Dataset Information

Sarcopenia as a predictor of all-cause mortality among older nursing home residents: a systematic review and meta-analysis.

ABSTRACT:

Objectives

This study aims to review the evidence of sarcopenia as a predictor of all-cause mortality among nursing home residents.

Design

Systematic review and meta-analysis of observational cohort studies.

Data sources

PubMed, EMBASE and the Cochrane Library databases were searched for relevant articles.

Participants

Nursing home residents.

Primary and secondary outcome measures

All-cause mortality.

Data analysis

Summary-adjusted HRs or risk ratios (RRs) were calculated by fixed-effects model. The risk of bias was assessed by Newcastle-Ottawa Scale.

Results

Of 2292 studies identified through the systematic review, six studies (1494 participants) were included in the meta-analysis. Sarcopenia was significantly associated with a higher risk for all-cause mortality among nursing home residents (pooled HR 1.86, 95% CI 1.42 to 2.45, p<0.001, I²=0). In addition, the subgroup analysis demonstrated that sarcopenia was associated with all-cause mortality (pooled HR 1.87,95% CI 1.38 to 2.52, p<0.001) when studies with a follow-up period of 1 year or more were analysed; however, this was not found for studies with the follow-up period less than 1 year. Furthermore, sarcopenia was significantly associated with the risk of mortality among older nursing home residents when using bioelectrical impedance analysis to diagnosis muscle mass (pooled HR 1.88, 95% CI 1.39 to 2.53, p<0.001); whereas, it was not found when anthropometric measures were used to diagnosis muscle mass.

Conclusion

Sarcopenia is a significant predictor of all-cause mortality among older nursing home residents. Therefore, it is important to diagnose and treat sarcopenia to reduce mortality rates among nursing home residents.

Prospero registration number

CRD42018081668.

SUBMITTER: Zhang X

PROVIDER: S-EPMC6252774 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Sarcopenia as a predictor of all-cause mortality among older nursing home residents: a systematic review and meta-analysis.

Zhang Xiaoming X Wang Conghua C Dou Qingli Q Zhang Wenwu W Yang Yunzhi Y Xie Xiaohua X

BMJ open 20181112 11

<h4>Objectives</h4>This study aims to review the evidence of sarcopenia as a predictor of all-cause mortality among nursing home residents.<h4>Design</h4>Systematic review and meta-analysis of observational cohort studies.<h4>Data sources</h4>PubMed, EMBASE and the Cochrane Library databases were searched for relevant articles.<h4>Participants</h4>Nursing home residents.<h4>Primary and secondary outcome measures</h4>All-cause mortality.<h4>Data analysis</h4>Summary-adjusted HRs or risk ratios (R ...[more]

PMID: 30420343

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Project description:Little is known about the prognostic value of the strength, assistance walking, rise from a chair, climb stairs, and falls questionnaire (SARC-F) and SARC-F combined with calf circumference (SARC-CalF) among elderly nursing home residents.To compare the prognostic value of SARC-F and SARC-CalF for mortality in this population.We conducted a prospective study in four nursing homes in western China. Sarcopenia was estimated using SARC-F and SARC-CalF, respectively. Nutrition status, activities of daily living, and other covariates were evaluated. The survival status was collected via medical records and telephone interviews at the 12th month after the baseline investigation. We used multivariate Cox proportional-hazard models to calculate the hazard ratio (HR) and 95% confidence interval (CI) for 1-year all-cause mortality by SARC-F-defined sarcopenia and SARC-CalF-defined sarcopenia, separately.We included 329 participants (median age: 85 years). The prevalences of SARC-F-defined sarcopenia and SARC-CalF-defined sarcopenia were 39.8% and 46.8%, respectively. During the 1-year follow-up period, 73 participants (22.7%) died. The mortality was 29.0% and 18.3% in the participants with or without SARC-F-defined sarcopenia, respectively (P = .025). The mortality was 26.6% and 19.0% in the participants with or without SARC-CalF-defined sarcopenia, respectively (P = .105). After adjusted for the relevant confounders including malnutrition, SARC-F-defined sarcopenia was independently associated with an increased risk of 1-year mortality (adjusted HR: 2.08; 95% CI: 1.27-3.42). However, SARC-CalF-defined sarcopenia was not an independent predictor of 1-year mortality (adjusted HR: 1.54; 95% CI: 0.95-2.47).Sarcopenia is highly prevalent in Chinese elderly nursing home residents according to SARC-F or SARC-CalF. SARC-F-defined sarcopenia appears to be better for predicting the 1-year mortality of Chinese nursing home residents than SARC-CalF-defined sarcopenia.

Project description:Background and aimsFew studies have meta-analyzed different prognostic models developed for older adults, especially nursing home residents. We aimed to systematically review and meta-analyze the performance of all published models that predicted all-cause mortality among older nursing home residents.MethodsWe systematically searched PubMed and EMBASE from the databases' inception to January 1, 2020 to capture studies developing and/or validating a prognostic/prediction model for all-cause mortality among nursing home residents. We then carried out both qualitative and quantitative analyses evaluating these models' risks of bias and applicability.ResultsThe systematic search yielded 23,975 articles. We identified 28 indices that predicted the risk of all-cause mortality from 14 days to 39 months among older adults in nursing homes. The most used predictors were age, sex, body weight, swallowing problem, congestive heart failure, shortness of breath, body mass index, and activities of daily living. Of the 28 indices, 8 (29%) and 3 (11%) were internally and externally validated, respectively. None of the indices was validated in more than one cohort. Of the 28 indices, 22 (79%) reported the C-statistic, while only 6 (6%) reported the 95% confidence interval for the C statistic in the development cohorts. In the validation cohorts, 11 (39%) reported the C-statistic and 8 (29%) reported the 95% confidence interval. The meta-analyzed C statistic for all indices is 0.733 (95% prediction interval: 0.669-0.797). All studies/indices had high risks of bias and high concern for applicability according to PROBAST.ConclusionWe identified 28 indices for predicting all-cause mortality among older nursing home residents. The overall quality of evidence was low due to a high degree of bias and poor reporting of model performance statistics. Before any prediction model could be recommended in routine care, future research is needed to rigorously validate existing prediction models and evaluate their applicability and develop new prediction models.

Project description:ImportanceThe coronavirus disease 2019 (COVID-19) pandemic has severely affected nursing homes. Vulnerable nursing home residents are at high risk for adverse outcomes, but improved understanding is needed to identify risk factors for mortality among nursing home residents.ObjectiveTo identify risk factors for 30-day all-cause mortality among US nursing home residents with COVID-19.Design, setting, and participantsThis cohort study was conducted at 351 US nursing homes among 5256 nursing home residents with COVID-19-related symptoms who had severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection confirmed by polymerase chain reaction testing between March 16 and September 15, 2020.ExposuresResident-level characteristics, including age, sex, race/ethnicity, symptoms, chronic conditions, and physical and cognitive function.Main outcomes and measuresDeath due to any cause within 30 days of the first positive SARS-CoV-2 test result.ResultsThe study included 5256 nursing home residents (3185 women [61%]; median age, 79 years [interquartile range, 69-88 years]; and 3741 White residents [71%], 909 Black residents [17%], and 586 individuals of other races/ethnicities [11%]) with COVID-19. Compared with residents aged 75 to 79 years, the odds of death were 1.46 (95% CI, 1.14-1.86) times higher for residents aged 80 to 84 years, 1.59 (95% CI, 1.25-2.03) times higher for residents aged 85 to 89 years, and 2.14 (95% CI, 1.70-2.69) times higher for residents aged 90 years or older. Women had lower risk for 30-day mortality than men (odds ratio [OR], 0.69 [95% CI, 0.60-0.80]). Two comorbidities were associated with mortality: diabetes (OR, 1.21 [95% CI, 1.05-1.40]) and chronic kidney disease (OR, 1.33 [95%, 1.11-1.61]). Fever (OR, 1.66 [95% CI, 1.41-1.96]), shortness of breath (OR, 2.52 [95% CI, 2.00-3.16]), tachycardia (OR, 1.31 [95% CI, 1.04-1.64]), and hypoxia (OR, 2.05 [95% CI, 1.68-2.50]) were also associated with increased risk of 30-day mortality. Compared with cognitively intact residents, the odds of death among residents with moderate cognitive impairment were 2.09 (95% CI, 1.68-2.59) times higher, and the odds of death among residents with severe cognitive impairment were 2.79 (95% CI, 2.14-3.66) times higher. Compared with residents with no or limited impairment in physical function, the odds of death among residents with moderate impairment were 1.49 (95% CI, 1.18-1.88) times higher, and the odds of death among residents with severe impairment were 1.64 (95% CI, 1.30-2.08) times higher.Conclusions and relevanceIn this cohort study of US nursing home residents with COVID-19, increased age, male sex, and impaired cognitive and physical function were independently associated with mortality. Understanding these risk factors can aid in the development of clinical prediction models of mortality in this population.

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Dataset Information

Sarcopenia as a predictor of all-cause mortality among older nursing home residents: a systematic review and meta-analysis.

Objectives

Design

Data sources

Participants

Primary and secondary outcome measures

Data analysis

Results

Conclusion

Prospero registration number

Publications

Sarcopenia as a predictor of all-cause mortality among older nursing home residents: a systematic review and meta-analysis.

Similar Datasets

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