Incorporating inflammatory biomarkers into a prognostic risk score in patients with non-ischemic heart failure: a machine learning approach Article Swipe
Jiayu Feng
,
Xuemei Zhao
,
Boping Huang
,
Liyan Huang
,
Yihang Wu
,
Jing Wang
,
Jingyuan Guan
,
Xinqing Li
,
Yuhui Zhang
,
Jian Zhang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fimmu.2023.1228018
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fimmu.2023.1228018
Objectives Inflammation is involved in the mechanisms of non-ischemic heart failure (NIHF). We aimed to investigate the prognostic value of 21 inflammatory biomarkers and construct a biomarker risk score to improve risk prediction for patients with NIHF. Methods Patients diagnosed with NIHF without infection during hospitalization were included. The primary outcome was defined as all-cause mortality and heart transplantations. We used elastic net Cox regression with cross-validation to select inflammatory biomarkers and construct the best biomarker risk score model. Discrimination, calibration, and reclassification were evaluated to assess the predictive value of the biomarker risk score. Results Of 1,250 patients included (median age, 53 years, 31.9% women), 436 patients (34.9%) experienced the primary outcome during a median of 2.8 years of follow-up. The final biomarker risk score included high-sensitivity C-reactive protein-to-albumin ratio (CAR) and red blood cell distribution width-standard deviation (RDW-SD), both of which were 100% selected in 1,000 times cross-validation folds. Incorporating the biomarker risk score into the best basic model improved the discrimination (Δ C -index = 0.012, 95% CI 0.003–0.018) and reclassification (IDI, 2.3%, 95% CI 0.7%–4.9%; NRI, 17.3% 95% CI 6.4%–32.3%) in risk identification. In the cross-validation sets, the mean time-dependent AUC ranged from 0.670 to 0.724 for the biomarker risk score and 0.705 to 0.804 for the basic model with a biomarker risk score, from 1 to 8 years. In multivariable Cox regression, the biomarker risk score was independently associated with the outcome in patients with NIHF (HR 1.76, 95% CI 1.49–2.08, p < 0.001, per 1 score increase). Conclusions An inflammatory biomarker-derived risk score significantly improved prognosis prediction and risk stratification, providing potential individualized therapeutic targets for NIHF patients.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fimmu.2023.1228018
- https://www.frontiersin.org/articles/10.3389/fimmu.2023.1228018/pdf
- OA Status
- gold
- Cited By
- 6
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385874811
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385874811Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fimmu.2023.1228018Digital Object Identifier
- Title
-
Incorporating inflammatory biomarkers into a prognostic risk score in patients with non-ischemic heart failure: a machine learning approachWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-15Full publication date if available
- Authors
-
Jiayu Feng, Xuemei Zhao, Boping Huang, Liyan Huang, Yihang Wu, Jing Wang, Jingyuan Guan, Xinqing Li, Yuhui Zhang, Jian ZhangList of authors in order
- Landing page
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https://doi.org/10.3389/fimmu.2023.1228018Publisher landing page
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https://www.frontiersin.org/articles/10.3389/fimmu.2023.1228018/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/articles/10.3389/fimmu.2023.1228018/pdfDirect OA link when available
- Concepts
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Biomarker, Medicine, Internal medicine, Framingham Risk Score, Proportional hazards model, C-reactive protein, Heart failure, Oncology, Inflammation, Disease, Chemistry, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 3Per-year citation counts (last 5 years)
- References (count)
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35Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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