Scholarlink

Background <p>Urinary tract infection (UTI) is a common clinical infection that can lead to sepsis, infectious shock, and death when severe. Urine output is a critical indicator of renal function and hemodynamic status, and its trajectory may hold vital value for clinical outcomes in people with UTI.</p> Objective <p>To investigate the association between 24-hour urine output trajectories and 28-day mortality in people managing UTI.</p> Methods <p>The data of individuals living with UTI were identified from the MIMIC-IV database. Latent class trajectory modeling (LCTM) classified urine output trajectories during the first 24&#xa0;h post-ICU admission. The fitness of the model was evaluated using Bayesian Information Criterion (BIC), Akaike Information Criterion (AIC), Sample Adjusted Bayesian Information Criterion (SABIC), Entropy and Likelihood Ratio. Survival curves, Cox regression (adjusted for potential confounding factors: age, sex, race, marital status, vital signs, laboratory indicators, and all other available important clinical covariates), and restricted cubic splines (RCS) were used to analyze associations between trajectory groups and 28-day mortality (adjusted for all confounding factors). Effect modifications were explored by subgroup analyses.</p> Results <p>LCTM ultimately identified three types of urine output trajectories: Class 1 (5.57%): first decreased and then rebounded urine output; Class 2 (11.60%): continuous slow and stable increase in urine output, reflecting good renal perfusion and fluid balance; Class 3 (82.83%): continuous low urine output (0–1 mL/kg/h), with mortality rates of 92.6%, 94.8%, and 88.0%, respectively. Class 2 was significantly associated with reduced 28-day mortality risk compared to Class 3 group (HR = 0.479, 95%CI: 0.269–0.855, <i>P</i> = 0.013). Schoenfeld residual analysis confirmed that the model satisfied the proportional hazards assumption, and Bootstrap internal validation showed that the core risk ratio estimation was stable, which jointly supported the robustness of the main findings of this study. According to the stratified analysis, increased urine output during hours 12–24 post-admission correlated with lower mortality (HR &lt; 1, <i>P</i> &lt; 0.05). RCS revealed a nonlinear relationship between urine output during hours 6–12 and survival (<i>P</i>-non-linear = 0.0280).</p> Conclusion <p>Dynamic 24-hour urine output changes significantly correlate with 28-day mortality in those with UTI, which underscores the clinical importance of urine output monitoring for optimizing treatment.</p> Clinical trial number <p>Not applicable.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Impact of 24-hour urine output trajectories on 28-day mortality in people with urinary tract infection: based on the MIMIC-IV database

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月15日

查看全文
  • Huanlong Du,
  • Dongzhi Wang,
  • Jinhai Shao,
  • Jiabin Wu,
  • Feng Zhang,
  • Lingna Du,
  • Xiaolin Zhu,
  • Yefei Zhao

摘要

Background

Urinary tract infection (UTI) is a common clinical infection that can lead to sepsis, infectious shock, and death when severe. Urine output is a critical indicator of renal function and hemodynamic status, and its trajectory may hold vital value for clinical outcomes in people with UTI.

Objective

To investigate the association between 24-hour urine output trajectories and 28-day mortality in people managing UTI.

Methods

The data of individuals living with UTI were identified from the MIMIC-IV database. Latent class trajectory modeling (LCTM) classified urine output trajectories during the first 24 h post-ICU admission. The fitness of the model was evaluated using Bayesian Information Criterion (BIC), Akaike Information Criterion (AIC), Sample Adjusted Bayesian Information Criterion (SABIC), Entropy and Likelihood Ratio. Survival curves, Cox regression (adjusted for potential confounding factors: age, sex, race, marital status, vital signs, laboratory indicators, and all other available important clinical covariates), and restricted cubic splines (RCS) were used to analyze associations between trajectory groups and 28-day mortality (adjusted for all confounding factors). Effect modifications were explored by subgroup analyses.

Results

LCTM ultimately identified three types of urine output trajectories: Class 1 (5.57%): first decreased and then rebounded urine output; Class 2 (11.60%): continuous slow and stable increase in urine output, reflecting good renal perfusion and fluid balance; Class 3 (82.83%): continuous low urine output (0–1 mL/kg/h), with mortality rates of 92.6%, 94.8%, and 88.0%, respectively. Class 2 was significantly associated with reduced 28-day mortality risk compared to Class 3 group (HR = 0.479, 95%CI: 0.269–0.855, P = 0.013). Schoenfeld residual analysis confirmed that the model satisfied the proportional hazards assumption, and Bootstrap internal validation showed that the core risk ratio estimation was stable, which jointly supported the robustness of the main findings of this study. According to the stratified analysis, increased urine output during hours 12–24 post-admission correlated with lower mortality (HR < 1, P < 0.05). RCS revealed a nonlinear relationship between urine output during hours 6–12 and survival (P-non-linear = 0.0280).

Conclusion

Dynamic 24-hour urine output changes significantly correlate with 28-day mortality in those with UTI, which underscores the clinical importance of urine output monitoring for optimizing treatment.

Clinical trial number

Not applicable.