Machine learning models to predict disease progression among veterans with hepatitis C virus

Abstract

BackgroundMachine learning (ML) algorithms provide effective ways to build prediction models using longitudinal information given their capacity to incorporate numerous predictor variables without compromising the accuracy of the risk prediction. Clinical risk prediction models in chronic hepatitis C virus (CHC) can be challenging due to non-linear nature of disease progression. We developed and compared two ML algorithms to predict cirrhosis development in a large CHC-infected cohort using longitudinal data.Methods and findingsWe used national Veterans Health Administration (VHA) data to identify CHC patients in care between 2000–2016. The primary outcome was cirrhosis development ascertained by two consecutive aspartate aminotransferase (AST)-to-platelet ratio indexes (APRIs) > 2 after time zero given the infrequency of liver biopsy in clinical practice and that APRI is a validated non-invasive biomarker of fibrosis in CHC. We excluded those with initial APRI > 2 or pre-existing diagnosis of cirrhosis, hepatocellular carcinoma or hepatic decompensation. Enrollment was defined as the date of the first APRI. Time zero was defined as 2 years after enrollment. Cross-sectional (CS) models used predictors at or closest before time zero as a comparison. Longitudinal models used CS predictors plus longitudinal summary variables (maximum, minimum, maximum of slope, minimum of slope and total variation) between enrollment and time zero. Covariates included demographics, labs, and body mass index. Model performance was evaluated using concordance and area under the receiver operating curve (AuROC). A total of 72,683 individuals with CHC were analyzed with the cohort having a mean age of 52.8, 96.8% male and 53% white. There are 11,616 individuals (16%) who met the primary outcome over a mean follow-up of 7 years. We found superior predictive performance for the longitudinal Cox model compared to the CS Cox model (concordance 0.764 vs 0.746), and for the longitudinal boosted-survival-tree model compared to the linear Cox model (concordance 0.774 vs 0.764). The accuracy of the longitudinal models at 1,3,5 years after time zero also showed superior performance compared to the CS model, based on AuROC.ConclusionsBoosted-survival-tree based models using longitudinal information are statistically superior to cross-sectional or linear models for predicting development of cirrhosis in CHC, though all four models were highly accurate. Similar statistical methods could be applied to predict outcomes in other non-linear chronic disease states.

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“…Additional studies have used machine learning to focus on the challenge of predicting progression in HCV patients. Konerman et al analysed approximately 11,000 HCV patients to identify those at risk of developing cirrhosis using the national Veterans Health Administration data 20 . They found that boosting survival tree-based models outperformed other methods with an area under the receiver operator characteristic curve of 0.77.…”

Pregnancy data enable identification of relevant biomarkers and a partial prognosis of autism at birth

“…Moreover, ML approaches have shown recently their power in disease prognosis with applications in e.g. hepatitis prediction [45], classification of diabetic patients [46,47] and lung cancer screening [48]. They have also recently enabled to give brain specific interactions probability of each gene with all the genes of the network and their probability association with ASD [49] but without differentiating NT and ASD babies.…”

“…It can detect complex underlying patterns of features to predict the binary target variable of belonging to the ASD group. This algorithm gives state-of-the-art results in a wide range of classification applications, especially in healthcare and diagnosis of diseases [45,46,72,73].…”

Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review

“…In a follow-up study utilizing a cohort of 72,683 veterans with CHC, boosted survival tree-based models using longitudinal data consistently outperformed prediction of development of cirrhosis in CHC patients at 1, 3, and 5 years as opposed to standard cross-sectional statistical methods. (51) PSC ML methods have been investigated in patients with PSC both preceding and following transplantation. In an effort to be able to better identify patients with PSC at risk for HD, Eaton et al examined a large cohort of 509 PSC patients to derive an algorithm predictive of HD (ascites, variceal hemorrhage, and encephalopathy) using the GBM algorithm.…”