1
|
He S, Park S, Kuklina E, Therrien NL, Lundeen EA, Wall HK, Lampley K, Kompaniyets L, Pierce SL, Sperling L, Jackson SL. Leveraging Electronic Health Records to Construct a Phenotype for Hypertension Surveillance in the United States. Am J Hypertens 2023; 36:677-685. [PMID: 37696605 PMCID: PMC10898654 DOI: 10.1093/ajh/hpad081] [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: 01/25/2023] [Revised: 05/10/2023] [Accepted: 09/06/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND Hypertension is an important risk factor for cardiovascular diseases. Electronic health records (EHRs) may augment chronic disease surveillance. We aimed to develop an electronic phenotype (e-phenotype) for hypertension surveillance. METHODS We included 11,031,368 eligible adults from the 2019 IQVIA Ambulatory Electronic Medical Records-US (AEMR-US) dataset. We identified hypertension using three criteria, alone or in combination: diagnosis codes, blood pressure (BP) measurements, and antihypertensive medications. We compared AEMR-US estimates of hypertension prevalence and control against those from the National Health and Nutrition Examination Survey (NHANES) 2017-18, which defined hypertension as BP ≥130/80 mm Hg or ≥1 antihypertensive medication. RESULTS The study population had a mean (SD) age of 52.3 (6.7) years, and 56.7% were women. The selected three-criteria e-phenotype (≥1 diagnosis code, ≥2 BP measurements of ≥130/80 mm Hg, or ≥1 antihypertensive medication) yielded similar trends in hypertension prevalence as NHANES: 42.2% (AEMR-US) vs. 44.9% (NHANES) overall, 39.0% vs. 38.7% among women, and 46.5% vs. 50.9% among men. The pattern of age-related increase in hypertension prevalence was similar between AEMR-US and NHANES. The prevalence of hypertension control in AEMR-US was 31.5% using the three-criteria e-phenotype, which was higher than NHANES (14.5%). CONCLUSIONS Using an EHR dataset of 11 million adults, we constructed a hypertension e-phenotype using three criteria, which can be used for surveillance of hypertension prevalence and control.
Collapse
Affiliation(s)
- Siran He
- Division for Heart Disease and Stroke Prevention, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Soyoun Park
- Division for Heart Disease and Stroke Prevention, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Elena Kuklina
- Division for Heart Disease and Stroke Prevention, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Nicole L Therrien
- Division for Heart Disease and Stroke Prevention, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Elizabeth A Lundeen
- Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Hilary K Wall
- Division for Heart Disease and Stroke Prevention, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Katrice Lampley
- Division for Heart Disease and Stroke Prevention, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA
- ASRT, INC, Smyrna, GA, USA
| | - Lyudmyla Kompaniyets
- Division of Nutrition, Physical Activity, and Obesity, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Samantha L Pierce
- Division of Nutrition, Physical Activity, and Obesity, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Laurence Sperling
- Division for Heart Disease and Stroke Prevention, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Sandra L Jackson
- Division for Heart Disease and Stroke Prevention, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA
| |
Collapse
|
2
|
Aubert CE, Chan CL, Terman SW, Hofer TP, Ha JK, Cushman WC, Sussman J, Min L. Evaluating alternative methods of comparing antihypertensive treatment intensity. THE AMERICAN JOURNAL OF MANAGED CARE 2022; 28:e157-e162. [PMID: 35546588 PMCID: PMC10694801 DOI: 10.37765/ajmc.2022.89146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
OBJECTIVES To change blood pressure treatment, clinicians can modify medication count or dose. However, existing studies have measured count modification, which may miss clinically important dose change in the absence of count change. This research demonstrates how dose modification captures more information about management than medication count alone. STUDY DESIGN Retrospective cohort study. METHODS We included patients 65 years and older with established primary care at the Veterans Health Administration (July 2011-June 2013). We captured medication count and standardized dose change over 90 to 120 days using a validated pharmacy fill algorithm. We determined frequency of dose change without count change (and vice versa), no change in either, change in same direction ("concordant"), and change in opposite direction ("discordant"). We compared change according to systolic blood pressure (SBP) and compared concordance using a minimum threshold definition of dose change of at least 50% (instead of any change) of baseline dose modification. RESULTS Among 440,801 patients, 64.2% had dose change; 22.0%, count change; 35.6%, no change in either; 42.4%, dose change without count modification; and 0.2%, count change without dose modification. Discordance occurred in 2.1% of observations. Using the minimum threshold definition of change, 68.7% had no change in either dose or count. Treatment was more frequently changed at SBP greater than 140 mm Hg. CONCLUSIONS Measuring change in antihypertensive treatment using medication count frequently missed an isolated dose change in treatment modification and less often misclassified regimen modifications where there was no modification in total dose. In future research, measuring dose modification using our new algorithm would capture change in hypertension treatment intensity more precisely than current methods.
Collapse
Affiliation(s)
- Carole E Aubert
- Department of General Internal Medicine, Bern University Hospital, Freiburgstrasse, 3010 Bern, Switzerland.
| | | | | | | | | | | | | | | |
Collapse
|
3
|
Aubert CE, Ha J, Kim HM, Rodondi N, Kerr EA, Hofer TP, Min L. Clinical outcomes of modifying hypertension treatment intensity in older adults treated to low blood pressure. J Am Geriatr Soc 2021; 69:2831-2841. [PMID: 34097300 PMCID: PMC8497391 DOI: 10.1111/jgs.17295] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 05/08/2021] [Accepted: 05/14/2021] [Indexed: 01/26/2023]
Abstract
BACKGROUND/OBJECTIVES Hypertension treatment reduces cardiovascular events. However, uncertainty remains about benefits and harms of deintensification or further intensification of antihypertensive medication when systolic blood pressure (SBP) is tightly controlled in older multimorbid patients, because of their frequent exclusion in trials. We assessed the association of hypertension treatment deintensification or intensification with clinical outcomes in older adults with tightly controlled SBP. DESIGN Longitudinal cohort study (2011-2013) with 9-month follow-up. SETTING U.S.-nationwide primary care Veterans Health Administration healthcare system. PARTICIPANTS Veterans aged 65 and older with baseline SBP <130 mmHg and ≥1 antihypertensive medication during ≥2 consecutive visits (N = 228,753). EXPOSURE Deintensification or intensification, compared with stable treatment. MAIN OUTCOMES AND MEASURES Cardiovascular events, syncope, or fall injury, as composite and distinct outcomes, within 9 months after exposure. Adjusted logistic regression and inverse probability of treatment weighting (IPTW, sensitivity analysis). RESULTS Among 228,753 patients (mean age 75 [SD 7.5] years), the composite outcome occurred in 11,982/93,793 (12.8%) patients with stable treatment, 14,768/72,672 (20.3%) with deintensification, and 11,821/62,288 (19.0%) with intensification. Adjusted absolute outcome risk (95% confidence interval) was higher for deintensification (18.3% [18.1%-18.6%]) and intensification (18.7% [18.4%-19.0%]), compared with stable treatment (14.8% [14.6%-15.0%]), p < 0.001 for both effects in the multivariable model). Deintensification was associated with fewer cardiovascular events than intensification. At baseline SBP <95 mmHg, cardiovascular event risk was similar for deintensification and stable treatment, and fall risk lower for deintensification than intensification. IPTW yielded similar results. Mean follow-up SBP was 124.1 mmHg for stable treatment, 125.1 mmHg after deintensification (p < 0.001), and 124.0 mmHg after intensification (p < 0.001). CONCLUSION Antihypertensive treatment deintensification in older patients with tightly controlled SBP was associated with worse outcomes than continuing same treatment intensity. Given higher mortality among patients with treatment modification, confounding by indication may not have been fully corrected by advanced statistical methods for observational data analysis.
Collapse
Affiliation(s)
- Carole E. Aubert
- Department of General Internal MedicineInselspital, Bern University Hospital, University of BernBernSwitzerland,Institute of Primary Health Care (BIHAM)University of BernBernSwitzerland,Center for Clinical Management ResearchVeterans Affairs Ann Arbor Healthcare SystemAnn ArborMichiganUSA,Institute for Healthcare Policy and InnovationUniversity of MichiganAnn ArborMichiganUSA
| | - Jin‐Kyung Ha
- Division of Geriatric and Palliative Medicine, Department of MedicineUniversity of MichiganAnn ArborMichiganUSA
| | - Hyungjin Myra Kim
- Consulting for Statistics, Computing & Analytics Research (CSCAR)University of MichiganAnn ArborMichiganUSA,Department of BiostatisticsUniversity of Michigan Medical SchoolAnn ArborMichiganUSA
| | - Nicolas Rodondi
- Department of General Internal MedicineInselspital, Bern University Hospital, University of BernBernSwitzerland,Institute of Primary Health Care (BIHAM)University of BernBernSwitzerland
| | - Eve A. Kerr
- Center for Clinical Management ResearchVeterans Affairs Ann Arbor Healthcare SystemAnn ArborMichiganUSA,Institute for Healthcare Policy and InnovationUniversity of MichiganAnn ArborMichiganUSA,Department of Internal MedicineUniversity of MichiganAnn ArborMichiganUSA
| | - Timothy P. Hofer
- Center for Clinical Management ResearchVeterans Affairs Ann Arbor Healthcare SystemAnn ArborMichiganUSA,Institute for Healthcare Policy and InnovationUniversity of MichiganAnn ArborMichiganUSA,Department of Internal MedicineUniversity of MichiganAnn ArborMichiganUSA
| | - Lillian Min
- Center for Clinical Management ResearchVeterans Affairs Ann Arbor Healthcare SystemAnn ArborMichiganUSA,Institute for Healthcare Policy and InnovationUniversity of MichiganAnn ArborMichiganUSA,Division of Geriatric and Palliative Medicine, Department of MedicineUniversity of MichiganAnn ArborMichiganUSA,VA Ann Arbor Medical Center VA Geriatric ResearchEducation, and Clinical Center (GRECC)Ann ArborMichiganUSA
| |
Collapse
|
4
|
Aubert CE, Ha JK, Kerr EA, Hofer TP, Min L. Factors associated with antihypertensive treatment intensification and deintensification in older outpatients. INTERNATIONAL JOURNAL CARDIOLOGY HYPERTENSION 2021; 9:100098. [PMID: 34258575 PMCID: PMC8254109 DOI: 10.1016/j.ijchy.2021.100098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 05/21/2021] [Accepted: 06/08/2021] [Indexed: 11/29/2022]
Abstract
Background New hypertension performance measures encourage more intensive treatment in older adults. Treatment intensification includes starting new medications and increasing the dose of old ones. Medication dose is particularly important to older adults, given their vulnerability to dose-related side effects. We previously validated a standardized measure of beneficial doses tested in hypertension trials, Hypertension Daily Dose (HDD). Aim of the study To test whether changes in treatment intensity using HDD was associated with systolic blood pressure (SBP) and patient characteristics. Methods Longitudinal study of all Veterans aged ≥65 years with a diagnosis of hypertension. We defined 3 groups of risk: 1) cardiovascular risk; 2) geriatric/frail; 3) low-risk (comparator). Using multinomial regression, we assessed the probability of deintensification, intensification, vs. stable treatment, according to SBP and group. Results Among 1,331,111 Veterans, 19.9% had deintensification, and 29.6% intensification. Deintensification decreased, while intensification increased, with SBP. Compared to low-risk patients, cardiovascular risk patients had 1.11 (95% CI 1.10-1.13) times the odds of intensifying, and geriatric/frail patients 1.45 (95%CI 1.43-1.47) times the odds of deintensifying. Discussion Patient-level HDD change was consistent with an expected association with cardiovascular risk and geriatric/frail conditions, suggesting that HDD can be used longitudinally to assess hypertension treatment modification in large health systems.
Collapse
Affiliation(s)
- Carole E Aubert
- Department of General Internal Medicine, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland.,Institute of Primary Health Care (BIHAM), University of Bern, Switzerland.,Center for Clinical Management Research, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, MI, USA.,Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA
| | - Jin-Kyung Ha
- Division of Geriatric and Palliative Medicine, Department of Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Eve A Kerr
- Center for Clinical Management Research, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, MI, USA.,Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA.,Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Timothy P Hofer
- Center for Clinical Management Research, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, MI, USA.,Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA.,Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Lillian Min
- Center for Clinical Management Research, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, MI, USA.,Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA.,Division of Geriatric and Palliative Medicine, Department of Medicine, University of Michigan, Ann Arbor, MI, USA.,VA Ann Arbor Medical Center VA Geriatric Research, Education, and Clinical Center (GRECC), Ann Arbor, MI, USA
| |
Collapse
|
5
|
Min L, Ha JK, Aubert CE, Hofer TP, Sussman JB, Langa KM, Tinetti M, Kim HM, Maciejewski ML, Gillon L, Larkin A, Chan CL, Kerr EA, Bravata D, Cushman WC. A Method to Quantify Mean Hypertension Treatment Daily Dose Intensity Using Health Care System Data. JAMA Netw Open 2021; 4:e2034059. [PMID: 33449097 PMCID: PMC7811181 DOI: 10.1001/jamanetworkopen.2020.34059] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 11/28/2020] [Indexed: 12/27/2022] Open
Abstract
Importance Simple measures of hypertension treatment, such as achievement of blood pressure (BP) targets, ignore the intensity of treatment once the BP target is met. High-intensity treatment involves increased treatment burden and can be associated with potential adverse effects in older adults. A method was previously developed to identify older patients receiving intense hypertension treatment by low BP and number of BP medications using national Veterans Health Administration and Medicare Part D administrative pharmacy data to evaluate which BP medications a patient is likely taking on any given day. Objective To further develop and validate a method to more precisely quantify dose intensity of hypertension treatment using only health system administrative pharmacy fill data. Design, Setting, and Participants Observational, cross-sectional study of 319 randomly selected older veterans in the national Veterans Health Administration health care system who were taking multiple BP-lowering medications and had a total of 3625 ambulatory care visits from July 1, 2011, to June 30, 2013. Measure development and medical record review occurred January 1, 2017, through November 30, 2018, and data analysis was conducted from December 1, 2019, to August 31, 2020. Main Outcomes and Measures For each BP-lowering medication, a moderate hypertension daily dose (HDD) was defined as half the maximum dose above which no further clinical benefit has been demonstrated by that medication in hypertension trials. Patients' total HDD was calculated using pharmacy data (pharmacy HDDs), accounting for substantial delays in refills (>30 days) when a patient's pill supply was stretched (eg, cutting existing pills in half). As an external comparison, the pharmacy HDDs were correlated with doses manually extracted from clinicians' visit notes (clinically noted HDDs). How well the pharmacy HDDs correlated with clinically noted HDDs was calculated (using C statistics). To facilitate interpretation, HDDs were described in association with the number of medications. Results A total of 316 patients (99.1%) were male; the mean (SD) age was 75.6 (7.2) years. Pharmacy HDDs were highly correlated (r = 0.92) with clinically noted HDDs, with a mean (SD) of 2.7 (1.8) for pharmacy HDDs and 2.8 (1.8) for clinically noted HDDs. Pharmacy HDDs correlated with high-intensity, clinically noted HDDs ranging from a C statistic of 92.8% (95% CI, 92.0%-93.7%) for 2 or more clinically noted HDDs to 88.1% (95% CI, 85.5%-90.6%) for 6 or more clinically noted HDDs. Conclusions and Relevance This study suggests that health system pharmacy data may be used to accurately quantify hypertension regimen dose intensity. Together with clinic-measured BP, this tool can be used in future health system-based research or quality improvement efforts to fine-tune, manage, and optimize hypertension treatment in older adults.
Collapse
Affiliation(s)
- Lillian Min
- Veterans Affairs Geriatric Research, Education, and Clinical Center, Veterans Affairs Ann Arbor Medical Center, Ann Arbor, Michigan
- Division of Geriatric and Palliative Medicine, Department of Medicine, University of Michigan, Ann Arbor
- Veterans Affairs Center for Clinical Management Research, Health Services Research and Development Center of Innovation, Ann Arbor, Michigan
- Institute of Healthcare Policy and Innovation, University of Michigan, Ann Arbor
| | - Jin-Kyung Ha
- Division of Geriatric and Palliative Medicine, Department of Medicine, University of Michigan, Ann Arbor
| | - Carole E. Aubert
- Veterans Affairs Center for Clinical Management Research, Health Services Research and Development Center of Innovation, Ann Arbor, Michigan
- Institute of Healthcare Policy and Innovation, University of Michigan, Ann Arbor
- Department of General Internal Medicine, Bern University Hospital, University of Bern, Bern, Switzerland
- Institute of Primary Healthcare, University of Bern, Bern, Switzerland
| | - Timothy P. Hofer
- Veterans Affairs Center for Clinical Management Research, Health Services Research and Development Center of Innovation, Ann Arbor, Michigan
- Institute of Healthcare Policy and Innovation, University of Michigan, Ann Arbor
- Division of General Internal Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor
| | - Jeremy B. Sussman
- Veterans Affairs Center for Clinical Management Research, Health Services Research and Development Center of Innovation, Ann Arbor, Michigan
- Institute of Healthcare Policy and Innovation, University of Michigan, Ann Arbor
- Division of General Internal Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor
| | - Kenneth M. Langa
- Veterans Affairs Geriatric Research, Education, and Clinical Center, Veterans Affairs Ann Arbor Medical Center, Ann Arbor, Michigan
- Institute of Healthcare Policy and Innovation, University of Michigan, Ann Arbor
- Division of General Internal Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor
- Institute for Social Research, University of Michigan, Ann Arbor
| | - Mary Tinetti
- Section of Geriatrics, Department of Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Hyungjin Myra Kim
- Consulting for Statistics, Computing & Analytics Research, University of Michigan, Ann Arbor
- Department of Biostatistics, University of Michigan Medical School, Ann Arbor
| | - Matthew L. Maciejewski
- Center of Innovation to Accelerate Discovery and Practice Transformation, Veterans Affairs Healthcare System, Durham, North Carolina
- Department of Population Health Sciences, Duke University, Durham, North Carolina
| | - Leah Gillon
- Veterans Affairs Center for Clinical Management Research, Health Services Research and Development Center of Innovation, Ann Arbor, Michigan
| | - Angela Larkin
- Veterans Affairs Center for Clinical Management Research, Health Services Research and Development Center of Innovation, Ann Arbor, Michigan
| | - Chiao-Li Chan
- Division of Geriatric and Palliative Medicine, Department of Medicine, University of Michigan, Ann Arbor
| | - Eve A. Kerr
- Veterans Affairs Center for Clinical Management Research, Health Services Research and Development Center of Innovation, Ann Arbor, Michigan
- Institute of Healthcare Policy and Innovation, University of Michigan, Ann Arbor
- Division of General Internal Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor
| | - Dawn Bravata
- Veterans Affairs Health Services Research and Development Center for Health Information and Communication, Richard L. Roudebush Veterans Affairs Medical Center, Indianapolis, Indiana
- Department of Medicine, Indiana University School of Medicine, Indianapolis
- Department of Neurology, Indiana University School of Medicine, Indianapolis
- Center for Health Services Research, Regenstrief Institute, Indianapolis, Indiana
| | - William C. Cushman
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis
- Medical Service, Memphis Veterans Affairs Medical Center, Memphis, Tennessee
| |
Collapse
|