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Justice AC, Tate JP, Howland F, Gaziano JM, Kelley MJ, McMahon B, Haiman C, Wadia R, Madduri R, Danciu I, Leppert JT, Leapman MS, Thurtle D, Gnanapragasam VJ. Adaption and National Validation of a Tool for Predicting Mortality from Other Causes Among Men with Nonmetastatic Prostate Cancer. Eur Urol Oncol 2024; 7:923-932. [PMID: 38171965 DOI: 10.1016/j.euo.2023.11.023] [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: 06/26/2023] [Revised: 10/24/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND An electronic health record-based tool could improve accuracy and eliminate bias in provider estimation of the risk of death from other causes among men with nonmetastatic cancer. OBJECTIVE To recalibrate and validate the Veterans Aging Cohort Study Charlson Comorbidity Index (VACS-CCI) to predict non-prostate cancer mortality (non-PCM) and to compare it with a tool predicting prostate cancer mortality (PCM). DESIGN, SETTING, AND PARTICIPANTS An observational cohort of men with biopsy-confirmed nonmetastatic prostate cancer, enrolled from 2001 to 2018 in the national US Veterans Health Administration (VA), was divided by the year of diagnosis into the development (2001-2006 and 2008-2018) and validation (2007) sets. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Mortality (all cause, non-PCM, and PCM) was evaluated. Accuracy was assessed using calibration curves and C statistic in the development, validation, and combined sets; overall; and by age (<65 and 65+ yr), race (White and Black), Hispanic ethnicity, and treatment groups. RESULTS AND LIMITATIONS Among 107 370 individuals, we observed 24 977 deaths (86% non-PCM). The median age was 65 yr, 4947 were Black, and 5010 were Hispanic. Compared with CCI and age alone (C statistic 0.67, 95% confidence interval [CI] 0.67-0.68), VACS-CCI demonstrated improved validated discrimination (C statistic 0.75, 95% CI 0.74-0.75 for non-PCM). The prostate cancer mortality tool also discriminated well in validation (C statistic 0.81, 95% CI 0.78-0.83). Both were well calibrated overall and within subgroups. Owing to missing data, 18 009/125 379 (14%) were excluded, and VACS-CCI should be validated outside the VA prior to outside application. CONCLUSIONS VACS-CCI is ready for implementation within the VA. Electronic health record-assisted calculation is feasible, improves accuracy over age and CCI alone, and could mitigate inaccuracy and bias in provider estimation. PATIENT SUMMARY Veterans Aging Cohort Study Charlson Comorbidity Index is ready for application within the Veterans Health Administration. Electronic health record-assisted calculation is feasible, improves accuracy over age and Charlson Comorbidity Index alone, and might help mitigate inaccuracy and bias in provider estimation of the risk of non-prostate cancer mortality.
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Affiliation(s)
- Amy C Justice
- VA Connecticut Healthcare, West Haven, CT, USA; Pain Research, Informatics, Multimorbidities, Education (PRIME) Center, VA Connecticut Healthcare System, West Haven, CT, USA; Department of Medicine, Yale School of Medicine, New Haven, CT, USA; School of Public Health, Yale University, New Haven, CT, USA.
| | - Janet P Tate
- VA Connecticut Healthcare, West Haven, CT, USA; Department of Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Frank Howland
- Wabash College Economics Department, Crawfordsville, IN, USA
| | | | - Michael J Kelley
- Durham VA Health Care System, Durham, NC, USA; Cancer Institute and Department of Medicine, Duke University, Durham, NC, USA
| | | | - Christopher Haiman
- Center for Genetic Epidemiology, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Roxanne Wadia
- Department of Anatomic Pathology and Lab Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Ravi Madduri
- Data Science Learning Division, Argonne Research Library, Lemont, IL, USA
| | - Ioana Danciu
- Oak Ridge National Laboratory, Oak Ridge, TN, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - John T Leppert
- Department of Urology, Stanford University, Stanford, CA, USA; VA Palo Alto Health Care System, Palo Alto, CA, USA
| | - Michael S Leapman
- VA Connecticut Healthcare, West Haven, CT, USA; Department of Urology, Yale School of Medicine, New Haven, CT, USA
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Smith M, Crnich C, Donskey C, Evans CT, Evans M, Goto M, Guerrero B, Gupta K, Harris A, Hicks N, Khader K, Kralovic S, McKinley L, Rubin M, Safdar N, Schweizer ML, Tovar S, Wilson G, Zabarsky T, Perencevich EN. Research agenda for transmission prevention within the Veterans Health Administration, 2024-2028. Infect Control Hosp Epidemiol 2024:1-10. [PMID: 38600795 DOI: 10.1017/ice.2024.40] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Affiliation(s)
- Matthew Smith
- Center for Access & Delivery Research and Evaluation, Iowa City Veterans Affairs Health Care System, Iowa City, IA, USA
- Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Chris Crnich
- William. S. Middleton Memorial VA Hospital, Madison, WI, USA
| | - Curtis Donskey
- Geriatric Research, Education and Clinical Center, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA
| | - Charlesnika T Evans
- Center of Innovation for Complex Chronic Healthcare, Hines VA Hospital, Hines, IL, USA
- Department of Preventive Medicine and Center for Health Services and Outcomes Research, Northwestern University of Feinberg School of Medicine, Chicago, IL, USA
| | - Martin Evans
- MRSA/MDRO Division, VHA National Infectious Diseases Service, Patient Care Services, VA Central Office and the Lexington VA Health Care System, Lexington, KY, USA
| | - Michihiko Goto
- Center for Access & Delivery Research and Evaluation, Iowa City Veterans Affairs Health Care System, Iowa City, IA, USA
- Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Bernardino Guerrero
- Environmental Programs Service (EPS), Veterans Affairs Central Office, Washington, DC, USA
| | - Kalpana Gupta
- VA Boston Healthcare System and Boston University School of Medicine, Boston, MA, USA
| | - Anthony Harris
- Department of Epidemiology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Natalie Hicks
- National Infectious Diseases Service, Specialty Care Services, Veterans Health Administration, US Department of Veterans Affairs, Washington, DC, USA
| | - Karim Khader
- DEAS Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah
- Division of Epidemiology, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah
| | - Stephen Kralovic
- Veterans Health Administration National Infectious Diseases Service, Washington, DC, USA
- Cincinnati VA Medical Center and University of Cincinnati, Cincinnati, OH, USA
| | - Linda McKinley
- William. S. Middleton Memorial VA Hospital, Madison, WI, USA
| | - Michael Rubin
- DEAS Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah
- Division of Epidemiology, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah
| | - Nasia Safdar
- William. S. Middleton Memorial VA Hospital, Madison, WI, USA
| | - Marin L Schweizer
- William. S. Middleton Memorial VA Hospital, Madison, WI, USA
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, and William S. Middleton Hospital, Madison, WI, USA
| | - Suzanne Tovar
- National Infectious Diseases Service (NIDS), Veterans Affairs Central Office, Washington, DC, USA
| | - Geneva Wilson
- Center of Innovation for Complex Chronic Healthcare (CINCCH), Hines Jr. Veterans Affairs Hospital, Hines, IL, USA
- Department of Preventive Medicine, Center for Health Services and Outcomes Research, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Trina Zabarsky
- Environmental Programs Service (EPS), Veterans Affairs Central Office, Washington, DC, USA
| | - Eli N Perencevich
- Center for Access & Delivery Research and Evaluation, Iowa City Veterans Affairs Health Care System, Iowa City, IA, USA
- Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, USA
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Smith CL, Fisher G, Dharmayani PNA, Wijekulasuriya S, Ellis LA, Spanos S, Dammery G, Zurynski Y, Braithwaite J. Progress with the Learning Health System 2.0: a rapid review of Learning Health Systems' responses to pandemics and climate change. BMC Med 2024; 22:131. [PMID: 38519952 PMCID: PMC10960489 DOI: 10.1186/s12916-024-03345-8] [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: 09/06/2023] [Accepted: 01/23/2024] [Indexed: 03/25/2024] Open
Abstract
BACKGROUND Pandemics and climate change each challenge health systems through increasing numbers and new types of patients. To adapt to these challenges, leading health systems have embraced a Learning Health System (LHS) approach, aiming to increase the efficiency with which data is translated into actionable knowledge. This rapid review sought to determine how these health systems have used LHS frameworks to both address the challenges posed by the COVID-19 pandemic and climate change, and to prepare for future disturbances, and thus transition towards the LHS2.0. METHODS Three databases (Embase, Scopus, and PubMed) were searched for peer-reviewed literature published in English in the five years to March 2023. Publications were included if they described a real-world LHS's response to one or more of the following: the COVID-19 pandemic, future pandemics, current climate events, future climate change events. Data were extracted and thematically analyzed using the five dimensions of the Institute of Medicine/Zurynski-Braithwaite's LHS framework: Science and Informatics, Patient-Clinician Partnerships, Continuous Learning Culture, Incentives, and Structure and Governance. RESULTS The search yielded 182 unique publications, four of which reported on LHSs and climate change. Backward citation tracking yielded 13 additional pandemic-related publications. None of the climate change-related papers met the inclusion criteria. Thirty-two publications were included after full-text review. Most were case studies (n = 12, 38%), narrative descriptions (n = 9, 28%) or empirical studies (n = 9, 28%). Science and Informatics (n = 31, 97%), Continuous Learning Culture (n = 26, 81%), Structure and Governance (n = 23, 72%) were the most frequently discussed LHS dimensions. Incentives (n = 21, 66%) and Patient-Clinician Partnerships (n = 18, 56%) received less attention. Twenty-nine papers (91%) discussed benefits or opportunities created by pandemics to furthering the development of an LHS, compared to 22 papers (69%) that discussed challenges. CONCLUSIONS An LHS 2.0 approach appears well-suited to responding to the rapidly changing and uncertain conditions of a pandemic, and, by extension, to preparing health systems for the effects of climate change. LHSs that embrace a continuous learning culture can inform patient care, public policy, and public messaging, and those that wisely use IT systems for decision-making can more readily enact surveillance systems for future pandemics and climate change-related events. TRIAL REGISTRATION PROSPERO pre-registration: CRD42023408896.
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Affiliation(s)
- Carolynn L Smith
- Centre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia.
- NHMRC Partnership Centre for Health System Sustainability, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia.
| | - Georgia Fisher
- Centre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
| | - Putu Novi Arfirsta Dharmayani
- Centre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
- NHMRC Partnership Centre for Health System Sustainability, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
| | - Shalini Wijekulasuriya
- Centre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
- NHMRC Partnership Centre for Health System Sustainability, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
| | - Louise A Ellis
- Centre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
- NHMRC Partnership Centre for Health System Sustainability, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
| | - Samantha Spanos
- Centre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
| | - Genevieve Dammery
- Centre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
- NHMRC Partnership Centre for Health System Sustainability, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
| | - Yvonne Zurynski
- Centre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
- NHMRC Partnership Centre for Health System Sustainability, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
| | - Jeffrey Braithwaite
- Centre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
- NHMRC Partnership Centre for Health System Sustainability, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
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Salem KM, Padberg FT. Venous thromboembolism risk models may be ready for a makeover. J Vasc Surg Venous Lymphat Disord 2024; 12:101732. [PMID: 38368022 DOI: 10.1016/j.jvsv.2023.101732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 12/04/2023] [Indexed: 02/19/2024]
Affiliation(s)
- Karim M Salem
- Division of Vascular Surgery, Department of Surgery, Rutgers, New Jersey Medical School, Newark, NJ
| | - Frank T Padberg
- Division of Vascular Surgery, Department of Surgery, Rutgers, New Jersey Medical School, Newark, NJ; Department of Surgery, VA New Jersey Health Care System, East Orange, NJ
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Trinkley KE, An R, Maw AM, Glasgow RE, Brownson RC. Leveraging artificial intelligence to advance implementation science: potential opportunities and cautions. Implement Sci 2024; 19:17. [PMID: 38383393 PMCID: PMC10880216 DOI: 10.1186/s13012-024-01346-y] [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: 10/30/2023] [Accepted: 01/25/2024] [Indexed: 02/23/2024] Open
Abstract
BACKGROUND The field of implementation science was developed to address the significant time delay between establishing an evidence-based practice and its widespread use. Although implementation science has contributed much toward bridging this gap, the evidence-to-practice chasm remains a challenge. There are some key aspects of implementation science in which advances are needed, including speed and assessing causality and mechanisms. The increasing availability of artificial intelligence applications offers opportunities to help address specific issues faced by the field of implementation science and expand its methods. MAIN TEXT This paper discusses the many ways artificial intelligence can address key challenges in applying implementation science methods while also considering potential pitfalls to the use of artificial intelligence. We answer the questions of "why" the field of implementation science should consider artificial intelligence, for "what" (the purpose and methods), and the "what" (consequences and challenges). We describe specific ways artificial intelligence can address implementation science challenges related to (1) speed, (2) sustainability, (3) equity, (4) generalizability, (5) assessing context and context-outcome relationships, and (6) assessing causality and mechanisms. Examples are provided from global health systems, public health, and precision health that illustrate both potential advantages and hazards of integrating artificial intelligence applications into implementation science methods. We conclude by providing recommendations and resources for implementation researchers and practitioners to leverage artificial intelligence in their work responsibly. CONCLUSIONS Artificial intelligence holds promise to advance implementation science methods ("why") and accelerate its goals of closing the evidence-to-practice gap ("purpose"). However, evaluation of artificial intelligence's potential unintended consequences must be considered and proactively monitored. Given the technical nature of artificial intelligence applications as well as their potential impact on the field, transdisciplinary collaboration is needed and may suggest the need for a subset of implementation scientists cross-trained in both fields to ensure artificial intelligence is used optimally and ethically.
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Affiliation(s)
- Katy E Trinkley
- Department of Family Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
- Adult and Child Center for Outcomes Research and Delivery Science Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
- Department of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
- Colorado Center for Personalized Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
| | - Ruopeng An
- Brown School and Division of Computational and Data Sciences at Washington University in St. Louis, St. Louis, MO, USA
| | - Anna M Maw
- Adult and Child Center for Outcomes Research and Delivery Science Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- School of Medicine, Division of Hospital Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Russell E Glasgow
- Department of Family Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Adult and Child Center for Outcomes Research and Delivery Science Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Ross C Brownson
- Prevention Research Center, Brown School at Washington University in St. Louis, St. Louis, MO, USA
- Department of Surgery, Division of Public Health Sciences, and Alvin J. Siteman Cancer Center, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
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Patel S, Trujillo Rivera EA, Raman VK, Faselis C, Wang V, Fink JC, Roseman JM, Morgan CJ, Zhang S, Sheriff HM, Heimall MS, Wu WC, Zeng-Treitler Q, Ahmed A. Impact of the COVID-19 Pandemic on the Provision of Dialysis Service and Mortality in Veterans Receiving Maintenance Hemodialysis in the VA: An Interrupted Time-Series Analysis. Am J Nephrol 2023; 54:508-515. [PMID: 37524062 PMCID: PMC10959175 DOI: 10.1159/000532105] [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: 05/08/2023] [Accepted: 07/17/2023] [Indexed: 08/02/2023]
Abstract
INTRODUCTION According to the US Renal Data System (USRDS), patients with end-stage kidney disease (ESKD) on maintenance dialysis had higher mortality during early COVID-19 pandemic. Less is known about the effect of the pandemic on the delivery of outpatient maintenance hemodialysis and its impact on death. We examined the effect of pandemic-related disruption on the delivery of dialysis treatment and mortality in patients with ESKD receiving maintenance hemodialysis in the Veterans Health Administration (VHA) facilities, the largest integrated national healthcare system in the USA. METHODS Using national VHA electronic health records data, we identified 7,302 Veterans with ESKD who received outpatient maintenance hemodialysis in VHA healthcare facilities during the COVID-19 pandemic (February 1, 2020, to December 31, 2021). We estimated the average change in the number of hemodialysis treatments received and deaths per 1,000 patients per month during the pandemic by conducting interrupted time-series analyses. We used seasonal autoregressive moving average (SARMA) models, in which February 2020 was used as the conditional intercept and months thereafter as conditional slope. The models were adjusted for seasonal variations and trends in rates during the pre-pandemic period (January 1, 2007, to January 31, 2020). RESULTS The number (95% CI) of hemodialysis treatments received per 1,000 patients per month during the pre-pandemic and pandemic periods were 12,670 (12,525-12,796) and 12,865 (12,729-13,002), respectively. Respective all-cause mortality rates (95% CI) were 17.1 (16.7-17.5) and 19.6 (18.5-20.7) per 1,000 patients per month. Findings from SARMA models demonstrate that there was no reduction in the dialysis treatments delivered during the pandemic (rate ratio: 0.999; 95% CI: 0.998-1.001), but there was a 2.3% (95% CI: 1.5-3.1%) increase in mortality. During the pandemic, the non-COVID hospitalization rate was 146 (95% CI: 143-149) per 1,000 patients per month, which was lower than the pre-pandemic rate of 175 (95% CI: 173-176). In contrast, there was evidence of higher use of telephone encounters during the pandemic (3,023; 95% CI: 2,957-3,089), compared with the pre-pandemic rate (1,282; 95% CI: 1,241-1,324). CONCLUSIONS We found no evidence that there was a disruption in the delivery of outpatient maintenance hemodialysis treatment in VHA facilities during the COVID-19 pandemic and that the modest rise in deaths during the pandemic is unlikely to be due to missed dialysis.
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Affiliation(s)
- Samir Patel
- Center for Data Science and Outcomes Research, Veterans Affairs Medical Center, Washington, DC, USA
- Department of Medicine, George Washington University, Washington, DC, USA
| | - Eduardo A. Trujillo Rivera
- Center for Data Science and Outcomes Research, Veterans Affairs Medical Center, Washington, DC, USA
- Department of Pediatrics, Georgetown University, Washington, DC, USA
| | - Venkatesh K. Raman
- Center for Data Science and Outcomes Research, Veterans Affairs Medical Center, Washington, DC, USA
- Department of Medicine, Georgetown University, Washington, DC, USA
| | - Charles Faselis
- Center for Data Science and Outcomes Research, Veterans Affairs Medical Center, Washington, DC, USA
- Department of Medicine, George Washington University, Washington, DC, USA
- Department of Medicine, Uniformed Services University, Washington, DC, USA
| | - Virginia Wang
- Center of Innovation to Accelerate Discovery and Practice Transformation, Durham VA Health Care System, Durham, NC, USA
- Departments of Population Health Sciences and Medicine, Duke University, Baltimore, MD, USA
| | - Jeffrey C. Fink
- Veterans Affairs Medical Center, Baltimore, MD, USA
- Department of Medicine, University of Maryland, Baltimore, MD, USA
| | - Jeffrey M. Roseman
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Charity J. Morgan
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Sijian Zhang
- Center for Data Science and Outcomes Research, Veterans Affairs Medical Center, Washington, DC, USA
| | - Helen M. Sheriff
- Center for Data Science and Outcomes Research, Veterans Affairs Medical Center, Washington, DC, USA
- Clinical Research and Leadership and Biomedical Informatics Center, George Washington University, Washington, DC, USA
| | - Michael S. Heimall
- Center for Data Science and Outcomes Research, Veterans Affairs Medical Center, Washington, DC, USA
| | - Wen-Chih Wu
- Medical service, Veterans Affairs Medical Center, Providence, RI, USA
- Department of Medicine, Brown University, Providence, RI, USA
| | - Qing Zeng-Treitler
- Center for Data Science and Outcomes Research, Veterans Affairs Medical Center, Washington, DC, USA
- Clinical Research and Leadership and Biomedical Informatics Center, George Washington University, Washington, DC, USA
| | - Ali Ahmed
- Center for Data Science and Outcomes Research, Veterans Affairs Medical Center, Washington, DC, USA
- Department of Medicine, George Washington University, Washington, DC, USA
- Department of Medicine, Georgetown University, Washington, DC, USA
- Clinical Research and Leadership and Biomedical Informatics Center, George Washington University, Washington, DC, USA
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Zhou Y, Dowsey M, Spelman T, Choong P, Schilling C. SMART choice (knee) tool: a patient-focused predictive model to predict improvement in health-related quality of life after total knee arthroplasty. ANZ J Surg 2023; 93:316-327. [PMID: 36637215 DOI: 10.1111/ans.18250] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 12/11/2022] [Accepted: 12/21/2022] [Indexed: 01/14/2023]
Abstract
BACKGROUND Current predictive tools for TKA focus on clinicians rather than patients as the intended user. The purpose of this study was to develop a patient-focused model to predict health-related quality of life outcomes at 1-year post-TKA. METHODS Patients who underwent primary TKA for osteoarthritis from a tertiary institutional registry after January 2006 were analysed. The primary outcome was improvement after TKA defined by the minimal clinically important difference in utility score at 1-year post-surgery. Potential predictors included demographic information, comorbidities, lifestyle factors, and patient-reported outcome measures. Four models were developed, including both conventional statistics and machine learning (artificial intelligence) methods: logistic regression, classification tree, extreme gradient boosted trees, and random forest models. Models were evaluated using discrimination and calibration metrics. RESULTS A total of 3755 patients were included in the study. The logistic regression model performed the best with respect to both discrimination (AUC = 0.712) and calibration (intercept = -0.083, slope = 1.123, Brier score = 0.202). Less than 2% (n = 52) of the data were missing and therefore removed for complete case analysis. The final model used age (categorical), sex, baseline utility score, and baseline Veterans-RAND 12 responses as predictors. CONCLUSION The logistic regression model performed better than machine learning algorithms with respect to AUC and calibration plot. The logistic regression model was well calibrated enough to stratify patients into risk deciles based on their likelihood of improvement after surgery. Further research is required to evaluate the performance of predictive tools through pragmatic clinical trials. LEVEL OF EVIDENCE Level II, decision analysis.
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Affiliation(s)
- Yushy Zhou
- Department of Surgery, The University of Melbourne, Melbourne, Victoria, Australia
| | - Michelle Dowsey
- Department of Surgery, The University of Melbourne, Melbourne, Victoria, Australia
| | - Tim Spelman
- Department of Surgery, The University of Melbourne, Melbourne, Victoria, Australia
| | - Peter Choong
- Department of Surgery, The University of Melbourne, Melbourne, Victoria, Australia
| | - Chris Schilling
- Department of Surgery, The University of Melbourne, Melbourne, Victoria, Australia
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