BACKGROUND AND AIMS: We aimed to develop and validate machine learning algorithms to predict direct-acting antiviral (DAA) treatment failure among patients with HCV infection. APPROACH AND RESULTS: We used HCV-TARGET registry data to identify HCV-infected adults receiving all-oral DAA treatment and having virologic outcome. Potential pretreatment predictors (n = 179) included sociodemographic, clinical characteristics, and virologic data. We applied multivariable logistic regression as well as elastic net, random forest, gradient boosting machine (GBM), and feedforward neural network machine learning algorithms to predict DAA treatment failure. Training (n = 4894) and validation (n = 1631) patient samples had similar sociodemographic and clinical characteristics (mean age, 57 years; 60% male; 66% White; 36% with cirrhosis). Of 6525 HCV-infected adults, 95.3% achieved sustained virologic response, whereas 4.7% experienced DAA treatment failure. In the validation sample, machine learning approaches performed similarly in predicting DAA treatment failure (C statistic [95% CI]: GBM, 0.69 [0.64-0.74]; random forest, 0.68 [0.63-0.73]; feedforward neural network, 0.66 [0.60-0.71]; elastic net, 0.64 [0.59-0.70]), and all four outperformed multivariable logistic regression (0.51 [0.46-0.57]). Using the Youden index to identify the balanced risk score threshold, GBM had 66.2% sensitivity and 65.1% specificity, and 12 individuals were needed to evaluate to identify 1 DAA treatment failure. Over 55% of patients with treatment failure were classified by the GBM in the top three risk decile subgroups (positive predictive value: 6%-14%). The top 10 GBM-identified predictors included albumin, liver enzymes (aspartate aminotransferase, alkaline phosphatase), total bilirubin levels, sex, HCV viral loads, sodium level, HCC, platelet levels, and tobacco use. CONCLUSIONS: Machine learning algorithms performed effectively for risk prediction and stratification of DAA treatment failure.
BACKGROUND AND AIMS: We aimed to develop and validate machine learning algorithms to predict direct-acting antiviral (DAA) treatment failure among patients with HCV infection. APPROACH AND RESULTS: We used HCV-TARGET registry data to identify HCV-infected adults receiving all-oral DAA treatment and having virologic outcome. Potential pretreatment predictors (n = 179) included sociodemographic, clinical characteristics, and virologic data. We applied multivariable logistic regression as well as elastic net, random forest, gradient boosting machine (GBM), and feedforward neural network machine learning algorithms to predict DAA treatment failure. Training (n = 4894) and validation (n = 1631) patient samples had similar sociodemographic and clinical characteristics (mean age, 57 years; 60% male; 66% White; 36% with cirrhosis). Of 6525 HCV-infected adults, 95.3% achieved sustained virologic response, whereas 4.7% experienced DAA treatment failure. In the validation sample, machine learning approaches performed similarly in predicting DAA treatment failure (C statistic [95% CI]: GBM, 0.69 [0.64-0.74]; random forest, 0.68 [0.63-0.73]; feedforward neural network, 0.66 [0.60-0.71]; elastic net, 0.64 [0.59-0.70]), and all four outperformed multivariable logistic regression (0.51 [0.46-0.57]). Using the Youden index to identify the balanced risk score threshold, GBM had 66.2% sensitivity and 65.1% specificity, and 12 individuals were needed to evaluate to identify 1 DAA treatment failure. Over 55% of patients with treatment failure were classified by the GBM in the top three risk decile subgroups (positive predictive value: 6%-14%). The top 10 GBM-identified predictors included albumin, liver enzymes (aspartate aminotransferase, alkaline phosphatase), total bilirubin levels, sex, HCV viral loads, sodium level, HCC, platelet levels, and tobacco use. CONCLUSIONS: Machine learning algorithms performed effectively for risk prediction and stratification of DAA treatment failure.
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Authors: Loreta A Kondili; Giovanni Battista Gaeta; Maurizia Rossana Brunetto; Alfredo Di Leo; Andrea Iannone; Teresa Antonia Santantonio; Adele Giammario; Giovanni Raimondo; Roberto Filomia; Carmine Coppola; Daniela Caterina Amoruso; Pierluigi Blanc; Barbara Del Pin; Liliana Chemello; Luisa Cavalletto; Filomena Morisco; Laura Donnarumma; Maria Grazia Rumi; Antonio Gasbarrini; Massimo Siciliano; Marco Massari; Romina Corsini; Barbara Coco; Salvatore Madonia; Marco Cannizzaro; Anna Linda Zignego; Monica Monti; Francesco Paolo Russo; Alberto Zanetto; Marcello Persico; Mario Masarone; Erica Villa; Veronica Bernabucci; Gloria Taliani; Elisa Biliotti; Luchino Chessa; Maria Cristina Pasetto; Pietro Andreone; Marzia Margotti; Giuseppina Brancaccio; Donatella Ieluzzi; Guglielmo Borgia; Emanuela Zappulo; Vincenza Calvaruso; Salvatore Petta; Loredana Falzano; Maria Giovanna Quaranta; Liliana Elena Weimer; Stefano Rosato; Stefano Vella; Edoardo Giovanni Giannini Journal: PLoS One Date: 2017-10-04 Impact factor: 3.240