Personalizing liver targeted treatments and transplantation for patients with alpha-1 antitrypsin deficiency

Pye, Anita; Khan, Sheeba; Whitehouse, Tony; Turner, Alice

doi:10.1080/23808993.2021.1862648

Cited by 1 publication

(1 citation statement)

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“…However, the risk factors potentially contributing to the progression of AATD-LD have been poorly studied 12 . The existing clinical research on AATD-LD mainly utilizes conventional correlation analysis or multivariate regression analysis based on a limited number of predictor variables such as demographics, baseline disease characteristics, serum tests and lifestyle 7 , 13 – 20 . Many studies have limitations due to small sample size or older data 7 .…”

Section: Introductionmentioning

confidence: 99%

A stacking ensemble machine learning model to predict alpha-1 antitrypsin deficiency-associated liver disease clinical outcomes based on UK Biobank data

Meng

Treem

Heap

et al. 2022

Sci Rep

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Alpha-1 antitrypsin deficiency associated liver disease (AATD-LD) is a rare genetic disorder and not well-recognized. Predicting the clinical outcomes of AATD-LD and defining patients more likely to progress to advanced liver disease are crucial for better understanding AATD-LD progression and promoting timely medical intervention. We aimed to develop a tailored machine learning (ML) model to predict the disease progression of AATD-LD. This analysis was conducted through a stacking ensemble learning model by combining five different ML algorithms with 58 predictor variables using nested five-fold cross-validation with repetitions based on the UK Biobank data. Performance of the model was assessed through prediction accuracy, area under the receiver operating characteristic (AUROC), and area under the precision-recall curve (AUPRC). The importance of predictor contributions was evaluated through a feature importance permutation method. The proposed stacking ensemble ML model showed clinically meaningful accuracy and appeared superior to any single ML algorithms in the ensemble, e.g., the AUROC for AATD-LD was 68.1%, 75.9%, 91.2%, and 67.7% for all-cause mortality, liver-related death, liver transplant, and all-cause mortality or liver transplant, respectively. This work supports the use of ML to address the unanswered clinical questions with clinically meaningful accuracy using real-world data.

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