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

Ali, Hassam; Shahzad, Maria; Sarfraz, Shiza; Sewell, Kerry B; Alqalyoobi, Shehabaldin; Mohan, Babu P

Ali, Hassam; Shahzad, Maria; Sarfraz, Shiza; Sewell, Kerry B; Alqalyoobi, Shehabaldin; Mohan, Babu P.

Afiliación

Ali H; Department of Gastroenterology and Hepatology, East Carolina University, Greenville, NC, USA.
Shahzad M; Department of Internal Medicine, University of Health Sciences, Lahore, Punjab, Pakistan.
Sarfraz S; Department of Internal Medicine, University of Health Sciences, Lahore, Punjab, Pakistan.
Sewell KB; Laupus Health Sciences Library, East Carolina University, Greenville, NC, USA.
Alqalyoobi S; Department of Pulmonary and Critical Care Medicine, East Carolina University, Greenville, NC, USA.
Mohan BP; Department of Bioinformatics and Biostatistics, School of Public Health and Information Sciences, University of Louisville, Louisville, KY, USA.

Indian J Gastroenterol ; 42(6): 780-790, 2023 Dec.

Article en En | MEDLINE | ID: mdl-37594652

RESUMEN

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.

Asunto(s)

Gastroenterología; Hepatopatías; Humanos; Pronóstico; Curva ROC; Tracto Gastrointestinal

Palabras clave

Clinical decision-making; Diagnostic accuracy; Esophageal cancer; Gastroenterology; High-dimensional data; Inflammatory bowel disease; Lasso regression; Liver disease; Machine learning; Prediction modeling; Regularization; Variable selection

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Gastroenterología / Hepatopatías Tipo de estudio: Guideline / Prognostic_studies / Systematic_reviews Límite: Humans Idioma: En Revista: Indian J Gastroenterol Asunto de la revista: GASTROENTEROLOGIA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: India

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