Lillian Min1,2,3,4, Jin-Kyung Ha2, Timothy P Hofer3,4,5, Jeremy Sussman3,4,5, Kenneth Langa1,4,5,6, William C Cushman7,8, Mary Tinetti9, Hyungjin Myra Kim10,11, Matthew L Maciejewski12,13, Leah Gillon3, Angela Larkin3, Chiao-Li Chan2, Eve Kerr3,4,5. 1. VA Ann Arbor Medical Center, Geriatric Research, Education, and Clinical Center, Ann Arbor, Michigan. 2. Division of Geriatric and Palliative Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor. 3. VA Center for Clinical Management Research, Health Services Research and Development Center of Innovation, Ann Arbor, Michigan. 4. Institute of Healthcare Policy and Innovation, University of Michigan, Ann Arbor. 5. Division of General Internal Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor. 6. Institute for Social Research, University of Michigan, Ann Arbor. 7. Preventive Medicine Section, Memphis VA Medical Center, Memphis, Tennessee. 8. Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis. 9. Department of Medicine, Section of Geriatrics, Yale School of Medicine, New Haven, Connecticut. 10. Consulting for Statistics, Computing and Analytics Research, University of Michigan, Ann Arbor. 11. Department of Biostatistics, University of Michigan Medical School, Ann Arbor. 12. Accelerate Discovery and Practice Transformation Center of Innovation, VA Health Care System, Durham, North Carolina. 13. Department of Population Health Sciences, Duke University, Durham, North Carolina.
Abstract
Importance: Blood pressure (BP) targets are the main measure of high-quality hypertension care in health systems. However, BP alone does not reflect intensity of pharmacological treatment, which should be carefully managed in older patients. Objectives: To develop and validate an electronic health record (EHR) data-only algorithm using pharmacy and BP data to capture intensive hypertension care (IHC), defined as 3 or more BP medications and BP less than 120 mm Hg, and to identify conditions associated with greater IHC, either through greater algorithm false-positive IHC, or by contributing clinically to delivering more IHC. Design, Setting, and Participants: This cross-sectional study was conducted among 319 randomly selected patients aged 65 years or older receiving IHC from the Veterans Health Administration (VHA) from July 1, 2011, to June 30, 2013. Data were collected from a total of 3625 primary care visits. Data were analyzed from January 2017 to March 2020. Exposures: Calibration and measurement of the algorithm for IHC (algorithm IHC). Main Outcomes and Measures: For each primary care visit, the reference standard, clinical IHC, was determined by detailed review of free-text clinical notes. The correlation in BP medication count between the EHR-only algorithm vs the reference standard and the sensitivity and specificity of the algorithm IHC were calculated. In addition, presence vs absence of contributing conditions acting in combination with hypertension management were measured to examine incidence of IHC associated with contributing conditions, including an acute condition that lowered BP (eg, dehydration), another condition requiring a BP target lower than the standard 140 mm Hg (eg, diabetes), or the patient needing a BP-lowering medication for a nonhypertension condition (eg, β-blocker for atrial fibrillation) resulting in low BP. Results: Among 319 patients with 3625 visits (mean [SD] age, 75.6 [7.2] years; 3592 [99.1%] men), 911 visits (25.1%) had clinical IHC by the reference standard. The algorithm for determining medication count was highly correlated with the reference standard (r = 0.84). Sensitivity of detecting clinical IHC was 92.2% (95% CI, 89.3%-95.1%), and specificity was 97.2% (95% CI, 96.1%-98.3%), suggesting that clinical IHC can be identified from routinely collected data. Only 75 visits (2.1%) were algorithm IHC false positives, 55 visits (1.5%) involved IHC with contributing conditions, and 125 visits (3.5%) involved either false-positive or IHC with contributing conditions. Among select contributing conditions, congestive heart failure (37 patients [5.2%]) was most associated with a prespecified combined false-positive or IHC with contributing conditions rate higher than 5%. Conclusions and Relevance: These findings suggest that health system data can be used reliably to estimate IHC.
Importance: Blood pressure (BP) targets are the main measure of high-quality hypertension care in health systems. However, BP alone does not reflect intensity of pharmacological treatment, which should be carefully managed in older patients. Objectives: To develop and validate an electronic health record (EHR) data-only algorithm using pharmacy and BP data to capture intensive hypertension care (IHC), defined as 3 or more BP medications and BP less than 120 mm Hg, and to identify conditions associated with greater IHC, either through greater algorithm false-positive IHC, or by contributing clinically to delivering more IHC. Design, Setting, and Participants: This cross-sectional study was conducted among 319 randomly selected patients aged 65 years or older receiving IHC from the Veterans Health Administration (VHA) from July 1, 2011, to June 30, 2013. Data were collected from a total of 3625 primary care visits. Data were analyzed from January 2017 to March 2020. Exposures: Calibration and measurement of the algorithm for IHC (algorithm IHC). Main Outcomes and Measures: For each primary care visit, the reference standard, clinical IHC, was determined by detailed review of free-text clinical notes. The correlation in BP medication count between the EHR-only algorithm vs the reference standard and the sensitivity and specificity of the algorithm IHC were calculated. In addition, presence vs absence of contributing conditions acting in combination with hypertension management were measured to examine incidence of IHC associated with contributing conditions, including an acute condition that lowered BP (eg, dehydration), another condition requiring a BP target lower than the standard 140 mm Hg (eg, diabetes), or the patient needing a BP-lowering medication for a nonhypertension condition (eg, β-blocker for atrial fibrillation) resulting in low BP. Results: Among 319 patients with 3625 visits (mean [SD] age, 75.6 [7.2] years; 3592 [99.1%] men), 911 visits (25.1%) had clinical IHC by the reference standard. The algorithm for determining medication count was highly correlated with the reference standard (r = 0.84). Sensitivity of detecting clinical IHC was 92.2% (95% CI, 89.3%-95.1%), and specificity was 97.2% (95% CI, 96.1%-98.3%), suggesting that clinical IHC can be identified from routinely collected data. Only 75 visits (2.1%) were algorithm IHC false positives, 55 visits (1.5%) involved IHC with contributing conditions, and 125 visits (3.5%) involved either false-positive or IHC with contributing conditions. Among select contributing conditions, congestive heart failure (37 patients [5.2%]) was most associated with a prespecified combined false-positive or IHC with contributing conditions rate higher than 5%. Conclusions and Relevance: These findings suggest that health system data can be used reliably to estimate IHC.
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