{
  "abstract": "Background Acute kidney injury (AKI) is a frequent and serious complication in trauma populations, associated with significant morbidity and mortality. Current risk stratification tools, derived from general medical populations, often lack trauma-specific physiological variables and demonstrate limited applicability in these patients. This study aimed to develop and validate a specialized risk prediction model for the development of AKI within 72 hours of hospitalization in trauma patients requiring surgical intervention.Methods A retrospective cohort study was conducted involving 157 consecutive trauma patients requiring surgical intervention at a level I trauma center (2020–2022). AKI was defined and staged according to the Kidney Disease Improving Global Outcomes criteria. Multivariable logistic regression analysis was used to identify independent predictors and develop a risk model, which was then internally validated using a prospective cohort of 53 additional trauma patients.Results Independent predictors of AKI included advanced age, decreased admission estimated glomerular filtration rate, higher Injury Severity Scores, and lower hemoglobin levels. While body mass index and diabetes mellitus were not independent predictors, their inclusion in the composite model improved predictive accuracy. The final model demonstrated reliable discriminative ability, with an area under the curve of 0.91. Prospective validation confirmed this performance, achieving a probability threshold of 0.91 with only two observed false negatives.Conclusions This novel trauma-specific risk calculator, using an additive weighting methodology, provides clinicians with a reliable tool for early AKI identification. By incorporating both patient characteristics and injury-related variables, the model may facilitate the timely initiation of renal-protective measures in high-risk trauma populations.Level of evidence Level II, prognostic/epidemiological.",
  "authors": [
    {
      "affiliations": [
        "Texas A&M University System, College Station, Texas, USA"
      ],
      "name": "Emad Ramadan"
    },
    {
      "affiliations": [
        "University of Texas McGovern Medical School, Houston, Texas, USA"
      ],
      "name": "Richard Cook"
    },
    {
      "affiliations": [
        "Baylor Scott & White Health, Dallas, Texas, USA"
      ],
      "name": "Raman Srinivasan"
    },
    {
      "affiliations": [
        "Texas A&M University System, College Station, Texas, USA"
      ],
      "name": "Alan Babuji"
    },
    {
      "affiliations": [
        "U.S. Anesthesia Partners (USAP), Dallas, Texas, USA"
      ],
      "name": "Deborah Latham"
    },
    {
      "affiliations": [
        "Baylor Scott & White Health, Dallas, Texas, USA",
        "U.S. Anesthesia Partners (USAP), Dallas, Texas, USA"
      ],
      "name": "Daniel Gunn"
    },
    {
      "affiliations": [
        "Baylor Scott & White Health, Dallas, Texas, USA"
      ],
      "name": "Michael Foreman"
    },
    {
      "affiliations": [
        "Texas A&M University System, College Station, Texas, USA",
        "Baylor Scott & White Health, Dallas, Texas, USA",
        "U.S. Anesthesia Partners (USAP), Dallas, Texas, USA"
      ],
      "name": "Saravanan Ramamoorthy"
    }
  ],
  "title": "Development of a risk calculator for acute kidney injury in trauma patients: an experimental study",
  "uid": "d7bd6cdc-b9fc-5556-b682-ffe870ac5ca3"
}