ArticleBMC medical informatics and decision making2025
Beyond NT-proBNP and troponin: How machine learning redefines light-chain cardiac amyloidosis risk assessment.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- The Current Role of Physiotherapy in Systemic Light-Chain (AL) Amyloidosis and Multiple Myeloma.Life (Basel, Switzerland) · 2026Review
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7 authors.
Funding
Abstract
objectiveTo develop and validate a machine learning-based prognostic model that provides enhanced risk stratification for AL cardiac amyloidosis patients beyond existing staging system.
methodsWe conducted a retrospective cohort study of newly diagnosed AL cardiac amyloidosis patients (2006–2024), randomly allocating participants into training and test sets (8:2 ratio). Cardiac involvement required elevated cardiac biomarkers (NT-proBNP > 332 ng/L) or increased wall thickness (mean wall thickness > 12 mm). Using all-cause mortality as the primary endpoint, we compared five machine learning algorithms (support vector machine, CoxBoost, random survival forest, multi-layer perceptron, and k-neighbors classifier) and a traditional Cox model against the 2015 European-modified Mayo staging system.
resultsAmong 132 enrolled patients (median age 60 years; 56.8% male), 83 deaths (62.8%) occurred during median 14.5-month follow-up. Feature selection identified six key predictors: cardiac response (36% vs. 15%, P < 0.001), complete hematological response (35% vs. 20%, P < 0.001), E/e’ ratio (15.0 [12.0, 20.0] vs. 19.0 [15.0, 25.0], P = 0.009), left ventricular global longitudinal strain (-14.1 [-11.2 – -16.1] vs. -10.7 [-8.8 – -13.6], P = 0.003), serum uric acid (341.0 [292.0–439.0] vs. 429.0 [345.5–518.0], P = 0.002), and weight loss (30.6% vs. 65.1%, P < 0.001). The CoxBoost model demonstrated superior discrimination (AUC 92%) and calibration (brier score 0.11). Conversely, the predictive value of the revised Mayo 2004 staging system was unsatisfactory, with an AUC of 74% and a brier score of 0.19.
conclusionsMachine learning incorporating multi-dimensional parameters (including myocardial strain and dynamic clinical variables) provides significantly more accurate prognostication than NT-proBNP/troponin-dependent staging, enabling a new era of personalized risk stratification in AL cardiac amyloidosis.
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