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@ARTICLE{Mahdavi:1048926,
      author       = {Mahdavi, Maryam and Kazemnejad, Anoshirvan and Asosheh,
                      Abbas and Khalili, Davood and Hosseinpour, Kamyab and
                      Tajari, Ahmadreza},
      title        = {{I}ntegrating {ECG}-derived features with conventional
                      {CVD} risk models},
      journal      = {Scientific reports},
      volume       = {15},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Springer Nature},
      reportid     = {FZJ-2025-05023},
      pages        = {39128},
      year         = {2025},
      abstract     = {Non-communicable diseases (NCDs), particularly
                      cardiovascular diseases (CVDs), have becomethe leading cause
                      of mortality worldwide, with Iran exhibiting
                      higher-than-average incidence andmortality rates. Early
                      detection of high-risk individuals is critical, as CVD often
                      progresses silently.Electrocardiogram (ECG) signals may
                      enhance risk prediction beyond Framingham risk score
                      (FRS).This study aimed to evaluate the predictive
                      performance of ECG signal features for incident CVD
                      usingsignal processing in a large population-based cohort
                      from the Tehran Lipid and Glucose Study (TLGS). Atotal of
                      4,637 adults aged 40 years devoid of past CVD at baseline
                      (2006–2008) were followed up until2018. Baseline
                      characteristics, laboratory measurements, and ECG signal
                      features were collected. CVDevents were defined as coronary
                      heart disease (CHD) or stroke. A recalibrated FRS (baseline)
                      modelassessed the association between ECG features and
                      incident CVD, with model performance evaluatedusing
                      Harrell’s C-index, Net Reclassification Index (NRI), and
                      Integrated Discrimination Improvement(IDI). Over a 10-year
                      follow-up, 483 participants $(10.4\%)$ developed CVD. The
                      introduction of ECGsignal features improved risk prediction
                      in women, increasing the Harrell’s C-index from 0.84 to
                      0.85and demonstrating significant reclassification
                      improvement (NRI: $55.7\%,$ IDI: $2.8\%).$ However,
                      nomeaningful improvement was observed in men. ECG-based
                      modeling outperformed FRS, particularlyfor intermediate-risk
                      categories among women. Incorporating ECG signal features
                      into risk modelssignificantly enhanced CVD prediction
                      performance in women, suggesting potential utility
                      forimproving individualized preventive strategies. Further
                      research is warranted to refine ECG-based riskstratification
                      tools for broader clinical application.},
      cin          = {IBI-1},
      ddc          = {600},
      cid          = {I:(DE-Juel1)IBI-1-20200312},
      pnm          = {5243 - Information Processing in Distributed Systems
                      (POF4-524) / GRK 2610 - GRK 2610: Innovative Schnittstellen
                      zur Retina für optimiertes künstliches Sehen -
                      InnoRetVision (424556709)},
      pid          = {G:(DE-HGF)POF4-5243 / G:(GEPRIS)424556709},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1038/s41598-025-26471-6},
      url          = {https://juser.fz-juelich.de/record/1048926},
}