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A simplified nutrition screen for hospitalized patients using readily available laboratory and patient information
Linda Brugler1, Ana K Stankovic, Madeleine Schlefer
1St. Francis Hospital, Wilmington, Delaware, USA.
Insights
Identifying patients at risk for malnutrition-related complications (MRCs) is crucial. Admission diagnosis, serum albumin, hemoglobin, and lymphocyte count effectively predict MRC risk, enabling timely nutritional therapy.
Area of Science:
- Clinical Nutrition
- Patient Assessment
- Medical Diagnostics
Background:
- Malnutrition-related complications (MRCs) pose significant risks to patient recovery.
- Early identification of patients at risk for MRCs is essential for effective nutritional intervention.
- Current admission screening methods for MRC risk lack a uniform, proven standard.
Purpose of the Study:
- To identify the most effective admission screening information for predicting malnutrition-related complications (MRCs).
- To evaluate patient characteristics that best correlate with assigned MRC risk levels.
Main Methods:
- Evaluated 13 patient characteristics in 448 adults over 3 months.
- Utilized advanced statistical modeling to assess the correlation between patient factors and MRC risk levels.
- Stratified patients into four risk levels: no, mild, moderate, and high risk for MRCs.
Main Results:
- Key predictors of MRC risk included: wound occurrence, poor oral intake, malnutrition-related admission diagnosis, serum albumin, hemoglobin, and total lymphocyte count.
- A four-variable model (admission diagnosis, albumin, hemoglobin, lymphocyte count) demonstrated high accuracy, comparable to a six-variable model.
- These findings highlight specific, routinely available data points for effective risk assessment.
Conclusions:
- Accurate assessment of MRC risk at admission is vital for initiating prompt nutritional therapy.
- Developed models utilizing readily available admission data can be uniformly applied by hospitals for patient screening.
- Standardized screening improves the timely management of patients at risk for malnutrition-related complications.
Objective:
We assessed admission screening information that best identifies patients who are at risk for malnutrition-related complications (MRCs).
Methods:
We evaluated 13 patient characteristics associated with MRC for adults screened over a 3-mo period (n = 448) to determine which factors correlated best with the risk level assigned. The existing screen stratified patients into four levels defined as no risk, mild risk, moderate, and high risk for MRC. The analyzed variables were weight for height, wound, surgery/cancer therapy, fever, vomiting/diarrhea, poor oral intake, no oral intake, unplanned weight loss, malnutrition-related admission diagnosis, serum albumin, white blood cell count, hemoglobin, and total lymphocyte count. We modeled the relation between assigned MRC and the predictors by using state-of-the-art methods.
Results:
The characteristics that correlated best with MRC risk level assignment were occurrence of a wound, poor oral intake, malnutrition-related admission diagnosis, serum albumin value, hemoglobin value, and total lymphocyte count. A model using four variables (malnutrition-related admission diagnosis, serum albumin value, hemoglobin value, and total lymphocyte count) was almost as good as that using six predictors.
Conclusions:
The ability of admission information to accurately reflect MRC risk is crucial to early initiation of restorative medical nutritional therapy. There is currently no uniform or proved standard for identifying MRC risk within 24 h of acute care admission. The ideal nutritional screen correlates well with the occurrence of MRC and also uses data routinely obtained at admission. The models described can be uniformly used by hospitals to screen patients for MRC risk.
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