scholarly journals Central sleep apnoea in heart failure with reduced ejection fraction, adaptive servo-ventilation, and left ventricular ejection fraction: the (still) missing link

2017 ◽  
Vol 20 (3) ◽  
pp. 545-547 ◽  
Author(s):  
Sergio H.R. Ramalho ◽  
Amil M. Shah
Keyword(s):  
Heart Failure ◽  
Left Ventricular Ejection Fraction ◽  
Ejection Fraction ◽  
Sleep Apnoea ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction ◽  
Adaptive Servo Ventilation ◽  
Reduced Ejection Fraction ◽  
Central Sleep Apnoea

scholarly journals Updating The Accuracy of Administrative Claims for Identifying Left Ventricular Ejection Fraction Among Patients with Heart Failure

2021 ◽  
Author(s):  
Alexander Sandhu ◽  
Jimmy Zheng ◽  
Paul A Heidenreich
Keyword(s):  
Heart Failure ◽  
Left Ventricular Ejection Fraction ◽  
Ejection Fraction ◽  
Predictive Accuracy ◽  
Left Ventricular ◽  
Ventricular Ejection ◽  
Left Ventricular Ejection ◽  
Administrative Claims ◽  
Ventricular Ejection Fraction ◽  
Reduced Ejection Fraction

Introduction: Left ventricular ejection fraction (EF) is an important factor for treatment decisions for heart failure. The EF is unavailable in administrative claims. We sought to evaluate the predictive accuracy of claims diagnoses for classifying heart failure with reduced ejection fraction (HFrEF) versus heart failure with preserved ejection fraction (HFpEF) with International Classification of Disease-Tenth Revision codes. Methods: We identified HF diagnoses for VA patients between 2017-2019 and extracted the EF from clinical notes and imaging reports using a VA natural language processing algorithm. We classified sets of codes as HFrEF-related, HFpEF-related, or non-specific based on the closest EF within 180 days. We selected a random heart failure diagnosis for each patient and tested the predictive accuracy of various algorithms for identifying HFrEF using the last 1 year of heart failure diagnoses. We performed sensitivity analyses on the EF thresholds, the cohort, and the diagnoses used. Results: Between 2017-2019, we identified 358,172 patients and 1,671,084 diagnoses with an EF recording within 180 days. After dividing diagnoses into HFrEF-related, HFpEF-related, or non-specific, we found using the proportion of specific diagnoses classified as HFrEF-related had an AUC of 0.76 for predicting EF≤40% and 0.80 for predicting EF<50%. However, 23.3% of patients could not be classified due to only having non-specific codes. Predictive accuracy increased among patients with ≥4 HF diagnoses over the preceding year. Discussion: In a VA cohort, administrative claims with ICD-10 codes had moderate accuracy for identifying reduced ejection fraction. This level of specificity is likely inadequate for performance measures. Administrative claims need to better align terminology with relevant clinical definitions.

scholarly journals Type 2 diabetes and heart failure: Characteristics and prognosis in preserved, mid-range and reduced ventricular function

2018 ◽  
Vol 15 (6) ◽  
pp. 494-503 ◽  
Author(s):  
Isabelle Johansson ◽  
Ulf Dahlström ◽  
Magnus Edner ◽  
Per Näsman ◽  
Lars Rydén ◽  
...  
Keyword(s):  
Heart Failure ◽  
Type 2 Diabetes ◽  
Left Ventricular Ejection Fraction ◽  
Ejection Fraction ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Preserved Ejection Fraction ◽  
Ventricular Ejection Fraction ◽  
Reduced Ejection Fraction

Objective: To study the characteristics and prognostic implications of type 2 diabetes in different heart failure entities from a nationwide perspective. Methods: This observational study comprised 30,696 heart failure patients prospectively included in the Swedish Heart Failure Registry (SwedeHF) 2003–2011 from specialist care, with mortality information available until December 2014. Patients were categorized into three heart failure entities by their left ventricular ejection fraction (heart failure with preserved ejection fraction: ⩾50%, heart failure with mid-range ejection fraction: 40%–49% and heart failure with reduced ejection fraction: <40%). All-cause mortality stratified by type 2 diabetes and heart failure entity was studied by Cox regression. Results: Among the patients, 22% had heart failure with preserved ejection fraction, 21% had heart failure with mid-range ejection fraction and 57% had heart failure with reduced ejection fraction. The proportion of type 2 diabetes was similar, ≈25% in each heart failure entity. Patients with type 2 diabetes and heart failure with preserved ejection fraction were older, more often female and burdened with hypertension and renal impairment compared with heart failure with mid-range ejection fraction and heart failure with reduced ejection fraction patients among whom ischaemic heart disease was more common. Type 2 diabetes remained an independent mortality predictor across all heart failure entities after multivariable adjustment, somewhat stronger in heart failure with left ventricular ejection fraction below 50% (hazard ratio, 95% confidence interval; heart failure with preserved ejection fraction: 1.32 [1.22–1.43], heart failure with mid-range ejection fraction: 1.51 [1.39–1.65], heart failure with reduced ejection fraction: 1.46 [1.39–1.54]; p-value for interaction, p = 0.0049). Conclusion: Type 2 diabetes is an independent mortality predictor across all heart failure entities increasing mortality risk by 30%–50%. In type 2 diabetes, the heart failure with mid-range ejection fraction entity resembles heart failure with reduced ejection fraction in clinical characteristics, risk factor pattern and prognosis.

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