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The hosts' susceptibility to infection depends on several factors. The integrity of the skin and mucous membranes helps protect the body against microbial attacks. When the skin is altered, the chance of infection, limb loss, and even death increases.
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Risk Factors and Development of a Predictive Model for In-Hospital Mortality in Hemodynamically Stable Older Adults

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Area of Science:

  • Geriatrics
  • Infectious Diseases
  • Emergency Medicine

Background:

  • Urinary tract infections (UTIs) are a common reason for emergency department (ED) visits and hospitalizations in older adults.
  • A significant number of elderly patients with UTIs deteriorate during hospitalization, lacking specific ED tools for risk stratification.
  • Identifying patients at high risk for in-hospital mortality is crucial for timely intervention.

Purpose of the Study:

  • To identify risk factors for in-hospital mortality in hemodynamically stable older adults presenting with UTIs.
  • To develop and validate a predictive model for in-hospital mortality in this patient population.

Main Methods:

  • Analysis of the MIMIC-IV-ED database (2011-2019) for culture-confirmed UTI patients aged ≥ 65 years, hemodynamically stable.
  • Utilized Least Absolute Shrinkage and Selection Operator (LASSO) regression for variable selection.
  • Assessed model performance using C-statistic, Hosmer-Lemeshow test, decision curve analysis, and bootstrap resampling.

Main Results:

  • The study included 1571 eligible encounters, with a 4.5% in-hospital mortality rate.
  • Six significant predictors of mortality were identified: age, systolic blood pressure, oxygen saturation, white blood cell count, red cell distribution width, and blood urea nitrogen.
  • The developed nomogram achieved a C-statistic of 0.73, outperforming existing early warning scores.

Conclusions:

  • A six-variable nomogram can effectively stratify mortality risk in hemodynamically stable older adults with UTIs.
  • The model shows promise for improving risk assessment in the emergency department setting.
  • External validation in multicenter cohorts is necessary to confirm the generalizability of these findings.