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[Optimisation of nutritional screening tool CIPA: are two parameters of protein really necessary?]
Alejandro Mora Mendoza1, José Pablo Suárez Llanos, Irina Delgado Brito
1. pablua@hotmail.com.
Introduction:
the preliminary nutritional screening tool CIPA (food intake, proteins, anthropometry) is positive when it fulfills one of the following: control food intake (CI) 48-72 h < 50%, albumin < 3 g/dl, total protein < 5 g/dl, body mass index (BMI) < 18,5 kg/m2 or mid-upper arm circumference (MUAC) ≤ 22,5 cm. The use of two protein parameters increases costs and difficulty; one of them can be suppressed without affecting validity.
Objectives:
to evaluate the effectiveness of screening CIPA after exclusion of total protein.
Method:
prospective study of hospitalized patients; prevalence or risk of malnutrition was evaluated through CIPA and Subjective Global Assessment (SGA). Hospital malnutrition according to complete CIPA screening (with total proteins and albumin, [CIPAc] and without total proteins [CIPAw/p]) and concordance between both methods were analyzed, as well as the association of the positive screening result with clinical outcomes.
Results:
three hundred and forty-three patients were analyzed. The prevalence or risk of malnutrition identified by complete CIPA (c) was 38.19% (33.02-43.36); by CIPA without protein (w/p), 37.32% (32.17-42.46); and SGA was 29.15% (24.32-33.99). Kappa index: 0.981 between both CIPAs, p < 0.001. Both CIPA and SGA detect patients with higher mortality in hospital and one month after discharge. Early readmission was higher in positive CIPA, statistical significantly in CIPAw/p (screening with positive results 21.88% vs screening with negative results 13.49%, p = 0.044), SGA 20.01% vs 15.23%, p = 0.28. Length of stay was higher in patients with positive screening in CIPAc, CIPAw/p and SGA.
Conclusions:
CIPAw/p is equally or more effective than the previously validated full CIPA; therefore, it could replace the latter saving costs. The prevalence of malnutrition is high in both variants and they are able to predict which patient has worse clinical prognosis.
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