A Phenomapping Tool and Clinical Score to Identify Low Diuretic Efficiency in Acute Decompensated Heart Failure.

BACKGROUND: Individuals with acute decompensated heart failure (ADHF) have a varying response to diuretic therapy. Strategies for the early identification of low diuretic efficiency to inform decongestion therapies are lacking. OBJECTIVES: The authors sought to develop and externally validate a machine learning-based phenomapping approach and integer-based diuresis score to identify patients with low diuretic efficiency. METHODS: Participants with ADHF from ROSE-AHF, CARRESS-HF, and ATHENA-HF were pooled in the derivation cohort (n = 794). Multivariable finite-mixture model-based phenomapping was performed to identify phenogroups based on diuretic efficiency (urine output over the first 72 hours per total intravenous furosemide equivalent loop diuretic dose). Phenogroups were externally validated in other pooled ADHF trials (DOSE/ESCAPE). An integer-based diuresis score (BAN-ADHF score: blood urea nitrogen, creatinine, natriuretic peptide levels, atrial fibrillation, diastolic blood pressure, hypertension and home diuretic, and heart failure hospitalization) was developed and validated based on predictors of the diuretic efficiency phenogroups to estimate the probability of low diuretic efficiency using the pooled ADHF trials described earlier. The associations of the BAN-ADHF score with markers and symptoms of congestion, length of stay, in-hospital mortality, and global well-being were assessed using adjusted regression models. RESULTS: Clustering identified 3 phenogroups based on diuretic efficiency: phenogroup 1 (n = 370; 47%) had lower diuretic efficiency (median: 13.1 mL/mg; Q1-Q3: 7.7-19.4 mL/mg) than phenogroups 2 (n = 290; 37%) and 3 (n = 134; 17%) (median: 17.8 mL/mg; Q1-Q3: 10.8-26.1 mL/mg and median: 35.3 mL/mg; Q1-Q3: 17.5-49.0 mL/mg, respectively) (P < 0.001). The median urine output difference in response to 80 mg intravenous twice-daily furosemide between the lowest and highest diuretic efficiency group (phenogroup 1 vs 3) was 3,520 mL/d. The BAN-ADHF score demonstrated good model performance for predicting the lowest diuretic efficiency phenogroup membership (C-index: 0.92 in DOSE/ESCAPE validation cohort) that was superior to measures of kidney function (creatinine or blood urea nitrogen), natriuretic peptide levels, or home diuretic dose (DeLong P < 0.001 for all). Net urine output in response to 80 mg intravenous twice-daily furosemide among patients with a low vs high (5 vs 20) BAN-ADHF score was 2,650 vs 660 mL per 24 hours, respectively. Participants with higher BAN-ADHF scores had significantly lower global well-being, higher natriuretic peptide levels on discharge, a longer in-hospital stay, and a higher risk of in-hospital mortality in both derivation and validation cohorts. CONCLUSIONS: The authors developed and validated a phenomapping strategy and diuresis score for individuals with ADHF and differential response to diuretic therapy, which was associated with length of stay and mortality.

Electronic Health Records-Based Cardio-Oncology Registry for Care Gap Identification and Pragmatic Research: Procedure and Observational Study.

BACKGROUND: Professional society guidelines are emerging for cardiovascular care in cancer patients. However, it is not yet clear how effectively the cancer survivor population is screened and treated for cardiomyopathy in contemporary clinical practice. As electronic health records (EHRs) are now widely used in clinical practice, we tested the hypothesis that an EHR-based cardio-oncology registry can address these questions. OBJECTIVE: The aim of this study was to develop an EHR-based pragmatic cardio-oncology registry and, as proof of principle, to investigate care gaps in the cardiovascular care of cancer patients. METHODS: We generated a programmatically deidentified, real-time EHR-based cardio-oncology registry from all patients in our institutional Cancer Population Registry (N=8275, 2011-2017). We investigated: (1) left ventricular ejection fraction (LVEF) assessment before and after treatment with potentially cardiotoxic agents; and (2) guideline-directed medical therapy (GDMT) for left ventricular dysfunction (LVD), defined as LVEF<50%, and symptomatic heart failure with reduced LVEF (HFrEF), defined as LVEF<50% and Problem List documentation of systolic congestive heart failure or dilated cardiomyopathy. RESULTS: Rapid development of an EHR-based cardio-oncology registry was feasible. Identification of tests and outcomes was similar using the EHR-based cardio-oncology registry and manual chart abstraction (100% sensitivity and 83% specificity for LVD). LVEF was documented prior to initiation of cancer therapy in 19.8% of patients. Prevalence of postchemotherapy LVD and HFrEF was relatively low (9.4% and 2.5%, respectively). Among patients with postchemotherapy LVD or HFrEF, those referred to cardiology had a significantly higher prescription rate of a GDMT. CONCLUSIONS: EHR data can efficiently populate a real-time, pragmatic cardio-oncology registry as a byproduct of clinical care for health care delivery investigations.

Electronic Medical Record-Based Radiation Oncology Toxicity Recording Instrument Aids Benchmarking and Quality Improvement in the Clinic.

PURPOSE: The on-treatment visit (OTV) for radiation oncology is essential for patient management. Radiation toxicities recorded during the OTV may be inconsistent because of the use of free text and the lack of treatment site-specific templates. We developed a radiation oncology toxicity recording instrument (ROTOX) in a health system electronic medical record (EMR). Our aims were to assess improvement in documentation of toxicities and to develop clinic toxicity benchmarks. METHODS: A ROTOX that was based on National Cancer Institute Common Terminology Criteria for Adverse Events (version 4.0) with flow-sheet functionality was developed in the EMR. Improvement in documentation was assessed at various time intervals. High-grade toxicities (ie, grade ≥ 3 by CTCAE) by site were audited to develop benchmarks and to track nursing and physician actions taken in response to these. RESULTS: A random sample of OTV notes from each clinic physician before ROTOX implementation was reviewed and assigned a numerical document quality score (DQS) that was based on completeness and comprehensiveness of toxicity grading. The mean DQS improved from an initial level of 41% to 99% (of the maximum possible DQS) when resampled at 6 months post-ROTOX. This high-level DQS was maintained 3 years after ROTOX implementation at 96% of the maximum. For months 7 to 9 after implementation (during a 3-month period), toxicity grading was recorded in 4,443 OTVs for 698 unique patients; 107 episodes of high-grade toxicity were identified during this period, and toxicity-specific intervention was documented in 95%. CONCLUSION: An EMR-based ROTOX enables consistent recording of treatment toxicity. In a uniform sample of patients, local population toxicity benchmarks can be developed, and clinic response can be tracked.