Application and impact of Lasso regression in gastroenterology: A systematic review

• Published in: Indian journal of gastroenterology Vol. 42; no. 6; pp. 780 - 790
• Main Authors: Ali, Hassam, Shahzad, Maria, Sarfraz, Shiza, Sewell, Kerry B., Alqalyoobi, Shehabaldin, Mohan, Babu P.
• Format: Journal Article
• Language: English
• Published: New Delhi Springer India 01.12.2023; Springer Nature B.V
• Subjects: Alcohol use; Algorithms; Business metrics; Cohort analysis; Fatty liver; Gastroenterology; Gastrointestinal Tract; Hepatitis; Hepatology; Humans; Liver cirrhosis; Liver Diseases; Machine learning; Medicine; Medicine & Public Health; Natural language; Observational studies; Oncology; Pediatrics; Prognosis; Regularization methods; ROC Curve; Systematic Review; Variables; Vocabularies & taxonomies; High-dimensional data; Inflammatory bowel disease; Lasso regression; Diagnostic accuracy; Gastroenterology; Liver disease; Machine learning; Clinical decision-making; Regularization; Esophageal cancer; Prediction modeling; Variable selection
• ISSN: 0254-8860; 0975-0711; 0975-0711
• DOI: 10.1007/s12664-023-01426-9

Least absolute shrinkage and selection operator (Lasso) regression is a statistical technique that can be used to study the effects of clinical variables in outcome prediction. In this study, we aimed at systematically reviewing the application of Lasso regression in gastroenterology for developing predictive models and providing a method of performing Lasso regression. A comprehensive search strategy was conducted in PubMed, Embase and Cochrane CENTRAL databases ( Keywords : lasso regression; gastrointestinal tract/diseases) following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies were screened for eligibility based on pre-defined selection criteria and the data was extracted using a standardized form. Total 16 studies were included, comprising a diverse range of gastroenterological disease-related outcomes. Sample sizes ranged from 134 to 8861 subjects. Eleven studies reported liver disease-related prediction models, while five focused on non-hepatic etiology models. Lasso regression was applied for variable selection, risk prediction and model development, with various validation methods and performance metrics used. Model performance metrics included Area Under the Receiver Operating Characteristics (AUROC), C-index and calibration plots. In gastroenterology, Lasso regression has been used in various diseases such as inflammatory bowel disease, liver disease and esophageal cancer. It is valuable for complex scenarios with many predictors. However, its effectiveness depends on high-quality and complete data. While it identifies important variables, it doesn't provide causal interpretations. Therefore, cautious interpretation is necessary considering the study design and data quality.