Risk Factors and Development of a Predictive Model for In-Hospital Mortality in Hemodynamically Stable Older Adults with Urinary Tract Infection

Abstract

Background and Objectives: Urinary tract infections (UTIs) are a major cause of emergency department (ED) visits and hospital admissions among older adults. Although most seniors present hemodynamically stable, a sizeable fraction deteriorate during hospitalization, and no ED-specific tool exists to identify those at greatest risk. We sought to determine risk factors for in-hospital mortality in this population and to develop a predictive model. Materials and Methods: We analyzed the MIMIC-IV-ED database (2011-2019) and enrolled culture-confirmed UTI patients aged ≥ 65 years who were hemodynamically stable-defined as a systolic blood pressure ≥ 100 mm Hg without vasopressor support. Demographics, comorbidities, triage vital signs, and initial laboratory tests were extracted. Least Absolute Shrinkage and Selection Operator (LASSO) regression with 10-fold cross-validation was performed for variable selection. Discrimination was quantified with the C-statistic, calibration with the Hosmer-Lemeshow test, and clinical utility with decision curve analysis. Internal validation was assessed via 1000-sample bootstrap resampling. Results: Among 1571 eligible encounters (median age 79 years, 33% male), in-hospital mortality was 4.5%. LASSO selected eight variables; six remained significant in multivariable analysis: age, systolic blood pressure, oxygen saturation, white blood cell count, red cell distribution width, and blood urea nitrogen. The predictive nomogram demonstrated a C-statistic of 0.73 (95% CI 0.66-0.79) and outperformed traditional early warning scores. Conclusions: A six-variable nomogram may stratify mortality risk in hemodynamically stable older adults with UTI. Because the model was developed in a single U.S. tertiary-care ED, it remains hypothesis-generating until validated in external, multicenter cohorts to confirm generalizability.

Keywords: emergency department; older patient; predictive model; urinary tract infection.

Figure 1

Flow diagram of patient selection. ED: emergency department; UTI: urinary tract infection; ICU: intensive care unit.

Figure 2

Predictive nomogram for calculating in-hospital mortality risk. Individual predictor values are converted to points via the upper scoring scale, totaled together, and subsequently mapped to the lower probability scale to determine mortality likelihood.

Figure 3

Decision curve analysis. The graph features the predictive model as the blue curve. The y-axis denotes net benefit, and the x-axis represents the threshold probability. The red and green lines indicate the clinical benefits for universal treatment and no treatment scenarios, respectively. The net clinical benefit provided by algorithms is shown by the vertical distance between the curves at different risk thresholds.

Figure 4

Receiver operating characteristic curves of the predictive model for in-hospital mortality compared with NEWS and MEWS. NEWS: national early warning score; MEWS: modified early warning score.