1
|
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.
Collapse
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
| | | | | |
Collapse
|
2
|
Gong J, Kim DM, De Hoedt AM, Bhowmick N, Figlin R, Kim HL, Sandler H, Theodorescu D, Posadas E, Freedland SJ. Disparities With Systemic Therapies for Black Men Having Advanced Prostate Cancer: Where Do We Stand? J Clin Oncol 2024; 42:228-236. [PMID: 37890125 PMCID: PMC10824384 DOI: 10.1200/jco.23.00949] [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: 04/28/2023] [Revised: 08/08/2023] [Accepted: 08/23/2023] [Indexed: 10/29/2023] Open
Abstract
PURPOSE Prostate cancer represents the most common cancer diagnosis in Black men and is the second leading cause of cancer death in this population. Multilevel disparities have been well-documented in Black men with prostate cancer and play a role in poorer survival outcomes when compared with White men with prostate cancer. In this review, we highlight the changing trend in disparities for systemic therapy outcomes in Black men diagnosed with metastatic prostate cancer. METHODS We reviewed data from real-world registries and prospective clinical trials with a particular focus on equal access settings to compare outcomes to systemic therapies between Black and White men with metastatic prostate cancer. RESULTS In metastatic prostate cancer, there is growing evidence to suggest that Black men may have similar, if not better, outcomes to systemic therapies than White men with advanced disease, as corroborated by prospective studies and clinical trials where health care delivery and follow-up are more likely to be standardized. CONCLUSION This review illustrates the importance of nonbiological drivers of racial disparities in Black men with advanced prostate cancer. Mitigating barriers to health care access and delivery as well as including participation in clinical trials will be pivotal to ongoing efforts to address disparities in systemic therapy outcomes for Black men with metastatic prostate cancer.
Collapse
Affiliation(s)
- Jun Gong
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Daniel M. Kim
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Amanda M. De Hoedt
- Urology Section, Department of Surgery, Veterans Affairs Health Care System, Durham, NC
| | - Neil Bhowmick
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Robert Figlin
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Hyung L. Kim
- Division of Urology, Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Howard Sandler
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Dan Theodorescu
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA
- Division of Urology, Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Edwin Posadas
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Stephen J. Freedland
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA
- Urology Section, Department of Surgery, Veterans Affairs Health Care System, Durham, NC
- Division of Urology, Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA
| |
Collapse
|
3
|
Dizon MP, Linos E, Swetter SM. Estimating remaining life expectancy in veterans with basal cell carcinoma using an automated electronic health record scoring system: A retrospective cohort study. J Am Acad Dermatol 2024; 90:98-105. [PMID: 37742837 DOI: 10.1016/j.jaad.2023.09.029] [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: 07/31/2023] [Revised: 08/26/2023] [Accepted: 09/03/2023] [Indexed: 09/26/2023]
Abstract
BACKGROUND Active surveillance may be considered for low-risk basal cell carcinomas (BCCs) in patients with limited life expectancy; however, estimates of life expectancy are not readily available. Veterans Health Administration's Care Assessment Need (CAN) score may address this problem. OBJECTIVE We examined the CAN score's performance in predicting 1-, 3-, and 5-year mortality in US veterans with BCC. METHODS This retrospective cohort study used national Veterans Health Administration's electronic medical record data. The CAN score's performance in the prediction of mortality in veterans with BCC was evaluated based on tests of goodness-of-fit, discrimination, and calibration. RESULTS For 54,744 veterans with BCC treatment encounters between 2013 and 2018, the CAN score performed well in the prediction of mortality based on multiple tests. A threshold CAN score of 90 had a positive predictive value of 55% for 3-year mortality, clinically useful in identifying patients with intermediate-term survival. LIMITATIONS The study relied upon the combination of diagnosis codes and procedure codes to identify BCC cases. CONCLUSION The CAN score has the potential to improve the quality of cancer care for veterans by providing clinicians with an estimate of life expectancy and facilitating conversations in cases where active surveillance can be considered.
Collapse
Affiliation(s)
- Matthew P Dizon
- Center for Innovation to Implementation, Veterans Affairs Palo Alto Health Care System, Menlo Park, California; Dermatology Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, California; Department of Health Policy, Stanford University School of Medicine, Palo Alto, California.
| | - Eleni Linos
- Program for Clinical Research and Technology, Stanford University, Stanford, California
| | - Susan M Swetter
- Dermatology Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, California; Department of Dermatology/Cutaneous Oncology, Stanford University Medical Center and Cancer Institute, Stanford, California
| |
Collapse
|
4
|
Jia-Richards M, Williams EC, Rosland AM, Boudreaux-Kelly MY, Luther JF, Mikolic J, Chinman MJ, Daniels K, Bachrach RL. Unhealthy alcohol use and brief intervention rates among high and low complexity veterans seeking primary care services in the Veterans Health Administration. JOURNAL OF SUBSTANCE USE AND ADDICTION TREATMENT 2023; 152:209117. [PMID: 37355154 PMCID: PMC10527472 DOI: 10.1016/j.josat.2023.209117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 03/31/2023] [Accepted: 06/21/2023] [Indexed: 06/26/2023]
Abstract
INTRODUCTION Brief intervention (BI) is recommended for all primary care (PC) patients who screen positive for unhealthy alcohol use; however, patients with multiple chronic health conditions who are at high-risk of hospitalization (i.e., "high complexity" patients) may face disparities in receiving BIs in PC. The current study investigated whether high complexity and low complexity patients in the Veterans Health Administration (VHA) differed regarding screening positive for unhealthy alcohol use, alcohol-use severity, and receipt of BI for those with unhealthy alcohol use. METHODS Patients were veterans receiving PC services at the VHA in a mid-Atlantic region of the United States. The study extracted VHA administrative and clinical data for a total of 282,242 patients who had ≥1 PC visits between 1/1/2014 and 12/31/2014, during which they were screened for unhealthy alcohol use by the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C). The study defined high complexity patients as those within and above the 90th percentile of risk for hospitalization per the VHA's Care Assessment Need Score. Logistic regression models assessed if being a high complexity patient was associated with screening positive for unhealthy alcohol use (AUDIT-C ≥ 5), severity of unhealthy alcohol use in those who screened positive (AUDIT-C score range 5-12), and receipt of BI in those who screened positive. RESULTS Our sample was 94.5% male, 83% White, 13% Black, 4% other race, and 1.7% Hispanic. A total of 10,813 (3.8%) patients screened positive for unhealthy alcohol use from which we identified 569 (5.3%) high complexity and 10,128 (93.6%) low complexity patients (n = 116 removed due to missing complexity data). Relative to low complexity patients, high complexity patients were less likely to screen positive for unhealthy alcohol use (3.3% vs. 4.1%, AOR = 0.59, p < .001); however, in patients who screened positive, high complexity patients had higher AUDIT-C scores (Mean AUDIT-C = 7.75 vs. 6.87, AOR = 1.46, p < .001) and were less likely to receive a BI (78.0% vs. 92.6%, AOR = 0.42, p < .001). CONCLUSIONS Disparities in BI exist for highly complex patients despite having more severe unhealthy alcohol use. Future research should examine the specific patient- and/or clinic-level factors impeding BI delivery for complex patients.
Collapse
Affiliation(s)
| | - Emily C Williams
- Department of Health Systems and Population Health, University of Washington, School of Public Health, Seattle, WA, USA; Health Services Research & Development (HSR&D) Center of Innovation for Veteran-Centered and Value-Driven Care, Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA
| | - Ann-Marie Rosland
- Center for Health Equity and Research Promotion, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA; Division of General Internal Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - James F Luther
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA; Mental Illness Research, Education, and Clinical Center, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
| | - Joseph Mikolic
- StatCore, Veterans Affairs Pittsburgh Healthcare System Research Office, Pittsburgh, PA, USA
| | - Matthew J Chinman
- Center for Health Equity and Research Promotion, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA; Mental Illness Research, Education, and Clinical Center, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA; The RAND Corporation, Pittsburgh, PA, USA
| | - Karin Daniels
- Center for Health Equity and Research Promotion, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
| | - Rachel L Bachrach
- Center for Health Equity and Research Promotion, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA; Division of General Internal Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA; Mental Illness Research, Education, and Clinical Center, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA; Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA.
| |
Collapse
|
5
|
Atkins D, Makridis CA, Alterovitz G, Ramoni R, Clancy C. Developing and Implementing Predictive Models in a Learning Healthcare System: Traditional and Artificial Intelligence Approaches in the Veterans Health Administration. Annu Rev Biomed Data Sci 2022; 5:393-413. [PMID: 35609894 DOI: 10.1146/annurev-biodatasci-122220-110053] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Predicting clinical risk is an important part of healthcare and can inform decisions about treatments, preventive interventions, and provision of extra services. The field of predictive models has been revolutionized over the past two decades by electronic health record data; the ability to link such data with other demographic, socioeconomic, and geographic information; the availability of high-capacity computing; and new machine learning and artificial intelligence methods for extracting insights from complex datasets. These advances have produced a new generation of computerized predictive models, but debate continues about their development, reporting, validation, evaluation, and implementation. In this review we reflect on more than 10 years of experience at the Veterans Health Administration, the largest integrated healthcare system in the United States, in developing, testing, and implementing such models at scale. We report lessons from the implementation of national risk prediction models and suggest an agenda for research. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Collapse
Affiliation(s)
- David Atkins
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, USA;
| | - Christos A Makridis
- National Artificial Intelligence Institute, Department of Veterans Affairs, Washington, DC, USA
| | - Gil Alterovitz
- National Artificial Intelligence Institute, Department of Veterans Affairs, Washington, DC, USA
| | - Rachel Ramoni
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, USA;
| | - Carolyn Clancy
- Office of Discovery, Education and Affiliate Networks, Department of Veterans Affairs, Washington, DC, USA
| |
Collapse
|