Background: Large-scale and population-based studies of heart failure (HF) incidence and prevalence are scarce in China. The study sought to estimate the prevalence, incidence, and cost of HF in China. Methods: We conducted a population-based study using records of 50.0 million individuals ≥25 years old from the national urban employee basic medical insurance from 6 provinces in China in 2017. Incident cases were individuals with a diagnosis of HF (International Classification of Diseases code, and text of diagnosis) in 2017 with a 4-year disease-free period (2013–2016). We calculated standardized rates by applying age standardization to the 2010 Chinese census population. Results: The age-standardized prevalence and incidence were 1.10% (1.10% among men and women) and 275 per 100 000 person-years (287 among men and 261 among women), respectively, accounting for 12.1 million patients with HF and 3.0 million patients with incident HF ≥25 years old. Both prevalence and incidence increased with increasing age (0.57%, 3.86%, and 7.55% for prevalence and 158, 892, and 1655 per 100 000 person-years for incidence among persons who were 25–64, 65–79, and ≥80 years of age, respectively). The inpatient mean cost per-capita was $4406.8 and the proportion with ≥3 hospitalizations among those hospitalized was 40.5%. The outpatient mean cost per-capita was $892.3. Conclusions: HF has placed a considerable burden on health systems in China, and strategies aimed at the prevention and treatment of HF are needed. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: ChiCTR2000029094.

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Context: Rivaroxaban and ticagrelor are two common drugs for the treatment of atrial fibrillation and acute coronary syndrome. However, the drug-drug interaction between them is still unknown. Objective: To investigate the effects of ticagrelor on the pharmacokinetics of rivaroxaban in rats both in vivo and in vitro. Materials and methods: A sensitive and reliable UPLC-MS/MS method was developed for the determination of rivaroxaban in rat plasma. Ten Sprague-Dawley rats were randomly divided into ticagrelor pretreated group (10 mg/kg/day for 14 days) and control group. The pharmacokinetics of orally administered rivaroxaban (10 mg/kg, single dose) with or without ticagrelor pre-treatment was investigated with developed UPLC-MS/MS method. Additionally, Sprague-Dawley rat liver microsomes were also used to investigate the drug-drug interaction between these two drugs in vitro. Results: The C max (221.34 ± 53.33 vs. 691.18 ± 238.31 ng/mL) and the AUC (0-t) (1060.97 ± 291.21 vs. 3483.03 ± 753.83 lgÁh/L) of rivaroxaban increased significantly (p < 0.05) with ticagrelor pre-treatment. The MRT (0-1) of rivaroxaban increased from 4.41 ± 0.79 to 5.97 ± 1.11 h, while the intrinsic clearance decreased from 9.93 ± 2.55 to 2.89 ± 0.63 L/h/kg (both p < 0.05) after pre-treated with ticagrelor. Enzyme kinetic study indicated that ticagrelor decreased rivaroxaban metabolic clearance with the IC 50 value of 14.04 lmol/L. Conclusions: Our in vivo and in vitro results demonstrated that there is a drug-drug interaction between ticagrelor and rivaroxaban in rats. Further studies need to be carried out to verify whether similar interactions truly apply in humans and whether these interactions have clinical significance.

Background: This study aimed to develop and validate an electronic frailty index (eFI) based on routine electronic health records (EHR) for older adult inpatients and to analyze the correlations between frailty and hospitalized events and costs.Methods: We created an eFI from routine EHR and validated the effectiveness by the consistency of the comprehensive geriatric assessment-frailty index (CGA-FI) with an independent prospective cohort. Then, we analyzed the correlations between frailty and hospitalized events and costs by regressions.Results: During the study period, 49,226 inpatients were included in the analysis, 42,821 (87.0%) of which had enough data to calculate an eFI. A strong correlation between the CGA-FI and eFI was shown in the validation cohort of 685 subjects (Pearson's r = 0.716, P &lt; 0.001). The sensitivity and specificity for an eFI≥0.15, the upper tertile, to identify frailty, defined as a CGA-FI≥0.25, were 64.8 and 88.7%, respectively. After adjusting for age, sex, and operation, an eFI≥0.15 showed an independent association with long hospital stay (odds ratio [OR] = 2.889, P &lt; 0.001) and death in hospital (OR = 19.97, P &lt; 0.001). Moreover, eFI values (per 0.1) were positively associated with total costs (β = 0.453, P &lt; 0.001), examination costs (β = 0.269, P &lt; 0.001), treatment costs (β = 0.414, P &lt; 0.001), nursing costs (β = 0.381, P &lt; 0.001), pharmacy costs (β = 0.524, P &lt; 0.001), and material costs (β = 0.578, P &lt; 0.001) after adjusting aforementioned factors.Conclusions: We successfully developed an effective eFI from routine EHR from a general hospital in China. Frailty is an independent risk factor for long hospital stay and death in hospital. As the degree of frailty increases, the hospitalized costs increase accordingly.

Purpose Elderly heart failure (HF) patients have different clinical characteristics and poorer prognosis compared with younger patients. Prognostic risk scores for HF have not been validated well in elderly patients. We aimed to validate the Seattle Heart Failure Model (SHFM) and the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score in an elderly Chinese HF cohort. Patients and Methods This retrospective study enrolled 675 elderly HF patients (age≥70 years) discharged from our hospital between 2012 and 2017. The performance of the two risk scores was evaluated in terms of discrimination, using receiver-operating characteristic analysis, and calibration using a calibration plot and Hosmer–Lemeshow (H-L) test. Absolute risk reclassification was used to compare the two scores. Results During the mean follow-up time of 32.6 months, 193 patients (28.6%) died, and 1-year mortality was 10.5%. The predicted median 1-year mortality was 8% for the SHFM and 18% for the MAGGIC score. A Kaplan–Meier survival curve demonstrated that event rates of all-cause mortality significantly increased with increasing SHFM and MAGGIC scores. The discriminatory capacity of the SHFM was greater than that of the MAGGIC score (c-statistics were 0.72 and 0.67, respectively; P = 0.05). The calibration plot for the SHFM was better than that for MAGGIC score for 1-year mortality (SHFM: H-L χ 2 =8.2, P = 0.41; MAGGIC: H-L χ 2 =18.8, P =0.02). Compared with the MAGGIC score, the net reclassification index (NRI) of the SHFM was 2.96% (Z=5.88, P< 0.0001). Conclusion The SHFM performs better than MAGGIC score, having good discrimination, calibration and risk classification for the prediction of 1-year mortality in elderly Chinese HF patients.

Background Frailty assessment based on routine electronic health records (EHR) may be a good alternative to time-consuming frailty scales. This study mainly aimed to develop and validate an electronic frailty index (eFI) using routine EHR for elderly inpatients and analyse the correlations between frailty and hospitalized events and costs.Methods Based on the cumulative deficit model, we created an eFI from routine EHR. In a prospective cohort, we validated the effectiveness of the eFI by consistency with the comprehensive geriatric assessment-frailty index (CGA-FI). Then, we analysed the correlations between frailty and hospitalized events by logistic regression and costs by generalized linear regression models.Results During the study period, 49,226 elderly inpatients from the EHR were included in the analysis. There were 42,821 (87.0%) patients with sufficient data to calculate an eFI. The cut-off value for the upper tertile of the eFI for these patients was 0.15. A strong correlation between the CGA-FI and eFI was shown in the validation cohort of 685 subjects (Pearson’s r = 0.716, P < 0.001). The sensitivity and specificity for an eFI ≥ 0.15 to identify frailty defined as a CGA-FI ≥ 0.25 were 64.8% and 88.7%, respectively. After adjusting for age, gender, and operation, an eFI ≥ 0.15 showed an independent association with long hospital stay (odds ratio [OR] = 2.889, P < 0.001) and death in hospital (OR = 19.97, P < 0.001) for elderly inpatients from all departments. Moreover, after adjusting for age, gender, and operation, eFI values (per 0.1) were positively associated with total costs (β = 0.453, P < 0.001), examination costs (β = 0.269, P < 0.001), treatment costs (β = 0.414, P < 0.001), nursing costs (β = 0.381, P < 0.001), pharmacy costs (β = 0.524, P < 0.001), and material costs (β = 0.578, P < 0.001) for elderly inpatients from all departments.Conclusions It is feasible to develop an effective eFI from routine EHR for elderly inpatients from a general hospital in China. Frailty is an independent risk factor for long hospital stay and death in hospital. As the degree of frailty increases, the hospitalized costs for elderly inpatients increase accordingly.

Background: Characteristics of heart failure with recovered ejection fraction (HFrecEF) have not yet been fully understood. The objective of this study is to identify potential biomarkers for the left ventricular ejection fraction(LVEF) recovery. Methods: Antibody microarrays were used to detect proteins in serum of healthy volunteers, patients with heart failure with reduced ejection fraction(HFrEF), and patients with HFrecEF, looking for specific proteins of HFrecEF patients.Results:1000 proteins were detected in the sera of healthy volunteers, HFrEF patients and HFrecEF patients using antibody microarrays (three in each group). There were dozens of different proteins between each group. Based on the signal strength, fold changes, clinical significance and Venn diagram analysis, 11 proteins were selected to be detected in the sera of 10 healthy volunteers ,47 HFrEF patients and 22 HFrecEF patients using antibody microarrays. Serum concentrations of cysteine dioxygenase type 1 (CDO1) and growth/differentiation factor 8 (GDF-8) were significantly downregulated in HFrecEF patients compared with HFrEF patients. ROC curve analysis showed that the area under the CDO1 curve was 0.662(95%CI 0.517-0.808,P=0.031).The sensitivity of CDO1 was 77%, the specificity was 54%, and diagnostic cut‑off points was 10198.5.The GDF-8 has no diagnostic value. Kaplan–Meier survival curves showed that the prognosis is better in HFrecEF patients than HFrEF patients about all cause death(P=0.011) and cardiovascular death(P=0.004).But we did not find that patients with low baseline CDO1 levels (<10198.5) had better outcomes than those with high CDO1 levels (≥10198.5).Conclusions: This pilot study indicates that CDO1 is a potential biomarker of LVEF recovery, which needs to be confirmed by further studies.

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