{"title":"Construction and validation of a frailty risk prediction model for geriatric hematologic neoplasms patients: A cross-sectional study.","authors":"Jinying Zhao, Yating Liu, Zhongfan Kan, Qianqian Zhang, Zenghui Sha, Zhiwei Xu, Rui Ma, Yandi Wang, Rui Hao, Wenxuan Wang, Lanna Song, Wenjun Xie","doi":"10.1177/09287329251363698","DOIUrl":null,"url":null,"abstract":"<p><p>BackgroundWith the increasing incidence of malignant hematological neoplasms in the elderly population, debilitating issues have gradually become an important challenge for patients.ObjectivesTo construct a prediction model, draw a nomogram, and perform internal validation of the model.Methods505 elderly patients with hematological neoplasms were included in the study. The survey was conducted using research tools such as a general information questionnaire, the Chinese version of the Geriatric 8. A risk prediction model was established and a line chart was drawn to visualize the model after univariate and multivariate Logistic regression analysis. Internal validation of the model was performed using Boot strap bootstrap sampling, calibration curve, receiver operating characteristic curve and area under curve, decision curve analysis to internally validate the model.ResultsAfter constructing the model and resampling, it was shown that the calibration curve matched the ideal curve well, and the decision analysis curve showed good calibration, discrimination, and clinical benefit within the 0.0-1.0 threshold range.ConclusionThe prediction model constructed in this study has good predictive effects and can help clinical medical staff to identify the risk of frailty in geriatric hematologic neoplasms patients at an early stage.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329251363698"},"PeriodicalIF":1.8000,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Technology and Health Care","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/09287329251363698","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
BackgroundWith the increasing incidence of malignant hematological neoplasms in the elderly population, debilitating issues have gradually become an important challenge for patients.ObjectivesTo construct a prediction model, draw a nomogram, and perform internal validation of the model.Methods505 elderly patients with hematological neoplasms were included in the study. The survey was conducted using research tools such as a general information questionnaire, the Chinese version of the Geriatric 8. A risk prediction model was established and a line chart was drawn to visualize the model after univariate and multivariate Logistic regression analysis. Internal validation of the model was performed using Boot strap bootstrap sampling, calibration curve, receiver operating characteristic curve and area under curve, decision curve analysis to internally validate the model.ResultsAfter constructing the model and resampling, it was shown that the calibration curve matched the ideal curve well, and the decision analysis curve showed good calibration, discrimination, and clinical benefit within the 0.0-1.0 threshold range.ConclusionThe prediction model constructed in this study has good predictive effects and can help clinical medical staff to identify the risk of frailty in geriatric hematologic neoplasms patients at an early stage.
期刊介绍:
Technology and Health Care is intended to serve as a forum for the presentation of original articles and technical notes, observing rigorous scientific standards. Furthermore, upon invitation, reviews, tutorials, discussion papers and minisymposia are featured. The main focus of THC is related to the overlapping areas of engineering and medicine. The following types of contributions are considered:
1.Original articles: New concepts, procedures and devices associated with the use of technology in medical research and clinical practice are presented to a readership with a widespread background in engineering and/or medicine. In particular, the clinical benefit deriving from the application of engineering methods and devices in clinical medicine should be demonstrated. Typically, full length original contributions have a length of 4000 words, thereby taking duly into account figures and tables.
2.Technical Notes and Short Communications: Technical Notes relate to novel technical developments with relevance for clinical medicine. In Short Communications, clinical applications are shortly described. 3.Both Technical Notes and Short Communications typically have a length of 1500 words.
Reviews and Tutorials (upon invitation only): Tutorial and educational articles for persons with a primarily medical background on principles of engineering with particular significance for biomedical applications and vice versa are presented. The Editorial Board is responsible for the selection of topics.
4.Minisymposia (upon invitation only): Under the leadership of a Special Editor, controversial or important issues relating to health care are highlighted and discussed by various authors.
5.Letters to the Editors: Discussions or short statements (not indexed).