Development and Preliminary Validation of a Medicare Claims-Based Model to Predict Left Ventricular Ejection Fraction Class in Patients With Heart Failure

Development and validation of algorithms to predict left ventricular ejection fraction class from healthcare claims data

AIMS

The use of large medical or healthcare claims databases is very useful for population-based studies on the burden of heart failure (HF). Clinical characteristics and management of HF patients differ according to categories of left ventricular ejection fraction (LVEF), but this information is often missing in such databases. We aimed to develop and validate algorithms to identify LVEF in healthcare databases where the information is lacking.

METHODS AND RESULTS

Algorithms were built by machine learning with a random forest approach. Algorithms were trained and reinforced using the French national claims database [Système National des Données de Santé (SNDS)] and a French HF registry. Variables were age, gender, and comorbidities, which could be identified by medico-administrative code-based proxies, Anatomical Therapeutic Chemical codes for drug delivery, International Classification of Diseases (Tenth Revision) coding for hospitalizations, and administrative codes for any other type of reimbursed care. The algorithms were validated by cross-validation and against a subset of the SNDS that includes LVEF information. The areas under the receiver operating characteristic curve were 0.84 for the algorithm identifying LVEF ≤ 40% and 0.79 for the algorithms identifying LVEF < 50% and ≥50%. For LVEF ≤ 40%, the reinforced algorithm identified 50% of patients in the validation dataset with a positive predictive value of 0.88 and a specificity of 0.96. The most important predictive variables were delivery of HF medication, sex, age, hospitalization, and testing for natriuretic peptides with different orders of positive or negative importance according to the LVEF category.

CONCLUSIONS

The algorithms identify reduced or preserved LVEF in HF patients within a nationwide healthcare claims database with high positive predictive value and low rates of false positives.

Use of Sodium-Glucose Cotransporter-2 Inhibitors and Angiotensin Receptor-Neprilysin Inhibitors in Patients With Atrial Fibrillation and Heart Failure From 2021 to 2022: An Analysis of Real-World Data

BACKGROUND

Contemporary use of sodium-glucose cotransporter-2 inhibitors (SGLT2i) and angiotensin receptor-neprilysin inhibitors (ARNi) in patients with atrial fibrillation (AF) and heart failure (HF) has not been described.

METHODS AND RESULTS

We analyzed the MarketScan databases for the period January 1, 2021 to July 30, 2022. Validated algorithms were used to identify patients with AF and HF, and to classify patients into HF with reduced ejection fraction (HFrEF) or HF with preserved ejection fraction (HFpEF). We assessed the prevalence of SGLT2i and ARNi use overall and by HF type. Additionally, we explored correlates of lower use, including demographics and comorbidities. The study population included 60 927 patients (mean age, 75 years; 43% women) diagnosed with AF and HF (85% with HFpEF, 15% with HFrEF). Prevalence of ARNi use was 11% overall (30% in HFrEF, 8% in HFpEF), whereas the corresponding figure was 6% for SGLT2i (13% in HFrEF, 5% in HFpEF). Use of both medications increased over the study period: ARNi from 9% to 12% (22%-29% in HFrEF, 6%-8% in HFpEF), and SGLT2i from 3% to 9% (6%-16% in HFrEF, 2%-7% in HFpEF). Female sex, older age, and specific comorbidities were associated with lower use of these 2 medication types overall and by HF type.

CONCLUSIONS

Use of ARNi and SGLT2i in patients with AF and HF is suboptimal, particularly among women and older individuals, though use is increasing. These results underscore the need for understanding reasons for these disparities and developing interventions to improve adoption of evidence-based therapies among patients with comorbid AF and HF.

Diagnostic Validity of Chronic Kidney Disease in Health Claims Data Over Time: Results from a Cohort of Community-Dwelling Older Adults in Germany

Purpose

The validity of ICD-10 diagnostic codes for chronic kidney disease (CKD) in health claims data has not been sufficiently studied in the general population and over time.

Patients and Methods

We used data from the Berlin Initiative Study (BIS), a prospective longitudinal cohort of community-dwelling individuals aged ≥70 years in Berlin, Germany. With estimated glomerular filtration rate (eGFR) as reference, we assessed the diagnostic validity (sensitivity, specificity, positive [PPV], and negative predictive values [NPV]) of different claims-based ICD-10 codes for CKD stages G3-5 (eGFR <60mL/min/1.73m²: ICD-10 N18.x-N19), G3 (eGFR 30-<60mL/min/1.73m²: N18.3), and G4-5 (eGFR <30mL/min/1.73m²: N18.4-5). We analysed trends over five study visits (2009-2019).

Results

We included data of 2068 participants at baseline (2009-2011) and 870 at follow-up 4 (2018-2019), of whom 784 (38.9%) and 440 (50.6%) had CKD G3-5, respectively. At baseline, sensitivity for CKD in claims data ranged from 0.25 (95%-confidence interval [CI] 0.22-0.28) to 0.51 (95%-CI 0.48-0.55) for G3-5, depending on the included ICD-10 codes, 0.20 (95%-CI 0.18-0.24) for G3, and 0.36 (95%-CI 0.25-0.49) for G4-5. Over the course of 10 years, sensitivity increased by 0.17 to 0.29 in all groups. Specificity, PPVs, and NPVs remained mostly stable over time and ranged from 0.82-0.99, 0.47-0.89, and 0.66-0.98 across all study visits, respectively.

Conclusion

German claims data showed overall agreeable performance in identifying older adults with CKD, while differentiation between stages was limited. Our results suggest increasing sensitivity over time possibly attributable to improved CKD diagnosis and awareness.

Comparative Effect of Loop Diuretic Prescription on Mortality and Heart Failure Readmission

Loop diuretics are a standard pharmacologic therapy in heart failure (HF) management. Although furosemide is most frequently used, torsemide and bumetanide are increasingly prescribed in clinical practice, possibly because of superior bioavailability. Few real-world comparative effectiveness studies have examined outcomes across all 3 loop diuretics. The study goal was to compare the effects of loop diuretic prescribing at HF hospitalization discharge on mortality and HF readmission. We identified patients in Medicare claims data initiating furosemide, torsemide, or bumetanide after an index HF hospitalization from 2007 to 2017. We estimated 6-month risks of all-cause mortality and a composite outcome (HF readmission or all-cause mortality) using inverse probability of treatment weighting to adjust for relevant confounders. We identified 62,632 furosemide, 1,720 torsemide, and 2,389 bumetanide initiators. The 6-month adjusted all-cause mortality risk was lowest for torsemide (13.2%), followed by furosemide (14.5%) and bumetanide (15.6%). The 6-month composite outcome risk was 21.4% for torsemide, 24.7% for furosemide, and 24.9% for bumetanide. Compared with furosemide, the 6-month all-cause mortality risk was 1.3% (95% confidence interval [CI]: -3.7, 1.0) lower for torsemide and 1.0% (95% CI: -1.2, 3.2) higher for bumetanide, and the 6-month composite outcome risk was 3.3% (95% CI: -6.3, -0.3) lower for torsemide and 0.2% (95% CI: -2.5, 2.9) higher for bumetanide. In conclusion, the findings suggested that the first prescribed loop diuretic following HF hospitalization is associated with clinically important differences in morbidity in older patients receiving torsemide, bumetanide, or furosemide. These differences were consistent for the effect of all-cause mortality alone, but were not statistically significant.

Frailty and uptake of angiotensin receptor neprilysin inhibitor for heart failure with reduced ejection fraction

BACKGROUND

Frail older adults may be less likely to receive guideline-directed medical therapy (GDMT)-renin-angiotensin blockers, beta-blockers, and mineralocorticoid receptor antagonists-for heart failure with reduced ejection fraction (HFrEF). We aimed to examine the uptake of angiotensin receptor neprilysin inhibitor (ARNI) and GDMT in frail older adults with HFrEF.

METHODS

Using 2015-2019 Medicare data, we estimated the proportion of beneficiaries with HFrEF receiving ARNI and GDMT each year by frailty status, defined by a claims-based frailty index. Logistic regression was used to identify clinical characteristics associated with ARNI initiation. Cox proportional hazards regression was used to examine the association of GDMT use in 2015 and death or heart failure hospitalization in 2016-2019.

RESULTS

Among 147,506-180,386 beneficiaries with HFrEF (mean age: 77 years; 27% women; 42.6-49.1% frail) in 2015-2019, the proportion of patients receiving ARNI increased in both non-frail (0.4%-16.4%) and frail (0.3%-13.7%) patients (p for yearly-trend-by-frailty = 0.970). Among those not receiving a renin-angiotensin system blocker, patients with age ≥ 85 years (odds ratio [95% CI], 0.89 [0.80-0.99]), dementia (0.88 [0.81-0.96]), and frailty (0.87 [0.81-0.94]) were less likely to initiate ARNI. The proportion of patients receiving all 3 GDMT classes increased in non-frail patients (22.0%-27.0%) but changed minimally in frail patients (19.6%-21.8%). Regardless of frailty status, treatment with at least 1 class of GDMT was associated with lower death or heart failure hospitalization than no GDMT medications (hazard ratio [95% CI], 0.94 [0.91-0.97], 0.92 [0.89-0.94], 0.94 [0.91-0.97] for 1, 2, and 3 classes, respectively).

CONCLUSIONS

Our results suggest an evidence-practice gap in the use of ARNI and GDMT in Medicare beneficiaries with HFrEF, particularly those with frailty. Efforts to narrow this gap are needed to reduce the burden of HFrEF in older adults.

Use of SGLT2i and ARNi in patients with atrial fibrillation and heart failure in 2021-2022: an analysis of real-world data

Objective

To evaluate utilization of sodium-glucose cotransporter-2 inhibitors (SGLT2i) and angiotensin receptor neprilysin inhibitors (ARNi) in patients with atrial fibrillation (AF) and heart failure (HF).

Methods

We analyzed the MarketScan databases for the period 1/1/2021 to 6/30/2022. Validated algorithms were used to identify patients with AF and HF, and to classify patients into HF with reduced ejection fraction (HFrEF) or preserved ejection fraction (HFpEF). We assessed the prevalence of SGLT2i and ARNi use overall and by HF type. Additionally, we explored correlates of lower utilization, including demographics and comorbidities.

Results

The study population included 60,927 patients (mean age 75, 43% female) diagnosed with AF and HF (85% with HFpEF, 15% with HFrEF). Prevalence of ARNi use was 11% overall (30% in HFrEF, 8% in HFpEF), while the corresponding figure was 6% for SGLT2i (13% in HFrEF, 5% in HFpEF). Use of both medications increased over the study period: ARNi from 9% to 12% (from 22% to 29% in HFrEF, from 6% to 8% in HFpEF), and SGLT2i from 3% to 9% (from 6% to 16% in HFrEF, from 2% to 7% in HFpEF). Female sex, older age, and specific comorbidities were associated with lower utilization of these two medication types overall and by HF type.

Conclusion

Use of ARNi and SGLT2i in patients with AF and HF is suboptimal, particularly among females and older individuals, though utilization is increasing. These results underscore the need for understanding reasons for these disparities and developing interventions to improve adoption of evidence-based therapies among patients with comorbid AF and HF.

Incidence and prevalence of heart failure in England: a descriptive analysis of linked primary and secondary care data - the PULSE study

BACKGROUND

Heart failure (HF) is associated with high morbidity and mortality, yet data on HF subtype (HF with reduced ejection fraction [HFrEF] and preserved ejection fraction [HFpEF]) in broad populations are lacking. Additionally, it is unknown whether current HF incidence and prevalence rates are consistent with historical data. Here, we estimate the incidence and prevalence of HF in England and describe the characteristics of patients with HF, both overall and by subtype.

METHODS

This was a non-interventional cohort study based on data from the UK Clinical Practice Research Datalink (CPRD), linked to Hospital Episode Statistics data and Office for National Statistics mortality data. Patients aged ≥ 18 years who were registered in the CPRD Aurum database between 1^st January 2015 and 31^st December 2019 formed the base cohort, from which patients with a recorded chronic HF diagnosis (historical or incident) from 2015-2019 contributed to the incidence and prevalence calculations.

RESULTS

The eligible denominator over the study period comprised 11,414,490 patients, from which 383,896 patients with HF were included as prevalent or incident HF cases. From 2015 to 2019, the incidence rate of newly diagnosed HF increased from 4.1/1,000 person-years to 4.9/1,000 person-years, and HF prevalence increased from 2.1% to 2.4%. Phenotype data were available for 100,224 (26.1%) patients, of which 68,780 patients had HFrEF and 31,444 had HFpEF (HFrEF/HFpEF ratio: 70.1%/29.9%). Comorbidity levels were high and broadly similar across HF subgroups.

CONCLUSIONS

Primary care recording of HF subtype is suboptimal, with more than 7/10 patients with HF lacking subtype data. In patients with a recorded subtype (n = 100,224), a HFrEF/HFpEF ratio of 70%/30% was observed. Comorbidity levels were high regardless of subtype. Between 2015 and 2019, we observed modest but consistent increases in the incidence and prevalence of chronic HF in adults, in line with historical data.

Comparative Safety of Gout Treatment Strategies on Cardiovascular Outcomes Using Observational Data: Clone-censor-weight Target Trial Emulation Approach

BACKGROUND

We sought to examine the cardiovascular safety of intensive treat-to-target serum urate strategies for gout using Medicare claims data linked to electronic health record laboratory data.

METHODS

We selected patients with gout who initiated urate-lowering therapy. We emulated a hypothetical trial comparing the rate of major adverse cardiovascular events (nonfatal myocardial infarction, nonfatal stroke, and cardiovascular death) among seven different strategies over 24 months. Three aspects were considered in defining increasingly intensive strategies: (1) continuation of urate-lowering therapy, (2) serum urate monitoring, and (3) modification of urate-lowering therapy when serum urate >6 mg/dl. We applied the "clone-censor-weight" method to account for baseline and time-varying confounding.

RESULTS

We identified 4402 patients with gout who initiated urate-lowering therapy (mean age 77; male 60%). During a total of 6611 person-years (PY) of follow-up under usual care, the rate of major cardiovascular events (first and recurrent) was 4.5/100 PY (95% CI = 4.0, 5.1). The rate ratios (RRs) suggested reductions (RR point estimates 0.88-0.84) compared with usual care. All 95% CIs were imprecise, but their upper bounds excluded substantial increase in RRs. RRs were closer to 1.0 for the analysis focusing on the first major adverse cardiovascular event during follow-up and on comparison to the strategy requiring continuation of urate-lowering therapy (but not necessarily titration).

CONCLUSIONS

Our treatment strategy trial emulation did not find increased risk of major adverse cardiovascular events with intensive urate-lowering strategies. Results may provide reassurance of the cardiovascular safety of intensive treat-to-target serum urate strategies recommended by rheumatology societies.

Sodium-glucose cotransporter 2 inhibitors vs. sitagliptin in heart failure and type 2 diabetes: an observational cohort study

AIMS

The effectiveness of sodium-glucose cotransporter 2 inhibitors (SGLT2i) in patients with heart failure (HF) in routine clinical practice is not extensively studied. This study aimed to evaluate the comparative effectiveness of SGLT2i vs. sitagliptin in older adults with HF and type 2 diabetes and to investigate whether there were any differences between agents within the SGLT2i class or for reduced and preserved ejection fraction.

METHODS AND RESULTS

Using Medicare claims data (April 2013 to December 2019), 16 253 SGLT2i initiators vs. 43 352 initiators of sitagliptin aged ≥65 years with type 2 diabetes and HF were included. The primary outcome was a composite of all-cause mortality, hospitalization for HF or urgent visit requiring intravenous diuretics; secondary outcomes included its individual components. Propensity score fine stratification weighted Cox regression was used to adjust for 100 pre-exposure characteristics. Mean age was 74 years; 49.8% were women. Initiation of SGLT2i vs. sitagliptin was associated with a lower risk of the primary composite outcome [adjusted hazard ratio (HR) 0.72; 95% confidence interval 0.67-0.77]. The adjusted HRs were 0.70 (0.63-0.78) for all-cause mortality, 0.64 (0.58-0.70) for hospitalization for HF, and 0.77 (0.69-0.86) for urgent visit requiring intravenous diuretics. Similar associations with the primary composite outcome were observed for all three agents within the SGLT2i class, for reduced and preserved ejection fraction, and subgroups based on demographics, comorbidities, and other HF treatments. Bias-calibrated HRs for the primary endpoint using negative and positive control outcomes ranged between 0.81 and 0.89, suggesting that the observed benefit could not be fully explained by residual confounding.

CONCLUSION

In routine US clinical practice, SGLT2i demonstrated robust clinical effectiveness in older adults with HF and type 2 diabetes compared with sitagliptin, with no evidence of heterogeneity across the SGLT2i class or across ejection fraction.

Development and Validation of a Novel Tool to Predict Model for End-Stage Liver Disease (MELD) Scores in Cirrhosis, Using Administrative Datasets

Background

The Model for End-Stage Liver Disease (MELD) score predicts disease severity and mortality in cirrhosis. To improve cirrhosis phenotyping in administrative databases lacking laboratory data, we aimed to develop and externally validate claims-based MELD prediction models, using claims data linked to electronic health records (EHR).

Methods

We included adults with established cirrhosis in two Medicare-linked EHR networks (training and internal validation; 2007-2017), and a Medicaid-linked EHR network (external validation; 2000-2014). Using least absolute shrinkage and selection operator (LASSO) with 5-fold cross-validation, we selected among 146 investigator-specified variables to develop models for predicting continuous MELD and relevant MELD categories (MELD<10, MELD≥15 and MELD≥20), with observed MELD calculated from laboratory data. Regression coefficients for each model were applied to the validation sets to predict patient-level MELD and assess model performance.

Results

We identified 4501 patients in the Medicare training set (mean age 75.1 years, 18.5% female, mean MELD=13.0), and 2435 patients in the Medicare validation set (mean age: 74.3 years, 31.7% female, mean MELD=12.3). Our final model for predicting continuous MELD included 112 variables, explaining 58% of observed MELD variability; in the Medicare validation set, the area-under-the-receiver operating characteristic curves (AUC) for MELD<10 and MELD≥15 were 0.84 and 0.90, respectively; the AUC for the model predicting MELD≥20 (using 27 variables) was 0.93. Overall, these models correctly classified 77% of patients with MELD<10 (95% CI=0.75-0.78), 85% of patients with MELD≥15 (95% CI=0.84-0.87), and 87% of patients with MELD≥20 (95% CI=0.86-0.88). Results were consistent in the external validation set (n=2240).

Conclusion

Our MELD prediction tools can be used to improve cirrhosis phenotyping in administrative datasets lacking laboratory data.

Risk of Acute Kidney Injury Among Older Adults With Heart Failure and With Reduced Ejection Fraction Treated With Angiotensin-Neprilysin Inhibitor vs Renin-Angiotensin System Inhibitor in Routine Clinical Care

BACKGROUND

The acute hemodynamic effects of sacubitril/valsartan, an angiotensin receptor neprilysin inhibitor (ARNI), may result in early changes in kidney function, raising concerns about acute kidney injury (AKI), particularly in those who are naïve to renin-angiotensin system inhibitors (RASis).

METHODS

We conducted a cohort study using U.S. Medicare fee-for-service claims data (2014-2017). Patients with HFrEF ≥ 65 years newly initiating ARNI or RASi, with no prior use of either drug class, were included. The primary outcome was hospitalization due to AKI as the primary discharge diagnosis, and the secondary outcome included AKI as a primary or secondary discharge diagnosis. AKI risks were described under an as-treated follow-up approach, with censoring on treatment discontinuation, switch, insurance disenrollment, death, or administrative censoring as well as an intent-to-treat approach. Propensity-score-based fine-stratification weighting was used to account for potential confounding by 81 pre-exposure characteristics. Cumulative incidence functions were used to report absolute risks, and Cox proportional hazards models were used to provide hazard ratios (HR) and 95% confidence intervals (CI).

RESULTS

We included 27,166 patients with a mean (SD) age of 73 (7.3) years, and 4155 (15.3%) were initiating ARNI. After propensity score weighting, the 180-day cumulative incidence was 2.7% (2.4%-3.1%) among RASi initiators and 2.7% (2.2%-3.5%) among ARNI initiators for the primary outcome, and it was 6.5% (6.0%-7.1%) and 6.1% (5.2%-7.1%), respectively, for the secondary outcome under as-treated follow-up. HR (95% CI) comparing ARNI with RASi were 0.91 (95% CI: 0.72-1.16) for the primary outcome and 0.92 (95% CI: 0.79-1.08) for the secondary outcome. Similar results were observed in the intent-to-treat analysis.

CONCLUSIONS

Among a large cohort of U.S. Medicare beneficiaries with HFrEF, ARNI treatment was not associated with higher rates of AKI than RASi treatment. These results provide reassurance for providers considering ARNI initiation in older patients who are RASi-naïve.

How many and who are patients with heart failure eligible to SGLT2 inhibitors? Responses from the combination of administrative healthcare and primary care databases

BACKGROUND

Recent successful findings (i.e. DAPA-HF trial) in patients with heart failure (HF) with/without diabetes treated with sodium-glucose co-transporter inhibitors (SGLT2-I) have fostered real-world data analyses. Fondazione Ricerca e Salute's (ReSD) administrative and Health Search's (HSD) primary healthcare databases were combined in the ReS-HS DB Consortium, to identify and characterize HF-patients eligible to SGLT2-I, and assess their costs charged to the Italian National Health Service (INHS).

METHODS AND RESULTS

Eligibility to SGLT2-I was HF diagnosis, age ≥ 18 years, reduced (≤40%) ejection fraction (HFrEF) and glomerular filtration rate (GFR) ≥30 ml/min. The HSD, including 13,313 HF-patients (1.5% of the total HSD population) was used to develop and test the algorithms for imputing HFrEF and GFR ≥ 30 ml/min, based on a set of covariates, to the ReSD, including 67,369 (1.5% of the total ReSD population). Subjects eligible to SGLT2-I were 2187 in HSD (61.1% of HFrEF); after the imputation, 15,145 in ReSD (58.8% of HFrEF). Prevalence of eligibility to SGLT2-I was higher in males then in females and increased with age; diabetic patients were 44.3% and 33.4% of HSD and ReSD populations eligible to SGLT2-I, respectively. Estimated from ReSD, the mean annual cost charged to the INHS per patient with HF eligible to SGLT2-I was €7122 (68% due to hospitalizations).

CONCLUSIONS

Approximately 20% of patients with HF was eligible to SGLT2-I. Real-world data can identify, quantify and characterize patients eligible to SGLT2-Is and assess related costs for the health care system, thus providing useful information to Regulatory Decision makers.

Cardiovascular Effectiveness of Sodium-Glucose Cotransporter 2 Inhibitors and Glucagon-Like Peptide-1 Receptor Agonists in Older Patients in Routine Clinical Care With or Without History of Atherosclerotic Cardiovascular Diseases or Heart Failure

Background Randomized trials demonstrate the cardioprotective effects of sodium-glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA). We evaluated their relative cardiovascular effectiveness in routine care populations with a broad spectrum of atherosclerotic cardiovascular diseases (CVDs) or heart failure (HF). Methods and Results We identified Medicare beneficiaries from 2013 to 2017, aged >65 years, initiating SGLT2i (n=24 747) or GLP-1RA (n=22 596) after a 1-year baseline. On the basis of diagnoses during baseline, we classified patients into: (1) no HF or CVD, (2) HF but no CVD, (3) no HF but CVD, and (4) both HF and CVD. We identified hospitalized HF and atherosclerotic CVD outcomes from drug initiation until treatment changes, death, or disenrollment. We estimated propensity score-weighted 2-year risk ratios (RRs) and risk differences, accounting for measured confounding, informative censoring, and competing risk. In patients with no CVD or HF, SGLT2i reduced the hospitalized HF risk compared with GLP-1RA (propensity score-weighted RR, 0.65; 95% CI, 0.43-0.96). The association was strongest in those who had HF but no CVD (RR, 0.48; 95% CI, 0.25-0.85). The combined myocardial infarction, stroke, and mortality outcome risk was slightly higher for SGLT2i compared with GLP-1RA in those without CVD or HF (RR, 1.31; 95% CI, 1.09-1.56). The association was favorable toward SGLT2i in subgroups with a history of HF. Conclusions SGLT2i reduced the cardiovascular risk versus GLP-1RA in patients with a history of HF but no CVD. Atherosclerotic CVD events were less frequent with GLP-1RA in those without prior CVD or HF.

Rates of Spironolactone Initiation and Subsequent Hyperkalemia Hospitalizations in Patients with Heart Failure with Preserved Ejection Fraction Following the TOPCAT trial: A Cohort Study of Medicare Beneficiaries

BACKGROUND

The impact of TOPCAT trial publication on spironolactone initiation and subsequent hospitalizations for hyperkalemia among patients with heart failure with preserved ejection fraction (HFpEF) has not been empirically evaluated.

METHODS

We designed a cohort study using US Medicare fee-for-service data. Annual cohorts of patients with HFpEF from 2013 to 2018 were identified based on a validated claims-based phenotyping model. We determined spironolactone initiation in each annual cohort overall and within subgroups with hyperkalemia risk factors of baseline renin-angiotensin system inhibitors use, chronic kidney disease, and diabetes. We reported incidence rates of hospitalization for hyperkalemia within 6 months of treatment initiation.

RESULTS

A total of 621,171 patients with HFpEF (mean age 80±8 years, 62.9% female) were included. We identified 40,241 initiations of spironolactone with initiation rate/100 person-years (95% confidence interval [CI]) of 16.8 (16.4-17.1) in 2013 and increasing to 19.9 (19.4-20.5) in 2018. Among initiators, we identified a total of 164 hyperkalemia hospitalization with stable incidence rates per 1,000 person years between 2013 (12.0, 95% CI 8.8-16.1) and 2018 (10.6, 95% CI 6.2-17.0). Results were consistent for all subgroups.

CONCLUSION

Following the dissemination of TOPCAT findings, we noted a steady increase in initiation of spironolactone, but not in hyperkalemia hospitalizations.

Longitudinal Data Discontinuity in Electronic Health Records and Consequences for Medication Effectiveness Studies

Electronic health record (EHR) discontinuity (i.e., receiving care outside of the study EHR system), can lead to information bias in EHR-based real-world evidence (RWE) studies. An algorithm has been previously developed to identify patients with high EHR-continuity. We sought to assess whether applying this algorithm to patient selection for inclusion can reduce bias caused by data-discontinuity in four RWE examples. Among Medicare beneficiaries aged >=65 years from 2007 to 2014, we established 4 cohorts assessing drug effects on short-term or long-term outcomes, respectively. We linked claims data with two US EHR systems and calculated %bias of the multivariable-adjusted effect estimates based on only EHR vs. linked EHR-claims data because the linked data capture medical information recorded outside of the study EHR. Our study cohort included 77,288 patients in system 1 and 60,309 in system 2. We found the subcohort in the lowest quartile of EHR-continuity captured 72-81% of the short-term and only 21-31% of the long-term outcome events, leading to %bias of 6-99% for the short-term and 62-112% for the long-term outcome examples. This trend appeared to be more pronounced in the example using a nonuser comparison rather than an active comparison. We did not find significant treatment effect heterogeneity by EHR-continuity for most subgroups across empirical examples. In EHR-based RWE studies, investigators may consider excluding patients with low algorithm-predicted EHR-continuity as the EHR data capture relatively few of their actual outcomes, and treatment effect estimates in these patients may be unreliable.

Supervised Analysis for Phenotype Identification: The Case of Heart Failure Ejection Fraction Class

Artificial Intelligence is creating a paradigm shift in health care, with phenotyping patients through clustering techniques being one of the areas of interest.

OBJECTIVE

To develop a predictive model to classify heart failure (HF) patients according to their left ventricular ejection fraction (LVEF), by using available data from Electronic Health Records (EHR).

SUBJECTS AND METHODS

2854 subjects over 25 years old with a diagnosis of HF and LVEF, measured by echocardiography, were selected to develop an algorithm to predict patients with reduced EF using supervised analysis. The performance of the developed algorithm was tested in heart failure patients from Primary Care. To select the most influentual variables, the LASSO algorithm setting was used, and to tackle the issue of one class exceeding the other one by a large amount, we used the Synthetic Minority Oversampling Technique (SMOTE). Finally, Random Forest (RF) and XGBoost models were constructed.

RESULTS

The full XGBoost model obtained the maximum accuracy, a high negative predictive value, and the highest positive predictive value. Gender, age, unstable angina, atrial fibrillation and acute myocardial infarct are the variables that most influence EF value. Applied in the EHR dataset, with a total of 25,594 patients with an ICD-code of HF and no regular follow-up in cardiology clinics, 6170 (21.1%) were identified as pertaining to the reduced EF group.

CONCLUSION

The obtained algorithm was able to identify a number of HF patients with reduced ejection fraction, who could benefit from a protocol with a strong possibility of success. Furthermore, the methodology can be used for studies using data extracted from the Electronic Health Records.

External validation of a claims-based model to predict left ventricular ejection fraction class in patients with heart failure

Background

Ejection fraction (EF) is an important prognostic factor in heart failure (HF), but administrative claims databases lack information on EF. We previously developed a model to predict EF class from Medicare claims. Here, we evaluated the performance of this model in an external validation sample of commercial insurance enrollees.

Methods

Truven MarketScan claims linked to electronic medical records (EMR) data (IBM Explorys) containing EF measurements were used to identify a cohort of US patients with HF between 01-01-2012 and 10-31-2019. By applying the previously developed model, patients were classified into HF with reduced EF (HFrEF) or preserved EF (HFpEF). EF values recorded in EMR data were used to define gold-standard HFpEF (LVEF ≥45%) and HFrEF (LVEF<45%). Model performance was reported in terms of overall accuracy, positive predicted values (PPV), and sensitivity for HFrEF and HFpEF.

Results

A total of 7,001 HF patients with an average age of 71 years were identified, 1,700 (24.3%) of whom had HFrEF. An overall accuracy of 0.81 (95% CI: 0.80–0.82) was seen in this external validation sample. For HFpEF, the model had sensitivity of 0.96 (95%CI, 0.95–0.97) and PPV of 0.81 (95% CI, 0.81–0.82); while for HFrEF, the sensitivity was 0.32 (95%CI, 0.30–0.34) and PPV was 0.73 (95%CI, 0.69–0.76). These results were consistent with what was previously published in US Medicare claims data.

Conclusions

The successful validation of the Medicare claims-based model provides evidence that this model may be used to identify patient subgroups with specific EF class in commercial claims databases as well.

Effectiveness of angiotensin-neprilysin inhibitor treatment versus renin-angiotensin system blockade in older adults with heart failure in clinical care

OBJECTIVE

To evaluate the effectiveness of angiotensin receptor-neprilysin inhibitor (ARNI) versus renin-angiotensin system (RAS) blockade alone in older adults with heart failure with reduced ejection fraction (HFrEF).

METHODS

We conducted a cohort study using US Medicare fee-for-service claims data (2014-2017). Patients with HFrEF ≥65 years were identified in two cohorts: (1) initiators of ARNI or RAS blockade alone (ACE inhibitor, ACEI; or angiotensin receptor blocker, ARB) and (2) switchers from an ACEI to either ARNI or ARB. HR with 95% CI from Cox proportional hazard regression and 1-year restricted mean survival time (RMST) difference with 95% CI were calculated for a composite outcome of time to first worsening heart failure event or all-cause mortality after adjustment for 71 pre-exposure characteristics through propensity score fine-stratification weighting. All analyses of initiator and switcher cohorts were conducted separately and then combined using fixed effects.

RESULTS

51 208 patients with a mean age of 76 years were included, with 16 193 in the ARNI group. Adjusted HRs comparing ARNI with RAS blockade alone were 0.92 (95% CI 0.84 to 1.00) among initiators and 0.79 (95% CI 0.74 to 0.85) among switchers, with a combined estimate of 0.84 (95% CI 0.80 to 0.89). Adjusted 1-year RMST difference (95% CI) was 4 days in the initiator cohort (-1 to 9) and 12 days (8 to 17) in the switcher cohort, resulting in a pooled estimate of 9 days (6 to 12) favouring ARNI.

CONCLUSION

ARNI treatment was associated with lower risk of a composite effectiveness endpoint compared with RAS blockade alone in older adults with HFrEF.

Epidemiologic Characterization of Heart Failure with Reduced or Preserved Ejection Fraction Populations Identified Using Medicare Claims

BACKGROUND

Administrative claims do not contain ejection fraction information for heart failure patients. We recently developed and validated a claims-based model to predict ejection fraction subtype.

METHODS

Heart failure patients aged 65 years or above from US Medicare fee-for-service claims were identified using diagnoses recorded after a 6-month baseline period of continuous enrollment, which was used to identify predictors and to apply the claims-based model to distinguish heart failure with reduced or preserved ejection fraction (HFrEF or HFpEF). Patients were followed for the composite outcome of time to first worsening heart failure event (heart failure hospitalization or outpatient intravenous diuretic treatment) or all-cause mortality.

RESULTS

A total of 3,134,414 heart failure patients with an average age of 79 years were identified, of which 200,950 (6.4%) were classified as HFrEF. Among those classified as HFrEF, men comprised a larger proportion (68% vs 41%) and the average age was lower (76 vs 79 years) compared with HFpEF. History of myocardial infarction was more common in HFrEF (32% vs 13%), while hypertension was more common in HFpEF (71% vs 77%). One-year cumulative incidence of the composite endpoint was 42.6% for HFrEF and 36.9% for HFpEF. One-year all-cause mortality incidence was similar between the groups (27.4% for HFrEF and 26.4% for HFpEF), however, cardiovascular mortality was higher for HFrEF (15.6% vs 11.3%), whereas noncardiovascular mortality was higher for HFpEF (11.8% vs 15.1%).

CONCLUSION

We replicated well-documented differences in key patient characteristics and cause-specific outcomes between HFrEF and HFpEF in populations identified based on the application of a claims-based model.

Contemporary economic burden in a real-world heart failure population with Commercial and Medicare supplemental plans

BACKGROUND

Limited real-world data exist on healthcare resource utilization (HCRU) and associated costs of patients with heart failure (HF) with reduced ejection fraction (HFrEF) and preserved EF (HFpEF), including urgent HF visits, which are assumed to be less burdensome than HF hospitalizations (hHFs) HYPOTHESIS: This study aimed to quantify the economic burden of HFrEF and HFpEF, via a retrospective, longitudinal cohort study, using IBM® linked claims/electronic health records (Commercial and Medicare Supplemental data only).

METHODS

Adult patients, indexed on HF diagnosis (ICD-10-CM: I50.x) from July 2012 through June 2018, with 6-month minimum baseline period and varying follow-up, were classified as HFrEF (I50.2x) or HFpEF (I50.3x) according to last-observed EF-specific diagnosis. HCRU/costs were assessed during follow-up.

RESULTS

About 109 721 HF patients (22% HFrEF, 31% HFpEF, 47% unclassified EF; median 18 months' follow-up) were identified. There were 3.2 all-cause outpatient visits per patient-month (HFrEF, 3.3; HFpEF, 3.6); 69% of patients required inpatient stays (HFrEF, 80%; HFpEF, 78%). Overall, 11% of patients experienced hHFs (HFrEF, 23%; HFpEF, 16%), 9% experienced urgent HF visits (HFrEF, 15%; HFpEF, 12%); 26% were hospitalized less than 30 days after first urgent HF visit versus 11% after first hHF. Mean monthly total direct healthcare cost per patient was $9290 (HFrEF, $11 053; HFpEF, $7482).

CONCLUSIONS

HF-related HCRU is substantial among contemporary real-world HF patients in US Commercial or Medicare supplemental health plans. Patients managed in urgent HF settings were over twice as likely to be hospitalized within 30 days versus those initially hospitalized, suggesting urgent HF visits are important clinical events and quality improvement targets.

Patient factors associated with new prescribing of potentially inappropriate medications in multimorbid US older adults using multiple medications

BACKGROUND

The use of potentially inappropriate medications (PIMs) is common in older adults and is associated with potential negative consequences, such as falls and cognitive decline. Our objective was to investigate measurable patient factors associated with new outpatient prescribing of potentially inappropriate medications in older multimorbid adults already using multiple medications.

METHODS

In this retrospective US cohort study, we used linked Medicare pharmacy and medical claims and electronic health record data from a large healthcare system in Massachusetts between 2007 and 2014. We identified patients aged ≥65 years with an office visit who had not been prescribed or used a PIM in the prior 180 days. PIMs were defined using 2019 Beers criteria of the American Geriatrics Society. To specifically evaluate factors in patients with polypharmacy and multimorbidity, we selected those who filled medications for ≥90 days (i.e., chronic use) from ≥5 pharmaceutical classes in the prior 180 days and had ≥2 chronic conditions. Multivariable Cox regression analysis was used to estimate the association between baseline demographic and clinical characteristics on the probability of being prescribed a PIM in the 90-day follow-up period.

RESULTS

In total, we identified 17,912 patients aged ≥65 years with multimorbidity and polypharmacy who were naïve to a PIM in the prior 180 days. Of those, 10,497 (58.6%) were female, and mean age was 78 (SD = 7.5). On average, patients had 5.1 (SD = 2.3) chronic conditions and previously filled 6.1 (SD = 1.4) chronic medications. In total, 447 patients (2.5%) were prescribed a PIM during the 90-day follow-up. Male sex (adjusted hazard ratio (HR) = 1.29; 95%CI: 1.06-1.57), age (≥85 years: HR = 0.75, 95%CI: 0.56-0.99, 75-84 years: HR = 0.87, 95%CI: 0.71-1.07; reference: 65-74 years), ambulatory visits (18-29 visits: HR = 1.42, 95%CI: 1.06-1.92; ≥30 visits: HR = 2.12, 95%CI: 1.53-2.95; reference: ≤9 visits), number of prescribing orders (HR = 1.02, 95%CI: 1.01-1.02 per 1-unit increase), and heart failure (HR = 1.38, 95%CI: 1.07-1.78) were independently associated with being newly prescribed a PIM.

CONCLUSION

Several demographic and clinical characteristics, including factors suggesting lack of care coordination and increased clinical complexity, were found to be associated with the new prescribing of potentially inappropriate medications. This knowledge could inform the design of interventions and policies to optimize pharmacotherapy for these patients.

Utilization and Spending on Potentially Inappropriate Medications by US Older Adults with Multiple Chronic Conditions using Multiple Medications

BACKGROUND

The utilization of potentially inappropriate medications (PIMs) in older adults can lead to adverse events and increased healthcare costs. Polypharmacy, the concurrent utilization of multiple medications, is common in older adults with multiple chronic conditions.

OBJECTIVE

To investigate the utilization and costs of PIMs in multimorbid older adults with polypharmacy over time.

METHODS

This retrospective cross-sectional study used linked Medicare claims and electronic health records from seven hospitals/medical centers in Massachusetts (2007-2014). Participants were ≥65 years old, had ≥2 chronic conditions (to define multimorbidity), and used drugs from ≥5 pharmaceutical classes for ≥90 days (to define polypharmacy). Chronic conditions were defined using the Chronic Conditions Indicator from the Agency for Health Research and Quality. PIMs were defined using the American Geriatrics Society 2019 version of the Beers criteria. We calculated the percentage of patients with ≥1 PIMs and the percentages of patients using different types of PIMs. We used logistic regression analyses to test the odds of taking ≥1 PIMs. We calculated mean costs spent on PIMs by dividing the costs spent on PIMs by the total medication cost.

RESULTS

≥69% of patients used ≥1 PIM. After adjusting for healthcare utilization, chronic conditions, medication intake, and demographic factors, female sex (2014: Odds ratio (OR)=1.27, 95%CI 1.25-1.30), age (2014: OR=0.92, 95%CI 0.90-0.93), and Hispanic ethnicity (2014: OR=1.41, 95%CI 1.27-1.56) were associated with PIM use. Gastrointestinal drugs and central nervous system drugs were the most commonly-used PIMs. In patients using ≥1 PIM, >10% of medication costs were spent on PIMs.

CONCLUSION

The utilization of PIMs in US older adults with multimorbidity and polypharmacy is high.

Evaluation of claims-based computable phenotypes to identify heart failure patients with preserved ejection fraction

The 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.

A registry-based algorithm to predict ejection fraction in patients with heart failure

Aims

Left ventricular ejection fraction (EF) is required to categorize heart failure (HF) [i.e. HF with preserved (HFpEF), mid‐range (HFmrEF), and reduced (HFrEF) EF] but is often not captured in population‐based cohorts or non‐HF registries. The aim was to create an algorithm that identifies EF subphenotypes for research purposes.

Methods and results

We included 42 061 HF patients from the Swedish Heart Failure Registry. As primary analysis, we performed two logistic regression models including 22 variables to predict (i) EF≥ vs. <50% and (ii) EF≥ vs. <40%. In the secondary analysis, we performed a multivariable multinomial analysis with 22 variables to create a model for all three separate EF subphenotypes: HFrEF vs. HFmrEF vs. HFpEF. The models were validated in the database from the CHECK‐HF study, a cross‐sectional survey of 10 627 patients from the Netherlands. The C‐statistic (discrimination) was 0.78 [95% confidence interval (CI) 0.77–0.78] for EF ≥50% and 0.76 (95% CI 0.75–0.76) for EF ≥40%. Similar results were achieved for HFrEF and HFpEF in the multinomial model, but the C‐statistic for HFmrEF was lower: 0.63 (95% CI 0.63–0.64). The external validation showed similar discriminative ability to the development cohort.

Conclusions

Routine clinical characteristics could potentially be used to identify different EF subphenotypes in databases where EF is not readily available. Accuracy was good for the prediction of HFpEF and HFrEF but lower for HFmrEF. The proposed algorithm enables more effective research on HF in the big data setting.