Background: The NAFLD decompensation risk score (the Iowa Model) was recently developed to identify patients with nonalcoholic fatty liver disease (NAFLD) at highest risk of developing hepatic events using three variables-age, platelet count, and diabetes.
Aims: We performed an external validation of the Iowa Model and compared it to existing non-invasive models.
Methods: We included 249 patients with NAFLD at Boston Medical Center, Boston, Massachusetts, in the external validation cohort and 949 patients in the combined internal/external validation cohort. The primary outcome was the development of hepatic events (ascites, hepatic encephalopathy, esophageal or gastric varices, or hepatocellular carcinoma). We used Cox proportional hazards to analyze the ability of the Iowa Model to predict hepatic events in the external validation (https://uihc.org/non-alcoholic-fatty-liver-disease-decompensation-risk-score-calculator). We compared the performance of the Iowa Model to the AST-to-platelet ratio index (APRI), NAFLD fibrosis score (NFS), and the FIB-4 index in the combined cohort.
Results: The Iowa Model significantly predicted the development of hepatic events with hazard ratio of 2.5 [95% confidence interval (CI) 1.7-3.9, < 0.001] and area under the receiver operating characteristic curve (AUROC) of 0.87 (CI 0.83-0.91). The AUROC of the Iowa Model (0.88, CI: 0.85-0.92) was comparable to the FIB-4 index (0.87, CI: 0.83-0.91) and higher than NFS (0.66, CI: 0.63-0.69) and APRI (0.76, CI: 0.73-0.79).
Conclusions: In an urban, racially and ethnically diverse population, the Iowa Model performed well to identify NAFLD patients at higher risk for liver-related complications. The model provides the individual probability of developing hepatic events and identifies patients in need of early intervention.
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http://dx.doi.org/10.1016/j.jceh.2022.11.005 | DOI Listing |
J Am Coll Health
January 2025
Department of Psychology, Fordham University, Fordham University, Bronx, New York, USA.
The transition to college is associated with rising rates of depressive symptoms and decreased well-being. It is critical to identify protective psychological factors for this period. One possible protective factor is psychological flexibility, or the ability to pursue self-identified values despite distressing thoughts and emotions.
View Article and Find Full Text PDFJSLS
January 2025
Wake Forest University Health Sciences, Department of Obstetrics and Gynecology, Winston-Salem, NC. (Drs. Cochrane and Moulder).
Background: Optimization of surgical scheduling represents an opportunity to improve resource utilization and increase patient access. Increasing body mass index (BMI) has been associated with increased operating time and may provide an opportunity to more accurately predict operating time.
Objective: To investigate the relationship between BMI and operative time for benign hysterectomy and develop a predictive model for hysterectomy operating time based on patient BMI.
Arthritis Rheumatol
January 2025
Medicine & Research Service, VA Nebraska-Western Iowa Health Care System, Omaha, NE.
Objective: Determine whether pollutants such as fire smoke-related particulate matter smaller than 2.5 microns (PM) are associated with incident rheumatoid arthritis (RA) and RA-associated interstitial lung disease (RA-ILD).
Methods: This case-control study used Veterans Affairs data 10/1/2009-12/31/2018.
J Cyst Fibros
January 2025
The Lundquist Institute, Harbor-UCLA Medical Center, Torrance 90502 CA, USA. Electronic address:
Background: Cystic Fibrosis-related Bone Disease is an emerging challenge faced by 50 % of adult people with cystic fibrosis (CF). The multifactorial causes of this comorbidity remain elusive. However, congenital bone defects have been observed in animal models with CFTR mutations, suggesting its importance.
View Article and Find Full Text PDFGeriatr Gerontol Int
January 2025
Division of Acute Care Surgery, Department of Surgery, University of Iowa Roy J. and Lucille A. Carver College of Medicine, Iowa City, Iowa, USA.
Aim: Pre-injury frailty has been investigated as a tool to predict outcomes of older trauma patients. Using artificial intelligence principles of machine learning, we aimed to identify a "signature" (combination of clinical variables) that could predict which older adults are at risk of fall-related hospital admission. We hypothesized that frailty, measured using the 5-item modified Frailty Index, could be utilized in combination with other factors as a predictor of admission for fall-related injuries.
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