{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T07:54:23Z","timestamp":1791618863284,"version":"4.3.4"},"reference-count":39,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T00:00:00Z","timestamp":1756425600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec><jats:title>Objectives<\/jats:title><jats:p>This study aims to develop and validate a nomogram for cognitive frailty in older patients with multimorbidity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>From April 2022 to December 1, 2024, a total of 711 older patients participated in the study. The study was conducted at a tertiary hospitals in Changsha, Hunan Province, China. We employed LASSO regression to identify initial variables associated with risk factors for older adults with multimorbidity and subsequently utilized regression analysis to develop predictive models. We collected encompassing demographic information, FRAIL scale scores, Mini-Mental State Examination (MMSE) results, Mini Nutritional Assessment Short Form (MNA-SF) evaluations, Patient Health Questionnaire-9 (PHQ-9) responses, and Athens Insomnia Scale (AIS) ratings. Statistical analyses were performed using R version 4.3.2. The model\u2019s predictive performance was evaluated using receiver operating characteristic (ROC) and area under the curve (AUC). Calibration was assessed via calibration curves, and clinical utility through decision curve analysis (DCA). Internal consistency was validated using bootstrap, and external validity with an independent test dataset.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this study, the training and validation sets were 498 and 213 patients, respectively. In the training set, there were 183 patients with cognitive frailty with a prevalence of 36.9%. Six initial variables were selected for the LASSO regression, including drinking, constipation, polypharmacy, chronic pain, nutrition, and depression. These six variables were included in the final predictive model. The model demonstrated a concordance index (C-index) of 0.818. Furthermore, AUC for the training and validation sets were determined to be 0.827 and 0.784, underscoring the model\u2019s robust predictive capability.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The high prevalence of cognitive frailty in older patients with multimorbidity should be noted. Efforts to diagnose cognitive frailty and develop targeted interventions in the context of an ageing population and young onset of dementia are of significance in delaying and reversing cognitive frailty.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fpubh.2025.1606505","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T04:11:18Z","timestamp":1756440678000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Development and validation of nomogram for predicting cognitive frailty with multimorbidity: a cross-sectional 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