Machine Learning Enables Prediction of Cardiac Amyloidosis by Routine Laboratory Parameters: A Proof-of-Concept Study
(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...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7290438/ https://www.ncbi.nlm.nih.gov/pubmed/32375287 http://dx.doi.org/10.3390/jcm9051334 |
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author | 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 |
author_facet | 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 |
author_sort | Agibetov, Asan |
collection | PubMed |
description | (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. |
format | Online Article Text |
id | pubmed-7290438 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-72904382020-06-15 Machine Learning Enables Prediction of Cardiac Amyloidosis by Routine Laboratory Parameters: A Proof-of-Concept Study 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 J Clin Med Article (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. MDPI 2020-05-03 /pmc/articles/PMC7290438/ /pubmed/32375287 http://dx.doi.org/10.3390/jcm9051334 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article 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 Machine Learning Enables Prediction of Cardiac Amyloidosis by Routine Laboratory Parameters: A Proof-of-Concept Study |
title | Machine Learning Enables Prediction of Cardiac Amyloidosis by Routine Laboratory Parameters: A Proof-of-Concept Study |
title_full | Machine Learning Enables Prediction of Cardiac Amyloidosis by Routine Laboratory Parameters: A Proof-of-Concept Study |
title_fullStr | Machine Learning Enables Prediction of Cardiac Amyloidosis by Routine Laboratory Parameters: A Proof-of-Concept Study |
title_full_unstemmed | Machine Learning Enables Prediction of Cardiac Amyloidosis by Routine Laboratory Parameters: A Proof-of-Concept Study |
title_short | Machine Learning Enables Prediction of Cardiac Amyloidosis by Routine Laboratory Parameters: A Proof-of-Concept Study |
title_sort | machine learning enables prediction of cardiac amyloidosis by routine laboratory parameters: a proof-of-concept study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7290438/ https://www.ncbi.nlm.nih.gov/pubmed/32375287 http://dx.doi.org/10.3390/jcm9051334 |
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