Screening for patients at risk for cardiac amyloidosis via electronic health records: A multicenter machine learning development and validation study

@article{spielvogel2026screening,
  title = {{Screening for patients at risk for cardiac amyloidosis via electronic health records: A multicenter machine learning development and validation study}},
  author = {Clemens P. Spielvogel and David Kersting and David Haberl and Maximilian Autherith and Laurenz Hauptmann and Josef Yu and Juliane Hennenberg and Kilian Kluge and Kim Moon and Stephan Settelmeier and Jing Ning and Katarina Kumpf and Markus Köfler and Felix Hofer and Katharina Mascherbauer and Julia Mascherbauer and Andreas Kammerlander and Tatjana Traub-Weidinger and Gregor Kasprian and Tienush Rassaf and Jens Kleesiek and Ken Herrmann and Philipp Bartko and Christian Hengstenberg and Marcus Hacker and Raffaella Calabretta and Christian Nitsche},
  journal = {PLOS Digital Health},
  year = {2026},
  month = oct,
  doi = {10.1371/journal.pdig.0001637},
  url = {https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001637},
  note = {CC BY 4.0},
}

TY  - JOUR
TI  - Screening for patients at risk for cardiac amyloidosis via electronic health records: A multicenter machine learning development and validation study
AU  - Clemens P. Spielvogel
AU  - David Kersting
AU  - David Haberl
AU  - Maximilian Autherith
AU  - Laurenz Hauptmann
AU  - Josef Yu
AU  - Juliane Hennenberg
AU  - Kilian Kluge
AU  - Kim Moon
AU  - Stephan Settelmeier
AU  - Jing Ning
AU  - Katarina Kumpf
AU  - Markus Köfler
AU  - Felix Hofer
AU  - Katharina Mascherbauer
AU  - Julia Mascherbauer
AU  - Andreas Kammerlander
AU  - Tatjana Traub-Weidinger
AU  - Gregor Kasprian
AU  - Tienush Rassaf
AU  - Jens Kleesiek
AU  - Ken Herrmann
AU  - Philipp Bartko
AU  - Christian Hengstenberg
AU  - Marcus Hacker
AU  - Raffaella Calabretta
AU  - Christian Nitsche
PY  - 2026
DA  - 2026/10/08
JO  - PLOS Digital Health
DO  - 10.1371/journal.pdig.0001637
UR  - https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001637
AB  - Timely detection is crucial to improve outcomes in patients with cardiac amyloidosis (CA) by initiation of guideline-directed disease-modifying treatments. Although confirmatory bone scintigraphy is highly accurate for CA detection, identifying at-risk patients for referral remains challenging. This study aimed to develop and validate a machine learning model, Amylo-Detect, using structured multimodal electronic health record (EHR) data to guide referrals for confirmatory scintigraphy and monoclonal protein testing. Consecutive all-comer patients (n = 11,616) referred for bone scintigraphy at the Vienna General Hospital (2010–2023) were retrospectively included. Patients referred before August 2020 formed the development cohort. The remaining patients comprised the internal validation cohort. External validation was performed at the University Hospital Essen (n = 1,521). Amylo-Detect was trained using 50 routinely available parameters to predict CA-suggestive uptake (Perugini grade ≥2) and compared with an existing score and clinical routine. High-grade uptake was present in 388 patients (3.0%). Amylo-Detect demonstrated excellent performance in development (AUC 0.93), independent internal validation (AUC 0.91), and external validation cohort (AUC 0.91), outperforming existing scoring systems and clinical routine. Results were consistent across subgroups, even when crucial predictors were missing. Of the 42/388 (10.8%) patients missed in clinical routine, 12/42 (29%) were additionally detected by Amylo-Detect. The model further conveyed significant prognostic value for mortality and heart failure hospitalization. We present Amylo-Detect, a validated EHR-based tool for CA risk prediction, available as a web app, allowing application and further evaluation. By improving timely detection and referral, Amylo-Detect may help address diagnostic delays, pending prospective evaluation. Author summary: Cardiac amyloidosis is a progressive and often fatal heart disease that is frequently diagnosed too late, even though effective treatments are now available. A major challenge is recognizing which patients should be referred for specialized testing, because early symptoms are diverse and often non-specific. In this study, we developed and validated a machine learning prediction system, called Amylo-Detect, that uses routinely collected information from electronic health records to identify patients who may be at risk for cardiac amyloidosis. We trained and externally validated the tool using data from 13,137 patients across two hospitals. We found that Amylo-Detect was highly accurate in identifying patients with disease-indicative findings on confirmatory scans and identified a substantial number of patients who were not initially suspected of having cardiac amyloidosis. Amylo-Detect, which we make publicly available via a web app, consistently outperformed existing risk scores and routine clinical decision-making. Our findings suggest that automated analysis of electronic health records can support clinicians in recognizing cardiac amyloidosis earlier, reduce diagnostic delays, and potentially improve patient outcomes by enabling timely treatment.
SN  - 2767-3170
ER  -