Chamberlain, Alanna M.

Objectives To describe the percentile distribution of multimorbidity across age by sex, race and ethnicity, and to demonstrate the utility of multimorbidity percentiles to predict mortality. Design Population-based descriptive study and cohort study. Setting Olmsted County, Minnesota (USA). Participants We used the medical records-linkage system of the Rochester Epidemiology Project (REP; http://www.rochesterproject.org ) to identify all residents of Olmsted County, Minnesota who reached one or more birthdays between 1 January 2005 and 31 December 2014 (10 years). Methods For each person, we obtained the count of chronic conditions (out of 20 conditions) present on each birthday by extracting all of the diagnostic codes received in the 5 years before the index birthday from the electronic indexes of the REP. To compare each person’s count to peers of same age, the counts were transformed into percentiles of the total population and displayed graphically across age by sex, race and ethnicity. In addition, quintiles 1, 2, 4 and 5 were compared with quintile 3 (reference) to predict the risk of death at 1 year, 5 years and through end of follow-up using time-to-event analyses. Follow-up was passive using the REP. Results We identified 238 010 persons who experienced a total of 1 458 094 birthdays during the study period (median of 6 birthdays per person; IQR 3–10). The percentiles of multimorbidity across age did not vary noticeably by sex, race or ethnicity. In general, there was an increased risk of mortality at 1 and 5 years for quintiles 4 and 5 of multimorbidity. The risk of mortality for quintile 5 was greater for younger age groups and for women. Conclusions The assignment of multimorbidity percentiles to persons in a population may be a simple and intuitive tool to assess relative health status, and to predict short-term mortality, especially in younger persons and in women.

Background Prior reports indicate that living in a rural area may be associated with worse health outcomes. However, data on rurality and heart failure (HF) outcomes are scarce. Methods and Results Residents from 6 southeastern Minnesota counties with a first‐ever code for HF ( International Classification of Diseases, Ninth Revision [ ICD‐9 ], code 428, and International Classification of Diseases, Tenth Revision [ ICD‐10 ] code I50) between January 1, 2013 and December 31, 2016, were identified. Resident address was classified according to the rural‐urban commuting area codes. Rurality was defined as living in a nonmetropolitan area. Cox regression was used to analyze the association between living in a rural versus urban area and death; Andersen‐Gill models were used for hospitalization and emergency department visits. Among 6003 patients with HF (mean age 74 years, 48% women), 43% lived in a rural area. Rural patients were older and had a lower educational attainment and less comorbidity compared with patients living in urban areas ( P <0.001). After a mean (SD) follow‐up of 2.8 (1.7) years, 2440 deaths, 20 506 emergency department visits, and 11 311 hospitalizations occurred. After adjustment, rurality was independently associated with an increased risk of death (hazard ratio [HR], 1.18; 95% CI, 1.09–1.29) and a reduced risk of emergency department visits (HR, 0.89; 95% CI, 0.82–0.97) and hospitalizations (HR, 0.78; 95% CI, 0.73–0.84). Conclusions Among patients with HF, living in a rural area is associated with an increased risk of death and fewer emergency department visits and hospitalizations. Further study to identify and address the mechanisms through which rural residence influences mortality and healthcare utilization in HF is needed in order to reduce disparities in rural health.

Objective: To determine whether a bi-directional relationship exists between depression and HF within a single population of individuals receiving primary care services, using longitudinal electronic health records (EHRs). Methods: This retrospective cohort study utilized EHRs for adults who received primary care services within a large healthcare system in 2006. Validated EHR-based algorithms identified 10,649 people with depression (depression cohort) and 5,911 people with HF (HF cohort) between January 1, 2006 and December 31, 2018. Each person with depression or HF was matched 1:1 with an unaffected referent on age, sex, and outpatient service use. Each cohort (with their matched referents) was followed up electronically to identify newly diagnosed HF (in the depression cohort) and depression (in the HF cohort) that occurred after the index diagnosis of depression or HF, respectively. The risks of these outcomes were compared (vs. referents) using marginal Cox proportional hazard models adjusted for 16 comorbid chronic conditions. Results: 2,024 occurrences of newly diagnosed HF were observed in the depression cohort and 944 occurrences of newly diagnosed depression were observed in the HF cohort over approximately 4–6 years of follow-up. People with depression had significantly increased risk for developing newly diagnosed HF (HR 2.08, 95% CI 1.89–2.28) and people with HF had a significantly increased risk of newly diagnosed depression (HR 1.34, 95% CI 1.17–1.54) after adjusting for all 16 comorbid chronic conditions. Conclusion: These results provide evidence of a bi-directional relationship between depression and HF independently of age, sex, and multimorbidity from chronic illnesses.

AbstractThe purpose of this analysis was to develop and validate computable phenotypes for heart failure (HF) with preserved ejection fraction (HFpEF) using claims‐type measures using the Rochester Epidemiology Project. This retrospective study utilized an existing cohort of Olmsted County, Minnesota residents aged ≥ 20 years diagnosed with HF between 2007 and 2015. The gold standard definition of HFpEF included meeting the validated Framingham criteria for HF and having an LVEF ≥ 50%. Computable phenotypes of claims‐type data elements (including ICD‐9/ICD‐10 diagnostic codes and lab test codes) both individually and in combinations were assessed via sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with respect to the gold standard. In the Framingham‐validated cohort, 2,035 patients had HF; 1,172 (58%) had HFpEF. One in‐patient or two out‐patient diagnosis codes of ICD‐9 428.3X or ICD‐10 I50.3X had 46% sensitivity, 88% specificity, 84% PPV, and 54% NPV. The addition of a BNP/NT‐proBNP test code reduced sensitivity to 35% while increasing specificity to 91% (PPV = 84%, NPV = 51%). Broadening the diagnostic codes to ICD‐9 428.0, 428.3X, and 428.9/ICD‐10 I50.3X and I50.9 increased sensitivity at the expense of decreasing specificity (diagnostic code‐only model: 87% sensitivity, 8% specificity, 56% PPV, 30% NPV; diagnostic code and BNP lab code model: 61% sensitivity, 43% specificity, 60% PPV, 45% NPV). In an analysis conducted to mimic real‐world use of the computable phenotypes, any one in‐patient or out‐patient code of ICD‐9 428/ICD‐10 150 among the broader population (N = 3,755) resulted in lower PPV values compared with the Framingham cohort. However, one in‐patient or two out‐patient instances of ICD‐9 428.0, 428.9, or 428.3X/ICD‐10 150.3X or 150.9 brought the PPV values from the two cohorts closer together. While some misclassification remains, the computable phenotypes defined here may be used in claims databases to identify HFpEF patients and to gain a greater understanding of the characteristics of patients with HFpEF.

AbstractIntroductionThe best management strategy for patients with atrial fibrillation (AF) with heart failure (HF) and preserved left ventricular ejection fraction (LVEF) is unknown.Methods and ResultsThis cohort study was conducted in Olmsted County, Minnesota, with resources of the Rochester Epidemiology Project. Patients with incident AF occurring between 2000 and 2014 with a prior or concurrent HF were included. Patients with LVEF ≥ 50% were designated as HF and preserved ejection fraction (HFpEF) and those with LVEF < 50% were designated as HF and reduced ejection fraction (HFrEF). Rhythm control in the first year after AF diagnosis was defined as prescriptions for an antiarrhythmic drug, catheter ablation, or maze procedure. The primary endpoint was all‐cause mortality. The secondary endpoints were cardiovascular death, cardiovascular hospitalization, and stroke or transient ischemic attack. Of 859 patients (age, 77.2 ± 12.1 years; 49.2%, female), 447 had HFpEF‐AF, and 412 had HFrEF‐AF. There was no difference in all‐cause mortality (10‐year mortality, 83% vs 79%; p = .54) or secondary endpoints between the HFpEF‐AF and HFrEF‐AF, respectively. Compared with the rate control strategy, rhythm control in HFpEF‐AF patients (n = 40, 15.9%) offered no survival benefits (adjusted HR, 0.70; 95% CI, 0.42–1.16; p = .16), whereas rhythm control in HFrEF‐AF patients (n = 52, 22.5%) decrease cardiovascular mortality (HR, 0.38; 95% CI, 0.17–0.86; p = .02).ConclusionsPatients with HFpEF‐AF and HFrEF‐AF had similar poor prognoses. Rhythm control strategy was seldom adopted in community care in patients with HF and AF. A rhythm control strategy may provide survival benefit for patients with HFrEF‐AF and the benefit of rhythm control in patients with HFpEF‐AF warrants further study.

Background Polypharmacy is highly prevalent in elderly people with chronic conditions, including atrial fibrillation ( AF ). The impact of polypharmacy on adverse outcomes and on treatment effectiveness in elderly patients with AF remains unaddressed. Methods and Results We studied 338 810 AF patients ≥75 years of age enrolled in the MarketScan Medicare Supplemental database in 2007–2015. Polypharmacy was defined as ≥5 active prescriptions at AF diagnosis (defined by the presence of International Classification of Diseases, Ninth Revision, Clinical Modification [ ICD‐9‐CM ] codes) based on outpatient pharmacy claims. AF treatments (oral anticoagulation, rhythm and rate control) and cardiovascular end points (ischemic stroke, bleeding, heart failure) were defined based on inpatient, outpatient, and pharmacy claims. Multivariable Cox models were used to estimate associations of polypharmacy with cardiovascular end points and the interaction between polypharmacy and AF treatments in relation to cardiovascular end points. Prevalence of polypharmacy was 52%. Patients with polypharmacy had increased risk of major bleeding (hazard ratio [ HR ], 1.16; 95% CI, 1.12–1.20) and heart failure ( HR , 1.33; 95% CI , 1.29–1.36) but not ischemic stroke ( HR , 0.96; 95% CI , 0.92–1.00), compared with those not receiving polypharmacy. Polypharmacy status did not consistently modify the effectiveness of oral anticoagulants. Rhythm control (versus rate control) was more effective in preventing heart failure hospitalization in patients not receiving polypharmacy ( HR , 0.87; 95% CI , 0.76–0.99) than among those with polypharmacy ( HR , 0.98; 95% CI , 0.91–1.07; P =0.02 for interaction). Conclusion Polypharmacy is common among patients ≥75 with AF , is associated with adverse outcomes, and may modify the effectiveness of AF treatments. Optimizing management of polypharmacy in AF patients ≥75 may lead to improved outcomes.