A Nomogram for Optimizing Sarcopenia Screening in Community-dwelling Older Adults: AB3C Model |
| |
| Institution: | 1. Department of General Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P. R. China;2. Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P. R. China;3. Department and Institute of Infectious Disease, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China;4. Community Health Service Centre, Wuhan, Hubei, P. R. China;5. Ernst & Young (China) Advisory Limited, Shanghai, P. R. China;6. National Medical Center for Major Public Health Events, Wuhan, Hubei, P. R. China;1. RTI International, Research Triangle Park, NC, USA;2. Shirley Ryan Ability Lab, Chicago, IL, USA;3. Feinberg School of Medicine, Northwestern University, Chicago, IL, USA;4. RTI International, Waltham, MA, USA;5. The Centers for Medicare and Medicaid Services, Woodlawn, MD, USA;1. University of Maryland, Baltimore, MD, USA;2. Arvada, CO, USA;3. University of Colorado, Denver School of Medicine, Denver, CO, USA;1. Center for Gerontology and Healthcare Research, Brown University School of Public Health, Providence, RI, USA;2. Department of Health Services, Policy & Practice, Brown University School of Public Health, Providence, RI, USA;3. Center of Innovation in Long-Term Services and Supports, Providence Veterans Affairs Medical Center, Providence, RI, USA;4. Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA;5. Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, MI, USA;6. Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA;7. Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA;8. Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA;1. Pharmacy Clinical Management Unit, Virgen del Rocío University Hospital, Sevilla, Spain;2. Department of Preventive Medicine and Public Health, University of Seville, Sevilla, Spain;3. Internal Medicine Clinical Management Unit, Virgen del Rocío University Hospital, Sevilla, Spain;1. Basel Pharmacoepidemiology Unit, Division of Clinical Pharmacy and Epidemiology, Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland;2. Neurorehabilitation and Research Department, ZURZACH Care, Bad Zurzach, Switzerland;3. Hospital Pharmacy, University Hospital Basel, Basel, Switzerland;4. Department of Neurology, University Hospital Zurich, Zurich, Switzerland;5. Boston Collaborative Drug Surveillance Program, Lexington, MA, USA;1. Department of Health Promotion, Maternal and Infant Care, Internal Medicine and Medical Specialties, “G. D''Alessandro”–PROMISE–University of Palermo, Palermo, Italy;2. School of Medicine and Surgery, University Kore of Enna, Enna, Italy |
| |
| Abstract: | ObjectivesSarcopenia is associated with significantly higher mortality risk, and earlier detection of sarcopenia has remarkable public health benefits. However, the model that predicts sarcopenia in the community has yet to be well identified. The study aimed to develop a nomogram for predicting the risk of sarcopenia and compare the performance with 3 sarcopenia screen models in community-dwelling older adults in China.DesignCross-sectional study.Setting and ParticipantsA total of 966 community-dwelling older adults.MethodsA total of 966 community-dwelling older adults were enrolled in the study, with 678 participants grouped into the Training Set and 288 participants grouped into the Validation Set according to a 7:3 randomization. Predictors were identified in the Training Set by univariate and multivariate logistic regression and then combined into a nomogram to predict the risk of sarcopenia. The performance of this nomogram was assessed by calibration, discrimination, and clinical utility.ResultsAge, body mass index, calf circumference, congestive heart failure, and chronic obstructive pulmonary disease were demonstrated to be predictors for sarcopenia. The nomogram (named as AB3C model) that was constructed based on these predictors showed excellent calibration and discrimination in the Training Set with an area under the receiver operating characteristic curve (AUC) of 0.930. The nomogram also showed perfect calibration and discrimination in the Validation Set with an AUC of 0.897. The clinical utility of the nomogram was supported by decision curve analysis. Comparing the performance with 3 sarcopenia screen models (SARC-F, Ishii, and Calf circumference), the AB3C model outperformed the other models regarding sensitivity and AUC.Conclusions and ImplicationsAB3C model, an easy-to-apply and cost-effective nomogram, was developed to predict the risk of sarcopenia, which may contribute to optimizing sarcopenia screening in community settings. |
| |
| Keywords: | Sarcopenia risk factor predicting nomogram screen community dwelling older adult |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
