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Leveraging the Electronic Health Records for Population Health: A Case Study of Patients With Markedly Elevated Blood Pressure.

ABSTRACT: Background The digital transformation of medical data provides opportunities to perform digital population health surveillance and identify people inadequately managed in usual care. We leveraged the electronic health records of a large health system to identify patients with markedly elevated blood pressure and characterize their follow-up care pattern. Methods and Results We included 373 861 patients aged 18 to 85 years, who had at least 1 outpatient encounter in the Yale New Haven Health System between January 2013 and December 2017. We described the prevalence and follow-up pattern of patients with at least 1 systolic blood pressure (SBP) ?160 mm Hg or diastolic blood pressure (DBP) ?100 mm Hg and patients with at least 1 SBP ?180 mm Hg or DBP ?120 mm Hg. Of 373 861 patients included, 56 909 (15.2%) had at least 1 SBP ?160 mm Hg or DBP ?100 mm Hg, and 10 476 (2.8%) had at least 1 SBP ?180 mm Hg or DBP ?120 mm Hg. Among patients with SBP ?160 mm Hg or DBP ?100 mm Hg, only 28.3% had a follow visit within 1 month (time window of follow-up recommended by the guideline) and 19.9% subsequently achieved control targets (SBP <130 mm Hg and DBP <80 mm Hg) within 6 months. Follow-up rate at 1 month and control rate at 6 months for patients with SBP ?180 mm Hg or DBP ?120 mm Hg was 31.9% and 17.2%. Conclusions Digital population health surveillance with an electronic health record identified a large number of patients with markedly elevated blood pressure and inadequate follow-up. Many of these patients subsequently failed to achieve control targets.

SUBMITTER: Lu Y

PROVIDER: S-EPMC7428633 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Leveraging the Electronic Health Records for Population Health: A Case Study of Patients With Markedly Elevated Blood Pressure.

Lu Yuan Y Huang Chenxi C Mahajan Shiwani S Schulz Wade L WL Nasir Khurram K Spatz Erica S ES Krumholz Harlan M HM

Journal of the American Heart Association 20200323 7

Background The digital transformation of medical data provides opportunities to perform digital population health surveillance and identify people inadequately managed in usual care. We leveraged the electronic health records of a large health system to identify patients with markedly elevated blood pressure and characterize their follow-up care pattern. Methods and Results We included 373 861 patients aged 18 to 85 years, who had at least 1 outpatient encounter in the Yale New Haven Health Syst ...[more]

PMID: 32200730

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Project description:Background How measures of long-term exposure to elevated blood pressure might add to the performance of "current" blood pressure in predicting future cardiovascular disease is unclear. We compared incident cardiovascular disease risk prediction using past, current, and usual systolic blood pressure alone or in combination. Methods and Results Using data from UK primary care linked electronic health records, we applied a landmark cohort study design and identified 80 964 people, aged 50 years (derivation cohort=64 772; validation cohort=16 192), who, at study entry, had recorded blood pressure, no prior cardiovascular disease, and no previous antihypertensive or lipid-lowering prescriptions. We used systolic blood pressure recorded up to 10 years before baseline to estimate past systolic blood pressure (mean, time-weighted mean, and variability) and usual systolic blood pressure (correcting current values for past time-dependent blood pressure fluctuations) and examined their prospective relation with incident cardiovascular disease (first hospitalization for or death from coronary heart disease or stroke/transient ischemic attack). We used Cox regression to estimate hazard ratios and applied Bayesian analysis within a machine learning framework in model development and validation. Predictive performance of models was assessed using discrimination (area under the receiver operating characteristic curve) and calibration metrics. We found that elevated past, current, and usual systolic blood pressure values were separately and independently associated with increased incident cardiovascular disease risk. When used alone, the hazard ratio (95% credible interval) per 20-mm Hg increase in current systolic blood pressure was 1.22 (1.18-1.30), but associations were stronger for past systolic blood pressure (mean and time-weighted mean) and usual systolic blood pressure (hazard ratio ranging from 1.39-1.45). The area under the receiver operating characteristic curve for a model that included current systolic blood pressure, sex, smoking, deprivation, diabetes mellitus, and lipid profile was 0.747 (95% credible interval, 0.722-0.811). The addition of past systolic blood pressure mean, time-weighted mean, or variability to this model increased the area under the receiver operating characteristic curve (95% credible interval) to 0.750 (0.727-0.811), 0.750 (0.726-0.811), and 0.748 (0.723-0.811), respectively, with all models showing good calibration. Similar small improvements in area under the receiver operating characteristic curve were observed when testing models on the validation cohort, in sex-stratified analyses, or by using different landmark ages (40 or 60 years). Conclusions Using multiple blood pressure recordings from patients' electronic health records showed stronger associations with incident cardiovascular disease than a single blood pressure measurement, but their addition to multivariate risk prediction models had negligible effects on model performance.

Project description:BACKGROUND:Administrative health records (AHRs) and electronic medical records (EMRs) are two key sources of population-based data for disease surveillance, but misclassification errors in the data can bias disease estimates. Methods that combine information from error-prone data sources can build on the strengths of AHRs and EMRs. We compared bias and error for four data-combining methods and applied them to estimate hypertension prevalence. METHODS:Our study included rule-based OR and AND methods that identify disease cases from either or both data sources, respectively, rule-based sensitivity-specificity adjusted (RSSA) method that corrects for inaccuracies using a deterministic rule, and probabilistic-based sensitivity-specificity adjusted (PSSA) method that corrects for error using a statistical model. Computer simulation was used to estimate relative bias (RB) and mean square error (MSE) under varying conditions of population disease prevalence, correlation amongst data sources, and amount of misclassification error. AHRs and EMRs for Manitoba, Canada were used to estimate hypertension prevalence using validated case definitions and multiple disease markers. RESULTS:The OR method had the lowest RB and MSE when population disease prevalence was 10%, and the RSSA method had the lowest RB and MSE when population prevalence increased to 20%. As the correlation between data sources increased, the OR method resulted in the lowest RB and MSE. Estimates of hypertension prevalence for AHRs and EMRs alone were 30.9% (95% CI: 30.6-31.2) and 24.9% (95% CI: 24.6-25.2), respectively. The estimates were 21.4% (95% CI: 21.1-21.7), for the AND method, 34.4% (95% CI: 34.1-34.8) for the OR method, 32.2% (95% CI: 31.8-32.6) for the RSSA method, and ranged from 34.3% (95% CI: 34.1-34.5) to 35.9% (95% CI, 35.7-36.1) for the PSSA method, depending on the statistical model. CONCLUSIONS:The OR and AND methods are influenced by correlation amongst the data sources, while the RSSA method is dependent on the accuracy of prior sensitivity and specificity estimates. The PSSA method performed well when population prevalence was high and average correlations amongst disease markers was low. This study will guide researchers to select a data-combining method that best suits their data characteristics.

Project description:BACKGROUND:Clinical trials show benefit from lowering systolic blood pressure (SBP) in people ?80 years of age, but nonrandomized epidemiological studies suggest lower SBP may be associated with higher mortality. This study aimed to evaluate associations of SBP with all-cause mortality by frailty category >80 years of age and to evaluate SBP trajectories before death. METHODS:A population-based cohort study was conducted using electronic health records of 144?403 participants ?80 years of age registered with family practices in the United Kingdom from 2001 to 2014. Participants were followed for ?5 years. Clinical records of SBP were analyzed. Frailty status was classified using the e-Frailty Index into the categories of fit, mild, moderate, and severe. All-cause mortality was evaluated by frailty status and mean SBP in Cox proportional-hazards models. SBP trajectories were evaluated using person months as observations, with mean SBP and antihypertensive treatment status estimated for each person month. Fractional polynomial models were used to estimate SBP trajectories over 5 years before death. RESULTS:During follow-up, 51?808 deaths occurred. Mortality rates increased with frailty level and were greatest at SBP <110 mm?Hg. In fit women, mortality was 7.7 per 100 person years at SBP 120 to 139 mm?Hg, 15.2 at SBP 110 to 119 mm?Hg, and 22.7 at SBP <110 mm?Hg. For women with severe frailty, rates were 16.8, 25.2, and 39.6, respectively. SBP trajectories showed an accelerated decline in the last 2 years of life. The relative odds of SBP <120 mm?Hg were higher in the last 3 months of life than 5 years previously in both treated (odds ratio, 6.06; 95% confidence interval, 5.40-6.81) and untreated (odds ratio, 6.31; 95% confidence interval, 5.30-7.52) patients. There was no evidence of intensification of antihypertensive therapy in the final 2 years of life. CONCLUSIONS:A terminal decline of SBP in the final 2 years of life suggests that nonrandomized epidemiological associations of low SBP with higher mortality may be accounted for by reverse causation if participants with lower blood pressure values are closer, on average, to the end of life.

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Leveraging the Electronic Health Records for Population Health: A Case Study of Patients With Markedly Elevated Blood Pressure.

Publications

Leveraging the Electronic Health Records for Population Health: A Case Study of Patients With Markedly Elevated Blood Pressure.

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