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fondamenti di nutrizione umana

Fondamenti di nutrizione umana (III Edizione) a cura di Lorenzo M. Donini, Anna Maria Giusti, Alessandro Pinto, Valeria del Balzo.

Nutrizione artificiale
Predicting the outcome of artificial nutrition by clinical and functional indices
(in stampa su Nutrition)

Lorenzo M. Donini, Claudia Savina**, Laura Maria Ricciardi, Cecilia Coletti**, Maddalena Paolini, Luciano Scavone, Maria Rosaria De Felice**, Alessandro Laviano*, Filippo Rossi Fanelli*, Carlo Cannella


Department of Medical Physiopathology (Food Science Section) and *Department of Clinical Medicine – “Sapienza” University of Rome (Italy)
** Rehabilitation Clinical Institute “Villa delle Querce” - Nemi (Rome – Italy)


Artificial nutrition (AN) is now considered as medical therapy and has progressively become one of the mainstays of the different therapeutic options available for either home or hospitalised patients, including surgical, medical and critically ill patients. 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 is to identify those indices, either clinical, functional or nutritional, which may reliably predict, before the start of AN, those patients who are likely not to benefit from the nutritional support

Methods
312 clinical charts of patients receiving AN in the period comprised between January 1999 and September 2006, were retrospectively examined.
Data registered before AN start were collected and analysed: general data (age, sex), clinical conditions (comorbidity, Quality of Life, frailty), anthropometric and biochemical indices, type of AN treatment (TEN, TPN. mixed AN) and outcome of the treatment..

Results
The percentage of negative outcomes (death or interrumption of AN for worsening of clinical conditions occurred within 10 days after start of AN) was meaningfully higher in subjects with: age>80 years, reduced social functions, higher comorbidity or/and frailty, reduced level of albumin, prealbumin, lymphocytes count and cholinesterase and higher level of CRP.
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 shows that the patient’s general status (i.e. comorbidity, social quality of life and frailty), nutritional and inflammatory status (i.e. lymphocyte count, albumin, prealbumin, CRP), have a good predictive value on artificial nutrition effectiveness.

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