Multi–task deep survival analysis links acute exacerbation frequency to hip fracture risk in advanced COPD.
• Developed first multitask deep learning model integrating hip fracture risk and acute exacerbation prediction in advanced COPD patients. • Multitask deep learning model predicts COPD hip fracture risk (C-index 0.725) and acute exacerbation frequency (MSE 0.522). • Each acute COPD exacerbation link...
| Publicado en: | Heart & Lung Vol. 76; pp. 164 - 174 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Mar2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=192300677&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 192300677 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01479563 04L jtl: Heart & Lung issn: 01479563 maglogo: N pubinfo: dt: Mar2026 vid: 76 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 192300677 192300677 192300677 10.1016/j.hrtlng.2025.10.008 192300677 ppf: 164 ppct: 10 formats: tig: atl: Multi–task deep survival analysis links acute exacerbation frequency to hip fracture risk in advanced COPD. aug: au: Xu, Pengyu Wang, Xiaoyu Li, Zhuying affil: Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang 150040, China sug: subj: Deep Learning Survival Analysis Disease Exacerbation Prediction Models Hip Fractures Risk Factors Pulmonary Disease, Chronic Obstructive Physiopathology Severity of Illness Human Multitasking Behavior Inflammation Osteoporotic Fractures Muscle, Skeletal China Prospective Studies Cox Proportional Hazards Model Biological Markers Confidence Intervals Risk Assessment ab: • Developed first multitask deep learning model integrating hip fracture risk and acute exacerbation prediction in advanced COPD patients. • Multitask deep learning model predicts COPD hip fracture risk (C-index 0.725) and acute exacerbation frequency (MSE 0.522). • Each acute COPD exacerbation linked to 28 % higher fracture hazard, revealing inflammation-bone fragility interplay. • Lung function decline drive skeletal vulnerability in COPD patients. Chronic obstructive pulmonary disease (COPD) is linked to elevated hip fracture risk, but validated prediction tools integrating disease–specific pathophysiology are lacking. To develop a multitask deep learning model predicting hip fracture risk and acute exacerbation frequency in COPD patients, and identify key predictors of skeletal vulnerability. This retrospective cohort study analyzed 4995 COPD patients (245 incident hip fractures) from the China Health and Retirement Longitudinal Study (CHARLS). A multitask deep survival model combined Cox proportional hazards (fracture prediction) and regression (exacerbation frequency) tasks, integrating demographic, clinical, and biomarker data. Performance was evaluated via concordance index (C–index) and mean squared error (MSE). The model achieved a C–index of 0.725 for fracture prediction and MSE of 0.522 for exacerbation frequency, outperforming conventional methods. Key predictors included acute exacerbation frequency (fracture group: 2.5 ± 4.4 vs. non–fracture: 1.1 ± 2.2 events/year; adjusted HR = 1.28 per additional event, 95 % CI: 1.19–1.38) and baseline lung function (fracture group: 262.7 ± 96.7 mL vs. non–fracture: 277.4 ± 85.7 mL). Frequent hospitalizations (≥2/year) increased fracture risk by 47 %. Systemic inflammation (elevated CRP/IL–6) and age further contributed to skeletal vulnerability. This study establishes the first multitask deep learning framework for COPD–related fracture risk, demonstrating superior performance through multidimensional feature synthesis. The model enables personalized prevention by highlighting exacerbation burden, lung function decline, and inflammation as critical risk factors. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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