ABSTRACT
BACKGROUND: Malnutrition is a significant concern reported in adult critically ill patients, yet there is no gold standard to assess nutritional status in this population. This study examines the association between nutritional status and clinical outcomes in intensive care unit (ICU) patients using nutritional risk assessment tools and aims to look for the best tool. METHOD: In a single-center prospective cohort study among 165 patients, the predictive performance of high or low malnutrition risk assessed by Nutritional Risk Screening (NRS), Modified Nutrition Risk in Critically Ill (m-NUTRIC), Mini-Nutritional-Assessment Short-Form (MNA-SF), Controlling Nutritional status (CONUT), and Prognostic Nutritional Index (PNI) were evaluated and compared for mortality, organ failure, length of hospitalization, and mechanical ventilation (MV). RESULTS: Different assessment tools showed various nutritional statuses. m-NUTRIC and NRS-2002 were found to be associated more strongly relative to other tools with mortality (RR = 1.72; 95% CI, 1.42-2.08) and (RR = 1.37; 95% CI, 1.08-1.72), organ failure (RR = 1.69; 95% CI, 1.44-1.96) and (RR = 1.22; 95% CI, 0.99-1.48), MV (RR = 1.46; 95% CI, 1.27-1.65) and (RR = 1.21; 95% CI, 1.04-1.39) respectively. There was no correlation between malnutrition levels assessed by mentioned tools except for NRS-2002 and length of hospitalization. In predicting mortality or illness severity, the cut points were different for some tools like NUTRIC-score and all assessed outcomes (3.5), MNA-SF and mortality (6.5), CONUT with mortality, and MV (6.5). CONCLUSIONS: A considerable proportion of patients admitted to the ICU are at high risk for malnutrition. Compared to other tools, m-NUTRIC and NRS-2002 proved superior in predicting clinical outcomes in critically ill patients. Other tools overestimated the risk of malnutrition in the ICU so couldn't predict clinical outcomes correctly.
ABSTRACT
BACKGROUND: Breast cancer (BC) was the fifth cause of mortality worldwide in 2015 and second cause of mortality in Iran in 2012. This study aimed to explore factors associated with survival of patients with BC using parametric survival models. MATERIALS AND METHODS: Data of 1154 patients that diagnosed with BC recorded in the East Azerbaijan population-based cancer registry database between March 2007 and March 2016. The parametric survival model with an accelerated failure time (AFT) approach was used to assess the association between sex, age, grade, and morphology with time to death. RESULTS: A total of 217 (18.8%) individuals experienced death due to BC by the end of the study. Among the fitted parametric survival models including exponential, Weibull, log logistic, and log-normal models, the log-normal model was the best model with the Akaike information criterion = 1441.47 and Bayesian information criterion = 1486.93 where patients with higher ages (time ratio [TR] =0.693; 95% confidence interval [CI] = [0.531, 0.904]) and higher grades (TR = 0.350; 95% CI = [0.201, 0.608]) had significantly lower survival while the lobular carcinoma type of morphology (TR = 1.975; 95% CI = [1.049, 3.720]) had significantly higher survival. CONCLUSION: Log-normal model showed to be an optimal tool to model the survival of patients with BC in the current study. Age, grade, and morphology showed significant association with time to death in patients with BC using AFT model. This finding could be recommended for planning and health policymaking in patients with BC. However, the impact of the models used for analysis on the significance and magnitude of estimated effects should be acknowledged.
