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An Electronic Health Record Model for Predicting Risk of Hepatic Fibrosis in Primary Care Patients.
Thrift, Aaron P; Nguyen Wenker, Theresa H; Godwin, Kyler; Balakrishnan, Maya; Duong, Hao T; Loomba, Rohit; Kanwal, Fasiha; El-Serag, Hashem B.
Afiliación
  • Thrift AP; Section of Epidemiology and Population Sciences, Baylor College of Medicine, Houston, TX, USA.
  • Nguyen Wenker TH; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA.
  • Godwin K; Section of Gastroenterology and Hepatology, Department of Medicine, Baylor College of Medicine, Cambridge Street, Houston, TX, 7200, USA.
  • Balakrishnan M; Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA.
  • Duong HT; Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA.
  • Loomba R; Section of Gastroenterology and Hepatology, Department of Medicine, Baylor College of Medicine, Cambridge Street, Houston, TX, 7200, USA.
  • Kanwal F; Section of Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
  • El-Serag HB; Division of Epidemiology, Department of Family Medicine and Public Health, University of California at San Diego, San Diego, CA, USA.
Dig Dis Sci ; 69(7): 2430-2436, 2024 Jul.
Article en En | MEDLINE | ID: mdl-38700632
ABSTRACT

BACKGROUND:

One challenge for primary care providers caring for patients with nonalcoholic fatty liver disease is to identify those at the highest risk for clinically significant liver disease.

AIM:

To derive a risk stratification tool using variables from structured electronic health record (EHR) data for use in populations which are disproportionately affected with obesity and diabetes.

METHODS:

We used data from 344 participants who underwent Fibroscan examination to measure liver fat and liver stiffness measurement [LSM]. Using two approaches, multivariable logistic regression and random forest classification, we assessed risk factors for any hepatic fibrosis (LSM > 7 kPa) and significant hepatic fibrosis (> 8 kPa). Possible predictors included data from the EHR for age, gender, diabetes, hypertension, FIB-4, body mass index (BMI), LDL, HDL, and triglycerides.

RESULTS:

Of 344 patients (56.4% women), 34 had any hepatic fibrosis, and 15 significant hepatic fibrosis. Three variables (BMI, FIB-4, diabetes) were identified from both approaches. When we used variable cut-offs defined by Youden's index, the final model predicting any hepatic fibrosis had an AUC of 0.75 (95% CI 0.67-0.84), NPV of 91.5% and PPV of 40.0%. The final model with variable categories based on standard clinical thresholds (i.e., BMI ≥ 30 kg/m2; FIB-4 ≥ 1.45) had lower discriminatory ability (AUC 0.65), but higher PPV (50.0%) and similar NPV (91.3%). We observed similar findings for predicting significant hepatic fibrosis.

CONCLUSIONS:

Our results demonstrate that standard thresholds for clinical risk factors/biomarkers may need to be modified for greater discriminatory ability among populations with high prevalence of obesity and diabetes.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Atención Primaria de Salud / Registros Electrónicos de Salud / Enfermedad del Hígado Graso no Alcohólico / Cirrosis Hepática Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Dig Dis Sci Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Atención Primaria de Salud / Registros Electrónicos de Salud / Enfermedad del Hígado Graso no Alcohólico / Cirrosis Hepática Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Dig Dis Sci Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos