practices. DATA SOURCES A systematic review of the literature was performed using the MEDLINE, Embase, and Cochrane Library databases for all articles published from database inception to January 23, 2023. A manual reference search of included articles was also undertaken to identify any additional relevant articles. STUDY SELECTION Studies developing machine learning (ML) models for the diagnosis of hip fractures from hip or pelvic radiographs or to predict any postoperative patient outcome following hip fracture surgery were included. DATA EXTRACTION AND SYNTHESIS This study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses and was registered with PROSPERO. Eligible full-text articles were evaluated and relevant data extracted independently using a template data extraction form. For studies that predicted postoperative outcomes, the performance of traditional predictive statistical models, either multivariable logistic or linear regression, was recorded and compared with the performance of the best ML model on the same out-of-sample data set. MAIN OUTCOMES AND MEASURES Diagnostic accuracy of AI models was compared with the diagnostic accuracy of expert clinicians using odds ratios (ORs) with 95% CIs. Areas under the curve for postoperative outcome prediction between traditional statistical models (multivariable linear or logistic regression) and ML models were compared. RESULTS Of 39 studies that met all criteria and were included in this analysis, 18 (46.2%) used AI models to diagnose hip fractures on plain radiographs and 21 (53.8%) used AI models to predict patient outcomes following hip fracture surgery. A total of 39 598 plain radiographs and 714 939 hip fractures were used for training, validating, and testing ML models specific to diagnosis and postoperative outcome prediction, respectively. Mortality and length of hospital stay were the most predicted outcomes. On pooled data analysis, compared with clinicians, the OR for diagnostic error of ML models was 0.79 (95% CI, 0.48-1.31; P = .36; I2 = 60%) for hip fracture radiographs. For the ML models, the mean (SD) sensitivity was 89.3% (8.5%), specificity was 87.5% (9.9%), and F1 score was 0.90 (0.06). The mean area under the curve for mortality prediction was 0.84 with ML models compared with 0.79 for alternative controls (P = .09). CONCLUSIONS AND RELEVANCE The findings of this systematic review and meta-analysis suggest that the potential applications of AI to aid with diagnosis from hip radiographs are promising. The performance of AI in diagnosing hip fractures was comparable with that of expert radiologists and surgeons. However, current implementations of AI for outcome prediction do not seem to provide substantial benefit over traditional multivariable predictive statistics.
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Affiliation(s)
- Johnathan R. Lex
- Division of Orthopaedic Surgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
- Orthopaedics Biomechanics Laboratory, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Joseph Di Michele
- Division of Orthopaedic Surgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Robert Koucheki
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
- Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Daniel Pincus
- Division of Orthopaedic Surgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Division of Orthopaedic Surgery, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Cari Whyne
- Orthopaedics Biomechanics Laboratory, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Bheeshma Ravi
- Division of Orthopaedic Surgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Division of Orthopaedic Surgery, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
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Qiu F, Li J, Zhang R, Legerlotz K. Use of artificial neural networks in the prognosis of musculoskeletal diseases-a scoping review. BMC Musculoskelet Disord 2023; 24:86. [PMID: 36726111 PMCID: PMC9890715 DOI: 10.1186/s12891-023-06195-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 01/24/2023] [Indexed: 02/03/2023] Open
Abstract
To determine the current evidence on artificial neural network (ANN) in prognostic studies of musculoskeletal diseases (MSD) and to assess the accuracy of ANN in predicting the prognosis of patients with MSD. The scoping review was reported under the Preferred Items for Systematic Reviews and the Meta-Analyses extension for Scope Reviews (PRISMA-ScR). Cochrane Library, Embase, Pubmed, and Web of science core collection were searched from inception to January 2023. Studies were eligible if they used ANN to make predictions about MSD prognosis. Variables, model prediction accuracy, and disease type used in the ANN model were extracted and charted, then presented as a table along with narrative synthesis. Eighteen Studies were included in this scoping review, with 16 different types of musculoskeletal diseases. The accuracy of the ANN model predictions ranged from 0.542 to 0.947. ANN models were more accurate compared to traditional logistic regression models. This scoping review suggests that ANN can predict the prognosis of musculoskeletal diseases, which has the potential to be applied to different types of MSD.
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Affiliation(s)
- Fanji Qiu
- grid.7468.d0000 0001 2248 7639Movement Biomechanics, Institute of Sport Sciences, Humboldt‐Universität zu Berlin, Unter Den Linden 6, 10099 Berlin, Germany
| | - Jinfeng Li
- grid.34421.300000 0004 1936 7312Department of Kinesiology, Iowa State University, Ames, 50011 IA USA
| | - Rongrong Zhang
- grid.261049.80000 0004 0645 4572School of Control and Computer Engineering, North China Electric Power University, 102206 Beijing, China
| | - Kirsten Legerlotz
- grid.7468.d0000 0001 2248 7639Movement Biomechanics, Institute of Sport Sciences, Humboldt‐Universität zu Berlin, Unter Den Linden 6, 10099 Berlin, Germany
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9
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Kitcharanant N, Chotiyarnwong P, Tanphiriyakun T, Vanitcharoenkul E, Mahaisavariya C, Boonyaprapa W, Unnanuntana A. Development and internal validation of a machine-learning-developed model for predicting 1-year mortality after fragility hip fracture. BMC Geriatr 2022; 22:451. [PMID: 35610589 PMCID: PMC9131628 DOI: 10.1186/s12877-022-03152-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 05/19/2022] [Indexed: 11/17/2022] Open
Abstract
Background Fragility hip fracture increases morbidity and mortality in older adult patients, especially within the first year. Identification of patients at high risk of death facilitates modification of associated perioperative factors that can reduce mortality. Various machine learning algorithms have been developed and are widely used in healthcare research, particularly for mortality prediction. This study aimed to develop and internally validate 7 machine learning models to predict 1-year mortality after fragility hip fracture. Methods This retrospective study included patients with fragility hip fractures from a single center (Siriraj Hospital, Bangkok, Thailand) from July 2016 to October 2018. A total of 492 patients were enrolled. They were randomly categorized into a training group (344 cases, 70%) or a testing group (148 cases, 30%). Various machine learning techniques were used: the Gradient Boosting Classifier (GB), Random Forests Classifier (RF), Artificial Neural Network Classifier (ANN), Logistic Regression Classifier (LR), Naive Bayes Classifier (NB), Support Vector Machine Classifier (SVM), and K-Nearest Neighbors Classifier (KNN). All models were internally validated by evaluating their performance and the area under a receiver operating characteristic curve (AUC). Results For the testing dataset, the accuracies were GB model = 0.93, RF model = 0.95, ANN model = 0.94, LR model = 0.91, NB model = 0.89, SVM model = 0.90, and KNN model = 0.90. All models achieved high AUCs that ranged between 0.81 and 0.99. The RF model also provided a negative predictive value of 0.96, a positive predictive value of 0.93, a specificity of 0.99, and a sensitivity of 0.68. Conclusions Our machine learning approach facilitated the successful development of an accurate model to predict 1-year mortality after fragility hip fracture. Several machine learning algorithms (eg, Gradient Boosting and Random Forest) had the potential to provide high predictive performance based on the clinical parameters of each patient. The web application is available at www.hipprediction.com. External validation in a larger group of patients or in different hospital settings is warranted to evaluate the clinical utility of this tool. Trial registration Thai Clinical Trials Registry (22 February 2021; reg. no. TCTR20210222003). Supplementary Information The online version contains supplementary material available at 10.1186/s12877-022-03152-x.
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Affiliation(s)
- Nitchanant Kitcharanant
- Department of Orthopaedics, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.,Department of Orthopaedic Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, 10700, Bangkok, Thailand
| | - Pojchong Chotiyarnwong
- Department of Orthopaedic Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, 10700, Bangkok, Thailand.
| | - Thiraphat Tanphiriyakun
- Department of Orthopaedics, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.,Biomedical Informatics Center, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Ekasame Vanitcharoenkul
- Department of Orthopaedic Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, 10700, Bangkok, Thailand
| | - Chantas Mahaisavariya
- Golden Jubilee Medical Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Wichian Boonyaprapa
- Siriraj Information Technology Department, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Aasis Unnanuntana
- Department of Orthopaedic Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, 10700, Bangkok, Thailand
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10
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Li YY, Wang JJ, Huang SH, Kuo CL, Chen JY, Liu CF, Chu CC. Implementation of a machine learning application in preoperative risk assessment for hip repair surgery. BMC Anesthesiol 2022; 22:116. [PMID: 35459103 PMCID: PMC9034633 DOI: 10.1186/s12871-022-01648-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Accepted: 04/07/2022] [Indexed: 12/22/2022] Open
Abstract
Background This study aims to develop a machine learning-based application in a real-world medical domain to assist anesthesiologists in assessing the risk of complications in patients after a hip surgery. Methods Data from adult patients who underwent hip repair surgery at Chi-Mei Medical Center and its 2 branch hospitals from January 1, 2013, to March 31, 2020, were analyzed. Patients with incomplete data were excluded. A total of 22 features were included in the algorithms, including demographics, comorbidities, and major preoperative laboratory data from the database. The primary outcome was a composite of adverse events (in-hospital mortality, acute myocardial infarction, stroke, respiratory, hepatic and renal failure, and sepsis). Secondary outcomes were intensive care unit (ICU) admission and prolonged length of stay (PLOS). The data obtained were imported into 7 machine learning algorithms to predict the risk of adverse outcomes. Seventy percent of the data were randomly selected for training, leaving 30% for testing. The performances of the models were evaluated by the area under the receiver operating characteristic curve (AUROC). The optimal algorithm with the highest AUROC was used to build a web-based application, then integrated into the hospital information system (HIS) for clinical use. Results Data from 4,448 patients were analyzed; 102 (2.3%), 160 (3.6%), and 401 (9.0%) patients had primary composite adverse outcomes, ICU admission, and PLOS, respectively. Our optimal model had a superior performance (AUROC by DeLong test) than that of ASA-PS in predicting the primary composite outcomes (0.810 vs. 0.629, p < 0.01), ICU admission (0.835 vs. 0.692, p < 0.01), and PLOS (0.832 vs. 0.618, p < 0.01). Conclusions The hospital-specific machine learning model outperformed the ASA-PS in risk assessment. This web-based application gained high satisfaction from anesthesiologists after online use.
Supplementary Information The online version contains supplementary material available at 10.1186/s12871-022-01648-y.
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Affiliation(s)
- Yu-Yu Li
- Department of Anesthesiology, Chi Mei Medical Center, Tainan, Taiwan
| | - Jhi-Joung Wang
- Department of Anesthesiology, Chi Mei Medical Center, Tainan, Taiwan
| | - Sheng-Han Huang
- Department of Anesthesiology, Chi Mei Medical Center, Tainan, Taiwan
| | - Chi-Lin Kuo
- Department of Anesthesiology, Chi Mei Medical Center, Tainan, Taiwan
| | - Jen-Yin Chen
- Department of Anesthesiology, Chi Mei Medical Center, Tainan, Taiwan
| | - Chung-Feng Liu
- Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan.
| | - Chin-Chen Chu
- Department of Anesthesiology, Chi Mei Medical Center, Tainan, Taiwan.
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11
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Issitt RW, Cortina-Borja M, Bryant W, Bowyer S, Taylor AM, Sebire N. Classification Performance of Neural Networks Versus Logistic Regression Models: Evidence From Healthcare Practice. Cureus 2022; 14:e22443. [PMID: 35345728 PMCID: PMC8942139 DOI: 10.7759/cureus.22443] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/20/2022] [Indexed: 12/19/2022] Open
Abstract
Machine learning encompasses statistical approaches such as logistic regression (LR) through to more computationally complex models such as neural networks (NN). The aim of this study is to review current published evidence for performance from studies directly comparing logistic regression, and neural network classification approaches in medicine. A literature review was carried out to identify primary research studies which provided information regarding comparative area under the curve (AUC) values for the overall performance of both LR and NN for a defined clinical healthcare-related problem. Following an initial search, articles were reviewed to remove those that did not meet the criteria and performance metrics were extracted from the included articles. Teh initial search revealed 114 articles; 21 studies were included in the study. In 13/21 (62%) of cases, NN had a greater AUC compared to LR, but in most the difference was small and unlikely to be of clinical significance; (unweighted mean difference in AUC 0.03 (95% CI 0-0.06) in favour of NN versus LR. In the majority of cases examined across a range of clinical settings, LR models provide reasonable performance that is only marginally improved using more complex methods such as NN. In many circumstances, the use of a relatively simple LR model is likely to be adequate for real-world needs but in specific circumstances in which large amounts of data are available, and where even small increases in performance would provide significant management value, the application of advanced analytic tools such as NNs may be indicated.
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Affiliation(s)
- Richard W Issitt
- Clinical Informatics, Great Ormond Street Hospital, National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) University College London (UCL), London, GBR
| | - Mario Cortina-Borja
- Statistics, Great Ormond Street Institute of Child Health, University College London (UCL), London, GBR
| | - William Bryant
- Clinical Informatics, Great Ormond Street Hospital, National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) University College London (UCL), London, GBR
| | - Stuart Bowyer
- Clinical Informatics, Great Ormond Street Hospital, National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) University College London (UCL), London, GBR
| | - Andrew M Taylor
- Clinical Informatics, Great Ormond Street Hospital, National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) University College London (UCL), London, GBR
| | - Neil Sebire
- Clinical Informatics, Great Ormond Street Hospital, National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) University College London (UCL), London, GBR
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12
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McLeod G, Kennedy I, Simpson E, Joss J, Goldmann K. A pilot project informing the design of a web-based dynamic nomogram in order to predict survival one year after hip fracture surgery (Preprint). Interact J Med Res 2021; 11:e34096. [PMID: 35238320 PMCID: PMC9008534 DOI: 10.2196/34096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 01/18/2022] [Accepted: 02/13/2022] [Indexed: 12/15/2022] Open
Affiliation(s)
- Graeme McLeod
- Department of Anaesthesia, Ninewells Hospital, National Health Service Tayside, Dundee, United Kingdom
- School of Medicine, University of Dundee, Ninewells Hospital, Dundee, United Kingdom
| | - Iain Kennedy
- Department of Anaesthesia, Ninewells Hospital, National Health Service Tayside, Dundee, United Kingdom
| | - Eilidh Simpson
- Crosshouse Hospital, National Health Service Ayrshire and Arran, Kilmarnock, United Kingdom
| | - Judith Joss
- Department of Anaesthesia, Ninewells Hospital, National Health Service Tayside, Dundee, United Kingdom
| | - Katriona Goldmann
- William Harvey Research Institute, Barts and the London School of Medicine & Dentistry, London, United Kingdom
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13
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Cao B, Zhang KC, Wei B, Chen L. Status quo and future prospects of artificial neural network from the perspective of gastroenterologists. World J Gastroenterol 2021; 27:2681-2709. [PMID: 34135549 PMCID: PMC8173384 DOI: 10.3748/wjg.v27.i21.2681] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/29/2021] [Accepted: 04/22/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) diseases. This state-of-the-art technique exhibits excellent performance in diagnosis, prognostic prediction, and treatment. Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements. However, the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice. In this review, we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists. Existing limitations and future directions are also proposed to optimize ANN’s clinical potential. In consideration of barriers to interdisciplinary knowledge, sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.
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Affiliation(s)
- Bo Cao
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Ke-Cheng Zhang
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Bo Wei
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Lin Chen
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
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14
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Cao Y, Forssten MP, Mohammad Ismail A, Borg T, Ioannidis I, Montgomery S, Mohseni S. Predictive Values of Preoperative Characteristics for 30-Day Mortality in Traumatic Hip Fracture Patients. J Pers Med 2021; 11:353. [PMID: 33924993 PMCID: PMC8146802 DOI: 10.3390/jpm11050353] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 03/21/2021] [Accepted: 04/23/2021] [Indexed: 12/23/2022] Open
Abstract
Hip fracture patients have a high risk of mortality after surgery, with 30-day postoperative rates as high as 10%. This study aimed to explore the predictive ability of preoperative characteristics in traumatic hip fracture patients as they relate to 30-day postoperative mortality using readily available variables in clinical practice. All adult patients who underwent primary emergency hip fracture surgery in Sweden between 2008 and 2017 were included in the analysis. Associations between the possible predictors and 30-day mortality was performed using a multivariate logistic regression (LR) model; the bidirectional stepwise method was used for variable selection. An LR model and convolutional neural network (CNN) were then fitted for prediction. The relative importance of individual predictors was evaluated using the permutation importance and Gini importance. A total of 134,915 traumatic hip fracture patients were included in the study. The CNN and LR models displayed an acceptable predictive ability for predicting 30-day postoperative mortality using a test dataset, displaying an area under the ROC curve (AUC) of as high as 0.76. The variables with the highest importance in prediction were age, sex, hypertension, dementia, American Society of Anesthesiologists (ASA) classification, and the Revised Cardiac Risk Index (RCRI). Both the CNN and LR models achieved an acceptable performance in identifying patients at risk of mortality 30 days after hip fracture surgery. The most important variables for prediction, based on the variables used in the current study are age, hypertension, dementia, sex, ASA classification, and RCRI.
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Affiliation(s)
- Yang Cao
- Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, 70182 Örebro, Sweden;
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institute, 17177 Stockholm, Sweden
| | - Maximilian Peter Forssten
- Department of Orthopedic Surgery, Orebro University Hospital, 70185 Orebro, Sweden; (M.P.F.); (A.M.I.); (T.B.); (I.I.)
- School of Medical Sciences, Orebro University, 70182 Orebro, Sweden;
| | - Ahmad Mohammad Ismail
- Department of Orthopedic Surgery, Orebro University Hospital, 70185 Orebro, Sweden; (M.P.F.); (A.M.I.); (T.B.); (I.I.)
- School of Medical Sciences, Orebro University, 70182 Orebro, Sweden;
| | - Tomas Borg
- Department of Orthopedic Surgery, Orebro University Hospital, 70185 Orebro, Sweden; (M.P.F.); (A.M.I.); (T.B.); (I.I.)
- School of Medical Sciences, Orebro University, 70182 Orebro, Sweden;
| | - Ioannis Ioannidis
- Department of Orthopedic Surgery, Orebro University Hospital, 70185 Orebro, Sweden; (M.P.F.); (A.M.I.); (T.B.); (I.I.)
- School of Medical Sciences, Orebro University, 70182 Orebro, Sweden;
| | - Scott Montgomery
- Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, 70182 Örebro, Sweden;
- Clinical Epidemiology Division, Department of Medicine, Karolinska Institutet, 17177 Stockholm, Sweden
- Department of Epidemiology and Public Health, University College London, London WC1E 6BT, UK
| | - Shahin Mohseni
- School of Medical Sciences, Orebro University, 70182 Orebro, Sweden;
- Division of Trauma and Emergency Surgery, Department of Surgery, Orebro University Hospital, 70185 Orebro, Sweden
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