Risk of malnutrition (over and under-nutrition): validation of the JaNuS screening tool.

BACKGROUND & AIMS: Malnutrition (over and under-nutrition) is highly prevalent in patients admitted to hospital and it is a well-known risk factor for increased morbidity and mortality. Nutritional problems are often misdiagnosed, and especially the coexistence of over and undernutrition is not usually recognized. We aimed to develop and validate a screening tool for the easy detection and reporting of both undernutrition and overnutrition, specifically identifying the clinical conditions where the two types of malnutrition coexist. METHODS: The study consisted of three phases: 1) selection of an appropriate study population (estimation sample) and of the hospital admission parameters to identify overnutrition and undernutrition; 2) combination of selected variables to create a screening tool to assess the nutritional risk in case of undernutrition, overnutrition, or the copresence of both the conditions, to be used by non-specialist health care professionals; 3) validation of the screening tool in a different patient sample (validation sample). RESULTS: Two groups of variables (12 for undernutrition, 7 for overnutrition) were identified in separate logistic models for their correlation with the outcome variables. Both models showed high efficacy, sensitivity and specificity (overnutrition, 97.7%, 99.6%, 66.6%, respectively; undernutrition, 84.4%, 83.6%, 84.8%). The logistic models were used to construct a two-faced test (named JaNuS - Just A Nutritional Screening) fitting into a two-dimension Cartesian coordinate graphic system. In the validation sample the JaNuS test confirmed its predictive value. Internal consistency and test-retest analysis provide evidence for the reliability of the test. CONCLUSION: The study provides a screening tool for the assessment of the nutritional risk, based on parameters easy-to-use by health care personnel lacking nutritional competence and characterized by excellent predictive validity. The test might be confidently applied in the clinical setting to determine the importance of malnutrition (including the copresence of over and undernutrition) as a risk factor for morbidity and mortality.

Predicting the outcome of artificial nutrition by clinical and functional indices.

OBJECTIVE: Artificial nutrition (AN) is now considered medical therapy and has progressively become one of the mainstays of the different therapeutic options available for home or hospitalized patients, including surgical, medical, and critically ill patients. The clinical relevance of any therapy is based on its efficacy and effectiveness and thus on the improvement of its cost efficiency, i.e., the ability to provide benefits to the patients with minimal wasting of human and financial resources. The aim of the present study was to identify those indices, clinical, functional, or nutritional, that may reliably predict, before the start of AN, those patients who are likely not to benefit from nutritional support. METHODS: Three hundred twelve clinical charts of patients receiving AN between January 1999 and September 2006 were retrospectively examined. Data registered before starting AN were collected and analyzed: general data (age, sex), clinical conditions (comorbidity, quality of life, frailty), anthropometric and biochemical indices, type of AN treatment (total enteral nutrition, total parenteral nutrition, mixed AN), and outcome of treatment. RESULTS: The percentage of negative outcomes (death or interruption of AN due to worsening clinical conditions within 10 d after starting AN) was meaningfully higher in subjects >80 y of age and with reduced social functions, higher comorbidity and/or frailty, reduced level of albumin, prealbumin, lymphocyte count, and cholinesterase and a higher level of C-reactive protein. The multivariate analysis showed that prealbumin and comorbidity were the best predictors of AN outcome. The logistic regression model with these variables showed a predictive value equal to 84.2%. CONCLUSION: Proper prognostic instruments are necessary to perform optimal evaluations. The present study showed that a patient's general status (i.e., comorbidity, social quality of life, frailty) and nutritional and inflammatory statuses (i.e., lymphocyte count, albumin, prealbumin, C-reactive protein) have good predictive value on the effectiveness of AN.