Home › Research › Digital health tools
ECG-based longitudinal risk prediction across diseases and organ systems
ye, y.; Zeng, Z.; Tian, X.; Yuan, Z.; Wang, J.; Zhu, Y.. medRxiv (preprint), 2026-09-02
Topic: Risk, prevention and care in population studies
Artificial intelligence applied to routine electrocardiograms (ECGs) has largely focused on detecting existing disease or predicting individual cardiovascular outcomes. Whether ECGs can support prediction of multiple future diseases across organ systems remains unclear. We developed ECG-RISK, a multitask survival model for 67 incident three-character ICD-10 endpoints using ECG waveforms, demographic characteristics and routinely collected laboratory data from 86,673 MIMIC-IV patients. Discrimination was highest for heart, brain, kidney and lung endpoints, with organ-level C-indices ranging from 0.796 to 0.825, whereas liver and pancreatic endpoints showed lower discrimination. The ECG-only model achieved strong discrimination across most endpoints, whereas the incremental improvement gained by incorporating ECG and laboratory inputs beyond demographic information varied substantially across endpoints. Across the nine exploratory aggregated outcomes, Kaplan-Meier curves showed clear separation among model-score tertiles. Discrimination was highest for dementia (C-index, 0.891) and heart failure (C-index, 0.857). These findings support the feasibility of ECG-based longitudinal risk prediction across multiple diseases. External validation and competing-risk analyses are required to assess generalisability and clinical utility.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
ye, y., Zeng, Z., Tian, X., Yuan, Z., Wang, J., & Zhu, Y. (2026). ECG-based longitudinal risk prediction across diseases and organ systems. medRxiv (preprint). https://doi.org/10.64898/2026.08.29.26361697
Vancouver
ye y, Zeng Z, Tian X, Yuan Z, Wang J, Zhu Y. ECG-based longitudinal risk prediction across diseases and organ systems. medRxiv (preprint). 2026. doi:10.64898/2026.08.29.26361697.
BibTeX
@article{ye2026-3ff605d3,
author = {ye, y and Zeng, Z. and Tian, X. and Yuan, Z. and Wang, J. and Zhu, Y.},
title = {{ECG-based longitudinal risk prediction across diseases and organ systems}},
journal = {medRxiv (preprint)},
year = {2026},
doi = {10.64898/2026.08.29.26361697},
url = {https://doi.org/10.64898/2026.08.29.26361697}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
Key findings
- These findings support the feasibility of ECG-based longitudinal risk prediction across multiple diseases.
Why it matters
This study found that a model using routine ECG data could predict future diagnoses across organ systems, with the highest discrimination for dementia. It is a prediction feasibility study requiring external validation and does not test any treatment, cure or prevention.
New to this subject? Start with our plain-English guide: Digital health →
Study details
- Study designlongitudinal study
- Evidence strengthModerate
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 28 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on impact for its field and year, so that was left out: it earned 26 of the 94 points we could judge it on, which is 28 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (medRxiv (preprint)) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 0 / 7 | Cited 0 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.0 citations a year over 2 year(s) since 2026 |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 0 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Yajie Zhu: h-index 15 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Hangzhou Normal University: institution h-index 311 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Points | 26 / 94 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 26 of the 94 points we could judge it on is 28 out of 100. |
| Total | 28 / 100 | Capped to 16 because this is not a peer-reviewed article. |
What each of these 20 components measures, and what this score is not →
Read the original
Free copy unknown: no free-copy check has an answer for this record yet. That does not mean it is paywalled.
Related studies in Digital health tools
Also in Digital health tools
178 studies in the library are filed under Digital health tools with no topic tag they share with another; these are this study's neighbours in a fixed tour of them. The Digital health tools hub.