Clinical characteristics and health care resource use of patients at risk for wild-type transthyretin amyloid cardiomyopathy identified by machine learning model.

BACKGROUND: Transthyretin amyloid cardiomyopathy (ATTR-CM) is a progressive, life-threatening systemic disorder that is an underrecognized cause of heart failure (HF). When the diagnosis of wild-type ATTR-CM (ATTRwt-CM) is delayed, patients often undergo additional assessments, deferring appropriate management as symptoms potentially worsen. Prompt recognition of patients at risk for ATTRwt-CM is essential to facilitate earlier diagnosis and disease-modifying treatment. A previously developed machine learning model performed well in identifying ATTRwt-CM in patients with HF vs controls with nonamyloid HF using medical claims/electronic health records, providing a systematic framework to raise disease suspicion. OBJECTIVE: To further evaluate this model's performance in identifying ATTRwt-CM using a large claims database of older adults with HF and confirmed ATTRwt-CM or nonamyloid HF; and to explore the characteristics and health care resource utilization (HCRU) of patients with confirmed and suspected ATTRwt-CM. METHODS: In this retrospective study, the prior model was applied using Humana administrative claims for patients diagnosed with ATTRwt-CM (cases) and nonamyloid HF (controls [1:1]). Patients were aged 65-89 years, had at least 2 claims for HF diagnosis (2015-2020), and were continuously enrolled in a Medicare Advantage prescription drug plan for at least 12 months before and at least 6 months after HF diagnosis. For the assessment of characteristics and HCRU, the suspected risk level was categorized based on the predicted probability (PP) from model output (high, moderate, and low risk: PP≥0.70; ≥0.50 and < 0.70; and < 0.50, respectively). RESULTS: Of 267,025 eligible patients, 119 (0.04%) had confirmed ATTRwt-CM; of 266,906 patients with nonamyloid HF, 10,997 (4.1%), 68,174 (25.5%), and 187,735 (70.3%) were categorized as high, moderate, and low risk for ATTRwt-CM, respectively. The model demonstrated sensitivity/specificity/accuracy/receiver operating characteristic area under the concentration-time curve of 88%/65%/77%/0.89, respectively, in differentiating ATTRwt-CM from nonamyloid HF. In patients with confirmed ATTRwt-CM, the mean (SD) time between HF and ATTRwt-CM diagnoses was 751 (528) days; 65% and 48% were hospitalized before and after ATTRwt-CM diagnosis, respectively. Atrial fibrillation was more common in patients with confirmed ATTRwt-CM and high risk (39% and 55%) vs low risk (27%). Hospitalization and emergency department visits after HF diagnosis were reported in 57% and 46% of patients with high ATTRwt-CM risk, respectively. CONCLUSIONS: The ATTRwt-CM predictive model performed well in identifying disease risk in the Humana Research Database. Patients at high risk for ATTRwt-CM had high HCRU and may benefit from the earlier suspicion of ATTRwt-CM. The model may be used as a tool to identify patients with a suspected high risk for the disease to facilitate earlier detection and treatment. DISCLOSURES: This study was sponsored by Pfizer. Medical writing support was provided by Donna McGuire of Engage Scientific Solutions and funded by Pfizer. Drs Bruno and Schepart and Mr Casey are currently employees of Pfizer and equity holders in this publicly traded company. Dr Reed was an employee of Pfizer at the time that this analysis was planned and conducted. Mr Sheer and Dr Simmons are currently employees of Humana, which received research funding from Pfizer. Dr Nair was an employee of Humana at the time that this analysis was planned and conducted.

Real-World Analysis of Guideline-Based Therapy After Hospitalization for Heart Failure.

Background Patients hospitalized with heart failure (HF) with reduced ejection fraction have high risk of rehospitalization or death. Despite guideline recommendations based on high-quality evidence, a substantial proportion of patients with HF with reduced ejection fraction receive suboptimal care and/or do not comply with optimal care following hospitalization. Methods and Results This retrospective observational study identified 17 106 patients with HF with reduced ejection fraction with an incident HF-related hospitalization using the Humana Medicare Advantage database (2008-2016). HF medication classes (beta-blockers, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, angiotensin receptor neprilysin inhibitors, or mineralocorticoid receptor antagonists) received in the year after hospitalization were recorded, and categorized by treatment intensity (ie, number of concomitant medication classes received: none [23% of patients; n=3987], monotherapy [22%; n=3777], dual therapy [41%; n=7056], or triple therapy [13%; n=2286]). Compared with no medication, risk of primary outcome (composite of death or rehospitalization) was significantly reduced (hazard ratio [95% CI]) with monotherapy (0.68 [0.64-0.71]), dual therapy (0.56 [0.53-0.59]), and triple therapy (0.45 [0.41-0.50]). Nearly half (46%) of patients who received post-discharge medication had no dose escalation. Overall, 59% of patients had follow-up with a primary care physician within 14 days of discharge, and 23% had follow-up with a cardiologist. Conclusions In real-world clinical practice, increasing treatment intensity reduced risk of death and rehospitalization among patients hospitalized for HF, though the use of guideline-recommended dual and triple HF therapy remained low. There are opportunities to improve post-discharge medical management for patients with HF with reduced ejection fraction such as optimizing dose titration and improving post-discharge follow-up with providers.