ArticleBMC infectious diseases2026
Impact of 24-hour urine output trajectories on 28-day mortality in people with urinary tract infection: based on the MIMIC-IV database.
Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUrinary 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.
objectiveTo investigate the association between 24-hour urine output trajectories and 28-day mortality in people managing UTI.
methodsThe 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.
resultsLCTM 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).
conclusionDynamic 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.