Quality of life in older individuals with joint contractures in geriatric care settings.

PURPOSE: The purpose of this study was to analyze the association between functioning and disability and quality of life (QoL) in older individuals with joint contractures in the geriatric care setting. More specifically, this study aimed to identify determinants of QoL out of a defined set of contracture-related categories of the International Classification of Functioning, Disability and Health (ICF). METHODS: Participants for this multicenter cross-sectional survey were recruited from acute geriatric rehabilitation hospitals, nursing homes, and community nursing facilities in Germany between February and October 2013. QoL was assessed using the validated German version of the EQ-5D index score and the EQ-5D visual analog scale (VAS). Manual and automatic variable selection methods were used to identify the most relevant variables out of 125 contracture-related ICF categories. RESULTS: A total of 241 eligible participants (34.9 % male, mean age 80.1 years) were included. The final models contained 14 ICF categories as predictors of the EQ-5D index score and 15 categories as predictors of the EQ-5D VAS. The statistically significant ICF categories from both models were 'muscle power functions (b730),' 'memory functions (b144),' 'taking care of plants (d6505),' 'recreation and leisure (d920),' 'religion and spirituality (d930),' 'drugs (e1101),' and 'products and technology for personal use in daily living (e115).' CONCLUSIONS: We identified the most relevant ICF categories for older individuals with joint contractures and their health-related quality of life. These items describe potential determinants of QoL which may provide the basis for future health interventions aiming to improve QoL for the patients with joint contractures.

Increasing disparities in the proportions of active treatment and 5-year overall survival over time by age groups among older patients with gastric cancer in Korea.

Purpose: As older patients with gastric cancer increase in Korea, no consensus indicative of anti-cancer treatment exists for the oldest old (age 85+). We investigated potential disparities in the proportion of surgery-including active treatment and the degree of survival improvement over time by age groups, and whether heterogeneity exists in the protective effect of time period on overall survival (OS) by age at diagnosis clusters. Materials and methods: A nationwide cohort (N = 63,975) of older patients with gastric cancer (age at diagnosis 70+) in 2005-2012 were followed until the end of 2018. Patients were categorized into four time period groups by their year of diagnosis. Cancer treatment patterns and 5-year OS were analyzed accordingly, and a random coefficients Cox model with random intercepts and random slopes of time period by age at diagnosis clusters was employed. Results: The mean age of patients was 76.4, and 60.4% were males. Most patients had 0-1 comorbidities (73.3%) and low-risk frailty scores (74.2%). Roughly two-thirds of patients received some form of anti-cancer treatment (62.4%), and while the number of comorbidities and the proportion of high-risk frailty scores trended toward an increase, the proportion of patients receiving anti-cancer treatment increased from 58% in 2005-2006 to 69.6% in 2011-2012. The proportion of surgery-including active treatment increased to over 70% in the 70-74 years old group, while stagnating at 10% in the 90+ years old group. Differences in the slope of 5-year OS improvement resulted in a widening survival gap between the old (age 70-84) and the oldest old. The protective effect of time period on OS hazard in the oldest old was not monotonically reduced with increasing "chronological" age but varied quite randomly, especially among female patients. Conclusion: Our study showed no upper age limit in terms of benefiting from the advances in the detection and treatment of gastric cancer over time. Thus, "functional" age rather than "chronological" age should be the criterion for anti-cancer screening and treatment, and actual implementation of proven treatments in the oldest old patients to reduce their non-compliance with treatment in clinical practice is needed to improve gastric cancer survival for all.

A multicenter prospective study on postoperative pulmonary complications prediction in geriatric patients with deep neural network model.

Aim: Postoperative pulmonary complications (PPCs) can increase the risk of postoperative mortality, and the geriatric population has high incidence of PPCs. Early identification of high-risk geriatric patients is of great value for clinical decision making and prognosis improvement. Existing prediction models are based purely on structured data, and they lack predictive accuracy in geriatric patients. We aimed to develop and validate a deep neural network model based on combined natural language data and structured data for improving the prediction of PPCs in geriatric patients. Methods: We consecutively enrolled patients aged ≥65 years who underwent surgery under general anesthesia at seven hospitals in China. Data from the West China Hospital of Sichuan University were used as the derivation dataset, and a deep neural network model was developed based on combined natural language data and structured data. Data from the six other hospitals were combined for external validation. Results: The derivation dataset included 12,240 geriatric patients, and 1949(15.9%) patients developed PPCs. Our deep neural network model outperformed other machine learning models with an area under the precision-recall curve (AUPRC) of 0.657(95% confidence interval [CI], 0.655-0.658) and an area under the receiver operating characteristic curve (AUROC) of 0.884(95% CI, 0.883-0.885). The external dataset included 7579 patients, and 776(10.2%) patients developed PPCs. In external validation, the AUPRC was 0.632(95%CI, 0.632-0.633) and the AUROC was 0.889(95%CI, 0.888-0.889). Conclusions: This study indicated that the deep neural network model based on combined natural language data and structured data could improve the prediction of PPCs in geriatric patients.