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55/100

✓ Peer-reviewed alzhub usefulness score 55 out of 100. Digital health tools

Topic: Amyloid, tau and blood-test diagnosis

ATTRACTnet was developed in an internal test set of 799 patients (mean [SD] age, 75.1 [11.1] years; 516 [64.7%] male and 283 [35.3%] female) using 5-fold cross-validation with an additional external test set of 422 patients. It had good discrimination for ATTR-CM detection with an area under the receiver operator characteristic curve of 0.85 (5-fold cross-validation, 0.77-0.85) in the internal set and 0.82 (95% CI, .81-0.83) in the external test set with similar performance in Hispanic, non-Hispanic Black, and non-Hispanic White patients. A total of 1471 patients were identified with positive AI model scores 0.5 or more during the study period, with 256 eligible patients who met study criteria. Of these patients, 50 underwent amyloidosis testing after physician and patient approval; 24 (48%) were diagnosed with ATTR-CM, and 21 (88%) initiated treatment within 3 months. The positivity rate was more than 2.8 times higher than historical controls (15.3%; 95% CI, 13.1%-17.9%; P < .001), with an 18% relative increase in new diagnoses vs the prior year. Conclusions and Relevance: AI-augmented screening may improve ATTR-CM detection and identify patients who are missed by usual care. Prospective randomized trials are needed to determine if outcomes are improved. Trial Registration: ClinicalTrials.gov Identifier: NCT06469372.

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

APA 7th

Jain, S. S., Sun, T., Pierson, E., Oliver, F. R., Malta, P. P., Castillo, M., et al. (2025). Detecting Transthyretin Cardiac Amyloidosis With Artificial Intelligence. JAMA Cardiology. https://doi.org/10.1001/jamacardio.2025.4591

Vancouver

Jain SS, Sun T, Pierson E, Oliver FR, Malta PP, Castillo M, et al. Detecting Transthyretin Cardiac Amyloidosis With Artificial Intelligence. JAMA Cardiology. 2025. doi:10.1001/jamacardio.2025.4591. PMID: 41213043.

BibTeX

@article{jain2025-41213043,
  author  = {Jain, Sneha S. and Sun, Tony and Pierson, Emma and Oliver, Francisco Roedan and Malta, Paloma P and Castillo, Michelle and Wan, Ningxin and Alishetti, Shudhanshu and others},
  title   = {{Detecting Transthyretin Cardiac Amyloidosis With Artificial Intelligence}},
  journal = {JAMA Cardiology},
  year    = {2025},
  doi     = {10.1001/jamacardio.2025.4591},
  pmid    = {41213043},
  url     = {https://doi.org/10.1001/jamacardio.2025.4591}
}

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Key findings

ATTRACTnet was developed in an internal test set of 799 patients (mean [SD] age, 75.1 [11.1] years; 516 [64.7%] male and 283 [35.3%] female) using 5-fold cross-validation with an additional external test set of 422 patients.
It had good discrimination for ATTR-CM detection with an area under the receiver operator characteristic curve of 0.85 (5-fold cross-validation, 0.77-0.85) in the internal set and 0.82 (95% CI, .81-0.83) in the external test set with similar performance in Hispanic, non-Hispanic Black, and non-Hispanic White patients.
A total of 1471 patients were identified with positive AI model scores 0.5 or more during the study period, with 256 eligible patients who met study criteria.
Of these patients, 50 underwent amyloidosis testing after physician and patient approval; 24 (48%) were diagnosed with ATTR-CM, and 21 (88%) initiated treatment within 3 months.
The positivity rate was more than 2.8 times higher than historical controls (15.3%; 95% CI, 13.1%-17.9%; P < .001), with an 18% relative increase in new diagnoses vs the prior year.

Why it matters

An artificial intelligence model detected transthyretin cardiac amyloidosis with good discrimination, and among tested patients, 24 of 50 were diagnosed and 21 started treatment within three months. This concerns heart amyloidosis detection, not treating, curing, or preventing Alzheimer's disease, and the authors say randomized trials are needed to show whether outcomes improve.

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Study details

Study designopen-label trial
Participants799
Effect size64.7%; 35.3%; 15.3%
Evidence strengthHigh

How this study scored 55/100

Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (JAMA Cardiology)
Journal's citation record	2 / 4	JAMA Cardiology: journal h-index 169 (OpenAlex, 2026-10-29)
Study design	0 / 12	Design recorded as “open-label trial”, which our rubric does not rank (not stated in the record — scored 0)
Strength of evidence	5 / 5	Rated “high” in our summary
Peer-review validation	10 / 10	Peer-reviewed. Published as an article in a named peer-reviewed journal.
How recent	4 / 5	2025 — within 3 year(s) of the newest study here
Sample size	2 / 4	799 participants
Effect reported	2 / 3	Effect reported as a number, without an interval or test
Studied in people	3 / 3	Studied in people (participant count stated)
Randomised / blinded	0 / 2	No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	2 / 7	Cited 12 times (OpenAlex, 2026-10-10)
Citations per year	2 / 5	4.0 citations a year over 3 year(s) since 2025
Impact for its field and year	3 / 6	Cited 3.1× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	0 / 6	No other study in this library cites it
Still being cited	1 / 4	Cited 12 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	2 / 4	Last-listed author Timothy Joseph Poterucha: h-index 27 (OpenAlex, whole career)
Institution's track record	3 / 3	Stanford University: institution h-index 2,684 (OpenAlex)
Multi-centre collaboration	3 / 3	OpenAlex lists 11 institution(s) in 1 country
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 41213043)
Free to read	2 / 2	Free to read
Total	55 / 100	—

What each of these 20 components measures, and what this score is not →

Read the original

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

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