Half a loaf is better than no bread: Epidemiology, clinical characteristics, determinants and prognosis of heart failure with partial improved ejection fraction

Half a loaf is better than no bread: Epidemiology, clinical characteristics, determinants and prognosis of heart failure with partial improved ejection fraction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Half a loaf is better than no bread: Epidemiology, clinical characteristics, determinants and prognosis of heart failure with partial improved ejection fraction Xuan Yin, Hengyi Mao, Feng Jiang, Fan Yang, Suyan Zhu, Hanbin Cui, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4690019/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background A subset of patients identified with heart failure (HF) with decreased ejection fraction (HFrEF) show a partial improvement in left ventricular ejection fraction (LVEF). Information regarding the epidemiology, clinical characteristics, and outlook for patients with HF exhibiting partially improved ejection fraction (HFpimpEF) is scarce. Methods Among 3691 adults HF patients with had two LVEF echocardiograms that were at least three months apart in Yinzhou District, 350 of these were initially categorized as HFrEF (LVEF ≤ 40%). Subtypes included pHFrEF (LVEF ≤ 40), HFpimpEF (LVEF 41–49%, improvement 40, LVEF improvement ≥ 10%). The main outcome was mortality or first HF-related readmission. Results During a median follow-up of 15.6 months, 62 (17.7%) were HFpimpEF. Using multivariable Cox models, HFpimpEF demonstrated a lower risk of readmission or death than pHFrEF after adjustments compared to pHFrEF (adjusted hazard ratio: 0.55; 95% CI, 0.31–0.96; P = 0.037). Conclusions Given its unique clinical presentation, HFpimpEF is supposed to be recognized as a distinct HF subtype. This subtype is characterized by a partial improvement in LVEF and generally has a more favorable prognosis compared to pHFrEF. Epidemiology heart failure risk factors treatment outcome Figures Figure 1 Figure 2 Figure 3 Introduction Heart failure (HF) is a clinical syndrome characterized by the inability of the heart to pump sufficient blood to meet the body’s metabolic demands. 1 HF is a significant public health concern affecting 64.3 million of individuals worldwide. Numerous epidemiological studies have demonstrated significant disease burdens associated with HF, with higher risk of morbidity and mortality poses considerable threats on healthcare systems and society as a whole. 2,3 In China, HF accounts for 20% of hospitalizations and 40% of deaths due to cardiovascular diseases and is the second most common cause of death. 4 HF patients can be classified into three major categories, HF with reduced ejection fraction (HFrEF), HF with mildly reduced ejection fraction (HFmrEF) and HF with preserved ejection fraction (HFpEF) according to their left ventricular function measured by ejection fraction (EF). 5,6 In recent years, patients who have recovered from HFrEF have received increasing attention and the definition of HF with improved ejection fraction (HFimpEF) was formally proposed and recognized by the medical community as a novel HF subtype with distinct clinical characteristics in 2021. It was defined as LVEF ≤ 40% at baseline with improvement of up to 40% and at least a ≥ 10% increase. 1 However, a non-negligible number of patients with improvement of EF following active treatment may still fail to meet the updated ACCF/AHA guideline criteria for HFimpEF (patients who experience partial recovery from HFrEF, characterized by a baseline LVEF of less than 40% and subsequently recover to over 40% but improve by less than 10%). With a follow-up LVEF greater than 40%, partial improvement of LVEF is likely to be beneficial to patients with previous HFrEF. Additionally, clinical characteristics, pharmacotherapy patterns and prognosis remain poorly understood for this subset of patients with partial EF recovery. Therefore, the main objective of this study was to comprehensively assess the epidemiological aspects, clinical features, and prognosis of HF patients with partial improved ejection fraction (HFpimpEF) from a population-based cohort of 12637 HF patients, with a specific focus on subjects demonstrating improvement in LVEF. We hypothesized that age, sex, comorbidities, laboratory test results and guideline-directed management and therapy (GDMT) would be associated with notable advantages in terms of augmenting LVEF. A secondary hypothesis envisioned the prognosis among HFpimpEF patients would be superior to patients with pHFrEF, but inferior to patients with HFimpEF. Methods Data Sources Yinzhou is located in the coastal city of Ningbo in the eastern part of the People's Republic of China, with a population of approximately 1.6 million. Yinzhou Health and Family Planning Commission established a regional healthcare big data platform since 2005, by integrating electronic healthcare records (EHR) from all local hospitals and community health centers. The platform serves as a repository for EHR-based database across all healthcare facility levels, encompassing individual patient demographic characteristics, healthcare encounter records, lifestyle behaviors, as well as imaging and laboratory test results. The healthcare big data database is further linked with disease registries as well as mortality records administered by the District Centre for Disease Control and Prevention. 7 Notably, diagnoses and procedures are recorded using International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM). Personal information and identifiers were anonymized to ensure privacy protection. Study Design and Population We conducted a retrospective cohort study by including all adult individuals with newly diagnosed HF (aged 18 years and above at HF diagnosis) between 1st January 2005 and 30th September 2022. Eligible patients received two or more echocardiograms, with at least a three-month interval between the examinations. Conversely, patients who did not undergo two or more echocardiograms separated by at least three months or were lacking valid LVEF quantification in both echocardiograms were excluded from the study. The present study was exempt from informed consent requirements due to the deidentified nature of the data. It was approved by the Institutional Review Board (IRB) of First Affiliated Hospital of Ningbo University (Ningbo First Hospital), and conformed to the principles of the Declaration of Helsinki (2021-R075). The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines were followed throughout this study and a STROBE checklist was provided (Supplemental table 1 ). 8 Definition of HF Types Eligible patients with HF were identified based on the presence of the ICD-10 code I50.x, and associated terminologies such as "heart failure" or "cardiac insufficiency" in the recorded diagnosis information. Ultimately, the selected HF cohort comprised patients who had received a single HF diagnosis during their hospitalization or emergency department (ED) visit, or two such diagnoses within a 30-day interval in the outpatient setting. HF patients were categorized into three groups based on their baseline LVEF as follows: HFrEF at an LVEF of ≤ 40%; HFmrEF at an LVEF of 41–49%; and HFpEF at an LVEF of ≥ 50%. Specifically, the baseline echocardiogram was determined as the earliest record indicative of reduced ejection fraction (EF ≤ 40%). Types and magnitude of EF improvement were determined using the subsequent echocardiogram that was at least three months apart from the baseline. Patients of varying degree of EF improvement were categorized as follows: 1) pHFrEF (subsequent EF ≤ 40%); 2) HFpimpEF, (subsequent EF was 41–49% and had EF improvement 40% and had an absolute EF improvement ≥ 10%). Baseline demographic and clinical characteristics were captured using diagnosis and procedure codes from 365 days prior to the date of index echocardiogram. Treatment period was defined as the varying time period between baseline and subsequent echocardiogram. The follow-up period was defined as after the 2nd echocardiogram 3 months apart from the baseline one. (Supplemental Fig. 1) Clinical Outcomes The primary outcome of the present study was composite outcome consisted of all-cause mortality and/or first HF related readmission. The study also examined an array of secondary outcomes including first all-cause readmission and worsening chronic heart failure (WCHF) events. WCHF was defined as rehospitalization or emergencies in patients with heart failure, discharging from hospitalization or ED, or the occurrence of symptom exacerbation and escalation of diuretic therapy such as intravenous diuretic treatment in outpatient with heart failure. 9–11 Covariate Definitions and Measurements Covariate set included individual sociodemographic variables, such as age, sex, and ethnicity, marital status, and insurance status. Moreover, baseline characteristics, such as age, sex, baseline EF, body mass index (BMI), marital and education status, comorbid conditions including hypertension, hyperlipidemia, diabetes mellitus, atrial fibrillation, stroke, chronic obstructive pulmonary disease (COPD), chronic kidney disease, coronary artery disease, peripheral arterial disease, anemia and myocardial infarction were defined based on International Classification of Diseases, ICD-10 diagnostic codes in the 365 days prior to the baseline echocardiogram. BMI was calculated as weight/height2 (kilograms per square meter). Further, Charlson Comorbidity Index (CCI) was defined Dr. Mary Charlson in 1987, which includes age factor and comorbidities and has been used and validated in other studies. 12 Pharmacotherapy administered thought the study period, encompassing a variety of medications such as diuretics, angiotensin converting enzyme inhibitors (ACEi), angiotensin receptor blockers (ARB), angiotensin receptor-neprilysin inhibitors (ARNi), β blockers, mineralocorticoid receptor antagonists (MRA), triple therapy (defined as the combination of ACEi/ARB/ARNi, β blockers and MRA), sodium-dependent glucose transporters 2 (SGLT2), digoxin, and ivabradine. The purpose of this inquiry was to evaluate patients' receipt of GDMT defined as ever-exposure of triple therapy within a defined period. Laboratory test results (including creatinine, estimated glomerular filtration rate (eGFR), hemoglobin, brain natriuretic peptide (BNP), NT-proBNP, sodium, potassium, and urine protein) were analyzed. The eGFR was calculated using previously published equations: eGFR (mL/min/1.73 m2) = 194 × (serum creatinine) −1.094 × (age) −0.287 (× 0.739, if female). 13 Statistical Analysis Baseline characteristics were summarized using descriptive statistics, with continuous variables expressed as means and standard deviation (SDs) for normally distributed continuous variables, medians and interquartile ranges (IQRs) for skewed continuous variables. Categorical variables presented as frequencies and percentages. In order to compare patient characteristics across different HF groups, statistical tests such as t-tests, χ2 tests, Wilcoxon tests, or rank sum tests were utilized as appropriate. Patients missing laboratory indicators were excluded from the specific indicator calculations. Ordinal logistic regression models were used to evaluate patient characteristics across pHFrEF, HFimpEF, and HFpimpEF. Baseline individual-level patient characteristics, encompassing age, gender, concurrent comorbid conditions, and the administration of GDMT between initial and subsequent echocardiograms, were incorporated into the multivariable regression models. The findings are presented as odds ratios (OR) accompanied by 95% confidence intervals (CIs) to elucidate associations between key variables. Survival between different HF subtypes was first assessed using Kaplan-Meier curves and differences between HF subtypes was assessed using log-rank test. The association between HF subtypes and clinically meaningful outcomes was estimated in baseline HFrEF population using Cox proportional hazards model with adjustment for age, sex, baseline comorbidities and time-fixed treatment covariates in sequential model. The proportional hazard assumption was verified through visual examination of the scaled Schoenfeld residual. 14 R version 4.0.5 (R Foundation for Statistical Computing) was used to conduct all statistical analysis, a two-side P -values < 0.05 considered statistically significant. Results Study Population and HF Classification Overall, among 12,637 adult patients diagnosed with HF, 3,691 patients were eligible for analysis. All patients included underwent two or more echocardiograms, with an interval of at least three months, during which their LVEF was objectively measured. Based on their baseline LVEF measurements, 3,128 (84.7%) were classified as having HFpEF, 213 (5.8%) were classified as having HFmrEF, and 350 (9.5%) were classified as having HFrEF. Among 350 patients initially classified as having HFrEF, 110 (31.4%) remained persistently in the HFrEF category, 62 (17.7%) were reclassified as having HFpimpEF, and 178 (50.9%) were reclassified as having HFimpEF based on subsequent LVEF measurements (Fig. 1 ). Among the patients with pHFrEF, the median baseline LVEF was 35% (IQR, 31%-38%). For patients with HFpimpEF, the median baseline LVEF was significantly higher at 39% (IQR, 37%-40%) ( P < 0.001 compared to pHFrEF), and for patients with HFimpEF, the median baseline LVEF was 36% (IQR, 32%-39%). In terms of subsequent LVEF measurements, patients with pHFrEF had a median of 36% (IQR, 33%-38%), while those with HFpimpEF had a significantly higher median of 44% (IQR, 42%-47%) (P < 0.001 compared to pHFrEF). Patients with HFimpEF displayed the highest median subsequent LVEF at 53% (IQR, 48%-60%) ( P < 0.001 compared to pHFrEF) (Supplementary Table 2). Baseline Characteristics At baseline, the median age of the patients was 73 years, with 32.9% being female. The most prevalent comorbidity among the patients was hypertension, with an overall prevalence rate of 50.9%, followed by coronary artery disease (36.9%), COPD (20.9%), and hyperlipidemia (19.4%). 279 (79.7%) exhibited high baseline comorbidity burdens as evidenced by a CCI exceeding 5. Regarding baseline pharmacotherapy, 149 (42.6%) of the patients had received renin-angiotensin system inhibitors (RASi), while 132 (37.7%) had received diuretics. Patients with HFimpEF were more likely to receive diuretics (43.8% vs. 30.0%, P = 0.027) and ARB (37.6% vs. 23.6%, P = 0.019). No significant differences were observed in terms of sex, comorbidities, laboratory test results, and other pharmacotherapy among the three cohorts during the baseline period (Table 1 , Supplementary Table 3–7). Table 1 Baseline characteristics Characteristics Entire cohort N = 350 HFimpEF N = 178 HFpimpEF N = 62 pHFrEF N = 110 P -value (HFpimpEF vs. pHFrEF) P -value (HFimpEF vs. pHFrEF) Demographics Age at first echo (yr, mean ± SD) 70.7 ± 13.0 71.9 ± 13.4 68.4 ± 14.4 70.2 ± 11.4 0.394 0.256 Age at first echo (yr, median, IQR) 73 (63, 80) 75 (63, 83) 72.5 (58, 77) 70.5 (63, 80) 18–39 8 (2.3%) 5 (2.8%) 2 (3.2%) 1 (0.9%) 0.009 0.233 40–59 63 (18.0%) 27 (15.2%) 16 (25.8%) 20 (18.2%) 60–69 68 (19.4%) 32 (18.0%) 7 (11.3%) 29 (26.4%) 70–79 111 (31.7%) 56 (31.5%) 26 (41.9%) 29 (26.4%) 80–89 89 (25.4%) 50 (28.1%) 9 (14.5%) 30 (27.3%) 90+ 11 (3.1%) 8 (4.5%) 2 (3.2%) 1 (0.9%) Female sex 115 (32.9%) 59 (33.1%) 17 (27.4%) 39 (35.5%) 0.363 0.784 Comorbidity Hypertension 178 (50.9%) 96 (53.9%) 31 (50.0%) 51 (46.4%) 0.765 0.260 Hyperlipidemia 68 (19.4%) 32 (18.0%) 16 (25.8%) 20 (18.2%) 0.325 1.000 Diabetes mellitus 63 (18.0%) 30 (16.9%) 11 (17.7%) 22 (20.0%) 0.873 0.605 Atrial fibrillation 50 (14.3%) 25 (14.0%) 10 (16.1%) 15 (13.6%) 0.826 1.000 Stroke 25 (7.1%) 14 (7.9%) 3 (4.8%) 8 (7.3%) 0.748 1.000 COPD 73 (20.9%) 38 (21.3%) 12 (19.4%) 23 (20.9%) 0.963 1.000 Chronic kidney disease 37 (10.6%) 18 (10.1%) 8 (12.9%) 11 (10.0%) 0.742 1.000 Coronary artery disease 129 (36.9%) 66 (37.1%) 24 (38.7%) 39 (35.5%) 0.794 0.879 Peripheral arterial disease 6 (1.7%) 3 (1.7%) 1 (1.6%) 2 (1.8%) 1.000 1.000 Anemia 22 (6.3%) 11 (6.2%) 5 (8.1%) 6 (5.5%) 0.528 1.000 Myocardial infarction 9 (2.6%) 4 (2.2%) 2 (3.2%) 3 (2.7%) 1.000 1.000 CCI 6 ( 5 , 8 ) 6.5 (5, 8.8) 6 (5, 8.8) 6 ( 5 , 8 ) 0.966 0.374 0–2 14 (4.0%) 9 (5.1%) 2 (3.2%) 3 (2.7%) 0.943 0.276 3–4 57 (16.3%) 23 (12.9%) 13 (21.0%) 21 (19.1%) 5+ 279 (79.7%) 146 (82.0%) 47 (75.8%) 86 (78.2%) Lab results Creatinine, µmol/l (median, IQR) 87.0 (69.0, 130.5) 83.0 (66.0, 137.1) 90.5 (71.7, 126.3) 90.5 (73.0, 121.2) 0.669 0.582 eGFR (mL/min/1.73 m 2 ) (median, IQR) 34.5 (22.5, 45.4) 35.6 (21.1, 46.6) 33.9 (21.7, 40.3) 32.2 (24.1, 43.4) 0.677 0.540 60 mL/min 13 (6.9%) 7 (7.3%) 2 (5.6%) 4 (7.1%) Hemoglobin, g/dL (median, IQR) 124.0 (103.5, 139.0) 119.0 (99.0, 187.0) 126.0 (106.0, 139.0) 128.0 (109.0, 139.0) 0.939 0.102 BNP (pg/mL) (median, IQR) 1801.0 (704.0, 3208.0) 1801.0 (646.6, 3451.5) 1722.5 (867.5, 3153.5) 1801.0 (1268.0, 2671.0) 0.777 0.839 NT-proBNP (pg/mL) (median, IQR) 7592 (2882, 15304) 5800 (2766, 12965) 7002 (2561, 16771) 4322 (8287, 15786) 0.534 0.105 Urine protein (median, IQR) 0.0 (0.0, 1.0) 0.0 (0.0, 1.0) 0.0 (0.0, 1.0) 0.0 (0.0, 1.0) 0.321 0.193 Pharmacotherapy Diuretics 132 (37.7%) 78 (43.8%) 21 (33.9%) 33 (30.0%) 0.723 0.027 CCB 93 (26.6%) 51 (28.7%) 22 (35.5%) 20 (18.2%) 0.019 0.063 ACEi 50 (14.3%) 26 (14.6%) 6 (9.7%) 18 (16.4%) 0.324 0.815 ARB 111 (31.7%) 67 (37.6%) 18 (29.0%) 26 (23.6%) 0.551 0.019 ARNi 9 (2.6%) 2 (1.1%) 3 (4.8%) 4 (3.6%) 0.704 0.207 ACEi/ ARB/ ARNi 149 (42.6%) 84 (47.2%) 25 (40.3%) 40 (36.4%) 0.726 0.093 BB 93 (26.6%) 53 (29.8%) 15 (24.2%) 25 (22.7%) 0.976 0.242 MRA 92 (26.3%) 53 (29.8%) 14 (22.6%) 25 (22.7%) 1.000 0.242 Triple therapy 34 (9.7%) 20 (11.2%) 5 (8.1%) 9 (8.2%) 1.000 0.525 SGLT2 1 (0.3%) 0 (0.0%) 0 (0.0%) 1 (0.9%) 1.000 0.382 Digoxin 42 (12.0%) 22 (12.5%) 5 (8.1%) 15 (13.6%) 0.397 0.894 Ivabradine 1 (0.3%) 0 (0.0%) 0 (0.0%) 1 (0.9%) 1.000 0.382 Abbreviations: HFimpEF, heart failure with improved ejection fraction; HFpimpEF, heart failure with partial improved ejection fraction; pHFrEF, persistent heart failure with reduced ejection fraction; SD, standard deviation; IQR, interquartile range; COPD, chronic obstructive pulmoriary disease; CCI, Charlson Comorbidity Index; eGFR, epidermal growth factor impeptor; BNP, brain natriuretic peptide; NT-proBNP, N-terminal pro-B-type natriuretic peptide; CCB, calcium channel blockers; ACEi, angiotensin converting enzyme inhibitor; ARB, angiotensin impeptor blocker; ARNi, angiotensin impeptor-neprilysin inhibitor; BB, β blockers; MRA, mineralocorticoid impeptor antagonist; SGLT2, sodium-dependent glucose transporters 2. Pharmacotherapy Between Echocardiograms Median treatment time was 1.1 years (IQR, 0.6 to 2.2), and no significant differences were found among the different subtypes of HFrEF. In treatment period, utilization rates for medications indicated for HF increased. 117 (33.4%) patients used GDMT during the treatment period compared to baseline 34 (9.7%). Patients with HFimpEF were more likely to receive ARB (51.1% vs. 33.6%, P = 0.005) compared with patients with pHFrEF. No significant differences were observed in other pharmacotherapy among three HF subtypes between the two echocardiograms (Supplementary Fig. 2, Supplementary Table 8). Associations Between GDMT and LVEF Improvement As shown in Supplementary Table 9, exposure to GDMT was not statistically associated with subsequent HF subtypes. Despite positive association between GDMT and EF improvement in HFimpEF vs. pHFrEF (OR: 1.05; 95% CI, 0.65–1.70; P = 0.851) and HFpimpEF vs. pHFrEF (OR: 1.05; 95% CI, 0.65–1.70; P = 0.851), the associations were still not statistically significant after adjustment for sex and age and CCI (aOR: 1.08; 95% CI, 0.67–1.77; P = 0.746). Clinical Outcomes at Follow-up Median follow-up time was 1.3 years (IQR, 0.7 to 2.9), and no significant differences were found among the different subtypes of HFrEF. On follow-up, 36 patients met the end point of all-cause death (pHFrEF: n = 14 [9.0%] vs. HFpimpEF: n = 6 [5.8%] vs. HFimpEF: n = 16 [4.3%]), and 123 experienced HF-related readmission or all-cause death (pHFrEF: n = 38 [37.5%] vs. HFpimpEF: n = 19 [22.9%] vs. HFimpEF: n = 66 [24.2%]), and 219 experienced all-cause readmission or all-cause death (pHFrEF: n = 56 [72.6%] vs. HFpimpEF: n = 42 [78.5%] vs. HFimpEF: n = 121 [77.3%]), and 72 experienced WCHF or all-cause death (pHFrEF: n = 26 [20.4%] vs. HFpimpEF: n = 13 [13.7%] vs. HFimpEF: n = 33 [9.6%]) (Table 2 , Supplementary Fig. 3). The Kaplan-Meier survival curve shows that those in the HFpimpEF experienced a lower incidence of prespecified adverse end points than the pHFrEF group but higher than HFimpEF group (Fig. 2 ). Table 2 Incidence of clinically relevant outcomes End point pHFrEF HFpimpEF HFimpEF Risk ratio (95%CI) No./total No. Follow-up time (PY) Events/100 PYs (95%CI) No./total No. Follow-up time (PY) Events/100 py (95%CI) No./total No. Follow-up time (PY) Events/100 PYs (95%CI) HFpimpEF vs. pHFrEF HFimpEF vs. pHFrEF HFpimpEF vs. HFimpEF All-cause death 14 155.2 9.0 (4.5, 13.5) 6 104.0 5.8 (1.3, 10.3) 16 373.3 4.3 (2.2, 6.3) 0.64 (0.25, 1.61) 0.48 (0.24, 0.95) 1.35 (0.54, 3.35) First HF related readmission or all-cause death 38 101.3 37.5 (28.1, 46.9) 19 83.1 22.9 (13.8, 31.9) 66 273.3 24.2 (19.1, 29.2) 0.61 (0.38, 0.97) 0.64 (0.46, 0.89) 0.95 (0.61, 1.48) First all-cause readmission or all-cause death 56 77.1 72.6 (62.7, 82.6) 42 53.5 78.5 (67.5, 89.5) 121 156.5 77.3 (70.8, 83.9) 1.08 (0.89, 1.31) 1.06 (0.91, 1.25) 1.02 (0.86, 1.20) First WCHF or all-cause death 26 127.5 20.4 (13.4, 27.4) 13 95.1 13.7 (6.8, 20.6) 33 343.0 9.6 (6.5, 12.7) 0.67 (0.36, 1.23) 0.47 (0.29, 0.76) 1.42 (0.78, 2.59) Abbreviations: HF, heart failure; HFimpEF, heart failure with improved ejection fraction; HFpimpEF, heart failure with partial improved ejection fraction; pHFrEF, persistent heart failure with reduced ejection fraction; WCHF, worsening chronic heart failure; PY, person year. Cox regression analysis demonstrated that patients with HFpimpEF had lower risk of first HF related readmission or all-cause death after adjusting for sex, age, CCI, and GDMT (adjusted hazard ratio [aHR]: 0.55; 95% CI, 0.31–0.96; P = 0.037). Patients with HFimpEF had lower risks of all-cause death (aHR: 0.47; 95% CI, 0.23–0.98; P = 0.045), first HF related readmission or all-cause death (aHR: 0.67; 95% CI, 0.44-1.00; P = 0.051), and first WCHF or all-cause death (aHR: 0.47; 95% CI, 0.28–0.79; P = 0.005). No significant differences were observed in the rates of the first all-cause readmission or all-cause death across the subtypes of HF (Fig. 3 , Supplementary Table 10). Discussion Summary of Main Results In the present cohort study, we investigated the epidemiological aspects, clinical features, and prognosis of HFpimpEF patients. We found that approximately one-fifth of patients with baseline HFrEF may experience a partial LVEF improvement. The median age of patients with HFpimpEF were 1.8 years younger compared to patients with pHFrEF at baseline. Furthermore, the results demonstrated that those with HFpimpEF were 3.2% less likely to develop all-cause death events and 14.6% less for HF admission or all-cause death than the pHFrEF group but higher than the HFimpEF group. No significant association was observed between GDMT and LVEF improvement. Mechanistic Interpretation HF as complex syndrome resulting from various etiologies, such as myocardial ischemia, hypertension, valvular heart disease, and cardiomyopathies, could experience improvement in LVEF through complex mechanisms including reverse ventricular remodeling, cellular and molecular adaptations, and myocardial perfusion via angiogenesis. 15 Reverse ventricular remodeling is the process of reversing adverse structural changes in patients with HF. This reversal leads to improved left ventricular ejection fraction (LVEF) and better cardiac function. Factors contributing to reverse remodeling include reduced neurohormonal activation, decreased wall stress on the heart, and enhanced cardiomyocyte function due to improved calcium handling and cellular contractility. 16 This positive remodeling can be achieved through appropriate medical treatment, lifestyle modifications, and interventions. These findings highlight the importance of early and effective heart failure management, known as Guideline-Directed Medical Therapy (GDMT), to facilitate reverse ventricular remodeling and improve patient outcomes. 17 However, we failed to identify associations between the use of GDMT and LVEF improvement, suggestive of insufficient duration of follow-up period coupled with a limited number of subjects exposed to SGLT-2i. Additionally, cellular and molecular adaptations in heart failure involve changes in contractile proteins, calcium handling, ion channels, cardiac metabolism, and angiogenesis, which collectively influence cardiac function. These mechanisms are crucial in reversing adverse remodeling and improving LVEF. 18 When studying heart failure in Asian subjects, it is essential to consider potential ethnic-specific variations that may impact the prevalence, severity, and response to treatment. 19–21 Lastly, myocardial perfusion via angiogenesis can be triggered by chronic ischemia and tissue hypoxia in the failing heart. Angiogenic factors such as VEGF and FGF stimulate the formation of collateral vessels, which provide an alternative route for blood flow and help bypass blocked or dysfunctional coronary arteries. 22,23 Therapeutic approaches involving angiogenic gene therapy, 24–26 stem cell-based therapies, 27 or growth factor administration 28 aim to enhance myocardial perfusion and improve cardiac function, potentially reducing myocardial damage and alleviating heart failure symptoms. However, with conflicting and inconclusive findings, further research is needed to fully understand the intricacies of angiogenesis in heart failure and optimize therapeutic strategies for promoting effective myocardial perfusion. Strengths and Limitations The interpretation of our findings necessitates consideration of both the strengths and limitations of our study. Firstly, we introduced the novel concept of HFpimpEF, conceptualizing it as a distinct subtype of HF that is independent of pHFrEF and HFimpEF. This pioneering identification represents a groundbreaking contribution to the field. Secondly, our study is the first population-based retrospective multicenter cohort study in China to analyze the predictors and clinic outcomes among HFimpEF and HFpimpEF patients. Thirdly, we implemented a rigorous data cleaning process, transforming unstructured echocardiogram reports from EHR into standardized cardiac ultrasound datasets, which had not been commonly practiced in previous retrospective investigations. Finally, our study considered WCHF as a clinical endpoint for patients, contributing to the diversity of endpoints considered in HFimpEF research. However, it is imperative to address several limitations that should be considered when interpreting the findings of this study. Firstly, there is some referral bias as we only included medical records from local hospitals and community health centers within the Yinzhou District. Therefore, most patients had mild symptoms, less comorbidities and better cardiac function. Moreover, patients who were transferred to higher-level hospitals outside of this district for treatment due to worsening HF would be excluded from our study and subsequent adverse outcomes will not be observed, leading to an underestimation of the mortality risk. Secondly, we cannot discount the potential impact of survival bias and selection bias in our study population as patients were required to have completed two echocardiograms with a minimum interval of three months. Thirdly, classification of HF subtypes was based on clinically available echocardiographic data which were extracted from a retrospective database. Although the measurement method used was consistent across different medical centers, variations in the equipment used and lack of strict quality control could introduce measurement errors that we are unable to identify or account for. Fourthly, HF and comorbidities were obtained using diagnosis codes, which might lead to misclassification. Fifthly, drugs obtained through pharmacy retail channels were not included in medical records, resulting in an extremely low usage rate of GDMT observed in this study. Sixthly, we cannot rule out residual confounding or prove causal relations because of the observational nature of our study. Seventhly, the primary population of our study consisted mainly of the permanent residents of the Yinzhou District in Ningbo City, a developed region along the eastern coast of China. Therefore, the generalizability of our findings to other geographical areas or ethnicities should be performed with caution. Finally, this study had relatively small sample size. A lager cohort is needed to verify the predictive factors of LVEF improvement and prognosis of HFpimpEF patients in future prospective research. Conclusion In conclusion, the cohort study identifies HFpimpEF as a distinct subtype of heart failure, characterized by partial LVEF improvement and favorable prognosis compared to pHFrEF patients but higher than HFimpEF patients. While the findings contribute valuable insights to the field, further research with larger and more diverse cohorts is needed to validate and expand upon these results. Abbreviations GDMT guideline-directed medical therapy HFpimpEF heart failure with partial improved ejection fraction HFimpEF heart failure with improved ejection fraction HFrEF heart failure with reduced ejection fraction pHFrEF persistent heart failure with reduced ejection fraction WCHF worsening chronic heart failure Declarations Ethics approval and consent to participate The current research was granted ethical approval by the Institutional Review Board (IRB) of First Affiliated Hospital of Ningbo University (Ningbo First Hospital), adhering to the principles outlined in the Declaration of Helsinki (2021-R075). Informed consent was waived by the IRB of First Affiliated Hospital of Ningbo University (Ningbo First Hospital) because of the anonymization of data. Consent for publication Not applicable. Availability of data and materials The data that support the findings of this study are available from the Yinzhou Health and Family Planning Commission, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This study received sponsorship from Bayer (Beijing, China). However, the contents of this manuscript are the exclusive responsibility of the authors and do not necessarily mirror the official positions or opinions of the sponsoring entity. Authors' contributions Xuan Yin : conception and design, acquisition of data, analysis and interpretation of data, drafting the manuscript and revising it critically for important intellectual content. Hengyi Mao : conception and design, analysis and interpretation of data, drafting the manuscript and revising it critically for important intellectual content. Feng Jiang : acquisition of data, analysis and interpretation of data. Fan Yang : revising the manuscript critically for important intellectual content. Suyan Zhu : revising the manuscript critically for important intellectual content. Hanbin Cui : conception and design, revising the manuscript critically for important intellectual content, final approval of the version to be published. Jifang Zhou : conception and design, analysis and interpretation of data, revising the manuscript critically for important intellectual content, final approval of the version to be published. Acknowledgments The authors are grateful for Dr. Fei Yu from First Affiliated Hospital of Ningbo University for her contribution to the methodology part of the paper. 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JACC Heart Fail. 2022;10:512–24. 10.1016/j.jchf.2022.05.004 . Kitai T, Miyakoshi C, Morimoto T, Yaku H, Murai R, Kaji S, Furukawa Y, Inuzuka Y, Nagao K, Tamaki Y, et al. Mode of Death Among Japanese Adults With Heart Failure With Preserved, Midrange, and Reduced Ejection Fraction. JAMA Netw Open. 2020;3:e204296. 10.1001/jamanetworkopen.2020.4296 . Tromp J, Seekings PJ, Hung CL, Iversen MB, Frost MJ, Ouwerkerk W, Jiang Z, Eisenhaber F, Goh RSM, Zhao H, et al. Automated interpretation of systolic and diastolic function on the echocardiogram: a multicohort study. Lancet Digit Health. 2022;4:e46–54. 10.1016/s2589-7500(21)00235-1 . Vijay A, Tay WT, Teng TK, Teramoto K, Tromp J, Ouwerkerk W, Lo SY, Shimizu W, Huffman MD, Lam CSP, et al. Polypill Eligibility for Patients with Heart Failure with Reduced Ejection Fraction in the ASIAN-HF Registry: A Cross-Sectional Analysis. Glob Heart. 2023;18:33. 10.5334/gh.1215 . Collén A, Bergenhem N, Carlsson L, Chien KR, Hoge S, Gan LM, Fritsche-Danielson R. VEGFA mRNA for regenerative treatment of heart failure. Nat Rev Drug Discov. 2022;21:79–80. 10.1038/s41573-021-00355-6 . Bergmark BA, Udell JA, Morrow DA, Cannon CP, Steen DL, Jarolim P, Budaj A, Hamm C, Guo J, Im K, et al. Association of Fibroblast Growth Factor 23 With Recurrent Cardiovascular Events in Patients After an Acute Coronary Syndrome: A Secondary Analysis of a Randomized Clinical Trial. JAMA Cardiol. 2018;3:473–80. 10.1001/jamacardio.2018.0653 . Ylä-Herttuala S, Bridges C, Katz MG, Korpisalo P. Angiogenic gene therapy in cardiovascular diseases: dream or vision? Eur Heart J. 2017;38:1365–71. 10.1093/eurheartj/ehw547 . Hinkel R, Trenkwalder T, Kupatt C. Gene therapy for ischemic heart disease. Expert Opin Biol Ther. 2011;11:723–37. 10.1517/14712598.2011.570749 . Eckhouse SR, Jones JA, Spinale FG. Gene targeting in ischemic heart disease and failure: translational and clinical studies. Biochem Pharmacol. 2013;85:1–11. 10.1016/j.bcp.2012.08.018 . Tang YL, Wang YJ, Chen LJ, Pan YH, Zhang L, Weintraub NL. Cardiac-derived stem cell-based therapy for heart failure: progress and clinical applications. Exp Biol Med (Maywood). 2013;238:294–300. 10.1177/1535370213477982 . Banquet S, Gomez E, Nicol L, Edwards-Lévy F, Henry JP, Cao R, Schapman D, Dautreaux B, Lallemand F, Bauer F, et al. Arteriogenic therapy by intramyocardial sustained delivery of a novel growth factor combination prevents chronic heart failure. Circulation. 2011;124:1059–69. 10.1161/circulationaha.110.010264 . Additional Declarations No competing interests reported. Supplementary Files Supplementarytables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4690019","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":336125493,"identity":"16bbc1f5-d7d2-4562-a8a8-9f5175202f62","order_by":0,"name":"Xuan Yin","email":"","orcid":"","institution":"China Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Yin","suffix":""},{"id":336125494,"identity":"b7a9dccc-669f-438f-9e04-d561c67e3a99","order_by":1,"name":"Hengyi Mao","email":"","orcid":"","institution":"First Affiliated Hospital of Ningbo University (Ningbo First Hospital), Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Hengyi","middleName":"","lastName":"Mao","suffix":""},{"id":336125496,"identity":"3dac5d81-d6a1-4245-a5b6-70211b0592d7","order_by":2,"name":"Feng Jiang","email":"","orcid":"","institution":"China Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Jiang","suffix":""},{"id":336125498,"identity":"731d3e89-4cec-4469-9131-87825f0c3bbc","order_by":3,"name":"Fan Yang","email":"","orcid":"","institution":"China Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Yang","suffix":""},{"id":336125499,"identity":"6f37414b-e587-4b25-b5d8-5a9258cdb3e7","order_by":4,"name":"Suyan Zhu","email":"","orcid":"","institution":"First Affiliated Hospital of Ningbo University (Ningbo First Hospital), Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Suyan","middleName":"","lastName":"Zhu","suffix":""},{"id":336125500,"identity":"b8c43ecf-cc14-4a80-8334-2bdf72f9cda0","order_by":5,"name":"Hanbin Cui","email":"","orcid":"","institution":"First Affiliated Hospital of Ningbo University (Ningbo First Hospital), Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Hanbin","middleName":"","lastName":"Cui","suffix":""},{"id":336125501,"identity":"87cad60d-80da-4d5a-a653-9c42afb46f81","order_by":6,"name":"Jifang Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIie3LsWrDMBDG8TMHl0VxVgWD/AoKgkx5GJlAplBSCiVbAwZtpavzFoFCZwWDsyidvXrpnOK10NbNVAqyxw76c3DwwQ8gFPqHEaK1F7kQNMntdbI9JB6ZrCm2KxXzSg8jgjmlmCszAWs5jBDXcz42qAnce3trQMS1jtqNn6z41NAN4eNzsjegprXGpPCTis8MuyM4v+DYQHaoNSHzksxcD9ZvHXnoJ6xEaZ3sCHVEy14yMlGz22pFvFJJ8cpne9fkiY+k+eRSfshPkT7lTbu5X6TxaXlsfeRXCMC/X7QbCn5IKBQKhf72BajbSOeJp8N+AAAAAElFTkSuQmCC","orcid":"","institution":"China Pharmaceutical University","correspondingAuthor":true,"prefix":"","firstName":"Jifang","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2024-07-05 06:21:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4690019/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4690019/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62190414,"identity":"fa6cba4f-3f78-4671-b88c-174690c92891","added_by":"auto","created_at":"2024-08-10 12:27:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83229,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flow diagram\u003c/p\u003e\n\u003cp\u003eAbbreviations: HF, heart failure; HFimpEF, heart failure with improved ejection fraction; HFpimpEF, heart failure with partial improved ejection fraction; pHFrEF, persistent heart failure with reduced ejection fraction; LVEF, left ventricular ejection fraction.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4690019/v1/7a8665a92ee431127dd9c5ee.png"},{"id":62190417,"identity":"d64f8284-6569-4c92-a7cc-afd0ff67afa4","added_by":"auto","created_at":"2024-08-10 12:27:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":453143,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves of clinically relevant outcomes\u003c/p\u003e\n\u003cp\u003eAbbreviations: HFimpEF, heart failure with improved ejection fraction; HFpimpEF, heart failure with partial improved ejection fraction; pHFrEF, persistent heart failure with reduced ejection fraction; WCHF, worsening chronic heart failure.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4690019/v1/4abfd7a6fa9e51e211b30b36.png"},{"id":62190884,"identity":"e62afe92-8902-4ca7-ae40-5c71168ab3e0","added_by":"auto","created_at":"2024-08-10 12:35:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":86397,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between heart failure subtypes and clinically relevant outcomes\u003c/p\u003e\n\u003cp\u003eAbbreviations: HF, heart failure; HFimpEF, heart failure with improved ejection fraction; HFpimpEF, heart failure with partial improved ejection fraction; pHFrEF, persistent heart failure with reduced ejection fraction; WCHF, worsening chronic heart failure; HR, hazard ratio.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4690019/v1/81a7f8a9bcaf4230c9c00481.png"},{"id":91332740,"identity":"0deedf25-d94e-4d49-8380-a563ad40ad5f","added_by":"auto","created_at":"2025-09-15 11:17:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1670772,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4690019/v1/13765b6f-2472-4cc2-9a68-75027e15c263.pdf"},{"id":62190415,"identity":"cafac6f4-3b5b-4df0-8766-a922f9ff505d","added_by":"auto","created_at":"2024-08-10 12:27:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":268592,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.docx","url":"https://assets-eu.researchsquare.com/files/rs-4690019/v1/f4b3a0d5eb741171e74a1d74.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Half a loaf is better than no bread: Epidemiology, clinical characteristics, determinants and prognosis of heart failure with partial improved ejection fraction","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHeart failure (HF) is a clinical syndrome characterized by the inability of the heart to pump sufficient blood to meet the body\u0026rsquo;s metabolic demands.\u003csup\u003e1\u003c/sup\u003e HF is a significant public health concern affecting 64.3\u0026nbsp;million of individuals worldwide. Numerous epidemiological studies have demonstrated significant disease burdens associated with HF, with higher risk of morbidity and mortality poses considerable threats on healthcare systems and society as a whole.\u003csup\u003e2,3\u003c/sup\u003e In China, HF accounts for 20% of hospitalizations and 40% of deaths due to cardiovascular diseases and is the second most common cause of death.\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eHF patients can be classified into three major categories, HF with reduced ejection fraction (HFrEF), HF with mildly reduced ejection fraction (HFmrEF) and HF with preserved ejection fraction (HFpEF) according to their left ventricular function measured by ejection fraction (EF).\u003csup\u003e5,6\u003c/sup\u003e In recent years, patients who have recovered from HFrEF have received increasing attention and the definition of HF with improved ejection fraction (HFimpEF) was formally proposed and recognized by the medical community as a novel HF subtype with distinct clinical characteristics in 2021. It was defined as LVEF\u0026thinsp;\u0026le;\u0026thinsp;40% at baseline with improvement of up to 40% and at least a\u0026thinsp;\u0026ge;\u0026thinsp;10% increase.\u003csup\u003e1\u003c/sup\u003e However, a non-negligible number of patients with improvement of EF following active treatment may still fail to meet the updated ACCF/AHA guideline criteria for HFimpEF (patients who experience partial recovery from HFrEF, characterized by a baseline LVEF of less than 40% and subsequently recover to over 40% but improve by less than 10%). With a follow-up LVEF greater than 40%, partial improvement of LVEF is likely to be beneficial to patients with previous HFrEF. Additionally, clinical characteristics, pharmacotherapy patterns and prognosis remain poorly understood for this subset of patients with partial EF recovery.\u003c/p\u003e \u003cp\u003eTherefore, the main objective of this study was to comprehensively assess the epidemiological aspects, clinical features, and prognosis of HF patients with partial improved ejection fraction (HFpimpEF) from a population-based cohort of 12637 HF patients, with a specific focus on subjects demonstrating improvement in LVEF. We hypothesized that age, sex, comorbidities, laboratory test results and guideline-directed management and therapy (GDMT) would be associated with notable advantages in terms of augmenting LVEF. A secondary hypothesis envisioned the prognosis among HFpimpEF patients would be superior to patients with pHFrEF, but inferior to patients with HFimpEF.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Sources\u003c/h2\u003e \u003cp\u003eYinzhou is located in the coastal city of Ningbo in the eastern part of the People's Republic of China, with a population of approximately 1.6\u0026nbsp;million. Yinzhou Health and Family Planning Commission established a regional healthcare big data platform since 2005, by integrating electronic healthcare records (EHR) from all local hospitals and community health centers. The platform serves as a repository for EHR-based database across all healthcare facility levels, encompassing individual patient demographic characteristics, healthcare encounter records, lifestyle behaviors, as well as imaging and laboratory test results. The healthcare big data database is further linked with disease registries as well as mortality records administered by the District Centre for Disease Control and Prevention.\u003csup\u003e7\u003c/sup\u003e Notably, diagnoses and procedures are recorded using International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM). Personal information and identifiers were anonymized to ensure privacy protection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Population\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cohort study by including all adult individuals with newly diagnosed HF (aged 18 years and above at HF diagnosis) between 1st January 2005 and 30th September 2022.\u003c/p\u003e \u003cp\u003eEligible patients received two or more echocardiograms, with at least a three-month interval between the examinations. Conversely, patients who did not undergo two or more echocardiograms separated by at least three months or were lacking valid LVEF quantification in both echocardiograms were excluded from the study.\u003c/p\u003e \u003cp\u003e The present study was exempt from informed consent requirements due to the deidentified nature of the data. It was approved by the Institutional Review Board (IRB) of First Affiliated Hospital of Ningbo University (Ningbo First Hospital), and conformed to the principles of the Declaration of Helsinki (2021-R075). The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines were followed throughout this study and a STROBE checklist was provided (Supplemental table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDefinition of HF Types\u003c/h2\u003e \u003cp\u003eEligible patients with HF were identified based on the presence of the ICD-10 code I50.x, and associated terminologies such as \"heart failure\" or \"cardiac insufficiency\" in the recorded diagnosis information. Ultimately, the selected HF cohort comprised patients who had received a single HF diagnosis during their hospitalization or emergency department (ED) visit, or two such diagnoses within a 30-day interval in the outpatient setting. HF patients were categorized into three groups based on their baseline LVEF as follows: HFrEF at an LVEF of \u0026le;\u0026thinsp;40%; HFmrEF at an LVEF of 41\u0026ndash;49%; and HFpEF at an LVEF of \u0026ge;\u0026thinsp;50%.\u003c/p\u003e \u003cp\u003eSpecifically, the baseline echocardiogram was determined as the earliest record indicative of reduced ejection fraction (EF\u0026thinsp;\u0026le;\u0026thinsp;40%). Types and magnitude of EF improvement were determined using the subsequent echocardiogram that was at least three months apart from the baseline. Patients of varying degree of EF improvement were categorized as follows: 1) pHFrEF (subsequent EF\u0026thinsp;\u0026le;\u0026thinsp;40%); 2) HFpimpEF, (subsequent EF was 41\u0026ndash;49% and had EF improvement\u0026thinsp;\u0026lt;\u0026thinsp;10%) and 3) HFimpEF (subsequent echocardiogram\u0026thinsp;\u0026gt;\u0026thinsp;40% and had an absolute EF improvement\u0026thinsp;\u0026ge;\u0026thinsp;10%).\u003c/p\u003e \u003cp\u003eBaseline demographic and clinical characteristics were captured using diagnosis and procedure codes from 365 days prior to the date of index echocardiogram. Treatment period was defined as the varying time period between baseline and subsequent echocardiogram. The follow-up period was defined as after the 2nd echocardiogram 3 months apart from the baseline one. (Supplemental Fig.\u0026nbsp;1)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eClinical Outcomes\u003c/h2\u003e \u003cp\u003eThe primary outcome of the present study was composite outcome consisted of all-cause mortality and/or first HF related readmission. The study also examined an array of secondary outcomes including first all-cause readmission and worsening chronic heart failure (WCHF) events. WCHF was defined as rehospitalization or emergencies in patients with heart failure, discharging from hospitalization or ED, or the occurrence of symptom exacerbation and escalation of diuretic therapy such as intravenous diuretic treatment in outpatient with heart failure.\u003csup\u003e9\u0026ndash;11\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCovariate Definitions and Measurements\u003c/h2\u003e \u003cp\u003eCovariate set included individual sociodemographic variables, such as age, sex, and ethnicity, marital status, and insurance status. Moreover, baseline characteristics, such as age, sex, baseline EF, body mass index (BMI), marital and education status, comorbid conditions including hypertension, hyperlipidemia, diabetes mellitus, atrial fibrillation, stroke, chronic obstructive pulmonary disease (COPD), chronic kidney disease, coronary artery disease, peripheral arterial disease, anemia and myocardial infarction were defined based on International Classification of Diseases, ICD-10 diagnostic codes in the 365 days prior to the baseline echocardiogram. BMI was calculated as weight/height2 (kilograms per square meter). Further, Charlson Comorbidity Index (CCI) was defined Dr. Mary Charlson in 1987, which includes age factor and comorbidities and has been used and validated in other studies.\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePharmacotherapy administered thought the study period, encompassing a variety of medications such as diuretics, angiotensin converting enzyme inhibitors (ACEi), angiotensin receptor blockers (ARB), angiotensin receptor-neprilysin inhibitors (ARNi), β blockers, mineralocorticoid receptor antagonists (MRA), triple therapy (defined as the combination of ACEi/ARB/ARNi, β blockers and MRA), sodium-dependent glucose transporters 2 (SGLT2), digoxin, and ivabradine. The purpose of this inquiry was to evaluate patients' receipt of GDMT defined as ever-exposure of triple therapy within a defined period.\u003c/p\u003e \u003cp\u003eLaboratory test results (including creatinine, estimated glomerular filtration rate (eGFR), hemoglobin, brain natriuretic peptide (BNP), NT-proBNP, sodium, potassium, and urine protein) were analyzed. The eGFR was calculated using previously published equations: eGFR (mL/min/1.73 m2)\u0026thinsp;=\u0026thinsp;194 \u0026times; (serum creatinine) \u003csup\u003e\u0026minus;1.094\u003c/sup\u003e \u0026times; (age)\u003csup\u003e\u0026minus;0.287\u003c/sup\u003e (\u0026times; 0.739, if female).\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eBaseline characteristics were summarized using descriptive statistics, with continuous variables expressed as means and standard deviation (SDs) for normally distributed continuous variables, medians and interquartile ranges (IQRs) for skewed continuous variables. Categorical variables presented as frequencies and percentages. In order to compare patient characteristics across different HF groups, statistical tests such as t-tests, χ2 tests, Wilcoxon tests, or rank sum tests were utilized as appropriate. Patients missing laboratory indicators were excluded from the specific indicator calculations.\u003c/p\u003e \u003cp\u003eOrdinal logistic regression models were used to evaluate patient characteristics across pHFrEF, HFimpEF, and HFpimpEF. Baseline individual-level patient characteristics, encompassing age, gender, concurrent comorbid conditions, and the administration of GDMT between initial and subsequent echocardiograms, were incorporated into the multivariable regression models. The findings are presented as odds ratios (OR) accompanied by 95% confidence intervals (CIs) to elucidate associations between key variables. Survival between different HF subtypes was first assessed using Kaplan-Meier curves and differences between HF subtypes was assessed using log-rank test. The association between HF subtypes and clinically meaningful outcomes was estimated in baseline HFrEF population using Cox proportional hazards model with adjustment for age, sex, baseline comorbidities and time-fixed treatment covariates in sequential model. The proportional hazard assumption was verified through visual examination of the scaled Schoenfeld residual.\u003csup\u003e14\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eR version 4.0.5 (R Foundation for Statistical Computing) was used to conduct all statistical analysis, a two-side \u003cem\u003eP\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population and HF Classification\u003c/h2\u003e \u003cp\u003eOverall, among 12,637 adult patients diagnosed with HF, 3,691 patients were eligible for analysis. All patients included underwent two or more echocardiograms, with an interval of at least three months, during which their LVEF was objectively measured. Based on their baseline LVEF measurements, 3,128 (84.7%) were classified as having HFpEF, 213 (5.8%) were classified as having HFmrEF, and 350 (9.5%) were classified as having HFrEF.\u003c/p\u003e \u003cp\u003eAmong 350 patients initially classified as having HFrEF, 110 (31.4%) remained persistently in the HFrEF category, 62 (17.7%) were reclassified as having HFpimpEF, and 178 (50.9%) were reclassified as having HFimpEF based on subsequent LVEF measurements (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong the patients with pHFrEF, the median baseline LVEF was 35% (IQR, 31%-38%). For patients with HFpimpEF, the median baseline LVEF was significantly higher at 39% (IQR, 37%-40%) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 compared to pHFrEF), and for patients with HFimpEF, the median baseline LVEF was 36% (IQR, 32%-39%). In terms of subsequent LVEF measurements, patients with pHFrEF had a median of 36% (IQR, 33%-38%), while those with HFpimpEF had a significantly higher median of 44% (IQR, 42%-47%) \u003cem\u003e(P\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 compared to pHFrEF). Patients with HFimpEF displayed the highest median subsequent LVEF at 53% (IQR, 48%-60%) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 compared to pHFrEF) (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics\u003c/h2\u003e \u003cp\u003eAt baseline, the median age of the patients was 73 years, with 32.9% being female. The most prevalent comorbidity among the patients was hypertension, with an overall prevalence rate of 50.9%, followed by coronary artery disease (36.9%), COPD (20.9%), and hyperlipidemia (19.4%). 279 (79.7%) exhibited high baseline comorbidity burdens as evidenced by a CCI exceeding 5.\u003c/p\u003e \u003cp\u003eRegarding baseline pharmacotherapy, 149 (42.6%) of the patients had received renin-angiotensin system inhibitors (RASi), while 132 (37.7%) had received diuretics. Patients with HFimpEF were more likely to receive diuretics (43.8% \u003cem\u003evs.\u003c/em\u003e 30.0%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027) and ARB (37.6% \u003cem\u003evs.\u003c/em\u003e 23.6%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019). No significant differences were observed in terms of sex, comorbidities, laboratory test results, and other pharmacotherapy among the three cohorts during the baseline period (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Table\u0026nbsp;3\u0026ndash;7).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEntire cohort\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;350\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHFimpEF\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;178\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHFpimpEF\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;62\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epHFrEF\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;110\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003cp\u003e(HFpimpEF \u003cem\u003evs.\u003c/em\u003e pHFrEF)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003cp\u003e(HFimpEF \u003cem\u003evs.\u003c/em\u003e pHFrEF)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at first echo (yr, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.9\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at first echo (yr, median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (63, 80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (63, 83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.5 (58, 77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.5 (63, 80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (18.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (19.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (18.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 (26.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111 (31.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (31.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (41.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 (26.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u0026ndash;89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89 (25.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (28.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115 (32.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (33.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (27.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39 (35.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178 (50.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96 (53.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51 (46.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (19.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (18.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (18.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (16.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (17.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (14.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (16.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (20.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (21.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (19.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (20.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (10.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (10.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary artery disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129 (36.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (37.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (38.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39 (35.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral arterial disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (5.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardial infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5 (5, 8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (5, 8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (16.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (21.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21 (19.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e279 (79.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146 (82.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (75.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86 (78.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLab results\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, \u0026micro;mol/l (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87.0 (69.0, 130.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.0 (66.0, 137.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.5 (71.7, 126.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90.5 (73.0, 121.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e) (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.5 (22.5, 45.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.6 (21.1, 46.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.9 (21.7, 40.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.2 (24.1, 43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;30 mL/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (38.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (38.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (41.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;60 mL/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102 (54.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (55.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (55.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 (51.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60 mL/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (6.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/dL (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124.0 (103.5, 139.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.0 (99.0, 187.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126.0 (106.0, 139.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e128.0 (109.0, 139.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBNP (pg/mL) (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1801.0 (704.0, 3208.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1801.0 (646.6, 3451.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1722.5 (867.5, 3153.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1801.0 (1268.0, 2671.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNT-proBNP (pg/mL) (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7592 (2882, 15304)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5800 (2766, 12965)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7002 (2561, 16771)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4322 (8287, 15786)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrine protein (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0 (0.0, 1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0 (0.0, 1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0 (0.0, 1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0 (0.0, 1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePharmacotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiuretics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132 (37.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (43.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (33.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33 (30.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93 (26.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (28.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (35.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (16.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111 (31.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (37.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (29.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26 (23.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARNi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEi/ ARB/ ARNi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149 (42.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (47.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (40.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 (36.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93 (26.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (29.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (24.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (29.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (22.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriple therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (11.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSGLT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigoxin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (12.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIvabradine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: HFimpEF, heart failure with improved ejection fraction; HFpimpEF, heart failure with partial improved ejection fraction; pHFrEF, persistent heart failure with reduced ejection fraction; SD, standard deviation; IQR, interquartile range; COPD, chronic obstructive pulmoriary disease; CCI, Charlson Comorbidity Index; eGFR, epidermal growth factor impeptor; BNP, brain natriuretic peptide; NT-proBNP, N-terminal pro-B-type natriuretic peptide; CCB, calcium channel blockers; ACEi, angiotensin converting enzyme inhibitor; ARB, angiotensin impeptor blocker; ARNi, angiotensin impeptor-neprilysin inhibitor; BB, β blockers; MRA, mineralocorticoid impeptor antagonist; SGLT2, sodium-dependent glucose transporters 2.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePharmacotherapy Between Echocardiograms\u003c/h2\u003e \u003cp\u003eMedian treatment time was 1.1 years (IQR, 0.6 to 2.2), and no significant differences were found among the different subtypes of HFrEF. In treatment period, utilization rates for medications indicated for HF increased. 117 (33.4%) patients used GDMT during the treatment period compared to baseline 34 (9.7%). Patients with HFimpEF were more likely to receive ARB (51.1% \u003cem\u003evs.\u003c/em\u003e 33.6%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) compared with patients with pHFrEF. No significant differences were observed in other pharmacotherapy among three HF subtypes between the two echocardiograms (Supplementary Fig.\u0026nbsp;2, Supplementary Table\u0026nbsp;8).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociations Between GDMT and LVEF Improvement\u003c/h2\u003e \u003cp\u003eAs shown in Supplementary Table\u0026nbsp;9, exposure to GDMT was not statistically associated with subsequent HF subtypes. Despite positive association between GDMT and EF improvement in HFimpEF \u003cem\u003evs.\u003c/em\u003e pHFrEF (OR: 1.05; 95% CI, 0.65\u0026ndash;1.70; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.851) and HFpimpEF \u003cem\u003evs.\u003c/em\u003e pHFrEF (OR: 1.05; 95% CI, 0.65\u0026ndash;1.70; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.851), the associations were still not statistically significant after adjustment for sex and age and CCI (aOR: 1.08; 95% CI, 0.67\u0026ndash;1.77; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.746).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eClinical Outcomes at Follow-up\u003c/h2\u003e \u003cp\u003eMedian follow-up time was 1.3 years (IQR, 0.7 to 2.9), and no significant differences were found among the different subtypes of HFrEF.\u003c/p\u003e \u003cp\u003eOn follow-up, 36 patients met the end point of all-cause death (pHFrEF: n\u0026thinsp;=\u0026thinsp;14 [9.0%] \u003cem\u003evs.\u003c/em\u003e HFpimpEF: n\u0026thinsp;=\u0026thinsp;6 [5.8%] \u003cem\u003evs.\u003c/em\u003e HFimpEF: n\u0026thinsp;=\u0026thinsp;16 [4.3%]), and 123 experienced HF-related readmission or all-cause death (pHFrEF: n\u0026thinsp;=\u0026thinsp;38 [37.5%] \u003cem\u003evs.\u003c/em\u003e HFpimpEF: n\u0026thinsp;=\u0026thinsp;19 [22.9%] \u003cem\u003evs.\u003c/em\u003e HFimpEF: n\u0026thinsp;=\u0026thinsp;66 [24.2%]), and 219 experienced all-cause readmission or all-cause death (pHFrEF: n\u0026thinsp;=\u0026thinsp;56 [72.6%] \u003cem\u003evs.\u003c/em\u003e HFpimpEF: n\u0026thinsp;=\u0026thinsp;42 [78.5%] \u003cem\u003evs.\u003c/em\u003e HFimpEF: n\u0026thinsp;=\u0026thinsp;121 [77.3%]), and 72 experienced WCHF or all-cause death (pHFrEF: n\u0026thinsp;=\u0026thinsp;26 [20.4%] \u003cem\u003evs.\u003c/em\u003e HFpimpEF: n\u0026thinsp;=\u0026thinsp;13 [13.7%] \u003cem\u003evs.\u003c/em\u003e HFimpEF: n\u0026thinsp;=\u0026thinsp;33 [9.6%]) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Supplementary Fig.\u0026nbsp;3). The Kaplan-Meier survival curve shows that those in the HFpimpEF experienced a lower incidence of prespecified adverse end points than the pHFrEF group but higher than HFimpEF group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIncidence of clinically relevant outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEnd point\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003epHFrEF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eHFpimpEF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eHFimpEF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eRisk ratio (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo./total No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFollow-up time (PY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvents/100 PYs (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo./total No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFollow-up time (PY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEvents/100 py (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo./total No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFollow-up time (PY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEvents/100 PYs (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHFpimpEF \u003cem\u003evs.\u003c/em\u003e pHFrEF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eHFimpEF \u003cem\u003evs.\u003c/em\u003e pHFrEF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eHFpimpEF \u003cem\u003evs.\u003c/em\u003e HFimpEF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAll-cause death\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e155.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.0 (4.5, 13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e104.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.8 (1.3, 10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e373.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.3 (2.2, 6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.64 (0.25, 1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.48 (0.24, 0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.35 (0.54, 3.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFirst HF related readmission or all-cause death\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.5 (28.1, 46.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e83.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.9 (13.8, 31.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e273.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e24.2 (19.1, 29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.61 (0.38, 0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.64 (0.46, 0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.95 (0.61, 1.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFirst all-cause readmission or all-cause death\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.6 (62.7, 82.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e53.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e78.5 (67.5, 89.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e156.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e77.3 (70.8, 83.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.08 (0.89, 1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.06 (0.91, 1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.02 (0.86, 1.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFirst WCHF or all-cause death\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.4 (13.4, 27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.7 (6.8, 20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e343.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9.6 (6.5, 12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.67 (0.36, 1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.47 (0.29, 0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.42 (0.78, 2.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eAbbreviations: HF, heart failure; HFimpEF, heart failure with improved ejection fraction; HFpimpEF, heart failure with partial improved ejection fraction; pHFrEF, persistent heart failure with reduced ejection fraction; WCHF, worsening chronic heart failure; PY, person year.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCox regression analysis demonstrated that patients with HFpimpEF had lower risk of first HF related readmission or all-cause death after adjusting for sex, age, CCI, and GDMT (adjusted hazard ratio [aHR]: 0.55; 95% CI, 0.31\u0026ndash;0.96; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037). Patients with HFimpEF had lower risks of all-cause death (aHR: 0.47; 95% CI, 0.23\u0026ndash;0.98; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045), first HF related readmission or all-cause death (aHR: 0.67; 95% CI, 0.44-1.00; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.051), and first WCHF or all-cause death (aHR: 0.47; 95% CI, 0.28\u0026ndash;0.79; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005). No significant differences were observed in the rates of the first all-cause readmission or all-cause death across the subtypes of HF (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary Table\u0026nbsp;10).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSummary of Main Results\u003c/h2\u003e \u003cp\u003eIn the present cohort study, we investigated the epidemiological aspects, clinical features, and prognosis of HFpimpEF patients. We found that approximately one-fifth of patients with baseline HFrEF may experience a partial LVEF improvement. The median age of patients with HFpimpEF were 1.8 years younger compared to patients with pHFrEF at baseline. Furthermore, the results demonstrated that those with HFpimpEF were 3.2% less likely to develop all-cause death events and 14.6% less for HF admission or all-cause death than the pHFrEF group but higher than the HFimpEF group. No significant association was observed between GDMT and LVEF improvement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMechanistic Interpretation\u003c/h2\u003e \u003cp\u003eHF as complex syndrome resulting from various etiologies, such as myocardial ischemia, hypertension, valvular heart disease, and cardiomyopathies, could experience improvement in LVEF through complex mechanisms including reverse ventricular remodeling, cellular and molecular adaptations, and myocardial perfusion via angiogenesis.\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eReverse ventricular remodeling is the process of reversing adverse structural changes in patients with HF. This reversal leads to improved left ventricular ejection fraction (LVEF) and better cardiac function. Factors contributing to reverse remodeling include reduced neurohormonal activation, decreased wall stress on the heart, and enhanced cardiomyocyte function due to improved calcium handling and cellular contractility.\u003csup\u003e16\u003c/sup\u003e This positive remodeling can be achieved through appropriate medical treatment, lifestyle modifications, and interventions. These findings highlight the importance of early and effective heart failure management, known as Guideline-Directed Medical Therapy (GDMT), to facilitate reverse ventricular remodeling and improve patient outcomes.\u003csup\u003e17\u003c/sup\u003e However, we failed to identify associations between the use of GDMT and LVEF improvement, suggestive of insufficient duration of follow-up period coupled with a limited number of subjects exposed to SGLT-2i.\u003c/p\u003e \u003cp\u003eAdditionally, cellular and molecular adaptations in heart failure involve changes in contractile proteins, calcium handling, ion channels, cardiac metabolism, and angiogenesis, which collectively influence cardiac function. These mechanisms are crucial in reversing adverse remodeling and improving LVEF.\u003csup\u003e18\u003c/sup\u003e When studying heart failure in Asian subjects, it is essential to consider potential ethnic-specific variations that may impact the prevalence, severity, and response to treatment.\u003csup\u003e19\u0026ndash;21\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eLastly, myocardial perfusion via angiogenesis can be triggered by chronic ischemia and tissue hypoxia in the failing heart. Angiogenic factors such as VEGF and FGF stimulate the formation of collateral vessels, which provide an alternative route for blood flow and help bypass blocked or dysfunctional coronary arteries.\u003csup\u003e22,23\u003c/sup\u003e Therapeutic approaches involving angiogenic gene therapy,\u003csup\u003e24\u0026ndash;26\u003c/sup\u003e stem cell-based therapies,\u003csup\u003e27\u003c/sup\u003e or growth factor administration\u003csup\u003e28\u003c/sup\u003e aim to enhance myocardial perfusion and improve cardiac function, potentially reducing myocardial damage and alleviating heart failure symptoms. However, with conflicting and inconclusive findings, further research is needed to fully understand the intricacies of angiogenesis in heart failure and optimize therapeutic strategies for promoting effective myocardial perfusion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eThe interpretation of our findings necessitates consideration of both the strengths and limitations of our study. Firstly, we introduced the novel concept of HFpimpEF, conceptualizing it as a distinct subtype of HF that is independent of pHFrEF and HFimpEF. This pioneering identification represents a groundbreaking contribution to the field. Secondly, our study is the first population-based retrospective multicenter cohort study in China to analyze the predictors and clinic outcomes among HFimpEF and HFpimpEF patients. Thirdly, we implemented a rigorous data cleaning process, transforming unstructured echocardiogram reports from EHR into standardized cardiac ultrasound datasets, which had not been commonly practiced in previous retrospective investigations. Finally, our study considered WCHF as a clinical endpoint for patients, contributing to the diversity of endpoints considered in HFimpEF research.\u003c/p\u003e \u003cp\u003eHowever, it is imperative to address several limitations that should be considered when interpreting the findings of this study. Firstly, there is some referral bias as we only included medical records from local hospitals and community health centers within the Yinzhou District. Therefore, most patients had mild symptoms, less comorbidities and better cardiac function. Moreover, patients who were transferred to higher-level hospitals outside of this district for treatment due to worsening HF would be excluded from our study and subsequent adverse outcomes will not be observed, leading to an underestimation of the mortality risk. Secondly, we cannot discount the potential impact of survival bias and selection bias in our study population as patients were required to have completed two echocardiograms with a minimum interval of three months. Thirdly, classification of HF subtypes was based on clinically available echocardiographic data which were extracted from a retrospective database. Although the measurement method used was consistent across different medical centers, variations in the equipment used and lack of strict quality control could introduce measurement errors that we are unable to identify or account for. Fourthly, HF and comorbidities were obtained using diagnosis codes, which might lead to misclassification. Fifthly, drugs obtained through pharmacy retail channels were not included in medical records, resulting in an extremely low usage rate of GDMT observed in this study. Sixthly, we cannot rule out residual confounding or prove causal relations because of the observational nature of our study. Seventhly, the primary population of our study consisted mainly of the permanent residents of the Yinzhou District in Ningbo City, a developed region along the eastern coast of China. Therefore, the generalizability of our findings to other geographical areas or ethnicities should be performed with caution. Finally, this study had relatively small sample size. A lager cohort is needed to verify the predictive factors of LVEF improvement and prognosis of HFpimpEF patients in future prospective research.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the cohort study identifies HFpimpEF as a distinct subtype of heart failure, characterized by partial LVEF improvement and favorable prognosis compared to pHFrEF patients but higher than HFimpEF patients. While the findings contribute valuable insights to the field, further research with larger and more diverse cohorts is needed to validate and expand upon these results.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"518\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.077220077220076%\"\u003e\n \u003cp\u003eGDMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.92277992277992%\"\u003e\n \u003cp\u003eguideline-directed medical therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.077220077220076%\"\u003e\n \u003cp\u003eHFpimpEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.92277992277992%\"\u003e\n \u003cp\u003eheart failure with partial improved ejection fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.077220077220076%\"\u003e\n \u003cp\u003eHFimpEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.92277992277992%\"\u003e\n \u003cp\u003eheart failure with improved ejection fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.077220077220076%\"\u003e\n \u003cp\u003eHFrEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.92277992277992%\"\u003e\n \u003cp\u003eheart failure with reduced ejection fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.077220077220076%\"\u003e\n \u003cp\u003epHFrEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.92277992277992%\"\u003e\n \u003cp\u003epersistent heart failure with reduced ejection fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.077220077220076%\"\u003e\n \u003cp\u003eWCHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.92277992277992%\"\u003e\n \u003cp\u003eworsening chronic heart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current research was granted ethical approval by the Institutional Review Board (IRB) of First Affiliated Hospital of Ningbo University (Ningbo First Hospital), adhering to the principles outlined in the Declaration of Helsinki (2021-R075). Informed consent was waived by the IRB of First Affiliated Hospital of Ningbo University (Ningbo First Hospital) because of the anonymization of data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the Yinzhou Health and Family Planning Commission, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received sponsorship from Bayer (Beijing, China). However, the contents of this manuscript are the exclusive responsibility of the authors and do not necessarily mirror the official positions or opinions of the sponsoring entity. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXuan Yin\u003c/strong\u003e:\u0026nbsp;conception and design, acquisition of data,\u0026nbsp;analysis and interpretation of data,\u0026nbsp;drafting the manuscript and revising it critically for important intellectual content. \u003cstrong\u003eHengyi Mao\u003c/strong\u003e: conception and design, analysis and interpretation of data, drafting the manuscript and revising it critically for important intellectual content. \u003cstrong\u003eFeng Jiang\u003c/strong\u003e:\u0026nbsp;acquisition of data,\u0026nbsp;analysis and interpretation of data. \u003cstrong\u003eFan Yang\u003c/strong\u003e:\u0026nbsp;revising the manuscript critically for important intellectual content.\u003cstrong\u003e\u0026nbsp;Suyan Zhu\u003c/strong\u003e:\u0026nbsp;revising the manuscript critically for important intellectual content.\u003cstrong\u003e\u0026nbsp;Hanbin Cui\u003c/strong\u003e:\u0026nbsp;conception and design, revising the manuscript critically for important intellectual content,\u0026nbsp;final approval of the version to be published. \u003cstrong\u003eJifang Zhou\u003c/strong\u003e:\u0026nbsp;conception and design, analysis and interpretation of data,\u0026nbsp;revising the manuscript critically for important intellectual content,\u0026nbsp;final approval of the version to be published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful for Dr. Fei Yu from First Affiliated Hospital of Ningbo University for her contribution to the methodology part of the paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBozkurt B, Coats AJS, Tsutsui H, Abdelhamid CM, Adamopoulos S, Albert N, Anker SD, Atherton J, B\u0026ouml;hm M, Butler J, et al. 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Circulation. 2011;124:1059\u0026ndash;69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/circulationaha.110.010264\u003c/span\u003e\u003cspan address=\"10.1161/circulationaha.110.010264\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Epidemiology, heart failure, risk factors, treatment outcome","lastPublishedDoi":"10.21203/rs.3.rs-4690019/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4690019/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eA subset of patients identified with heart failure (HF) with decreased ejection fraction (HFrEF) show a partial improvement in left ventricular ejection fraction (LVEF). Information regarding the epidemiology, clinical characteristics, and outlook for patients with HF exhibiting partially improved ejection fraction (HFpimpEF) is scarce.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAmong 3691 adults HF patients with had two LVEF echocardiograms that were at least three months apart in Yinzhou District, 350 of these were initially categorized as HFrEF (LVEF\u0026thinsp;\u0026le;\u0026thinsp;40%). Subtypes included pHFrEF (LVEF\u0026thinsp;\u0026le;\u0026thinsp;40), HFpimpEF (LVEF 41\u0026ndash;49%, improvement\u0026thinsp;\u0026lt;\u0026thinsp;10%), and HFimpEF (echocardiogram\u0026thinsp;\u0026gt;\u0026thinsp;40, LVEF improvement\u0026thinsp;\u0026ge;\u0026thinsp;10%). The main outcome was mortality or first HF-related readmission.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDuring a median follow-up of 15.6 months, 62 (17.7%) were HFpimpEF. Using multivariable Cox models, HFpimpEF demonstrated a lower risk of readmission or death than pHFrEF after adjustments compared to pHFrEF (adjusted hazard ratio: 0.55; 95% CI, 0.31\u0026ndash;0.96; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eGiven its unique clinical presentation, HFpimpEF is supposed to be recognized as a distinct HF subtype. This subtype is characterized by a partial improvement in LVEF and generally has a more favorable prognosis compared to pHFrEF.\u003c/p\u003e","manuscriptTitle":"Half a loaf is better than no bread: Epidemiology, clinical characteristics, determinants and prognosis of heart failure with partial improved ejection fraction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-10 12:27:29","doi":"10.21203/rs.3.rs-4690019/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c3cc1d2b-6d51-40db-b456-a7fdd1169dba","owner":[],"postedDate":"August 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-15T11:09:01+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-10 12:27:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4690019","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4690019","identity":"rs-4690019","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}