This retrospective cohort study utilized latent class trajectory modeling to identify distinct patterns of nursing intensity (NI) during the first week of an intensive care unit (ICU) stay and evaluate their links to patient outcomes.

Shifting focus away from traditional, static measurements taken at a single point in time, the researchers analyzed data from 7,334 adult patients admitted to the medical and surgical ICUs at Beth Israel Deaconess Medical Center between 2008 and 2019 who remained in the ICU for at least seven days. Daily NI was quantified using a composite score that tracked sedation management, monitoring frequency, and care complexity.

The modeling successfully uncovered four distinct trajectory classes that proved to be highly predictive of hospital mortality and ICU length of stay, entirely independent of baseline illness severity.

Class 1 (“Rapid Improvement”; 9.6% of patients) exhibited steeply declining NI scores alongside the lowest mortality rate (9.6%) and shortest median stay (8.7 days).

In stark contrast, Class 2 (“Late Escalation”; 4.9% of patients) featured sharply rising NI scores, capturing a population that suffered the highest mortality rate (39.3%) and the longest median stay (16.6 days).

The remaining two groups, Class 3 (“Moderate Stable”) and Class 4 (“Persistent High”), captured intermediate outcomes.

Ultimately, after adjusting for age, sex, and baseline SOFA scores, patients in the Rapid Improvement class had 74% lower odds of mortality, while those in the Late Escalation class faced 86% higher odds of mortality compared to the Persistent High cohort.

Bootstrap validation and sensitivity analyses confirmed the stability and robustness of these classes.

The study concludes that tracking the real-time ebb and flow of nursing workload provides a valuable, dynamic window into patient prognosis, though these hypothesis-generating findings require prospective validation before they can actively guide clinical practice.

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