Dataset Information

Use of Electronic Health Data to Estimate Heart Failure Events in a Population-Based Cohort with CKD.

ABSTRACT:

Background and objectives

Studies that use electronic health data typically identify heart failure (HF) events from hospitalizations with a principal diagnosis of HF. This approach may underestimate the total burden of HF among persons with CKD. We assessed the accuracy of algorithms for identifying validated HF events from hospitalizations and outpatient encounters, and we used this validation information to estimate the rate of HF events in a large CKD population.

Design, setting, participants, & measurements

We identified a cohort of 15,141 adults age 18-89 years with an eGFR<60 ml/min per 1.73 m² from 2008 to 2011. Potential HF events during follow-up were randomly sampled for validation with medical record review. Positive predictive values from the validation study were used to estimate the rate of validated HF events in the full cohort.

Results

A total of 1864 participants had at least one health care encounter that qualified as a potential HF event during 2.7 years of mean follow-up. Among 313 potential events that were randomly sampled for validation, positive predictive values were 92% for hospitalizations with a principal diagnosis of HF, 32% for hospitalizations with a secondary diagnosis of HF, and 70% for qualifying outpatient HF encounters. Through use of this validation information in the full cohort, the rate of validated HF events estimated from the most comprehensive algorithm that included principal and secondary diagnosis hospitalizations and outpatient encounters was 35.2 events/1000 person-years (95% confidence interval, 33.1 to 37.4), compared with 9.5 events/1000 person-years (95% confidence interval, 8.7 to 10.5) from the algorithm that included only principal diagnosis hospitalizations. Outpatient encounters accounted for 20% of the total number of validated HF events.

Conclusions

In studies that rely on electronic health data, algorithms that include hospitalizations with a secondary diagnosis of HF and outpatient HF encounters more fully capture the burden of HF, although validation of HF events may be necessary with this approach.

SUBMITTER: Floyd JS

PROVIDER: S-EPMC5108195 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Publications

Use of Electronic Health Data to Estimate Heart Failure Events in a Population-Based Cohort with CKD.

Floyd James S JS Wellman Robert R Fuller Sharon S Bansal Nisha N Psaty Bruce M BM de Boer Ian H IH Scholes Delia D

Clinical journal of the American Society of Nephrology : CJASN 20160809 11

<h4>Background and objectives</h4>Studies that use electronic health data typically identify heart failure (HF) events from hospitalizations with a principal diagnosis of HF. This approach may underestimate the total burden of HF among persons with CKD. We assessed the accuracy of algorithms for identifying validated HF events from hospitalizations and outpatient encounters, and we used this validation information to estimate the rate of HF events in a large CKD population.<h4>Design, setting, p ...[more]

PMID: 27507770

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Project description:CKD is common in patients with heart failure, associated with high mortality and morbidity, which is even higher in people undergoing long-term dialysis. Despite increasing use of evidence-based drug and device therapy in patients with heart failure in the general population, patients with CKD have not benefitted. This review discusses prevalence and evidence of kidney replacement, device, and drug therapies for heart failure in CKD. Evidence for treatment with β-blockers, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, angiotensin receptor neprilysin inhibitors, and sodium-glucose cotransporter inhibitors in mild-to-moderate CKD has emerged from general population studies in patients with heart failure with reduced ejection fraction (HFrEF). β-Blockers have been shown to improve outcomes in patients with HFrEF in all stages of CKD, including patients on dialysis. However, studies of HFrEF selected patients with creatinine <2.5 mg/dl for ACE inhibitors, <3.0 mg/dl for angiotensin-receptor blockers, and <2.5 mg/dl for mineralocorticoid receptor antagonists, excluding patients with severe CKD. Angiotensin receptor neprilysin inhibitor therapy was successfully used in randomized trials in patients with eGFR as low as 20 ml/min per 1.73 m2 Hence, the benefits of renin-angiotensin-aldosterone axis inhibitor therapy in patients with mild-to-moderate CKD have been demonstrated, yet such therapy is not used in all suitable patients because of fear of hyperkalemia and worsening kidney function. Sodium-glucose cotransporter inhibitor therapy improved mortality and hospitalization in patients with HFrEF and CKD stages 3 and 4 (eGFR>20 ml/min per 1.73 m2). High-dose and combination diuretic therapy, often necessary, may be complicated with worsening kidney function and electrolyte imbalances, but has been used successfully in patients with CKD stages 3 and 4. Intravenous iron improved symptoms in patients with heart failure and CKD stage 3; and high-dose iron reduced heart failure hospitalizations by 44% in patients on dialysis. Cardiac resynchronization therapy reduced death and hospitalizations in patients with heart failure and CKD stage 3. Peritoneal dialysis in patients with symptomatic fluid overload improved symptoms and prevented hospital admissions. Evidence suggests that combined cardiology-nephrology clinics may help improve management of patients with HFrEF and CKD. A multidisciplinary approach may be necessary for implementation of evidence-based therapy.

Project description:ImportanceAccurate, real-time case identification is needed to target interventions to improve quality and outcomes for hospitalized patients with heart failure. Problem lists may be useful for case identification but are often inaccurate or incomplete. Machine-learning approaches may improve accuracy of identification but can be limited by complexity of implementation.ObjectiveTo develop algorithms that use readily available clinical data to identify patients with heart failure while in the hospital.Design, setting, and participantsWe performed a retrospective study of hospitalizations at an academic medical center. Hospitalizations for patients 18 years or older who were admitted after January 1, 2013, and discharged before February 28, 2015, were included. From a random 75% sample of hospitalizations, we developed 5 algorithms for heart failure identification using electronic health record data: (1) heart failure on problem list; (2) presence of at least 1 of 3 characteristics: heart failure on problem list, inpatient loop diuretic, or brain natriuretic peptide level of 500 pg/mL or higher; (3) logistic regression of 30 clinically relevant structured data elements; (4) machine-learning approach using unstructured notes; and (5) machine-learning approach using structured and unstructured data.Main outcomes and measuresHeart failure diagnosis based on discharge diagnosis and physician review of sampled medical records.ResultsA total of 47 119 hospitalizations were included in this study (mean [SD] age, 60.9 [18.15] years; 23 952 female [50.8%], 5258 black/African American [11.2%], and 3667 Hispanic/Latino [7.8%] patients). Of these hospitalizations, 6549 (13.9%) had a discharge diagnosis of heart failure. Inclusion of heart failure on the problem list (algorithm 1) had a sensitivity of 0.40 and a positive predictive value (PPV) of 0.96 for heart failure identification. Algorithm 2 improved sensitivity to 0.77 at the expense of a PPV of 0.64. Algorithms 3, 4, and 5 had areas under the receiver operating characteristic curves of 0.953, 0.969, and 0.974, respectively. With a PPV of 0.9, these algorithms had associated sensitivities of 0.68, 0.77, and 0.83, respectively.Conclusions and relevanceThe problem list is insufficient for real-time identification of hospitalized patients with heart failure. The high predictive accuracy of machine learning using free text demonstrates that support of such analytics in future electronic health record systems can improve cohort identification.

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Dataset Information

Use of Electronic Health Data to Estimate Heart Failure Events in a Population-Based Cohort with CKD.

Background and objectives

Design, setting, participants, & measurements

Results

Conclusions

Publications

Use of Electronic Health Data to Estimate Heart Failure Events in a Population-Based Cohort with CKD.

Similar Datasets

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