Evidence map›Paper›PMID 42404736›Full record

ArticleInternational journal of nursing studies advances2026

Nursing intensity trajectory patterns and clinical outcomes in intensive care units: a latent class analysis.

Qinqin Shu, Yan Li

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Article in International journal of nursing studies advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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2 authors.

Qinqin ShuDepartment of Emergency Medicine, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, 1279 Sanmen Road, Hongkou District, Shanghai, 200434, China.
Yan LiDepartment of Emergency Medicine, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, 1279 Sanmen Road, Hongkou District, Shanghai, 200434, China.

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6 · The paper itself

Abstract

Background: Temporal patterns of nursing intensity during ICU stay may identify clinically meaningful patient subgroups, yet existing studies rely primarily on static measurements at single time points. Objectives: To identify distinct nursing intensity trajectory patterns during the first 7 ICU days and examine their associations with hospital mortality and ICU length of stay, independent of baseline illness severity. Design: Retrospective cohort study using latent class trajectory modeling. This study was not prospectively registered as it involved retrospective analysis of an existing database. Setting: Medical and surgical intensive care units at Beth Israel Deaconess Medical Center (2008-2019). Participants: 7,334 adult ICU patients (≥18 years) with ICU length of stay ≥7 days. Methods: Nursing intensity was measured daily using a composite score incorporating three dimensions: sedation management (RASS scores), monitoring frequency (nursing charting counts), and care complexity (organ support requirements). Latent class trajectory modeling identified distinct patterns over ICU Days 1-7. Multivariable logistic regression examined associations with hospital mortality, adjusting for age, sex, and SOFA score. Results: Four distinct trajectory classes were identified (entropy = 0.948): Class 1 'Rapid Improvement' (n = 702, 9.6%) showed steeply declining NI from 56.2 to 29.8, with the lowest mortality (9.6%) and shortest ICU stay (median 8.7 days). Class 2 'Late Escalation' (n = 360, 4.9%) showed rising NI from 38.5 to 61.7, with the highest mortality (39.3%) and longest stay (median 16.6 days). Class 3 'Moderate Stable' (n = 1,798, 24.5%) and Class 4 'Persistent High' (n = 4,474, 61.0%) showed intermediate outcomes. After adjusting for age, sex, and SOFA, Rapid Improvement was associated with 74% lower mortality odds (OR = 0.26, 95% CI: 0.18-0.36, p < 0.001) and Late Escalation with 86% higher odds (OR = 1.86, 95% CI: 1.45-2.39, p < 0.001) compared to Persistent High. Sensitivity analysis excluding medical intervention variables confirmed trajectory robustness (Adjusted Rand Index = 0.565). Bootstrap validation (500 resamples) confirmed class stability. Conclusions: Four distinct nursing intensity trajectory patterns were identified, each characterized by different clinical outcome profiles. Declining NI trajectories characterized patients with lower mortality and shorter ICU stays, while escalating trajectories were observed in patients with worse outcomes, independent of baseline illness severity. These observational findings are hypothesis-generating and require prospective validation before informing clinical practice.

Indexed as

Intensive careLatent class analysisNursing intensityPatient outcomesSOFA scoreTrajectory analysis

Identifiers

PMID42404736
PMCPMC13332457

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