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Falconer N, Scott IA, Abdel-Hafez A, Cottrell N, Long D, Morris C, Snoswell C, Aziz E, Jie Lam JY, Barras M. The adverse inpatient medication event and frailty (AIME-frail) risk prediction model. Res Social Adm Pharm 2024; 20:796-803. [PMID: 38772838 DOI: 10.1016/j.sapharm.2024.05.003] [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: 07/17/2023] [Revised: 03/04/2024] [Accepted: 05/07/2024] [Indexed: 05/23/2024]
Abstract
BACKGROUND Medication harm affects between 5 and 15% of hospitalised patients, with approximately half of the harm events considered preventable through timely intervention. The Adverse Inpatient Medication Event (AIME) risk prediction model was previously developed to guide a systematic approach to patient prioritisation for targeted clinician review, but frailty was not tested as a candidate predictor variable. AIM To evaluate the predictive performance of an updated AIME model, incorporating a measure of frailty, when applied to a new multisite cohort of hospitalised adult inpatients. METHODS A retrospective cohort study was conducted at two tertiary Australian hospitals on patients discharged between 1st January and April 31, 2020. Data were extracted from electronic medical records (EMRs) and clinical coding databases. Medication harm was identified using ICD-10 Y-codes and confirmed by senior pharmacist review of medical records. The Hospital Frailty Risk Score (HFRS) was calculated for each patient. Logistic regression analysis was used to construct a modified AIME model. Candidate variables of the original AIME model, together with new variables including HFRS were tested. Performance of the final model was reported using area under the curve (AUC) and decision curve analysis (DCA). RESULTS A total of 4089 patient admissions were included, with a mean age ± standard deviation (SD) of 64 years (±19 years), 2050 patients (50%) were males, and mean HFRS was 6.2 (±5.9). 184 patients (4.5%) experienced one or more medication harm events during hospitalisation. The new AIME-Frail risk model incorporated 5 of the original variables: length of stay (LOS), anti-psychotics, antiarrhythmics, immunosuppressants, and INR greater than 3, as well as 5 new variables: HFRS, anticoagulants, antibiotics, insulin, and opioid use. The AUC was 0.79 (95% CI: 0.76-0.83) which was superior to the original model (AUC = 0.70, 95% CI: 0.65-0.74) with a sensitivity of 69%, specificity of 81%, positive predictive value of 0.14 (95% CI: 0.10-0.17) and negative predictive value of 0.98 (95% CI: 0.97-0.99). The DCA identified the model as having potential clinical utility between the probability thresholds of 0.05-0.4. CONCLUSION The inclusion of a frailty measure improved the predictive performance of the AIME model. Screening inpatients using the AIME-Frail tool could identify more patients at high-risk of medication harm who warrant timely clinician review.
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Affiliation(s)
- Nazanin Falconer
- Department of Pharmacy, Princess Alexandra Hospital, Metro South Health, 199 Ipswich Road, Brisbane, QLD, 4102, Australia; School of Pharmacy, Faculty of Health and Behavioural Sciences, The University of Queensland, Brisbane, QLD, 4102, Australia; UQ Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, QLD, 4102, Australia.
| | - Ian A Scott
- Department of Internal Medicine, Princess Alexandra Hospital, Woolloongabba, QLD, 4102, Australia
| | - Ahmad Abdel-Hafez
- Clinical Informatics, Metro South Health, 199 Ipswich Road, Woolloongabba, QLD, 4102, Australia; University of Doha for Science and Technology, Doha, Qatar
| | - Neil Cottrell
- School of Pharmacy, Faculty of Health and Behavioural Sciences, The University of Queensland, Brisbane, QLD, 4102, Australia
| | - Duncan Long
- Department of Pharmacy, Princess Alexandra Hospital, Metro South Health, 199 Ipswich Road, Brisbane, QLD, 4102, Australia
| | - Christopher Morris
- Department of Internal Medicine, Princess Alexandra Hospital, Woolloongabba, QLD, 4102, Australia
| | - Centaine Snoswell
- School of Pharmacy, Faculty of Health and Behavioural Sciences, The University of Queensland, Brisbane, QLD, 4102, Australia; UQ Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, QLD, 4102, Australia
| | - Ebtyhal Aziz
- School of Pharmacy, Faculty of Health and Behavioural Sciences, The University of Queensland, Brisbane, QLD, 4102, Australia; Logan Hospital, Armstrong Rd and Loganlea Rd, Meadowbrook, Queensland QLD, 4131, Australia
| | - Jonathan Yong Jie Lam
- Department of Pharmacy, Princess Alexandra Hospital, Metro South Health, 199 Ipswich Road, Brisbane, QLD, 4102, Australia; School of Pharmacy, Faculty of Health and Behavioural Sciences, The University of Queensland, Brisbane, QLD, 4102, Australia
| | - Michael Barras
- Department of Pharmacy, Princess Alexandra Hospital, Metro South Health, 199 Ipswich Road, Brisbane, QLD, 4102, Australia; School of Pharmacy, Faculty of Health and Behavioural Sciences, The University of Queensland, Brisbane, QLD, 4102, Australia
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Li B, Eisenberg N, Beaton D, Lee DS, Al-Omran L, Wijeysundera DN, Hussain MA, Rotstein OD, de Mestral C, Mamdani M, Roche-Nagle G, Al-Omran M. Predicting inferior vena cava filter complications using machine learning. J Vasc Surg Venous Lymphat Disord 2024:101943. [PMID: 39084408 DOI: 10.1016/j.jvsv.2024.101943] [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: 04/28/2024] [Revised: 06/03/2024] [Accepted: 06/26/2024] [Indexed: 08/02/2024]
Abstract
OBJECTIVE Inferior vena cava (IVC) filter placement is associated with important long-term complications. Predictive models for filter-related complications may help guide clinical decision-making but remain limited. We developed machine learning (ML) algorithms that predict 1-year IVC filter complications using pre-operative data. METHODS The Vascular Quality Initiative (VQI) database was used to identify patients who underwent IVC filter placement between 2013-2024. We identified 77 pre-operative demographic/clinical features from the index hospitalization when the filter was placed. The primary outcome was 1-year filter-related complications (composite of filter thrombosis, migration, angulation, fracture, and embolization or fragmentation, vein perforation, new caval or iliac vein thrombosis, new pulmonary embolism, access site thrombosis, or failed retrieval). The data were divided into training (70%) and test (30%) sets. Six ML models were trained using pre-operative features with 10-fold cross-validation (Extreme Gradient Boosting [XGBoost], random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). Model robustness was assessed using calibration plot and Brier score. Performance was evaluated across subgroups based on age, sex, race, ethnicity, rurality, median Area Deprivation Index, planned duration of filter, landing site of filter, and presence of prior IVC filter placement. RESULTS Overall, 14,476 patients underwent IVC filter placement and 584 (4.0%) experienced 1-year filter-related complications. Patients with a primary outcome were younger (59.3 [SD 16.7] vs. 63.8 [SD 16.0] years, p < 0.001) and more likely to have thrombotic risk factors including thrombophilia, prior venous thromboembolism (VTE), and family history of VTE. The best prediction model was XGBoost, achieving an AUROC (95% CI) of 0.93 (0.92-0.94). In comparison, logistic regression had an AUROC (95% CI) of 0.63 (0.61-0.65). Calibration plot showed good agreement between predicted/observed event probabilities with a Brier score of 0.07. The top 10 predictors of 1-year filter-related complications were 1) thrombophilia, 2) prior VTE, 3) antiphospholipid antibodies, 4) Factor V Leiden mutation, 5) family history of VTE, 6) planned duration of IVC filter (temporary), 7) unable to maintain therapeutic anticoagulation, 8) malignancy, 9) recent/active bleeding, and 10) age. Model performance remained robust across all subgroups. CONCLUSIONS We developed ML models that can accurately predict 1-year IVC filter complications, performing better than logistic regression. These algorithms have potential for important utility in guiding patient selection for filter placement, counselling, peri-operative management, and follow-up to mitigate filter-related complications and improve outcomes.
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Affiliation(s)
- Ben Li
- Department of Surgery, University of Toronto, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Canada; Institute of Medical Science, University of Toronto, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Canada
| | - Naomi Eisenberg
- Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Canada
| | - Derek Beaton
- Data Science & Advanced Analytics, Unity Health Toronto, University of Toronto, Canada
| | - Douglas S Lee
- Division of Cardiology, Peter Munk Cardiac Centre, University Health Network, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Canada; ICES, University of Toronto, Canada
| | - Leen Al-Omran
- School of Medicine, Alfaisal University, Saudi Arabia
| | - Duminda N Wijeysundera
- Institute of Health Policy, Management and Evaluation, University of Toronto, Canada; ICES, University of Toronto, Canada; Department of Anesthesia, St. Michael's Hospital, Unity Health Toronto, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Canada
| | - Mohamad A Hussain
- Division of Vascular and Endovascular Surgery and the Center for Surgery and Public Health, Brigham and Women's Hospital, Harvard Medical School, United States
| | - Ori D Rotstein
- Department of Surgery, University of Toronto, Canada; Institute of Medical Science, University of Toronto, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Canada; Division of General Surgery, St. Michael's Hospital, Unity Health Toronto, Canada
| | - Charles de Mestral
- Department of Surgery, University of Toronto, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Canada; ICES, University of Toronto, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Canada
| | - Muhammad Mamdani
- Institute of Medical Science, University of Toronto, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Canada; Data Science & Advanced Analytics, Unity Health Toronto, University of Toronto, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Canada; ICES, University of Toronto, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Canada; Leslie Dan Faculty of Pharmacy, University of Toronto, Canada
| | - Graham Roche-Nagle
- Department of Surgery, University of Toronto, Canada; Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Canada; Division of Vascular and Interventional Radiology, University Health Network, Canada
| | - Mohammed Al-Omran
- Department of Surgery, University of Toronto, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Canada; Institute of Medical Science, University of Toronto, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Canada; Department of Surgery, King Faisal Specialist Hospital and Research Center, Saudi Arabia.
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Sharma V, McDermott J, Keen J, Foster S, Whelan P, Newman W. Pharmacogenetics Clinical Decision Support Systems for Primary Care in England: Co-Design Study. J Med Internet Res 2024; 26:e49230. [PMID: 39042886 PMCID: PMC11303890 DOI: 10.2196/49230] [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: 05/23/2023] [Revised: 12/22/2023] [Accepted: 05/13/2024] [Indexed: 07/25/2024] Open
Abstract
BACKGROUND Pharmacogenetics can impact patient care and outcomes through personalizing the selection of medicines, resulting in improved efficacy and a reduction in harmful side effects. Despite the existence of compelling clinical evidence and international guidelines highlighting the benefits of pharmacogenetics in clinical practice, implementation within the National Health Service in the United Kingdom is limited. An important barrier to overcome is the development of IT solutions that support the integration of pharmacogenetic data into health care systems. This necessitates a better understanding of the role of electronic health records (EHRs) and the design of clinical decision support systems that are acceptable to clinicians, particularly those in primary care. OBJECTIVE Explore the needs and requirements of a pharmacogenetic service from the perspective of primary care clinicians with a view to co-design a prototype solution. METHODS We used ethnographic and think-aloud observations, user research workshops, and prototyping. The participants for this study included general practitioners and pharmacists. In total, we undertook 5 sessions of ethnographic observation to understand current practices and workflows. This was followed by 3 user research workshops, each with its own topic guide starting with personas and early ideation, through to exploring the potential of clinical decision support systems and prototype design. We subsequently analyzed workshop data using affinity diagramming and refined the key requirements for the solution collaboratively as a multidisciplinary project team. RESULTS User research results identified that pharmacogenetic data must be incorporated within existing EHRs rather than through a stand-alone portal. The information presented through clinical decision support systems must be clear, accessible, and user-friendly as the service will be used by a range of end users. Critically, the information should be displayed within the prescribing workflow, rather than discrete results stored statically in the EHR. Finally, the prescribing recommendations should be authoritative to provide confidence in the validity of the results. Based on these findings we co-designed an interactive prototype, demonstrating pharmacogenetic clinical decision support integrated within the prescribing workflow of an EHR. CONCLUSIONS This study marks a significant step forward in the design of systems that support pharmacogenetic-guided prescribing in primary care settings. Clinical decision support systems have the potential to enhance the personalization of medicines, provided they are effectively implemented within EHRs and present pharmacogenetic data in a user-friendly, actionable, and standardized format. Achieving this requires the development of a decoupled, standards-based architecture that allows for the separation of data from application, facilitating integration across various EHRs through the use of application programming interfaces (APIs). More globally, this study demonstrates the role of health informatics and user-centered design in realizing the potential of personalized medicine at scale and ensuring that the benefits of genomic innovation reach patients and populations effectively.
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Affiliation(s)
- Videha Sharma
- Centre for Health Informatics, Division of Informatics, Imaging and Data Science, University of Manchester, Manchester, United Kingdom
- Pankhurst Institute for Health Technology Research and Innovation, University of Manchester, Manchester, United Kingdom
| | - John McDermott
- Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, United Kingdom
- Division of Evolution, Infection and Genomics, School of Biological Sciences, University of Manchester, Manchester, United Kingdom
| | - Jessica Keen
- Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | - Simon Foster
- Centre for Health Informatics, Division of Informatics, Imaging and Data Science, University of Manchester, Manchester, United Kingdom
| | - Pauline Whelan
- Centre for Health Informatics, Division of Informatics, Imaging and Data Science, University of Manchester, Manchester, United Kingdom
| | - William Newman
- Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, United Kingdom
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Singh D, Han MK, Hawkins NM, Hurst JR, Kocks JWH, Skolnik N, Stolz D, El Khoury J, Gale CP. Implications of Cardiopulmonary Risk for the Management of COPD: A Narrative Review. Adv Ther 2024; 41:2151-2167. [PMID: 38664329 PMCID: PMC11133105 DOI: 10.1007/s12325-024-02855-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 03/22/2024] [Indexed: 05/29/2024]
Abstract
Chronic obstructive pulmonary disease (COPD) constitutes a major global health burden and is the third leading cause of death worldwide. A high proportion of patients with COPD have cardiovascular disease, but there is also evidence that COPD is a risk factor for adverse outcomes in cardiovascular disease. Patients with COPD frequently die of respiratory and cardiovascular causes, yet the identification and management of cardiopulmonary risk remain suboptimal owing to limited awareness and clinical intervention. Acute exacerbations punctuate the progression of COPD in many patients, reducing lung function and increasing the risk of subsequent exacerbations and cardiovascular events that may lead to early death. This narrative review defines and summarises the principles of COPD-associated cardiopulmonary risk, and examines respiratory interventions currently available to modify this risk, as well as providing expert opinion on future approaches to addressing cardiopulmonary risk.
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Affiliation(s)
- Dave Singh
- Medicines Evaluation Unit, Manchester University NHS Foundation Trust, University of Manchester, Manchester, M23 9QZ, UK.
| | - MeiLan K Han
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | | | - John R Hurst
- UCL Respiratory, University College London, London, UK
| | - Janwillem W H Kocks
- General Practitioners Research Institute, Groningen, The Netherlands
- Observational and Pragmatic Research Institute, Singapore, Singapore
- Groningen Research Institute for Asthma and COPD, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Pulmonology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | | | - Daiana Stolz
- Clinic of Respiratory Medicine, Medical Center and Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | | | - Chris P Gale
- Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
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Li B, Aljabri B, Verma R, Beaton D, Hussain MA, Lee DS, Wijeysundera DN, de Mestral C, Mamdani M, Al‐Omran M. Predicting Outcomes Following Lower Extremity Endovascular Revascularization Using Machine Learning. J Am Heart Assoc 2024; 13:e033194. [PMID: 38639373 PMCID: PMC11179886 DOI: 10.1161/jaha.123.033194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 03/01/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND Lower extremity endovascular revascularization for peripheral artery disease carries nonnegligible perioperative risks; however, outcome prediction tools remain limited. Using machine learning, we developed automated algorithms that predict 30-day outcomes following lower extremity endovascular revascularization. METHODS AND RESULTS The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity endovascular revascularization (angioplasty, stent, or atherectomy) for peripheral artery disease between 2011 and 2021. Input features included 38 preoperative demographic/clinical variables. The primary outcome was 30-day postprocedural major adverse limb event (composite of major reintervention, untreated loss of patency, or major amputation) or death. Data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using preoperative features. The primary model evaluation metric was area under the receiver operating characteristic curve. Overall, 21 886 patients were included, and 30-day major adverse limb event/death occurred in 1964 (9.0%) individuals. The best performing model for predicting 30-day major adverse limb event/death was extreme gradient boosting, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI, 0.92-0.94). In comparison, logistic regression had an area under the receiver operating characteristic curve of 0.72 (95% CI, 0.70-0.74). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.09. The top 3 predictive features in our algorithm were (1) chronic limb-threatening ischemia, (2) tibial intervention, and (3) congestive heart failure. CONCLUSIONS Our machine learning models accurately predict 30-day outcomes following lower extremity endovascular revascularization using preoperative data with good discrimination and calibration. Prospective validation is warranted to assess for generalizability and external validity.
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Affiliation(s)
- Ben Li
- Department of SurgeryUniversity of TorontoCanada
- Division of Vascular SurgerySt. Michael’s Hospital, Unity Health Toronto, University of TorontoTorontoCanada
- Institute of Medical Science, University of TorontoTorontoCanada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T‐CAIREM)University of TorontoTorontoCanada
| | - Badr Aljabri
- Department of SurgeryKing Saud UniversityRiyadhSaudi Arabia
| | - Raj Verma
- School of Medicine, Royal College of Surgeons in IrelandUniversity of Medicine and Health SciencesDublinIreland
| | - Derek Beaton
- Data Science & Advanced Analytics, Unity Health TorontoUniversity of TorontoTorontoCanada
| | - Mohamad A. Hussain
- Division of Vascular and Endovascular Surgery and the Center for Surgery and Public Health, Brigham and Women’s HospitalHarvard Medical SchoolBostonMAUSA
| | - Douglas S. Lee
- Division of Cardiology, Peter Munk Cardiac CentreUniversity Health NetworkTorontoCanada
- Institute of Health Policy, Management and Evaluation, University of TorontoTorontoCanada
- ICES, University of TorontoTorontoCanada
| | - Duminda N. Wijeysundera
- Institute of Health Policy, Management and Evaluation, University of TorontoTorontoCanada
- ICES, University of TorontoTorontoCanada
- Department of AnesthesiaSt. Michael’s Hospital, Unity Health TorontoTorontoCanada
- Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Unity Health TorontoTorontoCanada
| | - Charles de Mestral
- Department of SurgeryUniversity of TorontoCanada
- Division of Vascular SurgerySt. Michael’s Hospital, Unity Health Toronto, University of TorontoTorontoCanada
- Institute of Health Policy, Management and Evaluation, University of TorontoTorontoCanada
- ICES, University of TorontoTorontoCanada
- Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Unity Health TorontoTorontoCanada
| | - Muhammad Mamdani
- Institute of Medical Science, University of TorontoTorontoCanada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T‐CAIREM)University of TorontoTorontoCanada
- Data Science & Advanced Analytics, Unity Health TorontoUniversity of TorontoTorontoCanada
- Institute of Health Policy, Management and Evaluation, University of TorontoTorontoCanada
- ICES, University of TorontoTorontoCanada
- Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Unity Health TorontoTorontoCanada
- Leslie Dan Faculty of PharmacyUniversity of TorontoTorontoCanada
| | - Mohammed Al‐Omran
- Department of SurgeryUniversity of TorontoCanada
- Division of Vascular SurgerySt. Michael’s Hospital, Unity Health Toronto, University of TorontoTorontoCanada
- Institute of Medical Science, University of TorontoTorontoCanada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T‐CAIREM)University of TorontoTorontoCanada
- Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Unity Health TorontoTorontoCanada
- Department of SurgeryKing Faisal Specialist Hospital and Research CenterRiyadhSaudi Arabia
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Mickley JP, Grove AF, Rouzrokh P, Yang L, Larson AN, Sanchez-Sotello J, Maradit Kremers H, Wyles CC. A Stepwise Approach to Analyzing Musculoskeletal Imaging Data With Artificial Intelligence. Arthritis Care Res (Hoboken) 2024; 76:590-599. [PMID: 37849415 DOI: 10.1002/acr.25260] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 08/27/2023] [Accepted: 10/13/2023] [Indexed: 10/19/2023]
Abstract
The digitization of medical records and expanding electronic health records has created an era of "Big Data" with an abundance of available information ranging from clinical notes to imaging studies. In the field of rheumatology, medical imaging is used to guide both diagnosis and treatment of a wide variety of rheumatic conditions. Although there is an abundance of data to analyze, traditional methods of image analysis are human resource intensive. Fortunately, the growth of artificial intelligence (AI) may be a solution to handle large datasets. In particular, computer vision is a field within AI that analyzes images and extracts information. Computer vision has impressive capabilities and can be applied to rheumatologic conditions, necessitating a need to understand how computer vision works. In this article, we provide an overview of AI in rheumatology and conclude with a five step process to plan and conduct research in the field of computer vision. The five steps include (1) project definition, (2) data handling, (3) model development, (4) performance evaluation, and (5) deployment into clinical care.
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Wiens MO, Nguyen V, Bone JN, Kumbakumba E, Businge S, Tagoola A, Sherine SO, Byaruhanga E, Ssemwanga E, Barigye C, Nsungwa J, Olaro C, Ansermino JM, Kissoon N, Singer J, Larson CP, Lavoie PM, Dunsmuir D, Moschovis PP, Novakowski S, Komugisha C, Tayebwa M, Mwesigwa D, Knappett M, West N, Mugisha NK, Kabakyenga J. Prediction models for post-discharge mortality among under-five children with suspected sepsis in Uganda: A multicohort analysis. PLOS GLOBAL PUBLIC HEALTH 2024; 4:e0003050. [PMID: 38683787 PMCID: PMC11057737 DOI: 10.1371/journal.pgph.0003050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 04/04/2024] [Indexed: 05/02/2024]
Abstract
In many low-income countries, over five percent of hospitalized children die following hospital discharge. The lack of available tools to identify those at risk of post-discharge mortality has limited the ability to make progress towards improving outcomes. We aimed to develop algorithms designed to predict post-discharge mortality among children admitted with suspected sepsis. Four prospective cohort studies of children in two age groups (0-6 and 6-60 months) were conducted between 2012-2021 in six Ugandan hospitals. Prediction models were derived for six-months post-discharge mortality, based on candidate predictors collected at admission, each with a maximum of eight variables, and internally validated using 10-fold cross-validation. 8,810 children were enrolled: 470 (5.3%) died in hospital; 257 (7.7%) and 233 (4.8%) post-discharge deaths occurred in the 0-6-month and 6-60-month age groups, respectively. The primary models had an area under the receiver operating characteristic curve (AUROC) of 0.77 (95%CI 0.74-0.80) for 0-6-month-olds and 0.75 (95%CI 0.72-0.79) for 6-60-month-olds; mean AUROCs among the 10 cross-validation folds were 0.75 and 0.73, respectively. Calibration across risk strata was good: Brier scores were 0.07 and 0.04, respectively. The most important variables included anthropometry and oxygen saturation. Additional variables included: illness duration, jaundice-age interaction, and a bulging fontanelle among 0-6-month-olds; and prior admissions, coma score, temperature, age-respiratory rate interaction, and HIV status among 6-60-month-olds. Simple prediction models at admission with suspected sepsis can identify children at risk of post-discharge mortality. Further external validation is recommended for different contexts. Models can be digitally integrated into existing processes to improve peri-discharge care as children transition from the hospital to the community.
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Affiliation(s)
- Matthew O. Wiens
- Institute for Global Health at BC Children’s and Women’s Hospital, Vancouver, Canada
- Department of Anesthesiology, Pharmacology & Therapeutics, University of British Columbia, Vancouver, Canada
- BC Children’s Hospital Research Institute, Vancouver, Canada
- Walimu, Kampala, Uganda
| | - Vuong Nguyen
- Institute for Global Health at BC Children’s and Women’s Hospital, Vancouver, Canada
| | - Jeffrey N. Bone
- BC Children’s Hospital Research Institute, Vancouver, Canada
| | - Elias Kumbakumba
- Department of Paediatrics and Child Health, Mbarara University of Science and Technology, Mbarara, Uganda
| | | | - Abner Tagoola
- Jinja Regional Referral Hospital, Jinja City, Uganda
| | | | | | | | | | - Jesca Nsungwa
- Ministry of Health for the Republic of Uganda, Kampala, Uganda
| | - Charles Olaro
- Ministry of Health for the Republic of Uganda, Kampala, Uganda
| | - J. Mark Ansermino
- Institute for Global Health at BC Children’s and Women’s Hospital, Vancouver, Canada
- Department of Anesthesiology, Pharmacology & Therapeutics, University of British Columbia, Vancouver, Canada
- BC Children’s Hospital Research Institute, Vancouver, Canada
| | - Niranjan Kissoon
- BC Children’s Hospital Research Institute, Vancouver, Canada
- Department of Pediatrics, University of British Columbia, Vancouver, Canada
| | - Joel Singer
- School of Population and Public Health, University of British Columbia, Vancouver, Canada
| | - Charles P. Larson
- School of Population and Global Health, McGill University, Montréal, Canada
| | - Pascal M. Lavoie
- BC Children’s Hospital Research Institute, Vancouver, Canada
- Department of Pediatrics, University of British Columbia, Vancouver, Canada
| | - Dustin Dunsmuir
- Institute for Global Health at BC Children’s and Women’s Hospital, Vancouver, Canada
- BC Children’s Hospital Research Institute, Vancouver, Canada
| | - Peter P. Moschovis
- Division of Global Health, Massachusetts General Hospital, Boston, MA, United States of America
| | - Stefanie Novakowski
- Institute for Global Health at BC Children’s and Women’s Hospital, Vancouver, Canada
- Department of Anesthesiology, Pharmacology & Therapeutics, University of British Columbia, Vancouver, Canada
| | | | | | | | - Martina Knappett
- Institute for Global Health at BC Children’s and Women’s Hospital, Vancouver, Canada
| | - Nicholas West
- BC Children’s Hospital Research Institute, Vancouver, Canada
| | | | - Jerome Kabakyenga
- Maternal Newborn & Child Health Institute, Mbarara University of Science and Technology, Mbarara, Uganda
- Faculty of Medicine, Department of Community Health, Mbarara University of Science and Technology, Mbarara, Uganda
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Merenda M, Earnest A, Ruseckaite R, Tse WC, Elder E, Hopper I, Ahern S. Patient-Reported Outcome Measures in High-Risk Medical Device Registries: A Scoping Review. Aesthet Surg J Open Forum 2024; 6:ojae015. [PMID: 38650972 PMCID: PMC11033681 DOI: 10.1093/asjof/ojae015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024] Open
Abstract
Little is known about the methods and outcomes of patient-reported outcome measure (PROM) use among high-risk medical device registries. The objective of this scoping review was to assess the utility and predictive ability of PROMs in high-risk medical device registries. We searched Ovid Medline, Embase, APA PsychINFO, Cochrane Library, and Scopus databases for published literature. After searching, 4323 titles and abstracts were screened, and 262 full texts were assessed for their eligibility. Seventy-six papers from across orthopedic (n = 64), cardiac (n = 10), penile (n = 1), and hernia mesh (n = 1) device registries were identified. Studies predominantly used PROMs as an outcome measure when comparing cohorts or surgical approaches (n = 45) or to compare time points (n = 13) including pre- and postintervention. Fifteen papers considered the predictive ability of PROMs. Of these, 8 treated PROMs as an outcome, 5 treated PROMs as a risk factor through regression analysis, and 2 papers treated PROMs as both a risk factor and as an outcome. One paper described PROMs to study implant survival. To advance methods of PROM integration into clinical decision-making for medical devices, an understanding of their use in high-risk device registries is needed. This scoping review found that there is a paucity of studies using PROMs to predict long-term patient and clinical outcomes in high-risk medical device registries. Determination as to why PROMs are rarely used for predictive purposes in long-term data collection is needed if PROM data are to be considered suitable as real-world evidence for high-risk device regulatory purposes, as well as to support clinical decision-making. Level of Evidence 4
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Affiliation(s)
- Michelle Merenda
- Corresponding Author: Mrs Michelle Merenda, Level 3, 553 St Kilda Rd, Melbourne, Victoria 3004, Australia. E-mail:
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Li B, Warren BE, Eisenberg N, Beaton D, Lee DS, Aljabri B, Verma R, Wijeysundera DN, Rotstein OD, de Mestral C, Mamdani M, Roche-Nagle G, Al-Omran M. Machine Learning to Predict Outcomes of Endovascular Intervention for Patients With PAD. JAMA Netw Open 2024; 7:e242350. [PMID: 38483388 PMCID: PMC10940965 DOI: 10.1001/jamanetworkopen.2024.2350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 01/19/2024] [Indexed: 03/17/2024] Open
Abstract
Importance Endovascular intervention for peripheral artery disease (PAD) carries nonnegligible perioperative risks; however, outcome prediction tools are limited. Objective To develop machine learning (ML) algorithms that can predict outcomes following endovascular intervention for PAD. Design, Setting, and Participants This prognostic study included patients who underwent endovascular intervention for PAD between January 1, 2004, and July 5, 2023, with 1 year of follow-up. Data were obtained from the Vascular Quality Initiative (VQI), a multicenter registry containing data from vascular surgeons and interventionalists at more than 1000 academic and community hospitals. From an initial cohort of 262 242 patients, 26 565 were excluded due to treatment for acute limb ischemia (n = 14 642) or aneurysmal disease (n = 3456), unreported symptom status (n = 4401) or procedure type (n = 2319), or concurrent bypass (n = 1747). Data were split into training (70%) and test (30%) sets. Exposures A total of 112 predictive features (75 preoperative [demographic and clinical], 24 intraoperative [procedural], and 13 postoperative [in-hospital course and complications]) from the index hospitalization were identified. Main Outcomes and Measures Using 10-fold cross-validation, 6 ML models were trained using preoperative features to predict 1-year major adverse limb event (MALE; composite of thrombectomy or thrombolysis, surgical reintervention, or major amputation) or death. The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). After selecting the best performing algorithm, additional models were built using intraoperative and postoperative data. Results Overall, 235 677 patients who underwent endovascular intervention for PAD were included (mean [SD] age, 68.4 [11.1] years; 94 979 [40.3%] female) and 71 683 (30.4%) developed 1-year MALE or death. The best preoperative prediction model was extreme gradient boosting (XGBoost), achieving the following performance metrics: AUROC, 0.94 (95% CI, 0.93-0.95); accuracy, 0.86 (95% CI, 0.85-0.87); sensitivity, 0.87; specificity, 0.85; positive predictive value, 0.85; and negative predictive value, 0.87. In comparison, logistic regression had an AUROC of 0.67 (95% CI, 0.65-0.69). The XGBoost model maintained excellent performance at the intraoperative and postoperative stages, with AUROCs of 0.94 (95% CI, 0.93-0.95) and 0.98 (95% CI, 0.97-0.99), respectively. Conclusions and Relevance In this prognostic study, ML models were developed that accurately predicted outcomes following endovascular intervention for PAD, which performed better than logistic regression. These algorithms have potential for important utility in guiding perioperative risk-mitigation strategies to prevent adverse outcomes following endovascular intervention for PAD.
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Affiliation(s)
- Ben Li
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Division of Vascular Surgery, St Michael’s Hospital, Unity Health Toronto, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, Ontario, Canada
| | - Blair E. Warren
- Division of Vascular and Interventional Radiology, Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
| | - Naomi Eisenberg
- Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada
| | - Derek Beaton
- Data Science & Advanced Analytics, Unity Health Toronto, University of Toronto, Toronto, Ontario, Canada
| | - Douglas S. Lee
- Division of Cardiology, Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
- ICES, University of Toronto, Toronto, Ontario, Canada
| | - Badr Aljabri
- Department of Surgery, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Raj Verma
- School of Medicine, Royal College of Surgeons in Ireland, University of Medicine and Health Sciences, Dublin, Ireland
| | - Duminda N. Wijeysundera
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
- ICES, University of Toronto, Toronto, Ontario, Canada
- Department of Anesthesia, St Michael’s Hospital, Unity Health Toronto, Toronto, Ontario, Canada
- Li Ka Shing Knowledge Institute, St Michael’s Hospital, Unity Health Toronto, Toronto, Ontario, Canada
| | - Ori D. Rotstein
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
- Li Ka Shing Knowledge Institute, St Michael’s Hospital, Unity Health Toronto, Toronto, Ontario, Canada
- Division of General Surgery, St Michael’s Hospital, Unity Health Toronto, Toronto, Ontario, Canada
| | - Charles de Mestral
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Division of Vascular Surgery, St Michael’s Hospital, Unity Health Toronto, Toronto, Ontario, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
- ICES, University of Toronto, Toronto, Ontario, Canada
- Li Ka Shing Knowledge Institute, St Michael’s Hospital, Unity Health Toronto, Toronto, Ontario, Canada
| | - Muhammad Mamdani
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, Ontario, Canada
- Data Science & Advanced Analytics, Unity Health Toronto, University of Toronto, Toronto, Ontario, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
- ICES, University of Toronto, Toronto, Ontario, Canada
- Li Ka Shing Knowledge Institute, St Michael’s Hospital, Unity Health Toronto, Toronto, Ontario, Canada
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada
| | - Graham Roche-Nagle
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Division of Vascular and Interventional Radiology, Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
- Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada
| | - Mohammed Al-Omran
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Division of Vascular Surgery, St Michael’s Hospital, Unity Health Toronto, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, Ontario, Canada
- Li Ka Shing Knowledge Institute, St Michael’s Hospital, Unity Health Toronto, Toronto, Ontario, Canada
- Department of Surgery, King Faisal Specialist Hospital and Research Center, Riyadh, Kingdom of Saudi Arabia
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Li B, Eisenberg N, Beaton D, Lee DS, Aljabri B, Wijeysundera DN, Rotstein OD, de Mestral C, Mamdani M, Roche-Nagle G, Al-Omran M. Using machine learning to predict outcomes following suprainguinal bypass. J Vasc Surg 2024; 79:593-608.e8. [PMID: 37804954 DOI: 10.1016/j.jvs.2023.09.037] [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: 08/19/2023] [Revised: 09/20/2023] [Accepted: 09/24/2023] [Indexed: 10/09/2023]
Abstract
OBJECTIVE Suprainguinal bypass for peripheral artery disease (PAD) carries important surgical risks; however, outcome prediction tools remain limited. We developed machine learning (ML) algorithms that predict outcomes following suprainguinal bypass. METHODS The Vascular Quality Initiative database was used to identify patients who underwent suprainguinal bypass for PAD between 2003 and 2023. We identified 100 potential predictor variables from the index hospitalization (68 preoperative [demographic/clinical], 13 intraoperative [procedural], and 19 postoperative [in-hospital course/complications]). The primary outcomes were major adverse limb events (MALE; composite of untreated loss of patency, thrombectomy/thrombolysis, surgical revision, or major amputation) or death at 1 year following suprainguinal bypass. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained six ML models using preoperative features (Extreme Gradient Boosting [XGBoost], random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). The best performing algorithm was further trained using intra- and postoperative data. Model robustness was evaluated using calibration plots and Brier scores. Performance was assessed on subgroups based on age, sex, race, ethnicity, rurality, median Area Deprivation Index, symptom status, procedure type, prior intervention for PAD, concurrent interventions, and urgency. RESULTS Overall, 16,832 patients underwent suprainguinal bypass, and 3136 (18.6%) developed 1-year MALE or death. Patients with 1-year MALE or death were older (mean age, 64.9 vs 63.5 years; P < .001) with more comorbidities, had poorer functional status (65.7% vs 80.9% independent at baseline; P < .001), and were more likely to have chronic limb-threatening ischemia (67.4% vs 47.6%; P < .001) than those without an outcome. Despite being at higher cardiovascular risk, they were less likely to receive acetylsalicylic acid or statins preoperatively and at discharge. Our best performing prediction model at the preoperative stage was XGBoost, achieving an AUROC of 0.92 (95% confidence interval [CI], 0.91-0.93). In comparison, logistic regression had an AUROC of 0.67 (95% CI, 0.65-0.69). Our XGBoost model maintained excellent performance at the intra- and postoperative stages, with AUROCs of 0.93 (95% CI, 0.92-0.94) and 0.98 (95% CI, 0.97-0.99), respectively. Calibration plots showed good agreement between predicted and observed event probabilities with Brier scores of 0.12 (preoperative), 0.11 (intraoperative), and 0.10 (postoperative). Of the top 10 predictors, nine were preoperative features including chronic limb-threatening ischemia, previous procedures, comorbidities, and functional status. Model performance remained robust on all subgroup analyses. CONCLUSIONS We developed ML models that accurately predict outcomes following suprainguinal bypass, performing better than logistic regression. Our algorithms have potential for important utility in guiding perioperative risk mitigation strategies to prevent adverse outcomes following suprainguinal bypass.
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Affiliation(s)
- Ben Li
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, ON, Canada
| | - Naomi Eisenberg
- Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Derek Beaton
- Data Science & Advanced Analytics, Unity Health Toronto, University of Toronto, Toronto, ON, Canada
| | - Douglas S Lee
- Division of Cardiology, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada
| | - Badr Aljabri
- Department of Surgery, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Duminda N Wijeysundera
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada; Department of Anesthesia, St. Michael's Hospital, Unity Health Toronto, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Canada
| | - Ori D Rotstein
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Canada; Division of General Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Charles de Mestral
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Canada
| | - Muhammad Mamdani
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, ON, Canada; Data Science & Advanced Analytics, Unity Health Toronto, University of Toronto, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Canada; Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada
| | - Graham Roche-Nagle
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Mohammed Al-Omran
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Canada; Department of Surgery, King Faisal Specialist Hospital and Research Center, Riyadh, Kingdom of Saudi Arabia.
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Li B, Verma R, Beaton D, Tamim H, Hussain MA, Hoballah JJ, Lee DS, Wijeysundera DN, de Mestral C, Mamdani M, Al-Omran M. Predicting outcomes following lower extremity open revascularization using machine learning. Sci Rep 2024; 14:2899. [PMID: 38316811 PMCID: PMC10844206 DOI: 10.1038/s41598-024-52944-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 01/25/2024] [Indexed: 02/07/2024] Open
Abstract
Lower extremity open revascularization is a treatment option for peripheral artery disease that carries significant peri-operative risks; however, outcome prediction tools remain limited. Using machine learning (ML), we developed automated algorithms that predict 30-day outcomes following lower extremity open revascularization. The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity open revascularization for chronic atherosclerotic disease between 2011 and 2021. Input features included 37 pre-operative demographic/clinical variables. The primary outcome was 30-day major adverse limb event (MALE; composite of untreated loss of patency, major reintervention, or major amputation) or death. Our data were split into training (70%) and test (30%) sets. Using tenfold cross-validation, we trained 6 ML models. Overall, 24,309 patients were included. The primary outcome of 30-day MALE or death occurred in 2349 (9.3%) patients. Our best performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve (95% CI) of 0.93 (0.92-0.94). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.08. Our ML algorithm has potential for important utility in guiding risk mitigation strategies for patients being considered for lower extremity open revascularization to improve outcomes.
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Affiliation(s)
- Ben Li
- Department of Surgery, University of Toronto, Toronto, Canada
- Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, University of Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada
- Institute of Medical Science, University of Toronto, Toronto, Canada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, Canada
| | - Raj Verma
- School of Medicine, Royal College of Surgeons in Ireland, University of Medicine and Health Sciences, Dublin, Ireland
| | - Derek Beaton
- Data Science & Advanced Analytics, Unity Health Toronto, University of Toronto, Toronto, Canada
| | - Hani Tamim
- Faculty of Medicine, Clinical Research Institute, American University of Beirut Medical Center, Beirut, Lebanon
- College of Medicine, Alfaisal University, Riyadh, Kingdom of Saudi Arabia
| | - Mohamad A Hussain
- Division of Vascular and Endovascular Surgery and the Center for Surgery and Public Health, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Jamal J Hoballah
- Division of Vascular and Endovascular Surgery, Department of Surgery, American University of Beirut Medical Center, Beirut, Lebanon
| | - Douglas S Lee
- Division of Cardiology, Peter Munk Cardiac Centre, University Health Network, Toronto, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
- ICES, University of Toronto, Toronto, Canada
| | - Duminda N Wijeysundera
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
- ICES, University of Toronto, Toronto, Canada
- Department of Anesthesia, St. Michael's Hospital, Unity Health Toronto, Toronto, Canada
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, Canada
| | - Charles de Mestral
- Department of Surgery, University of Toronto, Toronto, Canada
- Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, University of Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
- ICES, University of Toronto, Toronto, Canada
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, Canada
| | - Muhammad Mamdani
- Institute of Medical Science, University of Toronto, Toronto, Canada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, Canada
- Data Science & Advanced Analytics, Unity Health Toronto, University of Toronto, Toronto, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
- ICES, University of Toronto, Toronto, Canada
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, Canada
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Canada
| | - Mohammed Al-Omran
- Department of Surgery, University of Toronto, Toronto, Canada.
- Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, University of Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada.
- Institute of Medical Science, University of Toronto, Toronto, Canada.
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, Canada.
- College of Medicine, Alfaisal University, Riyadh, Kingdom of Saudi Arabia.
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, Canada.
- Department of Surgery, King Faisal Specialist Hospital and Research Center, Riyadh, Kingdom of Saudi Arabia.
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Loos NL, Hoogendam L, Souer JS, van Uchelen JH, Slijper HP, Wouters RM, Selles RW. Algorithm Versus Expert: Machine Learning Versus Surgeon-Predicted Symptom Improvement After Carpal Tunnel Release. Neurosurgery 2024; 95:00006123-990000000-01037. [PMID: 38299861 PMCID: PMC11155572 DOI: 10.1227/neu.0000000000002848] [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: 09/22/2023] [Accepted: 12/12/2023] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Surgeons rely on clinical experience when making predictions about treatment effects. Incorporating algorithm-based predictions of symptom improvement after carpal tunnel release (CTR) could support medical decision-making. However, these algorithm-based predictions need to outperform predictions made by surgeons to add value. We compared predictions of a validated prediction model for symptom improvement after CTR with predictions made by surgeons. METHODS This cohort study included 97 patients scheduled for CTR. Preoperatively, surgeons estimated each patient's probability of improvement 6 months after surgery, defined as reaching the minimally clinically important difference on the Boston Carpal Tunnel Syndrome Symptom Severity Score. We assessed model and surgeon performance using calibration (calibration belts), discrimination (area under the curve [AUC]), sensitivity, and specificity. In addition, we assessed the net benefit of decision-making based on the prediction model's estimates vs the surgeon's judgement. RESULTS The surgeon predictions had poor calibration and suboptimal discrimination (AUC 0.62, 95%-CI 0.49-0.74), while the prediction model showed good calibration and appropriate discrimination (AUC 0.77, 95%-CI 0.66-0.89, P = .05). The accuracy of surgeon predictions was 0.65 (95%-CI 0.37-0.78) vs 0.78 (95%-CI 0.67-0.89) for the prediction model ( P = .03). The sensitivity of surgeon predictions and the prediction model was 0.72 (95%-CI 0.15-0.96) and 0.85 (95%-CI 0.62-0.97), respectively ( P = .04). The specificity of the surgeon predictions was similar to the model's specificity ( P = .25). The net benefit analysis showed better decision-making based on the prediction model compared with the surgeons' decision-making (ie, more correctly predicted improvements and/or fewer incorrectly predicted improvements). CONCLUSION The prediction model outperformed surgeon predictions of improvement after CTR in terms of calibration, accuracy, and sensitivity. Furthermore, the net benefit analysis indicated that using the prediction model instead of relying solely on surgeon decision-making increases the number of patients who will improve after CTR, without increasing the number of unnecessary surgeries.
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Affiliation(s)
- Nina Louisa Loos
- Department of Rehabilitation Medicine, Erasmus MC, Rotterdam, The Netherlands
- Department of Plastic and Reconstructive Surgery and Hand Surgery, Erasmus MC, Rotterdam, The Netherlands
| | - Lisa Hoogendam
- Department of Rehabilitation Medicine, Erasmus MC, Rotterdam, The Netherlands
- Department of Plastic and Reconstructive Surgery and Hand Surgery, Erasmus MC, Rotterdam, The Netherlands
- Hand and Wrist Center, Xpert Clinics, Eindhoven, The Netherlands
| | | | | | | | - Robbert Maarten Wouters
- Department of Rehabilitation Medicine, Erasmus MC, Rotterdam, The Netherlands
- Department of Plastic and Reconstructive Surgery and Hand Surgery, Erasmus MC, Rotterdam, The Netherlands
| | - Ruud Willem Selles
- Department of Rehabilitation Medicine, Erasmus MC, Rotterdam, The Netherlands
- Department of Plastic and Reconstructive Surgery and Hand Surgery, Erasmus MC, Rotterdam, The Netherlands
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Henriksen MB, Hansen TF, Jensen LH, Brasen CL, Peimankar A, Ebrahimi A, Wiil UK, Hilberg O. A collection of multiregistry data on patients at high risk of lung cancer-a Danish retrospective cohort study of nearly 40,000 patients. Transl Lung Cancer Res 2023; 12:2392-2411. [PMID: 38205206 PMCID: PMC10774999 DOI: 10.21037/tlcr-23-495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 12/07/2023] [Indexed: 01/12/2024]
Abstract
Background Lung cancer (LC) is the leading cause of cancer related deaths, and several countries are implementing screening programs. Risk models have been introduced to refine the LC screening criteria, but the use of real-world data for this task demands a robust data infrastructure and quality. In this retrospective cohort study, we aim to address the different relevant risk factors in terms of data sources, descriptive statistics, completeness and quality. Methods Data on comorbidity, prescription medication, smoking history, consultations, symptoms, familial predispositions, exposures, laboratory data among others were collected for all patients examined on a risk of LC over a 10-year period in the Region of Southern Denmark. Data were delivered from the regional data warehouse as well as the Danish Lung Cancer Registry. Associations between LC and non-LC groups were examined through Chi-squared test (categorical variables) and Wilcoxon signed-rank test (continuous variables that were non-parametric). These associations were investigated on both the original datasets and the subset of patients with complete data. Results The number of examined individuals increased over the study period and more patients were diagnosed with LC in stage I-II, from 18% in 2009 to 31% in 2018. LC patients were more likely to be older, smoker, with a registered prescription of the included medication. They also exhibited differences in laboratory analysis indicating inflammation and hyponatremia. Weight loss, fatigue and pain were more prevalent in the LC group, while hemoptysis and fever were more common among the non-LC patients. Advanced-stage LC patients experienced a higher rate of symptoms compared to those in the low stages. Within the sub-cohort with complete dataset results, most observed trends persisted, although data on comorbidities were susceptibility to change. Conclusions This study provides key insights into LC risk assessment using a robust dataset of patients examined for suspected LC. A consistent positive trend in early-stage LC diagnosis was observed throughout the study period. LC patients exhibited distinct smoking behaviors, medication patterns, variations in lab results, and specific symptoms. These discoveries have the potential to enhance discrimination in machine learning-based prediction models, particularly those capable of handling complex distributions. Serving as a detailed account of real-world data collection and processing, the study establishes a foundation for future development of prediction models aimed at facilitating the early referral of LC patients.
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Affiliation(s)
| | | | | | - Claus Lohman Brasen
- Department of Biochemistry and Immunology, Vejle University Hospital, Vejle, Denmark
| | - Abdolrahman Peimankar
- SDU Health Informatics and Technology, Mærsk Mc-Kinney Møller Instituttet, University of Southern Denmark, Odense, Denmark
| | - Ali Ebrahimi
- SDU Health Informatics and Technology, Mærsk Mc-Kinney Møller Instituttet, University of Southern Denmark, Odense, Denmark
| | - Uffe Kock Wiil
- SDU Health Informatics and Technology, Mærsk Mc-Kinney Møller Instituttet, University of Southern Denmark, Odense, Denmark
| | - Ole Hilberg
- Department of Internal Medicine, Vejle University Hospital, Vejle, Denmark
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Li B, Aljabri B, Verma R, Beaton D, Eisenberg N, Lee DS, Wijeysundera DN, Forbes TL, Rotstein OD, de Mestral C, Mamdani M, Roche-Nagle G, Al-Omran M. Using machine learning to predict outcomes following open abdominal aortic aneurysm repair. J Vasc Surg 2023; 78:1426-1438.e6. [PMID: 37634621 DOI: 10.1016/j.jvs.2023.08.121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/15/2023] [Accepted: 08/19/2023] [Indexed: 08/29/2023]
Abstract
OBJECTIVE Prediction of outcomes following open abdominal aortic aneurysm (AAA) repair remains challenging with a lack of widely used tools to guide perioperative management. We developed machine learning (ML) algorithms that predict outcomes following open AAA repair. METHODS The Vascular Quality Initiative (VQI) database was used to identify patients who underwent elective open AAA repair between 2003 and 2023. Input features included 52 preoperative demographic/clinical variables. All available preoperative variables from VQI were used to maximize predictive performance. The primary outcome was in-hospital major adverse cardiovascular event (MACE; composite of myocardial infarction, stroke, or death). Secondary outcomes were individual components of the primary outcome, other in-hospital complications, and 1-year mortality and any reintervention. We split our data into training (70%) and test (30%) sets. Using 10-fold cross-validation, six ML models were trained using preoperative features (Extreme Gradient Boosting [XGBoost], random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). Model robustness was evaluated with calibration plot and Brier score. The top 10 predictive features in our final model were determined based on variable importance scores. Performance was assessed on subgroups based on age, sex, race, ethnicity, rurality, median area deprivation index, proximal clamp site, prior aortic surgery, and concomitant procedures. RESULTS Overall, 12,027 patients were included. The primary outcome of in-hospital MACE occurred in 630 patients (5.2%). Compared with patients without a primary outcome, those who developed in-hospital MACE were older with more comorbidities, demonstrated poorer functional status, had more complex aneurysms, and were more likely to require concomitant procedures. Our best performing prediction model for in-hospital MACE was XGBoost, achieving an AUROC of 0.93 (95% confidence interval, 0.92-0.94). Comparatively, logistic regression had an AUROC of 0.71 (95% confidence interval, 0.70-0.73). For secondary outcomes, XGBoost achieved AUROCs between 0.84 and 0.94. The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.05. These findings highlight the excellent predictive performance of the XGBoost model. The top three predictive features in our algorithm for in-hospital MACE following open AAA repair were: (1) coronary artery disease; (2) American Society of Anesthesiologists classification; and (3) proximal clamp site. Model performance remained robust on all subgroup analyses. CONCLUSIONS Open AAA repair outcomes can be accurately predicted using preoperative data with our ML models, which perform better than logistic regression. Our automated algorithms can help guide risk-mitigation strategies for patients being considered for open AAA repair to improve outcomes.
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Affiliation(s)
- Ben Li
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, ON, Canada
| | - Badr Aljabri
- Department of Surgery, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Raj Verma
- School of Medicine, Royal College of Surgeons in Ireland, University of Medicine and Health Sciences, Dublin, Ireland
| | - Derek Beaton
- Data Science and Advanced Analytics, Unity Health Toronto, University of Toronto, Toronto, ON, Canada
| | - Naomi Eisenberg
- Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Douglas S Lee
- Division of Cardiology, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada
| | - Duminda N Wijeysundera
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada; Department of Anesthesia, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Thomas L Forbes
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Ori D Rotstein
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Division of General Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Charles de Mestral
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Muhammad Mamdani
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, ON, Canada; Data Science and Advanced Analytics, Unity Health Toronto, University of Toronto, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada
| | - Graham Roche-Nagle
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Mohammed Al-Omran
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Department of Surgery, King Faisal Specialist Hospital and Research Center, Riyadh, Kingdom of Saudi Arabia.
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Ryan CT, Zeng Z, Chatterjee S, Wall MJ, Moon MR, Coselli JS, Rosengart TK, Li M, Ghanta RK. Machine learning for dynamic and early prediction of acute kidney injury after cardiac surgery. J Thorac Cardiovasc Surg 2023; 166:e551-e564. [PMID: 36347651 PMCID: PMC10071138 DOI: 10.1016/j.jtcvs.2022.09.045] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 08/29/2022] [Accepted: 09/10/2022] [Indexed: 11/05/2022]
Abstract
OBJECTIVE Acute kidney injury after cardiac surgery increases morbidity and mortality. Diagnosis relies on oliguria or increased serum creatinine, which develop 48 to 72 hours after injury. We hypothesized machine learning incorporating preoperative, operative, and intensive care unit data could dynamically predict acute kidney injury before conventional identification. METHODS Cardiac surgery patients at a tertiary hospital (2008-2019) were identified using electronic medical records in the Medical Information Mart for Intensive Care IV database. Preoperative and intraoperative parameters included demographics, Charlson Comorbidity subcategories, and operative details. Intensive care unit data included hemodynamics, medications, fluid intake/output, and laboratory results. Kidney Disease: Improving Global Outcomes creatinine criteria were used for acute kidney injury diagnosis. An ensemble machine learning model was trained for hourly predictions of future acute kidney injury within 48 hours. Performance was evaluated by area under the receiver operating characteristic curve and balanced accuracy. RESULTS Within the cohort (n = 4267), there were approximately 7 million data points. Median baseline creatinine was 1.0 g/dL (interquartile range, 0.8-1.2), with 17% (735/4267) of patients having chronic kidney disease. Postoperative stage 1 acute kidney injury occurred in 50% (2129/4267), stage 2 occurred in 8% (324/4267), and stage 3 occurred in 4% (183/4267). For hourly prediction of any acute kidney injury over the next 48 hours, area under the receiver operating characteristic curve was 0.82, and balanced accuracy was 75%. For hourly prediction of stage 2 or greater acute kidney injury over the next 48 hours, area under the receiver operating characteristic curve was 0.95 and balanced accuracy was 86%. The model predicted acute kidney injury before clinical detection in 89% of cases. CONCLUSIONS Ensemble machine learning models using electronic medical records data can dynamically predict acute kidney injury risk after cardiac surgery. Continuous postoperative risk assessment could facilitate interventions to limit or prevent renal injury.
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Affiliation(s)
- Christopher T Ryan
- Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex
| | - Zijian Zeng
- Department of Statistics, Rice University, Houston, Tex
| | - Subhasis Chatterjee
- Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex; Department of Cardiovascular Surgery, Texas Heart Institute, Houston, Tex
| | - Matthew J Wall
- Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex
| | - Marc R Moon
- Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex; Department of Cardiovascular Surgery, Texas Heart Institute, Houston, Tex
| | - Joseph S Coselli
- Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex; Department of Cardiovascular Surgery, Texas Heart Institute, Houston, Tex
| | - Todd K Rosengart
- Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex; Department of Cardiovascular Surgery, Texas Heart Institute, Houston, Tex
| | - Meng Li
- Department of Statistics, Rice University, Houston, Tex
| | - Ravi K Ghanta
- Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Tex.
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Li B, Verma R, Beaton D, Tamim H, Hussain MA, Hoballah JJ, Lee DS, Wijeysundera DN, de Mestral C, Mamdani M, Al-Omran M. Predicting outcomes following open revascularization for aortoiliac occlusive disease using machine learning. J Vasc Surg 2023; 78:1449-1460.e7. [PMID: 37454952 DOI: 10.1016/j.jvs.2023.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 06/12/2023] [Accepted: 07/08/2023] [Indexed: 07/18/2023]
Abstract
OBJECTIVE Open surgical treatment options for aortoiliac occlusive disease carry significant perioperative risks; however, outcome prediction tools remain limited. Using machine learning (ML), we developed automated algorithms that predict 30-day outcomes following open aortoiliac revascularization. METHODS The National Surgical Quality Improvement Program (NSQIP) targeted vascular database was used to identify patients who underwent open aortoiliac revascularization for atherosclerotic disease between 2011 and 2021. Input features included 38 preoperative demographic/clinical variables. The primary outcome was 30-day major adverse limb event (MALE; composite of untreated loss of patency, major reintervention, or major amputation) or death. The 30-day secondary outcomes were individual components of the primary outcome, major adverse cardiovascular event (MACE; composite of myocardial infarction, stroke, or death), individual components of MACE, wound complication, bleeding, other morbidity, non-home discharge, and unplanned readmission. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained six ML models using preoperative features. The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). Model robustness was evaluated with calibration plot and Brier score. Variable importance scores were calculated to determine the top 10 predictive features. Performance was assessed on subgroups based on age, sex, race, ethnicity, symptom status, procedure type, and urgency. RESULTS Overall, 9649 patients were included. The primary outcome of 30-day MALE or death occurred in 1021 patients (10.6%). Our best performing prediction model for 30-day MALE or death was XGBoost, achieving an AUROC of 0.95 (95% confidence interval [CI], 0.94-0.96). In comparison, logistic regression had an AUROC of 0.79 (95% CI, 0.77-0.81). For 30-day secondary outcomes, XGBoost achieved AUROCs between 0.87 and 0.97 (untreated loss of patency [0.95], major reintervention [0.88], major amputation [0.96], death [0.97], MACE [0.95], myocardial infarction [0.88], stroke [0.93], wound complication [0.94], bleeding [0.87], other morbidity [0.96], non-home discharge [0.90], and unplanned readmission [0.91]). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.05. The strongest predictive feature in our algorithm was chronic limb-threatening ischemia. Model performance remained robust on all subgroup analyses of specific demographic/clinical populations. CONCLUSIONS Our ML models accurately predict 30-day outcomes following open aortoiliac revascularization using preoperative data, performing better than logistic regression. They have potential for important utility in guiding risk-mitigation strategies for patients being considered for open aortoiliac revascularization to improve outcomes.
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Affiliation(s)
- Ben Li
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, ON, Canada
| | - Raj Verma
- School of Medicine, Royal College of Surgeons in Ireland, University of Medicine and Health Sciences, Dublin, Ireland
| | - Derek Beaton
- Department of Data Science and Advanced Analytics, Unity Health Toronto, University of Toronto, Toronto, ON, Canada
| | - Hani Tamim
- Faculty of Medicine, Clinical Research Institute, American University of Beirut Medical Center, Beirut, Lebanon; College of Medicine, Alfaisal University, Riyadh, Kingdom of Saudi Arabia
| | - Mohamad A Hussain
- Division of Vascular and Endovascular Surgery and the Center for Surgery and Public Health, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Jamal J Hoballah
- Division of Vascular and Endovascular Surgery, Department of Surgery, American University of Beirut Medical Center, Beirut, Lebanon
| | - Douglas S Lee
- Division of Cardiology, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; Institute for Clinical Evaluative Sciences, University of Toronto, Toronto, ON, Canada
| | - Duminda N Wijeysundera
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; Institute for Clinical Evaluative Sciences, University of Toronto, Toronto, ON, Canada; Department of Anesthesia, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Charles de Mestral
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; Institute for Clinical Evaluative Sciences, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Muhammad Mamdani
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, ON, Canada; Department of Data Science and Advanced Analytics, Unity Health Toronto, University of Toronto, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; Institute for Clinical Evaluative Sciences, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada
| | - Mohammed Al-Omran
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, ON, Canada; College of Medicine, Alfaisal University, Riyadh, Kingdom of Saudi Arabia; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Department of Surgery, King Faisal Specialist Hospital and Research Center, Riyadh, Kingdom of Saudi Arabia.
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Wiens MO, Trawin J, Pillay Y, Nguyen V, Komugisha C, Kenya-Mugisha N, Namala A, Bebell LM, Ansermino JM, Kissoon N, Payne BA, Vidler M, Christoffersen-Deb A, Lavoie PM, Ngonzi J. Prognostic algorithms for post-discharge readmission and mortality among mother-infant dyads: an observational study protocol. FRONTIERS IN EPIDEMIOLOGY 2023; 3:1233323. [PMID: 38455948 PMCID: PMC10911031 DOI: 10.3389/fepid.2023.1233323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 11/13/2023] [Indexed: 03/09/2024]
Abstract
Introduction In low-income country settings, the first six weeks after birth remain a critical period of vulnerability for both mother and newborn. Despite recommendations for routine follow-up after delivery and facility discharge, few mothers and newborns receive guideline recommended care during this period. Prediction modelling of post-delivery outcomes has the potential to improve outcomes for both mother and newborn by identifying high-risk dyads, improving risk communication, and informing a patient-centered approach to postnatal care interventions. This study aims to derive post-discharge risk prediction algorithms that identify mother-newborn dyads who are at risk of re-admission or death in the first six weeks after delivery at a health facility. Methods This prospective observational study will enroll 7,000 mother-newborn dyads from two regional referral hospitals in southwestern and eastern Uganda. Women and adolescent girls aged 12 and above delivering singletons and twins at the study hospitals will be eligible to participate. Candidate predictor variables will be collected prospectively by research nurses. Outcomes will be captured six weeks following delivery through a follow-up phone call, or an in-person visit if not reachable by phone. Two separate sets of prediction models will be built, one set of models for newborn outcomes and one set for maternal outcomes. Derivation of models will be based on optimization of the area under the receiver operator curve (AUROC) and specificity using an elastic net regression modelling approach. Internal validation will be conducted using 10-fold cross-validation. Our focus will be on the development of parsimonious models (5-10 predictor variables) with high sensitivity (>80%). AUROC, sensitivity, and specificity will be reported for each model, along with positive and negative predictive values. Discussion The current recommendations for routine postnatal care are largely absent of benefit to most mothers and newborns due to poor adherence. Data-driven improvements to postnatal care can facilitate a more patient-centered approach to such care. Increasing digitization of facility care across low-income settings can further facilitate the integration of prediction algorithms as decision support tools for routine care, leading to improved quality and efficiency. Such strategies are urgently required to improve newborn and maternal postnatal outcomes. Clinical trial registration https://clinicaltrials.gov/, identifier (NCT05730387).
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Affiliation(s)
- Matthew O. Wiens
- Institute for Global Health, BC Children’s and Women’s Hospitals, Vancouver, BC, Canada
- Department of Anesthesiology, Pharmacology & Therapeutics, University of British Columbia, Vancouver, BC, Canada
- WALIMU, Kampala, Uganda
| | - Jessica Trawin
- Institute for Global Health, BC Children’s and Women’s Hospitals, Vancouver, BC, Canada
| | - Yashodani Pillay
- Institute for Global Health, BC Children’s and Women’s Hospitals, Vancouver, BC, Canada
- Department of Anesthesiology, Pharmacology & Therapeutics, University of British Columbia, Vancouver, BC, Canada
| | - Vuong Nguyen
- Institute for Global Health, BC Children’s and Women’s Hospitals, Vancouver, BC, Canada
| | | | | | - Angella Namala
- Department of Obstetrics & Gynaecology, Jinja Regional Referral Hospital, Jinja, Uganda
| | - Lisa M. Bebell
- Department of Medicine, Division of Infectious Diseases, Medical Practice Evaluation Center, Center for Global Health, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - J. Mark Ansermino
- Institute for Global Health, BC Children’s and Women’s Hospitals, Vancouver, BC, Canada
- Department of Anesthesiology, Pharmacology & Therapeutics, University of British Columbia, Vancouver, BC, Canada
| | - Niranjan Kissoon
- Institute for Global Health, BC Children’s and Women’s Hospitals, Vancouver, BC, Canada
- Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada
| | - Beth A. Payne
- Digital Health Research, BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- School of Population and Public Health, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Marianne Vidler
- Department of Obstetrics & Gynaecology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Astrid Christoffersen-Deb
- Department of Obstetrics & Gynaecology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Pascal M. Lavoie
- Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada
- Digital Health Research, BC Children’s Hospital Research Institute, Vancouver, BC, Canada
| | - Joseph Ngonzi
- Department of Obstetrics and Gynaecology, Mbarara University of Science & Technology, Mbarara, Uganda
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Hayward J, Evans W, Miller E, Rafi I. Embedding genomics across the NHS: a primary care perspective. Future Healthc J 2023; 10:263-269. [PMID: 38162198 PMCID: PMC10753202 DOI: 10.7861/fhj.2023-0116] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Primary care remains the point of access to the NHS as well as having key roles in care coordination and prescribing. Therefore, embedding of genomic medicine in the NHS relies on successful implementation into the primary care landscape. Primary care is currently facing considerable challenges, including increasing numbers of patients and consultations per GP, multiple health conditions and polypharmacy, all contributing to increasing workload within a resource-constrained system. Although genomic medicine has enormous potential to benefit patients, its successful implementation demands alignment with existing skills and working practices, development of underpinning informatics infrastructure, integration into care pathways with consideration of commissioning and leadership. Here, we set out current initiatives and future strategies to support primary care colleagues in the delivery of genomic medicine, covering issues of workforce development and education, primary care leadership, and data and digital considerations.
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Affiliation(s)
- Judith Hayward
- National Genomics Education, RCGP joint clinical champion in genomics medicine, Royal College of General Practitioners, and honorary research fellow, St George's University London, London UK
| | - Will Evans
- Yorkshire Regional Genetics Service, and clinical assistant professor, Centre for Academic Primary Care, School of Medicine, University of Nottingham, UK
| | | | - Imran Rafi
- St George's University of London, London, UK, and joint clinical champion in genomics medicine, Royal College of General Practitioners
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Li B, Verma R, Beaton D, Tamim H, Hussain MA, Hoballah JJ, Lee DS, Wijeysundera DN, de Mestral C, Mamdani M, Al‐Omran M. Predicting Major Adverse Cardiovascular Events Following Carotid Endarterectomy Using Machine Learning. J Am Heart Assoc 2023; 12:e030508. [PMID: 37804197 PMCID: PMC10757546 DOI: 10.1161/jaha.123.030508] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 08/28/2023] [Indexed: 10/09/2023]
Abstract
Background Carotid endarterectomy (CEA) is a major vascular operation for stroke prevention that carries significant perioperative risks; however, outcome prediction tools remain limited. The authors developed machine learning algorithms to predict outcomes following CEA. Methods and Results The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent CEA between 2011 and 2021. Input features included 36 preoperative demographic/clinical variables. The primary outcome was 30-day major adverse cardiovascular events (composite of stroke, myocardial infarction, or death). The data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using preoperative features. The primary metric for evaluating model performance was area under the receiver operating characteristic curve. Model robustness was evaluated with calibration plot and Brier score. Overall, 38 853 patients underwent CEA during the study period. Thirty-day major adverse cardiovascular events occurred in 1683 (4.3%) patients. The best performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve of 0.91 (95% CI, 0.90-0.92). In comparison, logistic regression had an area under the receiver operating characteristic curve of 0.62 (95% CI, 0.60-0.64), and existing tools in the literature demonstrate area under the receiver operating characteristic curve values ranging from 0.58 to 0.74. The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.02. The strongest predictive feature in our algorithm was carotid symptom status. Conclusions The machine learning models accurately predicted 30-day outcomes following CEA using preoperative data and performed better than existing tools. They have potential for important utility in guiding risk-mitigation strategies to improve outcomes for patients being considered for CEA.
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Affiliation(s)
- Ben Li
- Department of SurgeryUniversity of TorontoCanada
- Division of Vascular Surgery, St. Michael’s Hospital, Unity Health TorontoUniversity of TorontoCanada
- Institute of Medical ScienceUniversity of TorontoCanada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T‐CAIREM)University of TorontoCanada
| | - Raj Verma
- School of Medicine, Royal College of Surgeons in IrelandUniversity of Medicine and Health SciencesDublinIreland
| | - Derek Beaton
- Data Science & Advanced Analytics, Unity Health TorontoUniversity of TorontoCanada
| | - Hani Tamim
- Faculty of Medicine, Clinical Research InstituteAmerican University of Beirut Medical CenterBeirutLebanon
- College of MedicineAlfaisal UniversityRiyadhKingdom of Saudi Arabia
| | - Mohamad A. Hussain
- Division of Vascular and Endovascular Surgery and the Center for Surgery and Public Health, Brigham and Women’s HospitalHarvard Medical SchoolBostonMAUSA
| | - Jamal J. Hoballah
- Division of Vascular and Endovascular Surgery, Department of SurgeryAmerican University of Beirut Medical CenterBeirutLebanon
| | - Douglas S. Lee
- Division of Cardiology, Peter Munk Cardiac CentreUniversity Health NetworkTorontoCanada
- Institute of Health Policy, Management and EvaluationUniversity of TorontoCanada
- ICESUniversity of TorontoCanada
| | - Duminda N. Wijeysundera
- Institute of Health Policy, Management and EvaluationUniversity of TorontoCanada
- ICESUniversity of TorontoCanada
- Department of AnesthesiaSt. Michael’s Hospital, Unity Health TorontoTorontoCanada
- Li Ka Shing Knowledge InstituteSt. Michael’s Hospital, Unity Health TorontoTorontoCanada
| | - Charles de Mestral
- Department of SurgeryUniversity of TorontoCanada
- Division of Vascular Surgery, St. Michael’s Hospital, Unity Health TorontoUniversity of TorontoCanada
- Institute of Health Policy, Management and EvaluationUniversity of TorontoCanada
- ICESUniversity of TorontoCanada
- Li Ka Shing Knowledge InstituteSt. Michael’s Hospital, Unity Health TorontoTorontoCanada
| | - Muhammad Mamdani
- Institute of Medical ScienceUniversity of TorontoCanada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T‐CAIREM)University of TorontoCanada
- Data Science & Advanced Analytics, Unity Health TorontoUniversity of TorontoCanada
- Institute of Health Policy, Management and EvaluationUniversity of TorontoCanada
- ICESUniversity of TorontoCanada
- Li Ka Shing Knowledge InstituteSt. Michael’s Hospital, Unity Health TorontoTorontoCanada
- Leslie Dan Faculty of PharmacyUniversity of TorontoCanada
| | - Mohammed Al‐Omran
- Department of SurgeryUniversity of TorontoCanada
- Division of Vascular Surgery, St. Michael’s Hospital, Unity Health TorontoUniversity of TorontoCanada
- Institute of Medical ScienceUniversity of TorontoCanada
- Temerty Centre for Artificial Intelligence Research and Education in Medicine (T‐CAIREM)University of TorontoCanada
- College of MedicineAlfaisal UniversityRiyadhKingdom of Saudi Arabia
- Li Ka Shing Knowledge InstituteSt. Michael’s Hospital, Unity Health TorontoTorontoCanada
- Department of SurgeryKing Faisal Specialist Hospital and Research CenterRiyadhKingdom of Saudi Arabia
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Li B, Beaton D, Eisenberg N, Lee DS, Wijeysundera DN, Lindsay TF, de Mestral C, Mamdani M, Roche-Nagle G, Al-Omran M. Using machine learning to predict outcomes following carotid endarterectomy. J Vasc Surg 2023; 78:973-987.e6. [PMID: 37211142 DOI: 10.1016/j.jvs.2023.05.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/08/2023] [Accepted: 05/13/2023] [Indexed: 05/23/2023]
Abstract
OBJECTIVE Prediction of outcomes following carotid endarterectomy (CEA) remains challenging, with a lack of standardized tools to guide perioperative management. We used machine learning (ML) to develop automated algorithms that predict outcomes following CEA. METHODS The Vascular Quality Initiative (VQI) database was used to identify patients who underwent CEA between 2003 and 2022. We identified 71 potential predictor variables (features) from the index hospitalization (43 preoperative [demographic/clinical], 21 intraoperative [procedural], and 7 postoperative [in-hospital complications]). The primary outcome was stroke or death at 1 year following CEA. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained six ML models using preoperative features (Extreme Gradient Boosting [XGBoost], random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). After selecting the best performing algorithm, additional models were built using intra- and postoperative data. Model robustness was evaluated using calibration plots and Brier scores. Performance was assessed on subgroups based on age, sex, race, ethnicity, insurance status, symptom status, and urgency of surgery. RESULTS Overall, 166,369 patients underwent CEA during the study period. In total, 7749 patients (4.7%) had the primary outcome of stroke or death at 1 year. Patients with an outcome were older with more comorbidities, had poorer functional status, and demonstrated higher risk anatomic features. They were also more likely to undergo intraoperative surgical re-exploration and have in-hospital complications. Our best performing prediction model at the preoperative stage was XGBoost, achieving an AUROC of 0.90 (95% confidence interval [CI], 0.89-0.91). In comparison, logistic regression had an AUROC of 0.65 (95% CI, 0.63-0.67), and existing tools in the literature demonstrate AUROCs ranging from 0.58 to 0.74. Our XGBoost models maintained excellent performance at the intra- and postoperative stages, with AUROCs of 0.90 (95% CI, 0.89-0.91) and 0.94 (95% CI, 0.93-0.95), respectively. Calibration plots showed good agreement between predicted and observed event probabilities with Brier scores of 0.15 (preoperative), 0.14 (intraoperative), and 0.11 (postoperative). Of the top 10 predictors, eight were preoperative features, including comorbidities, functional status, and previous procedures. Model performance remained robust on all subgroup analyses. CONCLUSIONS We developed ML models that accurately predict outcomes following CEA. Our algorithms perform better than logistic regression and existing tools, and therefore, have potential for important utility in guiding perioperative risk mitigation strategies to prevent adverse outcomes.
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Affiliation(s)
- Ben Li
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, ON, Canada
| | - Derek Beaton
- Data Science and Advanced Analytics Department, Unity Health Toronto, University of Toronto, Toronto, ON, Canada
| | - Naomi Eisenberg
- Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Douglas S Lee
- Division of Cardiology, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada
| | - Duminda N Wijeysundera
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada; Department of Anesthesia, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Thomas F Lindsay
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Charles de Mestral
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Muhammad Mamdani
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, ON, Canada; Data Science and Advanced Analytics Department, Unity Health Toronto, University of Toronto, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada; ICES, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada
| | - Graham Roche-Nagle
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Mohammed Al-Omran
- Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Department of Surgery, King Faisal Specialist Hospital and Research Center, Riyadh, Kingdom of Saudi Arabia.
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21
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Natae SF, Merzah MA, Sándor J, Ádány R, Bereczky Z, Fiatal S. A combination of strongly associated prothrombotic single nucleotide polymorphisms could efficiently predict venous thrombosis risk. Front Cardiovasc Med 2023; 10:1224462. [PMID: 37745125 PMCID: PMC10511882 DOI: 10.3389/fcvm.2023.1224462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 08/03/2023] [Indexed: 09/26/2023] Open
Abstract
Background Venous thrombosis (VT) is multifactorial trait that contributes to the global burden of cardiovascular diseases. Although abundant single nucleotide polymorphisms (SNPs) provoke the susceptibility of an individual to VT, research has found that the five most strongly associated SNPs, namely, rs6025 (F5 Leiden), rs2066865 (FGG), rs2036914 (F11), rs8176719 (ABO), and rs1799963 (F2), play the greatest role. Association and risk prediction models are rarely established by using merely the five strongly associated SNPs. This study aims to explore the combined VT risk predictability of the five SNPs and well-known non-genetic VT risk factors such as aging and obesity in the Hungarian population. Methods SNPs were genotyped in the VT group (n = 298) and control group (n = 400). Associations were established using standard genetic models. Genetic risk scores (GRS) [unweighted GRS (unGRS), weighted GRS (wGRS)] were also computed. Correspondingly, the areas under the receiver operating characteristic curves (AUCs) for genetic and non-genetic risk factors were estimated to explore their VT risk predictability in the study population. Results rs6025 was the most prevalent VT risk allele in the Hungarian population. Its risk allele frequency was 3.52-fold higher in the VT group than that in the control group [adjusted odds ratio (AOR) = 3.52, 95% CI: 2.50-4.95]. Using all genetic models, we found that rs6025 and rs2036914 remained significantly associated with VT risk after multiple correction testing was performed. However, rs8176719 remained statistically significant only in the multiplicative (AOR = 1.33, 95% CI: 1.07-1.64) and genotypic models (AOR = 1.77, 95% CI: 1.14-2.73). In addition, rs2066865 lost its significant association with VT risk after multiple correction testing was performed. Conversely, the prothrombin mutation (rs1799963) did not show any significant association. The AUC of Leiden mutation (rs6025) showed better discriminative accuracy than that of other SNPs (AUC = 0.62, 95% CI: 0.57-0.66). The wGRS was a better predictor for VT than the unGRS (AUC = 0.67 vs. 0.65). Furthermore, combining genetic and non-genetic VT risk factors significantly increased the AUC to 0.89 with statistically significant differences (Z = 3.924, p < 0.0001). Conclusions Our study revealed that the five strongly associated SNPs combined with non-genetic factors could efficiently predict individual VT risk susceptibility. The combined model was the best predictor of VT risk, so stratifying high-risk individuals based on their genetic profiling and well-known non-modifiable VT risk factors was important for the effective and efficient utilization of VT risk preventive and control measures. Furthermore, we urged further study that compares the VT risk predictability in the Hungarian population using the formerly discovered VT SNPs with the novel strongly associated VT SNPs.
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Affiliation(s)
- Shewaye Fituma Natae
- Department of Public Health and Epidemiology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
- Doctoral School of Health Sciences, University of Debrecen, Debrecen, Hungary
| | - Mohammed Abdulridha Merzah
- Department of Public Health and Epidemiology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
- Doctoral School of Health Sciences, University of Debrecen, Debrecen, Hungary
| | - János Sándor
- Department of Public Health and Epidemiology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
- ELKH-DE Public Health Research Group, Department of Public Health and Epidemiology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Róza Ádány
- Department of Public Health and Epidemiology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Zsuzsanna Bereczky
- Division of Clinical Laboratory Science, Department of Laboratory Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Szilvia Fiatal
- Department of Public Health and Epidemiology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
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22
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Bekker HL, Winterbottom AE, Gavaruzzi T, Finderup J, Mooney A. Decision aids to assist patients and professionals in choosing the right treatment for kidney failure. Clin Kidney J 2023; 16:i20-i38. [PMID: 37711634 PMCID: PMC10497379 DOI: 10.1093/ckj/sfad172] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Indexed: 09/16/2023] Open
Abstract
Background Kidney services vary in the way they involve people with kidney failure (PwKF) in treatment decisions as management needs change. We discuss how decision-science applications support proactively PwKF to make informed decisions between treatment options with kidney professionals. Methods A conceptual review of findings about decision making and use of decision aids in kidney services, synthesized with reference to: the Making Informed Decisions-Individually and Together (MIND-IT) multiple stakeholder decision makers framework; and the Medical Research Council-Complex Intervention Development and Evaluation research framework. Results This schema represents the different types of decision aids that support PwKF and professional reasoning as they manage kidney disease individually and together; adjustments at micro, meso and macro levels supports integration in practice. Conclusion Innovating services to meet clinical guidelines on enhancing shared decision making processes means enabling all stakeholders to use decision aids to meet their goals within kidney pathways at individual, service and organizational levels.
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Affiliation(s)
- Hilary L Bekker
- Leeds Unit of Complex Intervention Development (LUCID), Leeds Institute of Health Sciences, School of Medicine, University of Leeds, Leeds, UK
- Department of Public Health, Aarhus University, Denmark
- ResCenPI – Research Centre for Patient Involvement, Aarhus University, Aarhus and the Central Denmark Region, Denmark
| | - Anna E Winterbottom
- Leeds Unit of Complex Intervention Development (LUCID), Leeds Institute of Health Sciences, School of Medicine, University of Leeds, Leeds, UK
- Renal Unit, St James's University Hospital, Leeds Teaching Hospital Trust, Leeds, UK
| | - Teresa Gavaruzzi
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Bologna, Italy
| | - Jeanette Finderup
- ResCenPI – Research Centre for Patient Involvement, Aarhus University, Aarhus and the Central Denmark Region, Denmark
- Department of Renal Medicine, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Andrew Mooney
- Leeds Unit of Complex Intervention Development (LUCID), Leeds Institute of Health Sciences, School of Medicine, University of Leeds, Leeds, UK
- Renal Unit, St James's University Hospital, Leeds Teaching Hospital Trust, Leeds, UK
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23
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Lin FP. Delivering Data-Driven Precision and Equitable Artificial Intelligence Decision Tools in Oncology: Exploring the Informarker Concept. JCO Clin Cancer Inform 2023; 7:e2300142. [PMID: 37963314 DOI: 10.1200/cci.23.00142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 08/02/2023] [Indexed: 11/16/2023] Open
Abstract
Is it time to rethink the role of data and AI in cancer diagnosis and treatment through #informarkers?
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Affiliation(s)
- Frank P Lin
- Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, Australia
- School of Clinical Medicine, University of New South Wales, Sydney, Australia
- NHMRC Clinical Trials Centre, Sydney University, Camperdown, Australia
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24
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Friedemann Smith C, Duncombe S, Fleming S, Hirst Y, Black GB, Bankhead C, Nicholson BD. Electronic safety-netting tool features considered important by UK general practice staff: an interview and Delphi consensus study. BJGP Open 2023; 7:BJGPO.2022.0163. [PMID: 37277171 PMCID: PMC10646209 DOI: 10.3399/bjgpo.2022.0163] [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: 11/10/2022] [Revised: 03/16/2023] [Accepted: 04/03/2023] [Indexed: 06/07/2023] Open
Abstract
BACKGROUND The potential of the electronic health record to support safety netting has been recognised and a number of electronic safety-netting (E-SN) tools developed. AIM To establish the most important features of E-SN tools. DESIGN & SETTING User-experience interviews followed by a Delphi study in a primary care setting in the UK. METHOD The user-experience interviews were carried out remotely with primary care staff who had trialled the EMIS E-SN toolkit for suspected cancer. An electronic modified Delphi approach was used, with primary care staff involved in safety netting in any capacity, to measure consensus on tool features. RESULTS Thirteen user-experience interviews were carried out and features of E-SN tools seen as important formed the majority of the features included in the Delphi study. Three rounds of Delphi survey were administered. Sixteen responders (64%) completed all three rounds, and 28 out of 44 (64%) features reached consensus. Primary care staff preferred tools that were general in scope. CONCLUSION Primary care staff indicated that tools that were not specific to cancer or any other disease, and had features that promoted their flexible, efficient, and integrated use, were important. However, when the important features were discussed with the patient and public involvement (PPI) group, they expressed disappointment that features they believed would make E-SN tools robust and provide a safety net that is difficult to fall through did not reach consensus. The successful adoption of E-SN tools will rely on an evidence base of their effectiveness. Efforts should be made to assess the impact of these tools on patient outcomes.
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Affiliation(s)
| | | | - Susannah Fleming
- Nuffield Department of Primary Care Sciences, University of Oxford, Oxford, UK
| | - Yasemin Hirst
- Institute of Epidemiology & Health, University College London, Lancaster Medical School, Lancaster University, Lancaster, UK
| | - Georgia Bell Black
- Wolfson Institute of population Health, Queen Mary's University, London, UK
| | - Clare Bankhead
- Nuffield Department of Primary Care Sciences, University of Oxford, Oxford, UK
| | - Brian D Nicholson
- Nuffield Department of Primary Care Sciences, University of Oxford, Oxford, UK
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25
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Yamga E, Mantena S, Rosen D, Bucholz EM, Yeh RW, Celi LA, Ustun B, Butala NM. Optimized Risk Score to Predict Mortality in Patients With Cardiogenic Shock in the Cardiac Intensive Care Unit. J Am Heart Assoc 2023; 12:e029232. [PMID: 37345819 PMCID: PMC10356069 DOI: 10.1161/jaha.122.029232] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 05/01/2023] [Indexed: 06/23/2023]
Abstract
Background Mortality prediction in critically ill patients with cardiogenic shock can guide triage and selection of potentially high-risk treatment options. Methods and Results We developed and externally validated a checklist risk score to predict in-hospital mortality among adults admitted to the cardiac intensive care unit with Society for Cardiovascular Angiography & Interventions Shock Stage C or greater cardiogenic shock using 2 real-world data sets and Risk-Calibrated Super-sparse Linear Integer Modeling (RiskSLIM). We compared this model to those developed using conventional penalized logistic regression and published cardiogenic shock and intensive care unit mortality prediction models. There were 8815 patients in our training cohort (in-hospital mortality 13.4%) and 2237 patients in our validation cohort (in-hospital mortality 22.8%), and there were 39 candidate predictor variables. The final risk score (termed BOS,MA2) included maximum blood urea nitrogen ≥25 mg/dL, minimum oxygen saturation <88%, minimum systolic blood pressure <80 mm Hg, use of mechanical ventilation, age ≥60 years, and maximum anion gap ≥14 mmol/L, based on values recorded during the first 24 hours of intensive care unit stay. Predicted in-hospital mortality ranged from 0.5% for a score of 0 to 70.2% for a score of 6. The area under the receiver operating curve was 0.83 (0.82-0.84) in training and 0.76 (0.73-0.78) in validation, and the expected calibration error was 0.9% in training and 2.6% in validation. Conclusions Developed using a novel machine learning method and the largest cardiogenic shock cohorts among published models, BOS,MA2 is a simple, clinically interpretable risk score that has improved performance compared with existing cardiogenic-shock risk scores and better calibration than general intensive care unit risk scores.
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Affiliation(s)
- Eric Yamga
- Department of Medicine Centre Hospitalier de l'Université de Montréal (CHUM) Montreal QC Canada
| | | | - Darin Rosen
- Johns Hopkins School of Medicine Baltimore MD USA
| | - Emily M Bucholz
- University of Colorado School of Medicine Aurora CO USA
- Heart Institute, Children's Hospital of Colorado Aurora CO USA
| | - Robert W Yeh
- Harvard Medical School Boston MA USA
- Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Division of Cardiology Beth Israel Deaconess Medical Center Boston MA USA
| | - Leo A Celi
- Harvard Medical School Boston MA USA
- Laboratory for Computational Physiology, MIT Institute for Medical Engineering and Science, Massachusetts Institute of Technology Cambridge MA USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health Boston MA USA
| | - Berk Ustun
- Halıcıoğlu Data Science Institute University of California San Diego CA USA
| | - Neel M Butala
- University of Colorado School of Medicine Aurora CO USA
- Rocky Mountain Regional VA Medical Center Aurora CO USA
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26
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Ming DY, Zhao C, Tang X, Chung RJ, Rogers UA, Stirling A, Economou-Zavlanos NJ, Goldstein BA. Predictive Modeling to Identify Children With Complex Health Needs At Risk for Hospitalization. Hosp Pediatr 2023; 13:357-369. [PMID: 37092278 PMCID: PMC10158078 DOI: 10.1542/hpeds.2022-006861] [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: 04/25/2023]
Abstract
BACKGROUND Identifying children at high risk with complex health needs (CCHN) who have intersecting medical and social needs is challenging. This study's objectives were to (1) develop and evaluate an electronic health record (EHR)-based clinical predictive model ("model") for identifying high-risk CCHN and (2) compare the model's performance as a clinical decision support (CDS) to other CDS tools available for identifying high-risk CCHN. METHODS This retrospective cohort study included children aged 0 to 20 years with established care within a single health system. The model development/validation cohort included 33 months (January 1, 2016-September 30, 2018) and the testing cohort included 18 months (October 1, 2018-March 31, 2020) of EHR data. Machine learning methods generated a model that predicted probability (0%-100%) for hospitalization within 6 months. Model performance measures included sensitivity, positive predictive value, area under receiver-operator curve, and area under precision-recall curve. Three CDS rules for identifying high-risk CCHN were compared: (1) hospitalization probability ≥10% (model-predicted); (2) complex chronic disease classification (using Pediatric Medical Complexity Algorithm [PMCA]); and (3) previous high hospital utilization. RESULTS Model development and testing cohorts included 116 799 and 27 087 patients, respectively. The model demonstrated area under receiver-operator curve = 0.79 and area under precision-recall curve = 0.13. PMCA had the highest sensitivity (52.4%) and classified the most children as high risk (17.3%). Positive predictive value of the model-based CDS rule (19%) was higher than CDS based on the PMCA (1.9%) and previous hospital utilization (15%). CONCLUSIONS A novel EHR-based predictive model was developed and validated as a population-level CDS tool for identifying CCHN at high risk for future hospitalization.
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Affiliation(s)
- David Y. Ming
- Departments of Pediatrics
- Medicine
- Population Health Sciences
| | | | - Xinghong Tang
- Janssen Research & Development, LLC, Raritan, New Jersey
| | | | - Ursula A. Rogers
- Duke AI Health, Duke University School of Medicine, Durham, North Carolina
| | - Andrew Stirling
- Duke AI Health, Duke University School of Medicine, Durham, North Carolina
| | | | - Benjamin A. Goldstein
- Departments of Pediatrics
- Population Health Sciences
- Biostatistics & Bioinformatics, and
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Vernooij JEM, Koning NJ, Geurts JW, Holewijn S, Preckel B, Kalkman CJ, Vernooij LM. Performance and usability of pre-operative prediction models for 30-day peri-operative mortality risk: a systematic review. Anaesthesia 2023; 78:607-619. [PMID: 36823388 DOI: 10.1111/anae.15988] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/06/2023] [Indexed: 02/25/2023]
Abstract
Estimating pre-operative mortality risk may inform clinical decision-making for peri-operative care. However, pre-operative mortality risk prediction models are rarely implemented in routine clinical practice. High predictive accuracy and clinical usability are essential for acceptance and clinical implementation. In this systematic review, we identified and appraised prediction models for 30-day postoperative mortality in non-cardiac surgical cohorts. PubMed and Embase were searched up to December 2022 for studies investigating pre-operative prediction models for 30-day mortality. We assessed predictive performance in terms of discrimination and calibration. Risk of bias was evaluated using a tool to assess the risk of bias and applicability of prediction model studies. To further inform potential adoption, we also assessed clinical usability for selected models. In all, 15 studies evaluating 10 prediction models were included. Discrimination ranged from a c-statistic of 0.82 (MySurgeryRisk) to 0.96 (extreme gradient boosting machine learning model). Calibration was reported in only six studies. Model performance was highest for the surgical outcome risk tool (SORT) and its external validations. Clinical usability was highest for the surgical risk pre-operative assessment system. The SORT and risk quantification index also scored high on clinical usability. We found unclear or high risk of bias in the development of all models. The SORT showed the best combination of predictive performance and clinical usability and has been externally validated in several heterogeneous cohorts. To improve clinical uptake, full integration of reliable models with sufficient face validity within the electronic health record is imperative.
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Affiliation(s)
- J E M Vernooij
- Department of Anaesthesia, Rijnstate Hospital, the Netherlands
| | - N J Koning
- Department of Anaesthesia, Rijnstate Hospital, the Netherlands
| | - J W Geurts
- Department of Anaesthesia, Rijnstate Hospital, the Netherlands
| | - S Holewijn
- Department of Vascular Surgery, Rijnstate Hospital, the Netherlands
| | - B Preckel
- Department of Anaesthesia, Amsterdam UMC, Amsterdam, the Netherlands
| | - C J Kalkman
- University Medical Centre, Utrecht, the Netherlands
| | - L M Vernooij
- Department of Anaesthesia, University Medical Centre Utrecht, the Netherlands
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28
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Riester MR, Zullo AR. Prediction tool Development and Implementation in pharmacy praCTice (PreDICT) proposed guidance. Am J Health Syst Pharm 2023; 80:111-123. [PMID: 36242567 DOI: 10.1093/ajhp/zxac298] [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: 10/13/2022] [Indexed: 01/26/2023] Open
Abstract
PURPOSE Proposed guidance is presented for Prediction tool Development and Implementation in pharmacy praCTice (PreDICT). This guidance aims to assist pharmacists and their collaborators with planning, developing, and implementing custom risk prediction tools for use by pharmacists in their own health systems or practice settings. We aimed to describe general considerations that would be relevant to most prediction tools designed for use in health systems or other pharmacy practice settings. SUMMARY The PreDICT proposed guidance is organized into 3 sequential phases: (1) planning, (2) development and validation, and (3) testing and refining prediction tools for real-world use. Each phase is accompanied by a checklist of considerations designed to be used by pharmacists or their trainees (eg, residents) during the planning or conduct of a prediction tool project. Commentary and a worked example are also provided to highlight some of the most relevant and impactful considerations for each phase. CONCLUSION The proposed guidance for PreDICT is a pharmacist-focused set of checklists for planning, developing, and implementing prediction tools in pharmacy practice. The list of considerations and accompanying commentary can be used as a reference by pharmacists or their trainees before or during the completion of a prediction tool project.
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Affiliation(s)
- Melissa R Riester
- Department of Health Services, Policy, and Practice, Brown University School of Public Health, Providence, RI, USA
| | - Andrew R Zullo
- Departments of Health Services, Policy, and Practice and Epidemiology, Brown University School of Public Health, Providence, RI.,Department of Pharmacy, Rhode Island Hospital, Providence, RI, USA
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29
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McDermott JH, Sharma V, Keen J, Newman WG, Pirmohamed M. The Implementation of Pharmacogenetics in the United Kingdom. Handb Exp Pharmacol 2023; 280:3-32. [PMID: 37306816 DOI: 10.1007/164_2023_658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
There is considerable inter-individual variability in the effectiveness and safety of pharmaceutical interventions. This phenomenon can be attributed to a multitude of factors; however, it is widely acknowledged that common genetic variation affecting drug absorption or metabolism play a substantial contributory role. This is a concept known as pharmacogenetics. Understanding how common genetic variants influence responses to medications, and using this knowledge to inform prescribing practice, could yield significant advantages for both patients and healthcare systems. Some health services around the world have introduced pharmacogenetics into routine practice, whereas others are less advanced along the implementation pathway. This chapter introduces the field of pharmacogenetics, the existing body of evidence, and discusses barriers to implementation. The chapter will specifically focus on efforts to introduce pharmacogenetics in the NHS, highlighting key challenges related to scale, informatics, and education.
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Affiliation(s)
- John H McDermott
- Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, UK
- Division of Evolution and Genomic Sciences, School of Biological Sciences, University of Manchester, Manchester, UK
| | - Videha Sharma
- Division of Informatics, Imaging and Data Science, Centre for Health Informatics, The University of Manchester, Manchester, UK
| | - Jessica Keen
- Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, UK
| | - William G Newman
- Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, UK
- Division of Evolution and Genomic Sciences, School of Biological Sciences, University of Manchester, Manchester, UK
| | - Munir Pirmohamed
- Department of Pharmacology and Therapeutics, Wolfson Centre for Personalised Medicine, University of Liverpool, Liverpool, UK.
- Liverpool University Hospital Foundation NHS Trust, Liverpool, UK.
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30
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Kikano S, Kannankeril PJ. Precision Medicine in Pediatric Cardiology. Pediatr Ann 2022; 51:e390-e395. [PMID: 36215086 DOI: 10.3928/19382359-20220803-05] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Precision medicine is a developing strategy for individualized treatment of a wide range of diseases. Congenital heart disease is the most common of all congenital defects and carries a high degree of variability in outcomes because of unidentified causes. Advances have identified individual genetic and environmental factors that have helped understand variations in morbidity and mortality in pediatric cardiology. A focus on genomics and pharmacogenetics has also been key to risk prediction and improvement in drug safety and efficacy in the pediatric population. With the rapidly evolving understanding of these individual factors, there also come challenges in implementation of personalized medicine into our health care model. This review outlines the key features of precision medicine in pediatric cardiology and highlights the clinical effects of these findings in patients with congenital heart disease. [Pediatr Ann. 2022;51(10):e390-e395.].
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Wang F, Ma L, Moulton G, Wang M, Zhang L. Clinician Data Scientists-Preparing for the Future of Medicine in the Digital World. HEALTH DATA SCIENCE 2022; 2022:9832564. [PMID: 38487487 PMCID: PMC10880145 DOI: 10.34133/2022/9832564] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 09/12/2022] [Indexed: 03/17/2024]
Affiliation(s)
- Fulin Wang
- National Institute of Health Data Science at Peking University, Beijing, China
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Lin Ma
- Peking University Health Science Center, Beijing, China
| | - Georgina Moulton
- Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Mai Wang
- National Institute of Health Data Science at Peking University, Beijing, China
- Advanced Institute of Information Technology, Peking University, China
| | - Luxia Zhang
- National Institute of Health Data Science at Peking University, Beijing, China
- Advanced Institute of Information Technology, Peking University, China
- Institute of Nephrology, Peking University First Hospital, China
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Ananda Padmanabhan A, Balczewski EA, Singh K. Artificial Intelligence Systems in CKD: Where Do We Stand and What Will the Future Bring? Adv Chronic Kidney Dis 2022; 29:461-464. [PMID: 36253029 DOI: 10.1053/j.ackd.2022.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 06/14/2022] [Accepted: 06/22/2022] [Indexed: 01/25/2023]
Affiliation(s)
| | - Emily A Balczewski
- Medical Scientist Training Program University of Michigan Medical School Ann Arbor, MI
| | - Karandeep Singh
- School of Information University of Michigan Ann Arbor, MI; Department of Learning Health Sciences University of Michigan Medical School Ann Arbor, MI; Department of Internal Medicine University of Michigan Medical School Ann Arbor, MI
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McNamara MG. Editorial comment on: development and external validation of a model to predict overall survival in patients with resected gallbladder cancer. Hepatobiliary Surg Nutr 2022; 11:147-149. [PMID: 35284508 PMCID: PMC8847874 DOI: 10.21037/hbsn-2021-31] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 12/28/2021] [Indexed: 11/06/2022]
Affiliation(s)
- Mairéad G McNamara
- Division of Cancer Sciences, University of Manchester & The Christie NHS Foundation Trust, Manchester, UK
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Moskowitz CS. Toward Using Breast Cancer Risk Prediction Models for Guiding Screening Decisions. J Natl Cancer Inst 2022; 114:639-640. [PMID: 35026024 PMCID: PMC9086802 DOI: 10.1093/jnci/djac009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 01/10/2022] [Indexed: 01/16/2023] Open
Affiliation(s)
- Chaya S Moskowitz
- Correspondence to: Chaya S. Moskowitz, PhD, Department of Biostatistics and Epidemiology, Memorial Sloan Kettering Cancer Center, 485 Lexington Ave, 2nd Floor, New York, NY 10017, USA (e-mail: )
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Mou Z, Godat LN, El-Kareh R, Berndtson AE, Doucet JJ, Costantini TW. Electronic health record machine learning model predicts trauma inpatient mortality in real time: A validation study. J Trauma Acute Care Surg 2022; 92:74-80. [PMID: 34932043 PMCID: PMC9032917 DOI: 10.1097/ta.0000000000003431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
INTRODUCTION Patient outcome prediction models are underused in clinical practice because of lack of integration with real-time patient data. The electronic health record (EHR) has the ability to use machine learning (ML) to develop predictive models. While an EHR ML model has been developed to predict clinical deterioration, it has yet to be validated for use in trauma. We hypothesized that the Epic Deterioration Index (EDI) would predict mortality and unplanned intensive care unit (ICU) admission in trauma patients. METHODS A retrospective analysis of a trauma registry was used to identify patients admitted to a level 1 trauma center for >24 hours from October 2019 to July 2020. We evaluated the performance of the EDI, which is constructed from 125 objective patient measures within the EHR, in predicting mortality and unplanned ICU admissions. We performed a 5 to 1 match on age because it is a major component of EDI, then examined the area under the receiver operating characteristic curve (AUROC), and benchmarked it against Injury Severity Score (ISS) and new injury severity score (NISS). RESULTS The study cohort consisted of 1,325 patients admitted with a mean age of 52.5 years and 91% following blunt injury. The in-hospital mortality rate was 2%, and unplanned ICU admission rate was 2.6%. In predicting mortality, the maximum EDI within 24 hours of admission had an AUROC of 0.98 compared with 0.89 of ISS and 0.91 of NISS. For unplanned ICU admission, the EDI slope within 24 hours of ICU admission had a modest performance with an AUROC of 0.66. CONCLUSION Epic Deterioration Index appears to perform strongly in predicting in-patient mortality similarly to ISS and NISS. In addition, it can be used to predict unplanned ICU admissions. This study helps validate the use of this real-time EHR ML-based tool, suggesting that EDI should be incorporated into the daily care of trauma patients. LEVEL OF EVIDENCE Prognostic, level III.
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Affiliation(s)
- Zongyang Mou
- Department of Surgery, UC San Diego, San Diego, California
| | - Laura N. Godat
- Department of Surgery, Division of Trauma, Surgical Critical Care, Burns and Acute Care Surgery, UC San Diego, San Diego, California
| | - Robert El-Kareh
- Department of Medicine, University of California San Diego, San Diego, CA, United States
| | - Allison E. Berndtson
- Department of Surgery, Division of Trauma, Surgical Critical Care, Burns and Acute Care Surgery, UC San Diego, San Diego, California
| | - Jay J. Doucet
- Department of Surgery, Division of Trauma, Surgical Critical Care, Burns and Acute Care Surgery, UC San Diego, San Diego, California
| | - Todd W. Costantini
- Department of Surgery, Division of Trauma, Surgical Critical Care, Burns and Acute Care Surgery, UC San Diego, San Diego, California
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Sharma V, Davies A, Ainsworth J. Clinical risk prediction models: the canary in the coalmine for artificial intelligence in healthcare? BMJ Health Care Inform 2021; 28:bmjhci-2021-100421. [PMID: 34607819 PMCID: PMC8491286 DOI: 10.1136/bmjhci-2021-100421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 09/03/2021] [Indexed: 11/03/2022] Open
Affiliation(s)
- Videha Sharma
- Centre for Health Informatics, School of Health Sciences, The University of Manchester, Manchester, UK
| | - Angela Davies
- Centre for Health Informatics, School of Health Sciences, The University of Manchester, Manchester, UK
| | - John Ainsworth
- Centre for Health Informatics, School of Health Sciences, The University of Manchester, Manchester, UK
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Wilson A, Saeed H, Pringle C, Eleftheriou I, Bromiley PA, Brass A. Artificial intelligence projects in healthcare: 10 practical tips for success in a clinical environment. BMJ Health Care Inform 2021; 28:e100323. [PMID: 34326160 PMCID: PMC8323348 DOI: 10.1136/bmjhci-2021-100323] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 05/23/2021] [Indexed: 11/06/2022] Open
Abstract
There is much discussion concerning 'digital transformation' in healthcare and the potential of artificial intelligence (AI) in healthcare systems. Yet it remains rare to find AI solutions deployed in routine healthcare settings. This is in part due to the numerous challenges inherent in delivering an AI project in a clinical environment. In this article, several UK healthcare professionals and academics reflect on the challenges they have faced in building AI solutions using routinely collected healthcare data.These personal reflections are summarised as 10 practical tips. In our experience, these are essential considerations for an AI healthcare project to succeed. They are organised into four phases: conceptualisation, data management, AI application and clinical deployment. There is a focus on conceptualisation, reflecting our view that initial set-up is vital to success. We hope that our personal experiences will provide useful insights to others looking to improve patient care through optimal data use.
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Affiliation(s)
- Anthony Wilson
- Department of Adult Critical Care, Manchester University NHS Foundation Trust, Manchester, UK
| | - Haroon Saeed
- Department of Pediatric Ear Nose and Throat Surgery, Royal Manchester Children's Hospital, Manchester, UK
| | - Catherine Pringle
- Children's Brain Tumour Research Network, Royal Manchester Children's Hospital, Manchester, UK
- Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester, UK
| | - Iliada Eleftheriou
- Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester, UK
| | - Paul A Bromiley
- Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester, UK
| | - Andy Brass
- Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester, UK
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