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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
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
_version_ 1783545675749785600
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
work_keys_str_mv AT agibetovasan machinelearningenablespredictionofcardiacamyloidosisbyroutinelaboratoryparametersaproofofconceptstudy
AT seirerbenjamin machinelearningenablespredictionofcardiacamyloidosisbyroutinelaboratoryparametersaproofofconceptstudy
AT dachstheresamarie machinelearningenablespredictionofcardiacamyloidosisbyroutinelaboratoryparametersaproofofconceptstudy
AT koschutnikmatthias machinelearningenablespredictionofcardiacamyloidosisbyroutinelaboratoryparametersaproofofconceptstudy
AT dalosdaniel machinelearningenablespredictionofcardiacamyloidosisbyroutinelaboratoryparametersaproofofconceptstudy
AT rettlrene machinelearningenablespredictionofcardiacamyloidosisbyroutinelaboratoryparametersaproofofconceptstudy
AT ducafranz machinelearningenablespredictionofcardiacamyloidosisbyroutinelaboratoryparametersaproofofconceptstudy
AT schrutkalore machinelearningenablespredictionofcardiacamyloidosisbyroutinelaboratoryparametersaproofofconceptstudy
AT agishermine machinelearningenablespredictionofcardiacamyloidosisbyroutinelaboratoryparametersaproofofconceptstudy
AT kainrenate machinelearningenablespredictionofcardiacamyloidosisbyroutinelaboratoryparametersaproofofconceptstudy
AT auergrumbachmichela machinelearningenablespredictionofcardiacamyloidosisbyroutinelaboratoryparametersaproofofconceptstudy
AT binderchristina machinelearningenablespredictionofcardiacamyloidosisbyroutinelaboratoryparametersaproofofconceptstudy
AT mascherbauerjulia machinelearningenablespredictionofcardiacamyloidosisbyroutinelaboratoryparametersaproofofconceptstudy
AT hengstenbergchristian machinelearningenablespredictionofcardiacamyloidosisbyroutinelaboratoryparametersaproofofconceptstudy
AT samwaldmatthias machinelearningenablespredictionofcardiacamyloidosisbyroutinelaboratoryparametersaproofofconceptstudy
AT dorffnergeorg machinelearningenablespredictionofcardiacamyloidosisbyroutinelaboratoryparametersaproofofconceptstudy
AT bondermandiana machinelearningenablespredictionofcardiacamyloidosisbyroutinelaboratoryparametersaproofofconceptstudy