ArticlePLOS global public health2026
Performance of Nursing Activities Score in intensive care units of a teaching hospital in a low-middle-income country: An observational study.
Article in PLOS global public health, 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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Abstract
Nursing workload in intensive care units (ICUs) is a key determinant of patient outcomes, quality of care, and efficient resource utilization. The Nursing Activities Score (NAS) is a validated instrument for quantifying nursing workload; however, evidence regarding its use in low- and middle-income countries remains limited. This observational study assessed nursing workload using the NAS and evaluated its ability to predict patient outcomes among 501 adult patients admitted to the ICU of a teaching hospital between 7 March 2022 and 27 February 2023. NAS was recorded for each patient, and outcomes were classified as survivors and non-survivors. The predictive performance of NAS was evaluated using receiver operating characteristic (ROC) curve analysis, model calibration was assessed with the Hosmer-Lemeshow goodness-of-fit test, and logistic regression was used to examine the association between NAS and mortality. Differences in NAS scores between survivors and non-survivors were compared using the Mann-Whitney U test. The median NAS was 83.40 (IQR: 68.30-101.10; range: 39.2-134.4), indicating a high nursing workload. NAS demonstrated good discriminative ability for predicting patient outcomes (AUROC = 0.838, p < 0.001), with an optimal cut-off value of 90.40, yielding 73.5% sensitivity and 73.1% specificity. The model showed good calibration (Hosmer-Lemeshow p = 0.422). Higher NAS scores were significantly associated with mortality (Exp(B) = 0.937, p < 0.001), and non-survivors had significantly higher NAS scores than survivors (110.70 vs. 76.20, p < 0.001). These findings demonstrate that the NAS is a reliable tool for measuring ICU nursing workload and predicting patient outcomes, supporting its use to optimize nurse staffing, workload allocation, and critical care resource planning in resource-limited settings.
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