Development and validation of a predictive model for the risk of sarcopenia in the older adults in China.

BACKGROUND: Sarcopenia is a progressive age-related disease that can cause a range of adverse health outcomes in older adults, and older adults with severe sarcopenia are also at increased short-term mortality risk. The aim of this study was to construct and validate a risk prediction model for sarcopenia in Chinese older adults. METHODS: This study used data from the 2015 China Health and Retirement Longitudinal Study (CHARLS), a high-quality micro-level data representative of households and individuals aged 45 years and older adults in China. The study analyzed 65 indicators, including sociodemographic indicators, health-related indicators, and biochemical indicators. RESULTS: 3454 older adults enrolled in the CHARLS database in 2015 were included in the final analysis. A total of 997 (28.8%) had phenotypes of sarcopenia. Multivariate logistic regression analysis showed that sex, Body Mass Index (BMI), Mean Systolic Blood Pressure (MSBP), Mean Diastolic Blood Pressure (MDBP) and pain were predictive factors for sarcopenia in older adults. These factors were used to construct a nomogram model, which showed good consistency and accuracy. The AUC value of the prediction model in the training set was 0.77 (95% CI = 0.75-0.79); the AUC value in the validation set was 0.76 (95% CI = 0.73-0.79). Hosmer-Lemeshow test values were P = 0.5041 and P = 0.2668 (both P > 0.05). Calibration curves showed significant agreement between the nomogram model and actual observations. ROC and DCA showed that the nomograms had good predictive properties. CONCLUSIONS: The constructed sarcopenia risk prediction model, incorporating factors such as sex, BMI, MSBP, MDBP, and pain, demonstrates promising predictive capabilities. This model offers valuable insights for clinical practitioners, aiding in early screening and targeted interventions for sarcopenia in Chinese older adults.

ICU admission Braden score independently predicts delirium in critically ill patients with ischemic stroke.

BACKGROUND: Delirium is a common and severe complication in intensive care unit (ICU) patients with acute ischemic stroke, exacerbating cognitive and physical impairments. It prolongs hospitalization, increases healthcare costs, and raises mortality risk. Early prediction is crucial because it facilitates prompt interventions that could possibly reverse or alleviate the detrimental consequences of delirium. Braden scores, traditionally used to assess pressure injury risk, could also signal frailty, providing an early warning of delirium and aiding in prompt and effective patient management. OBJECTIVE: To examine the association between the Braden score and delirium. METHODS: A retrospective analysis of adult ischemic stroke patients in the ICU of a tertiary academic medical center in Boston from 2008 to 2019 was performed. Braden scores were obtained on admission for each patient. Delirium, the primary study outcome, was assessed using the Confusion Assessment Method for Intensive Care Unit and a review of nursing notes. The association between Braden score and delirium was determined using Cox proportional hazards modeling, with hazard ratios (HR) and 95% confidence intervals (CI) calculated. RESULTS: The study included 3,680 patients with a median age of 72 years, of whom 1,798 were women (48.9 %). The median Braden score at ICU admission was 15 (interquartile range 13-17). After adjustment for demographics, laboratory tests, severity of illness, and comorbidities, the Braden score was inversely associated with the risk of delirium (adjusted HR: 0.94, 95 % CI: 0.92-0.96, P < 0.001). CONCLUSIONS: The Braden score may serve as a convenient and simple screening tool to identify the risk of delirium in ICU patients with ischemic stroke. IMPLICATION FOR CLINICAL PRACTICE: The use of the Braden score as a predictor of delirium in ischemic stroke patients in the ICU allows early identification of high-risk patients. This facilitates timely intervention, thereby improving patient outcomes and potentially reducing healthcare costs.