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Introduction: Frailty has an important impact on the health outcomes of older patients, and frailty screening is recommended as part of perioperative evaluation. The Hospital Frailty Risk Score (HFRS) is a validated tool that highlights frailty risk using 109 International Classification of Diseases, 10th revision (ICD-10) codes. In this study, we aim to compare HFRS to the Charlson Comorbidity Index (CCI) and validate HFRS as a predictor of adverse outcomes in Asian patients admitted to surgical services. Method: A retrospective study of electronic health records (EHR) was undertaken in patients aged 65 years and above who were discharged from surgical services between 1 April 2022 to 31 July 2022. Patients were stratified into low (HFRS <5), intermediate (HFRS 5--15) and high (HFRS >15) risk of frailty. Results: Those at high risk of frailty were older and more likely to be men. They were also likely to have more comorbidities and a higher CCI than those at low risk of frailty. High HFRS scores were associated with an increased risk of adverse outcomes, such as mortality, hospital length of stay (LOS) and 30-day readmission. When used in combination with CCI, there was better prediction of mortality at 90 and 270 days, and 30-day readmission. Conclusion: To our knowledge, this is the first validation of HFRS in Singapore in surgical patients and confirms that high-risk HFRS predicts long LOS (≥7days), increased unplanned hospital readmissions (both 30-day and 270-day) and increased mortality (inpatient, 10-day, 30-day, 90-day, 270-day) compared with those at low risk of frailty. [ABSTRACT FROM AUTHOR]

Importance: Multimorbidity is a growing health care problem in aging societies and is strongly associated with epidemiologic characteristics and sociodemographic factors. Knowledge of these associations is important for the design of effective preventive and management strategies., Objectives: To determine the association between multimorbidity and sociodemographic factors (age, socioeconomic status [SES], sex, and race/ethnicity) and the association between mental health diseases and physical diseases, as well as their implications for the types and costs of health care use., Design, Setting, and Participants: This population-based cross-sectional study used deidentified Singapore Eastern Regional Health System data collected between January 1, 2012, and December 31, 2016. Patients who were alive as of January 1, 2016, and residing in the Regional Health System region in 2016 (N = 1 181 024) were included. Patients who had no year of birth records (n = 573), were born in 2017 (n = 93), or died before January 1, 2016 (n = 47 322), were excluded., Main Outcomes and Measures: Multimorbidity, age, sex, SES, mental health, race/ethnicity, and health care use., Results: In the study population of 1 181 024 individuals, the mean (SD) age was 39.6 (22.1) years, 51.2% were women, 70.1% were Chinese, 7.1% were Indian, 13.5% were Malayan, and 9.3% were other races/ethnicities. Multimorbidity, present in 26.2% of the population, was more prevalent in female (26.8%; 95% CI, 26.7%-26.9%) than in male (25.6%; 95% CI, 25.5%-25.7%) patients and among patients with low SES (41.6%) than those with high SES (20.1%). Mental health diseases were significantly more prevalent among individuals with low SES (5.2%; 95% CI, 5.1%-5.2%) than high SES (2.1%; 95% CI, 2.0%-2.1%; P < .001). The 3 most prevalent disease combinations were chronic kidney disease and hypertension, chronic kidney disease and lipid disorders, and hypertension and lipid disorders. Although chronic kidney disease, hypertension, lipid disorders, and type 1 and/or type 2 diabetes-related diseases had a low cost per capita, the large number of patients with these conditions caused the overall proportion of the cost incurred by health care use to be more than twice that incurred in other diseases., Conclusions and Relevance: These findings emphasize the association between multimorbidity and sociodemographic factors such as increasing age, lower SES, female sex, and increasing number of mental disorders. Health care policies need to take sociodemographic factors into account when tackling multimorbidity in a population.