PMID- 34055932
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220301
IS  - 2297-055X (Print)
IS  - 2297-055X (Electronic)
IS  - 2297-055X (Linking)
VI  - 8
DP  - 2021
TI  - Machine Learning for Predicting Heart Failure Progression in Hypertrophic 
      Cardiomyopathy.
PG  - 647857
LID - 10.3389/fcvm.2021.647857 [doi]
LID - 647857
AB  - Background: Development of advanced heart failure (HF) symptoms is the most 
      common adverse pathway in hypertrophic cardiomyopathy (HCM) patients. Currently, 
      there is a limited ability to identify HCM patients at risk of HF. Objectives: In 
      this study, we present a machine learning (ML)-based model to identify individual 
      HCM patients who are at high risk of developing advanced HF symptoms. Methods: 
      From a consecutive cohort of HCM patients evaluated at the Tufts HCM Institute 
      from 2001 to 2018, we extracted a set of 64 potential risk factors measured at 
      baseline. Only patients with New York Heart Association (NYHA) functional class 
      I/II and LV ejection fraction (LVEF) by echocardiography >35% were included. The 
      study cohort (n = 1,427 patients) was split into three disjoint subsets: 
      development (50%), model selection (10%), and independent validation (40%). The 
      least absolute shrinkage and selection operator was used to select the most 
      influential clinical variables. An ensemble of ML classifiers, including logistic 
      regression, was used to identify patients with high risk of developing a HF 
      outcome. Study outcomes were defined as progression to NYHA class III/IV, drop in 
      LVEF below 35%, septal reduction procedure, and/or heart transplantation. 
      Results: During a mean follow-up of 4.7 +/- 3.7 years, advanced HF occurred in 283 
      (20% out of 1,427) patients. The model features included patients' sex, NYHA 
      class (I or II), HCM type (i.e., obstructive or not), LV wall thickness, LVEF, 
      presence of HF symptoms (e.g., dyspnea, presyncope), comorbidities (atrial 
      fibrillation, hypertension, mitral regurgitation, and systolic anterior motion), 
      and type of cardiac medications. The developed risk stratification model showed 
      strong differentiation power to identify patients at advanced HF risk in the 
      testing dataset (c-statistics = 0.81; 95% confidence interval [CI]: 0.76, 0.86). 
      The model allowed correct identification of high-risk patients with accuracy 74% 
      (CI: 0.70, 0.78), sensitivity 80% (CI: 0.77, 0.83), and specificity 72% (CI: 
      0.68, 0.76). The model performance was comparable among different sex and age 
      groups. Conclusions: A 5-year risk prediction of progressive HF in HCM patients 
      can be accurately estimated using ML analysis of patients' clinical and imaging 
      parameters. A set of 17 clinical and imaging variables were identified as the 
      most important predictors of progressive HF in HCM.
CI  - Copyright (c) 2021 Fahmy, Rowin, Manning, Maron and Nezafat.
FAU - Fahmy, Ahmed S
AU  - Fahmy AS
AD  - Cardiovascular Division, Department of Medicine, Beth Israel Deaconess Medical 
      Center and Harvard Medical School, Boston, MA, United States.
FAU - Rowin, Ethan J
AU  - Rowin EJ
AD  - Division of Cardiology, Hypertrophic Cardiomyopathy Center, Tufts Medical Center, 
      Boston, MA, United States.
FAU - Manning, Warren J
AU  - Manning WJ
AD  - Cardiovascular Division, Department of Medicine, Beth Israel Deaconess Medical 
      Center and Harvard Medical School, Boston, MA, United States.
AD  - Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical 
      School, Boston, MA, United States.
FAU - Maron, Martin S
AU  - Maron MS
AD  - Division of Cardiology, Hypertrophic Cardiomyopathy Center, Tufts Medical Center, 
      Boston, MA, United States.
FAU - Nezafat, Reza
AU  - Nezafat R
AD  - Cardiovascular Division, Department of Medicine, Beth Israel Deaconess Medical 
      Center and Harvard Medical School, Boston, MA, United States.
LA  - eng
GR  - R01 HL158098/HL/NHLBI NIH HHS/United States
PT  - Journal Article
DEP - 20210513
PL  - Switzerland
TA  - Front Cardiovasc Med
JT  - Frontiers in cardiovascular medicine
JID - 101653388
PMC - PMC8155292
OTO - NOTNLM
OT  - heart failure
OT  - hypertrophic cardiomyopathy
OT  - machine learning
OT  - risk factors
OT  - risk stratification
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2021/06/01 06:00
MHDA- 2021/06/01 06:01
PMCR- 2021/01/01
CRDT- 2021/05/31 06:19
PHST- 2020/12/30 00:00 [received]
PHST- 2021/03/29 00:00 [accepted]
PHST- 2021/05/31 06:19 [entrez]
PHST- 2021/06/01 06:00 [pubmed]
PHST- 2021/06/01 06:01 [medline]
PHST- 2021/01/01 00:00 [pmc-release]
AID - 10.3389/fcvm.2021.647857 [doi]
PST - epublish
SO  - Front Cardiovasc Med. 2021 May 13;8:647857. doi: 10.3389/fcvm.2021.647857. 
      eCollection 2021.