17
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Nutritional Risk Screening and Assessment

      review-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Malnutrition is an independent risk factor that negatively influences patients’ clinical outcomes, quality of life, body function, and autonomy. Early identification of patients at risk of malnutrition or who are malnourished is crucial in order to start a timely and adequate nutritional support. Nutritional risk screening, a simple and rapid first-line tool to detect patients at risk of malnutrition, should be performed systematically in patients at hospital admission. Patients with nutritional risk should subsequently undergo a more detailed nutritional assessment to identify and quantify specific nutritional problems. Such an assessment includes subjective and objective parameters such as medical history, current and past dietary intake (including energy and protein balance), physical examination and anthropometric measurements, functional and mental assessment, quality of life, medications, and laboratory values. Nutritional care plans should be developed in a multidisciplinary approach, and implemented to maintain and improve patients’ nutritional condition. Standardized nutritional management including systematic risk screening and assessment may also contribute to reduced healthcare costs. Adequate and timely implementation of nutritional support has been linked with favorable outcomes such as a decrease in length of hospital stay, reduced mortality, and reductions in the rate of severe complications, as well as improvements in quality of life and functional status. The aim of this review article is to provide a comprehensive overview of nutritional screening and assessment methods that can contribute to an effective and well-structured nutritional management (process cascade) of hospitalized patients.

          Related collections

          Most cited references52

          • Record: found
          • Abstract: found
          • Article: not found

          Physical performance measures in the clinical setting.

          To assess the ability of gait speed alone and a three-item lower extremity performance battery to predict 12-month rates of hospitalization, decline in health, and decline in function in primary care settings serving older adults. Prospective cohort study. Primary care programs of a Medicare health maintenance organization (HMO) and Veterans Affairs (VA) system. Four hundred eighty-seven persons aged 65 and older. Lower extremity performance Established Population for Epidemiologic Studies of the Elderly (EPESE) battery including gait speed, chair stands, and tandem balance tests; demographics; health care use; health status; functional status; probability of repeated admission scale (Pra); and primary physician's hospitalization risk estimate. Veterans had poorer health and higher use than HMO members. Gait speed alone and the EPESE battery predicted hospitalization; 41% (21/51) of slow walkers (gait speed 1.0 m/s) (P <.0001). The relationship was stronger in the HMO than in the VA. Both performance measures remained independent predictors after accounting for Pra. The EPESE battery was superior to gait speed when both Pra and primary physician's risk estimate were included. Both performance measures predicted decline in function and health status in both health systems. Performance measures, alone or in combination with self-report measures, were more able to predict outcomes than self-report alone. Gait speed and a physical performance battery are brief, quantitative estimates of future risk for hospitalization and decline in health and function in clinical populations of older adults. Physical performance measures might serve as easily accessible "vital signs" to screen older adults in clinical settings.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Hand grip strength: outcome predictor and marker of nutritional status.

            Among all muscle function tests, measurement of hand grip strength has gained attention as a simple, non-invasive marker of muscle strength of upper extremities, well suitable for clinical use. This review outlines the prognostic relevance of grip strength in various clinical and epidemiologic settings and investigates its suitability as marker of nutritional status in cross-sectional as well as intervention studies. Studies investigating grip strength as prognostic marker or nutritional parameter in cross-sectional or intervention studies were summarized. Numerous clinical and epidemiological studies have shown the predictive potential of hand grip strength regarding short and long-term mortality and morbidity. In patients, impaired grip strength is an indicator of increased postoperative complications, increased length of hospitalization, higher rehospitalisation rate and decreased physical status. In elderly in particular, loss of grip strength implies loss of independence. Epidemiological studies have moreover demonstrated that low grip strength in healthy adults predicts increased risk of functional limitations and disability in higher age as well as all-cause mortality. As muscle function reacts early to nutritional deprivation, hand grip strength has also become a popular marker of nutritional status and is increasingly being employed as outcome variable in nutritional intervention studies. Copyright © 2010 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Validity of the Remote Food Photography Method (RFPM) for estimating energy and nutrient intake in near real-time.

              Two studies are reported; a pilot study to demonstrate feasibility followed by a larger validity study. Study 1's objective was to test the effect of two ecological momentary assessment (EMA) approaches that varied in intensity on the validity/accuracy of estimating energy intake (EI) with the Remote Food Photography Method (RFPM) over 6 days in free-living conditions. When using the RFPM, Smartphones are used to capture images of food selection and plate waste and to send the images to a server for food intake estimation. Consistent with EMA, prompts are sent to the Smartphones reminding participants to capture food images. During Study 1, EI estimated with the RFPM and the gold standard, doubly labeled water (DLW), were compared. Participants were assigned to receive Standard EMA Prompts (n = 24) or Customized Prompts (n = 16) (the latter received more reminders delivered at personalized meal times). The RFPM differed significantly from DLW at estimating EI when Standard (mean ± s.d. = -895 ± 770 kcal/day, P < 0.0001), but not Customized Prompts (-270 ± 748 kcal/day, P = 0.22) were used. Error (EI from the RFPM minus that from DLW) was significantly smaller with Customized vs. Standard Prompts. The objectives of Study 2 included testing the RFPM's ability to accurately estimate EI in free-living adults (N = 50) over 6 days, and energy and nutrient intake in laboratory-based meals. The RFPM did not differ significantly from DLW at estimating free-living EI (-152 ± 694 kcal/day, P = 0.16). During laboratory-based meals, estimating energy and macronutrient intake with the RFPM did not differ significantly compared to directly weighed intake.
                Bookmark

                Author and article information

                Journal
                J Clin Med
                J Clin Med
                jcm
                Journal of Clinical Medicine
                MDPI
                2077-0383
                20 July 2019
                July 2019
                : 8
                : 7
                : 1065
                Affiliations
                [1 ]Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Bern University Hospital, and University of Bern, Freiburgstrasse 15, 3010 Bern, Switzerland
                [2 ]The New York Academy of Sciences, 250 Greenwich Sweet, 40th floor, New York, NY 10007, USA
                [3 ]Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland
                [4 ]Medical University Department, Division of General Internal and Emergency Medicine, Kantonsspital Aarau, Tellstrasse 25, 5000 Aarau, Switzerland
                [5 ]Department for Clinical Research, Medical Faculty, University of Basel, 4001 Basel, Switzerland
                Author notes
                [* ]Correspondence: emilie.reber@ 123456insel.ch
                [†]

                Contributed equally to this manuscript.

                Author information
                https://orcid.org/0000-0001-8799-2708
                https://orcid.org/0000-0003-1702-1433
                Article
                jcm-08-01065
                10.3390/jcm8071065
                6679209
                31330781
                a2434b07-5b16-485f-8b35-ac70f52d1167
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 30 May 2019
                : 09 July 2019
                Categories
                Review

                nutritional risk screening,nutritional assessment,malnutrition

                Comments

                Comment on this article