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. 2025 Jun 30:7:1603314.
doi: 10.3389/fdgth.2025.1603314. eCollection 2025.

Applying a logistic regression-clustering joint model to analyze the causes of prolonged pre-analytic turnaround time for urine culture testing in hospital wards

Shuangshuang Lv  1 , Huan Ye  2 , Yuan Li  1 , Jian Zhang  1
Affiliations

Applying a logistic regression-clustering joint model to analyze the causes of prolonged pre-analytic turnaround time for urine culture testing in hospital wards

Shuangshuang Lv et al. Front Digit Health. .

Abstract

Introduction: In this study, we developed and validated a logistic regression-clustering joint model to: (1) quantify multistage workflow bottlenecks (collection/transport/reception) in urine culture pre-TAT prolongation (>115 min); and (2) assess the efficacy of targeted interventions derived from model-derived insights.

Methods: Using complete workflow data obtained from 1,343 urine culture specimens (January 2024-March 2024) collected at a tertiary hospital, we integrated binary logistic regression analysis with K-means clustering to quantify delay patterns. The analyzed variables included collection time, ward type, personnel roles, and patient demographics. Post-intervention data (May 2024-July 2024, *n* = 1,456) was also analyzed to assess the impact.

Results: Analysis of the critical risk factors revealed that specimens collected between 04:00-05:59/10:00-11:59 had 142.92-fold higher delay odds (95% CI: 58.81-347.37). Those collected on SICU/ICU wards showed 9.98-fold higher risk (95% CI: 5.05-19.72) than general wards. Regarding intervention efficacy, pre-TAT overtime rates decreased by 58.6% (13.48% → 7.55%, P < 0.01). Contamination rate decreased by 59.8% (5.67% → 2.28%, P < 0.01). The median pre-TAT decreased by 15.9% (44 → 37 min, P < 0.01).

Discussion: The joint model effectively identified workflow bottlenecks. Targeted interventions (dynamic transport scheduling, standardized training, and IoT alert systems) significantly optimized pre-TAT and specimen quality, providing a framework for improving clinical laboratory processes.

Keywords: logistic regression model; medical quality control; pre-analytical turnaround time (pre-TAT); process optimization; urine microbial culture.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flow chart of urine culture samples obtained from the ward.
Figure 2
Figure 2
(A) Scatter plot of each ward according to the pre-test overtime rate. (B) Scatter plot for each collection time window by the pre-test overtime rate.
Figure 3
Figure 3
(A) Scatter plot of SICU collected nurses by pre-test overtime rate. (B) Scatter plot of SICU collection time by the pre-test overtime rate.
Figure 4
Figure 4
(A) Scatter plot of ICU collected nurses by pre-test overtime rate. (B) Scatter plot of ICU collection time by the pre-test overtime rate.
See this image and copyright information in PMC

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