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Open AccessArticle

Machine Learning Enables Prediction of Cardiac Amyloidosis by Routine Laboratory Parameters: A Proof-of-Concept Study

1
Section for Artificial Intelligence and Decision Support, Medical University of Vienna, 1090 Vienna, Austria
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Division of Cardiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
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Division of Oncology, Medical University of Vienna, 1090 Vienna, Austria
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Division of Pathology, Medical University of Vienna, 1090 Vienna, Austria
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Division of Orthopedics and Traumatology, Medical University of Vienna, 1090 Vienna, Austria
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2020, 9(5), 1334; https://doi.org/10.3390/jcm9051334
Received: 19 March 2020 / Revised: 28 April 2020 / Accepted: 30 April 2020 / Published: 3 May 2020
(This article belongs to the Section Cardiology)
(1) Background: Cardiac amyloidosis (CA) is a rare and complex condition with poor prognosis. While novel therapies improve outcomes, many affected individuals remain undiagnosed due to a lack of awareness among clinicians. This study was undertaken to develop an expert-independent machine learning (ML) prediction model for CA relying on routinely determined laboratory parameters. (2) Methods: In a first step, we developed baseline linear models based on logistic regression. In a second step, we used an ML algorithm based on gradient tree boosting to improve our linear prediction model, and to perform non-linear prediction. Then, we compared the performance of all diagnostic algorithms. All prediction models were developed on a training cohort, consisting of patients with proven CA (positive cases, n = 121) and amyloidosis-unrelated heart failure (HF) patients (negative cases, n = 415). Performances of all prediction models were evaluated on a separate prognostic validation cohort with 37 CA-positive and 124 CA-negative patients. (3) Results: Our best model, based on gradient-boosted ensembles of decision trees, achieved an area under the receiver operating characteristic curve (ROC AUC) score of 0.86, with sensitivity and specificity of 89.2% and 78.2%, respectively. The best linear model had an ROC AUC score of 0.75, with sensitivity and specificity of 84.6 and 71.7, respectively. (4) Conclusions: Our work demonstrates that ML makes it possible to utilize basic laboratory parameters to generate a distinct CA-related HF profile compared with CA-unrelated HF patients. This proof-of-concept study opens a potential new avenue in the diagnostic workup of CA and may assist physicians in clinical reasoning. View Full-Text
Keywords: heart failure; cardiac amyloidosis; machine learning; artificial intelligence heart failure; cardiac amyloidosis; machine learning; artificial intelligence
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MDPI and ACS Style

Agibetov, A.; Seirer, B.; Dachs, T.-M.; Koschutnik, M.; Dalos, D.; Rettl, R.; Duca, F.; Schrutka, L.; Agis, H.; Kain, R.; Auer-Grumbach, M.; Binder, C.; Mascherbauer, J.; Hengstenberg, C.; Samwald, M.; Dorffner, G.; Bonderman, D. Machine Learning Enables Prediction of Cardiac Amyloidosis by Routine Laboratory Parameters: A Proof-of-Concept Study. J. Clin. Med. 2020, 9, 1334. https://doi.org/10.3390/jcm9051334

AMA Style

Agibetov A, Seirer B, Dachs T-M, Koschutnik M, Dalos D, Rettl R, Duca F, Schrutka L, Agis H, Kain R, Auer-Grumbach M, Binder C, Mascherbauer J, Hengstenberg C, Samwald M, Dorffner G, Bonderman D. Machine Learning Enables Prediction of Cardiac Amyloidosis by Routine Laboratory Parameters: A Proof-of-Concept Study. Journal of Clinical Medicine. 2020; 9(5):1334. https://doi.org/10.3390/jcm9051334

Chicago/Turabian Style

Agibetov, Asan; Seirer, Benjamin; Dachs, Theresa-Marie; Koschutnik, Matthias; Dalos, Daniel; Rettl, René; Duca, Franz; Schrutka, Lore; Agis, Hermine; Kain, Renate; Auer-Grumbach, Michela; Binder, Christina; Mascherbauer, Julia; Hengstenberg, Christian; Samwald, Matthias; Dorffner, Georg; Bonderman, Diana. 2020. "Machine Learning Enables Prediction of Cardiac Amyloidosis by Routine Laboratory Parameters: A Proof-of-Concept Study" J. Clin. Med. 9, no. 5: 1334. https://doi.org/10.3390/jcm9051334

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