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Development of tools for all-cause mortality prediction in heart failure patients: a systematic review — arXiv:2611.03665

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.03665
License (as archived): CC0-1.0
Archived: 2026-11-04T18:35:33.659Z
SHA-256 of the archived text: dd3c736ac6116019a837acd78c6bd52c8af6c6d1197cefb7e1bd9df55380525a
Stable URL of this page: https://metraipolis.com/documento/dc_bb60e2a09121

How to cite

Development of tools for all-cause mortality prediction in heart failure patients: a systematic review — arXiv:2611.03665 — arXiv (en), https://arxiv.org/abs/2611.03665 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bb60e2a09121 (Metraipolis archive dc_bb60e2a09121, sha256 dd3c736ac6116019a837acd78c6bd52c8af6c6d1197cefb7e1bd9df55380525a)

BibTeX

@misc{dcbb60e2a09121,
  title = {Development of tools for all-cause mortality prediction in heart failure patients: a systematic review — arXiv:2611.03665},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.03665},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bb60e2a09121; sha256 dd3c736ac6116019a837acd78c6bd52c8af6c6d1197cefb7e1bd9df55380525a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.03665. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Heart failure is a serious medical condition associated with risk of acute deterioration, recurrent hospitalizations and high mortality making accurate mortality prediction potentially valuable for patient care and healthcare planning. Numerous prognostic models have been developed using conventional statistics (CS), machine learning (ML) and deep learning (DL), but variation in study populations, heart failure definitions, prediction horizons and development practices may limit interpretation of applicability. We conducted a systematic review of mortality prediction models published between 2009 to the start of 2026 searching three databases (PubMed, IEEE and Scopus) for studies predicting all-cause mortality over a 1-60 months prediction horizon among patients with heart failure. Data extracted were cohort characteristics, heart failure definitions, model development, cohort size, validation practices, mortality, computational model design and reported performance. The risk of bias and applicability were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Of 1516 publications identified, 56 met the inclusion criteria. Publication numbers increased over the observation period, alongside increasing use of ML/DL models and decreasing use of CS approaches. Most development cohorts (64%) were classified as Unknown/Undefined heart failure, reflecting inconsistent reporting and terminology that did not correspond with established heart failure guidelines. External validation was reported in 23% of studies. Overall, research in modeling heart failure mortality has grown alongside increasing adoption of ML/DL approaches. Greater standardization of study design and reporting is needed for meaningful comparison, reproducibility and evaluation of models for clinical application.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Applications (stat.AP) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.03665 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-04T18:32:37.370Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).