This study revealed that malnutrition in an ICU setting was significant. The study also found a strong association between malnutrition and clinical outcomes, highlighting the importance of addressing this issue. In particular, the prognostic relevance of NUTRIC, MUST, and SGA was deemed to be of paramount importance.

This study revealed that the magnitude of malnutrition at admission was 29%. The cumulative incidence of malnutrition during follow-up was mild to moderate (27%, 95% CI: 22 to 31) and severe (22%, 95% CI: 19 to 27) with SGA score, compared to 65% (95% CI: 61 to 70) with modified NUTRIC and 32% (95% CI: 27 to 35) with MUST which is consistent with a study done in Singapore among 439 ICU patients, where the prevalence of malnutrition with SGA screening tool was 28% mildly malnourished, moderately malnourished 25%, and severely malnourished 3% [20].A similar finding was observed in a study by Kang MC et al. among 300 hospitalized patients, where malnutrition was 22% with SGA screening tool, strongly associated with older age, admission for medical treatment, and underlying pulmonary and oncologic problems [21].

In contrast to this study, studies conducted in Egypt among 68 ICU patients by Zaki et al. (50%) [22], Albania among 963 elderly ICU patients by Shpata et al. (71.24), and Pakistan among 139 ICU patients by Arbab et al.(71.9) [23] revealed a higher prevalence of malnutrition. The variations could be explained by the differences in socioeconomic status, diversity among the people participating in the study, disparities in the methods used to assess nutrition, differences in admission practices, and variations in types of intensive care units, discrepancies in the size of the sample, and discrepancies in the way ICU care is provided.

A systematic review conducted by Lew et al. with 20 studies demonstrated that the prevalence of malnutrition varied from 38 to 78%, which was more prevalent in the elderly (29%), acute kidney injury (78%), liver transplantation (53%) and cardiac patients (12.5%) [11]. Similarly, another systematic review conducted by Correia et al., including 66 studies with 29,474 patients from 12 Latin American countries, reported a higher rate of malnutrition, 40–60% in ICU patients [2]. These variations may be related to the underlying clinical illness. For example, most patients with gastrointestinal diseases typically experience nausea, vomiting, malabsorption, and diarrhoea, which could exacerbate malnutrition. Similarly, socioeconomic status, frailty in the elderly, dietary restriction for some patients, pattern of patient admission, variability of nutritional screening/assessment tools, and sample size might contribute to the inconsistent figure across regions over the years.

There are different types of malnutrition Screening and Assessment tools available that have been validated in various healthcare settings and age groups, including SGA, NUTRIC, MUST, MST, MNA, NRI, SNAQ, and NRS2002 in ICU, where MUST and NRI are screening [2, 11, 16]. This study demonstrated that MUST were found to be highly predictive of malnutrition risk, AUC = 0.81(95% CI: 0.77 to 0.85) compared to NUTRIC, AUC = 0.59(95% CI: 0.55 to 0.65), and SGA, AUC = 0.51(95% CI: 0.45 to 0.56 which consistent with a study done in Jordan by Al-Kalaldeh et al. among 321 ICU patients to investigate the predictability of MUST and Phase Angle screening tools, where MUST was a reasonably reliable screening tool along with Phase Angle screening tool [24]. Another study conducted in the USA by Canales et al. among 312 Adult patients Admitted to ICU to compare the effectiveness of NUTRIC and NRS2002 nutritional status screening tools showed that NUTRIC was superior compared to NRS2002 on screening of Malnutrition in ICU patients [25].

Furthermore, a study conducted in South Korea by Jeong et al. among 482 septic patients admitted to a medical ICU to compare the predictability of NUTRIC and modified NUTRIC tools on 28 days mortality showed that the two tools were consistent as depicted by Area under the curve (AUC = 0.76, 95% CI = 0.718–0.806 VS AUC = 0.757, 95% CI = 0.713–0.801 and P = 0.45) respectively [26]. Overall, there are inconsistent reports in the literature as to which nutrition screening and assessment tools are effective and reliable so far, and recent evidence recommends combining tools for further nutritional assessment rather than relying on a single tool [2, 7].

This study showed that the cumulative incidence of mortality among patients admitted to ICU was 47.9%( 95% CI: 43.2 to 52.6), whereas the mortality was very high in the malnourished compared to the well-nourished group, 54.9%( 95% CI: 49.6 to 60.1) and 21.1%( 95% CI: 13.9 to 30.8) respectively which is consistent with a systemic review conducted by Lew et al. (30.3-69.6%) [11], and study in Egypt by Zaki et al. (55.9%) [22], which is hypothesized that malnutrition causes a loss in muscle mass, which results in decreased respiratory drive, respiratory muscle weakness, and ventilator reliance, as well as an impairment in immune response, which is linked to a high prevalence of nosocomial infections. However, studies done in Turkey by Atalay et al. among 119 elderly patients and a study from Singapore by Lew et al. among 440 patients failed to show a significant association between malnutrition and mortality [14, 27]. The possible reason for such a discrepancy might be differences in pattern of disease, disease severity, ICU setting, study population, and sample size.

It is mainly reported in the literature that malnutrition is associated with an increased length of hospital stay and decreased median survival time, and this study showed consistent findings. Kaplan-Meier survival curve demonstrated that the median survival time was 7.0 (95% CI, 6 to 8) days. In contrast, the median survival time for well-nourished and malnourished was ten days (95% CI: 8 to 11) and five days (95% CI: 4 to 6) respectively. However, a study from Singapore by Lew et al. among 440 patients failed to demonstrate a significant association between malnutrition and ICU length of stay, β=-0.015(95% CI: -2.25, 1.67, p = 0.771) [14], where this inconsistency might be attributable to variations in nutritional status assessment tool, ICU type and setting, admission pattern, severity of disease, and socioeconomic status.

This study identified some independent mortality hazards among ICU patients, including nutritional status at admission, comorbidity, APACHE II score, and higher scores of malnutrition screening tools. Death hazards in a patient with malnutrition increased by 40% compared to well-nourished patients (aHR = 1.4, 95% CI: 1.33 to 2.56), which is consistent with a study conducted in Singapore by Lew et al. (aHR = 1.33, 95% CI:1.05 to 1.69) [13]. Similarly, another study conducted by Fontes et al. in Brazil among 185 ICU patients showed significant odds of mortality in patients with malnutrition (AOR = 8.12, 95% CI: 2.94 to 22.42, P < 0.05) [28].

A meta-analysis including eight studies with 4076 participants examining the pooled effect of malnutrition on mortality using modified NUTRIC demonstrated a high risk of mortality in patients with malnutrition(aHR = 2.03, 95% CI: 1.488 to 2.788, P < 0.001) [29].

However, a study by Coporossi et al. recruiting 248 medical and surgical ICU patients to examine the effect of malnutrition on mortality with the thickness of Adductor Pollicis muscle didn’t show a significant difference(AOR = 2.00, 95% CI:0.5 to 7.6) [28]. These differences might not be replicable because there might be a huge difference in the nutritional assessment tools, study population, and ICU setting, and most importantly, these studies used different statistical models and effect estimates, where the estimated parameter would be overinflated in the case of the odds ratio. Furthermore, this study revealed that APACHE II > 25 (aHR = 4.1, 95% CI: 2.51 to 6.67), higher NUTRIC score (aHR = 2.7, 95% CI: 1.67 to 4.49), being diabetic (aHR = 4.2, 95% CI: 2.12 to 8.28), and being asthmatic (aHR = 4.2, 95% CI: 2.25 to 7.9) were strong risks of 30-day mortality. Similarly, patients with a high risk of malnutrition at admission with MUST and SGA had a high risk of mortality (aHR = 2.2, 95% CI: 1.34 to 3.46 and aHR = 1.8, 95% CI: 1.19 to 2.70) respectively, which is consistent with a study conducted in an Albanian ICU where APACHE II > 15(AOR = 2.77, 95% CI = 1.69 to 4.57), and malnutrition (AOR = 2.68, 95% CI:1.74 to 4.18) were strongly associated with mortality [30]. However, the mortality magnitude might differ as it was determined with different statistical models and effect estimates.

This study is the first-ever prospective cohort study investigation of the effects of malnutrition on clinical outcomes of ICU patients with validated screening and assessment nutrition tools. To our knowledge, these screening and assessment tools were not validated in Sub-Saharan Africa in ICU settings. However, this study has limitations. Firstly, this study included heterogeneous participants concerning Sociodemographic characteristics, diagnosis, and comorbidities. Secondly, this was a single-centre study with limited biochemical tests to integrate with nutritional screening tools.

The burden of malnutrition is very high in low and middle-income countries, particularly in critically ill patients. However, nutritional risk screening and assessment is separate from clinical practice in most of this country, including in this study area; even the knowledge and practice of clinicians of these tools is minimal. Hence, the stakeholders should incorporate a protocol for screening, assessment and timing for nutrition supplementation for all patients admitted to the intensive care units.

This was a prospective cohort study with a relatively large sample size and follow-up time, but further observational multicenter studies with a homogenous population in age, diagnosis, and comorbidities are recommended as there was significant heterogeneity in Sociodemographic characteristics, admission pattern, and comorbidity in this study.