Determinants of hospital length of stay after transcatheter aortic valve implantation with self-expanding prostheses: a prospective, single centre observational study.

INTRODUCTION: We sought to identify baseline and periprocedural variables affecting hospital length of stay (LoS) in patients undergoing transcatheter aortic valve implantation (TAVI). METHODS: Data on 304 consecutive patients undergoing TAVI at a single centre between August 2008 and December 2017 were collected prospectively. All patients underwent a complete clinical, echocardiographic and laboratory evaluation including a comprehensive frailty assessment at baseline. LoS was defined as the number of in-hospital days after the TAVI procedure during the index hospitalisation until the time the patient left the hospital for home or a rehabilitation clinic. RESULTS: The mean LoS was 10.4 ± 7.1 days (median 8, interquartile range 5–12) with a significant trend towards shorter LoS over time (p <0.001). Patients discharged directly home were more likely to have shorter LoS (p = 0.007). All periprocedural complications were significantly associated with prolonged LoS (p <0.05 for all). Multivariate analysis showed an independent association between LoS and emergency admission (beta 3.24 ± 1.56, p = 0.039), baseline gait speed (beta: 0.39 ± 0.16, p = 0.018), baseline serum C-reactive protein (CRP, beta 0.14 ± 0.04, p = 0.001) and subclavian access (beta 8.27 ± 2 .9, p = 0.005). Gait speed and serum CRP remained significant determinants of LoS even after adjustment for periprocedural complications and patients’ discharge destination. CONCLUSION: Baseline gait speed and serum CRP are significant independent determinants of LoS after TAVI.

Impact of body composition changes on risk of all-cause mortality in older adults.

PURPOSE: This study evaluates the relationship between body mass index (BMI), fat mass index (FMI) and fat-free mass index (FFMI) changes and mortality in persons ≥65 years. METHODS: Adults ≥65 years with at least two body composition measurements (BCM) between 1990 and 2011 were included. We excluded persons who died within one month of the second BCM and who had two single BCM in a one-month timeframe. Mortality data was retrieved until December 2012. For each person, we calculated the regression slopes for BMI, FMI and FFMI changes. Significant positive slopes were categorized as "gain", negative slopes as "loss" and the others as "maintenance". The impact of body composition changes was evaluated by Cox regression models while adjusting for sex, age, co-morbidities and body composition at the last measurement. RESULTS: We included 791 persons with 3049 BCM. After adjustment for sex, and age and co-morbidities, a loss of FFMI, but not of FMI or BMI, increased the risk of mortality (HR 2.02, 95%CI 1.28-3.19). The prediction of mortality with FFMI loss remained significant when further adjusting for FMI loss and the last available body composition (HR 1.68, 95%CI 1.04-2.70). CONCLUSIONS: FFMI loss is related to increased mortality in older persons.

Body composition and all-cause mortality in subjects older than 65 y.

BACKGROUND: A low or high body mass index (BMI) has been associated with increased mortality risk in older subjects without taking fat mass index (FMI) and fat-free mass index (FFMI) into account. This information is essential because FMI is modulated through different healthcare strategies than is FFMI. OBJECTIVE: We aimed to determine the relation between body composition and mortality in older subjects. DESIGN: We included all adults ≥65 y old who were living in Switzerland and had a body-composition measurement by bioelectrical impedance analysis at the Geneva University Hospitals between 1990 and 2011. FMI and FFMI were divided into sex-specific quartiles. Quartile 1 (i.e., the reference category) corresponded to the lowest FMI or FFMI quartile. Mortality data were retrieved from the hospital database, the Geneva death register, and the Swiss National Cohort until December 2012. Comorbidities were assessed by using the Cumulative Illness Rating Scale. RESULTS: Of 3181 subjects included, 766 women and 1007 men died at a mean age of 82.8 and 78.5 y, respectively. Sex-specific Cox regression models, which were used to adjust for age, BMI, smoking, ambulatory or hospitalized state, and calendar time, showed that body composition did not predict mortality in women irrespective of whether comorbidities were taken into account. In men, risk of mortality was lower with FFMI in quartiles 3 and 4 [HR: 0.78 (95% CI: 0.62, 0.98) and 0.64 (95% CI: 0.49, 0.85), respectively] but was not affected by FMI. When comorbidities were adjusted for, FFMI in quartile 4 (>19.5 kg/m(2)) still predicted a lower risk of mortality (HR: 0.72; 95% CI: 0.54, 0.96). CONCLUSIONS: Low FFMI is a stronger predictor of mortality than is BMI in older men but not older women. FMI had no impact on mortality. These results suggest potential benefits of preventive interventions with the aim of maintaining muscle mass in older men. This trial was registered at clinicaltrials.gov as NCT01472679.