Left ventricular function recovery in peripartum cardiomyopathy: a cardiovascular magnetic resonance study by myocardial T1 and T2 mapping.

BACKGROUND: Peripartum cardiomyopathy (PPCM) is rare and potentially life-threatening; its etiology remains unclear. Imaging characteristics on cardiovascular magnetic resonance (CMR) and their prognostic significance have rarely been studied. We sought to determine CMR's prognostic value in PPCM by using T1 and T2 mapping techniques. METHODS: Data from 21 PPCM patients from our CMR registry database were analyzed. The control group comprised 20 healthy age-matched females. All subjects underwent comprehensive contrast-enhanced CMR. T1 and T2 mapping using modified Look-Locker inversion recovery and T2 prep balanced steady-state free precession sequences, respectively. Ventricular size and function, late gadolinium enhancement (LGE), myocardial T1 value, extracellular volume (ECV), and T2 value were analyzed. Transthoracic echocardiography was performed at baseline and during follow-up. The recovered left ventricular ejection fraction (LVEF) was defined as LVEF ≥50% on echocardiography follow-up after at least 6 months of the diagnosis. RESULTS: CMR imaging showed that the PPCM patients had severely impaired LVEF and right ventricular ejection fraction (LVEF: 26.8 ± 10.6%; RVEF: 33.9 ± 14.6%). LGE was seen in eight (38.1%) cases. PPCM patients had significantly higher native T1 and ECV (1345 ± 79 vs. 1212 ± 32 ms, P < 0.001; 33.9 ± 5.2% vs. 27.1 ± 3.1%, P < 0.001; respectively) and higher myocardial T2 value (42.3 ± 3.7 vs. 36.8 ± 2.3 ms, P < 0.001) than did the normal controls. After a median 2.5-year follow-up (range: 8 months-5 years), six patients required readmission for heart failure, two died, and 10 showed left ventricular function recovery. The LVEF-recovered group showed significantly lower ECV (30.7 ± 2.1% vs. 36.8 ± 5.6%, P = 0.005) and T2 (40.6 ± 3.0 vs. 43.9 ± 3.7 ms, P = 0.040) than the unrecovered group. Multivariable logistic regression analysis showed ECV (OR = 0.58 for per 1% increase, P = 0.032) was independently associated with left ventricular recovery in PPCM. CONCLUSIONS: Compared to normal controls, PPCM patients showed significantly higher native T1, ECV, and T2. Native T1, ECV, and T2 were associated with LVEF recovery in PPCM. Furthermore, ECV could independently predict left ventricular function recovery in PPCM.

Predicting non-elective hospital readmission or death using a composite assessment of cognitive and physical frailty in elderly inpatients with cardiovascular disease.

BACKGROUND: We aimed to assess the utility of the combination of the mini-mental state examination (MMSE) + clock drawing test (CDT) and the Fried phenotype for predicting non-elective hospital readmission or death within 6 months in elderly inpatients with cardiovascular disease (CVD). METHODS: A single-center prospective cohort was conducted from September 2018 to February 2019. Inpatients ≥65 years old were recruited. Predictive validity was tested using a Cox proportional hazards regression model analysis, and the discriminative ability was evaluated by the receiver operating characteristic (ROC) curve. RESULTS: A total of 542 patients were included. Overall, 12% (64/542) screened positive for cognitive impairment, 16% (86/542) were physically frail and 8% (44/542) had cognitive impairment combined with physical frailty, showing an older age (P < 0.001) and a lower education level (P < 0.001) than physically frail patients. A total of 113 patients (20.9%) died or were readmitted at 6 months. Frail participants with a normal (hazard ratio [HR]: 1.73, 95% confidence interval [CI]: 1.06-2.82, P = 0.028) or impaired cognition (HR: 2.50, 95% CI: 1.27-4.91, P = 0.008) had a higher risk of non-elective hospital readmission or death than robust patients after adjusting for the age, sex, education level, marital status, the presence of diabetes mellitus, heart failure, and history of stroke. The area under the ROC curve (AUC) showed that the discriminative ability in relation to 6 months readmission and death for the MMSE + CDT + Fried phenotype was 0.65 (95% CI: 0.60-0.71), and the AUC for men was 0.71 (95% CI: 0.63-0.78), while that for women was 0.60 (95% CI: 0.51-0.69). CONCLUSIONS: Accounting for cognitive impairment in the frailty phenotype may allow for the better prediction of non-elective hospital readmission or death in elderly inpatients with CVD in the short term. TRIAL REGISTRATION: ChiCTR1800017204; date of registration: 07/18/2018.

Caregiving burden and depression in paid caregivers of hospitalized patients: a pilot study in China.

BACKGROUND: Caregiving burden and depression in family caregivers have been investigated, but little is known about how they affect paid caregivers. The aim of this study was to investigate caregiving burden and depression in paid caregivers of hospitalized patients. METHODS: A cross-sectional survey study was conducted in a tertiary referral hospital (Chengdu, China) that enrolled 108 paid caregivers who worked in the inpatient department. The Caregiver Burden Inventory (CBI) and the Center for Epidemiologic Studies Depression (CES-D) scale were incorporated into a self-developed questionnaire to gather demographic information on the following four aspects: general, work, income, and family. RESULTS: The mean total CBI score was 29.7 ± 12.5. The time-dependence burden had the highest score of 15.3 ± 4.0, which was followed by the physical burden score of 6.5 ± 4.6, developmental burden score of 3.7 ± 4.0, social burden score of 3.2 ± 4.0, and emotional burden score of 2.4 ± 3.1. Multiple linear regression analysis showed that a higher CBI was associated with a longer time as a paid caregiver [ß=7.041, 95% Confidence Interval (CI):1.935 to 12.974, p = 0.009], lower income satisfaction (ß= - 6.573, 95% CI: -11.248 to -3.020, p = 0.001), and higher frequency of meeting with their relatives (ß=7.125, 95% CI: 2.019 to 12.456, p = 0.006). The mean CES-D score was 11.9 ± 8.7, and significant depression was found in 28 (25.9%) paid caregivers according to the CES-D score ≥ 16 cut-off. There was a moderate positive correlation between the CBI and CES-D scores (Pearson's r = 0.452, p < 0.001). CONCLUSIONS: A high caregiving burden was commonly observed in paid caregivers of hospitalized patients in China, as was a high prevalence of depression symptoms. Several associated factors were identified that could be areas for future interventions.

Development and Validation of an Electronic Frailty Index Using Routine Electronic Health Records: An Observational Study From a General Hospital in China.

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 < 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 < 0.001) and death in hospital (OR = 19.97, P < 0.001). Moreover, eFI values (per 0.1) were positively associated with total costs (ß = 0.453, P < 0.001), examination costs (ß = 0.269, P < 0.001), treatment costs (ß = 0.414, P < 0.001), nursing costs (ß = 0.381, P < 0.001), pharmacy costs (ß = 0.524, P < 0.001), and material costs (ß = 0.578, P < 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.

Urinary 8-oxo-7,8-dihydroguanosine as a potential biomarker of frailty for elderly patients with cardiovascular disease.

The diagnosis of frailty is usually subjective, which calls for objective biomarkers in clinical medicine. 8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodGsn) and 8-oxo-7, 8-dihydroguanosine (8-oxoGsn) in urine are two aging biomarkers that have not been explored deeply in cases of frailty. A total of 508 elderly patients with cardiovascular disease (mean age 75.0 ± 6.5 years, 50.8% males) were enrolled consecutively. Frailty was assessed by the Fried phenotype (robust: 0 score; pre-frail: 1-2 scores; frail: 3-5 scores). The concentrations of 8-oxoGsn and 8-oxodGsn in urine were measured by improved ultra-high-performance liquid chromatography-mass spectrometry (UPLC-MS/MS). Urinary creatinine (Cre) was tested to correct the 8-oxoGsn and 8-oxodGsn levels. According to the Fried phenotype score, the proportions of robust, pre-frail, and frail subjects were 20.5% (104/508), 53.9% (274/508), and 25.6% (130/508), respectively. The urinary 8-oxoGsn/Cre (P < 0.001) differed significantly among these 3 groups, but the urinary 8-oxodGsn/Cre (P = 0.600) showed no marked difference. Univariate and multivariate logistic regression showed that the age (odds ratio [OR] = 1.090, P < 0.001), systolic blood pressure (OR = 0.981, P = 0.008), 8-oxoGsn/Cre (OR = 1.203, P = 0.007), hemoglobin (OR = 0.980, P = 0.007), and sodium (OR = 0.915, P = 0.044) were independently associated with frailty. The sensitivity and specificity to identify frailty were 53.08% and 71.96%, respectively, for 8-oxoGsn/Cre at the optimal cut-off value of 3.879 µmol/mol according to the maximal Youden index. Urinary 8-oxoGsn, as a recognized biomarker of RNA oxidation, is independently associated with frailty in elderly patients with cardiovascular disease. However, the urinary 8-oxodGsn shows no obvious correlation with frailty. To obtain a better diagnostic performance for frailty, more biomarkers from different pathophysiological pathways should be explored in the future.

Identification of Frailty and Its Risk Factors in Elderly Hospitalized Patients from Different Wards: A Cross-Sectional Study in China.

OBJECTIVES: To survey the difference of frailty prevalence in elderly inpatients amongdifferent wards; to compare the diagnostic performance of five frailty measurements (Clinical Frailty Scale [CFS], FRAIL, Fried, Edmonton, Frailty Index [FI]) in identifying frailty; and to explore the risk factors of frailty in elderly inpatients. PARTICIPANTS AND METHODS: This was a cross-sectional study including 1000 inpatients (mean age 75.2±6.7 years, 51.5% male; 542, 229, and 229 patients from cardiology, non-surgical, and surgical wards, respectively) in a tertiary hospital from September 2018 to February 2019. We applied the combined index to integrate the five frailty measurements mentioned above as the gold standard of frailty diagnosis. Multivariate logistic regression models were used to determine the independent risk factors of frailty. RESULTS: Frailty prevalence was 32.3% (Fried), 36.2% (CFS), 19.2% (FRAIL), 25.2% (Edmonton), 35.1% (FI) in all patients. The frailty was more common in non-surgical wards, regardless of the frailty assessment tools used (non-surgical wards: 27.5% to 51.5%; cardiology ward: 14.9% to 29.3%; surgical wards: 18.8% to 41.9%). CFS≥5 showed a sensitivity of 94.1% and a specificity of 85.2% for all patients. FI≥0.25 showed a sensitivity of 94.8% and a specificity of 87.0% for all patients. Age [odds ratio (OR) = 1.089, P<0.001], education level (OR = 0.782, P=0.001), heart rate (OR = 1.025, P<0.001), albumin (OR = 0.911, P=0.002), log D-dimer (OR = 2.940, P<0.001), ≥5 comorbidities (OR = 2.164, P=0.002), and ≥5 medications (OR = 2.819, P<0.001) were independently associated with frailty in all participants. CONCLUSION: Frailty is common among elderly inpatients, especially in non-surgical wards. CFS is a preferred screening tool and FI may be an optimal assessment tool. Old age, low educational level, fast heart rate, low albumin, high D-dimer, ≥5 comorbidities, and polypharmacy are independent risk factors of frailty in elderly hospitalized patients.