{
  "abstract": "Objective This study aims to develop an algorithm to detect steatotic liver disease (SLD) risk in low-resource settings without requiring imaging.Methods This retrospective cohort study included 826 measurements from 444 participants aged 45–60 years who participated in the MAUCO+ study. Data included ultrasound, vibration-controlled transient elastography (VCTE), anthropometrics and biomarkers. Logistic multivariable regression was used to develop two predictive models for SLD risk, with and without ultrasound, using VCTE as gold standard. Missing data were minimal and retained in the analysis, as their proportion was not statistically relevant. Predictive performance (sensitivity, specificity, positive predictive value and negative predictive value) was compared with the clinically used Fatty Liver Index (FLI).Results The algorithm without ultrasound achieved a sensitivity of 81.1% (95% CI 71.7% to 88.4%) and specificity of 71.4% (95% CI 57.9% to 80.4%). The model with ultrasound demonstrated a sensitivity of 91.5% (95% CI 84.1% to 95.6%) and specificity of 70% (95% CI 59.9% to 80.7%). FLI showed an area under the curve (AUC) of 0.762, while our models achieved higher AUCs: 0.878 (with ultrasound) and 0.794 (without ultrasound).Discussion Our models offer screening tools for SLD in low-resource primary care. The model without ultrasound outperformed FLI, making it a feasible alternative where imaging is unavailable. The ultrasound-based model demonstrated higher performance, underscoring the value of ultrasound when it is accessible. Integrating these algorithms into preventive programmes could improve early diagnosis, especially in populations with a high burden of obesity and diabetes.Conclusions We developed two predictive models for SLD screening in a Chilean cohort. Both showed strong performance and potential for implementation in primary care to support early detection and better disease management.",
  "authors": [
    {
      "affiliations": [
        "Escuela de Salud Publica, Pontificia Universidad Católica de Chile, Santiago, Chile",
        "Escuela de Gobierno, Pontificia Universidad Católica de Chile, Santiago, Chile"
      ],
      "name": "Maria Spencer-Sandino"
    },
    {
      "affiliations": [
        "Escuela de Salud Publica, Pontificia Universidad Católica de Chile, Santiago, Chile",
        "Advance Center for Chronic Diseases, ACCDIS, Universidad de Chile and Pontificia Universidad Católica de Chile, Santiago, Chile"
      ],
      "name": "Franco Godoy"
    },
    {
      "affiliations": [
        "MRC Biostatistics Unit, University of Cambridge, Cambridge, UK"
      ],
      "name": "Danilo Alvares"
    },
    {
      "affiliations": [
        "Departamento de Matemática y Ciencia de la Computación, Universidad de Santiago de Chile, Santiago, Chile"
      ],
      "name": "Felipe Elorrieta"
    },
    {
      "affiliations": [
        "Department of Human Science, Georgetown University Medical Center, Washington, District of Columbia, USA"
      ],
      "name": "Ilona Argirion"
    },
    {
      "affiliations": [
        "Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA"
      ],
      "name": "Jill Koshiol"
    },
    {
      "affiliations": [
        "Hospital de Urgencia Asistencia Pública, Santiago, Chile"
      ],
      "name": "Claudio Vargas"
    },
    {
      "affiliations": [
        "Escuela de Salud Publica, Pontificia Universidad Católica de Chile, Santiago, Chile",
        "Advance Center for Chronic Diseases, ACCDIS, Universidad de Chile and Pontificia Universidad Católica de Chile, Santiago, Chile"
      ],
      "name": "Claudia Marco"
    },
    {
      "affiliations": [
        "Escuela de Salud Publica, Pontificia Universidad Católica de Chile, Santiago, Chile",
        "Instituto de Salud Pública de Chile, Santiago, Chile"
      ],
      "name": "Macarena Garrido"
    },
    {
      "affiliations": [
        "Centro de Investigación e Innovación en Biomedicina, Facultad de Medicina, Universidad de los Andes, Santiago, Chile",
        "Facultad de Ciencias Médicas, Universidad Bernardo O'Higgins, Santiago, Chile"
      ],
      "name": "Daniel Cabrera"
    },
    {
      "affiliations": [
        "Division of Gastroenterology, Hepatology, and Nutrition, Department of Internal Medicine, Virginia Commonwealth University School of Medicine, Richmond, Virginia, USA",
        "Departamento de Gastroenterología, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile"
      ],
      "name": "Juan Pablo Arab"
    },
    {
      "affiliations": [
        "Departamento de Gastroenterología, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile"
      ],
      "name": "Marco Arrese"
    },
    {
      "affiliations": [
        "Advance Center for Chronic Diseases, ACCDIS, Universidad de Chile and Pontificia Universidad Católica de Chile, Santiago, Chile",
        "Departamento de Ciencias Preclínicas, Universidad Catolica del Maule, Talca, Maule Region, Chile"
      ],
      "name": "Laura Huidobro"
    },
    {
      "affiliations": [
        "Departamento de Gastroenterología, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile"
      ],
      "name": "Francisco Barrera"
    },
    {
      "affiliations": [
        "Escuela de Salud Publica, Pontificia Universidad Católica de Chile, Santiago, Chile",
        "Instituto de Salud Pública de Chile, Santiago, Chile"
      ],
      "name": "Catterina Ferreccio"
    }
  ],
  "title": "Developing a non-invasive algorithm for the diagnosis of steatotic liver disease in primary healthcare: a retrospective cohort study",
  "uid": "be6b307f-1899-5e8b-be9d-e6dc560babb7"
}