METHODS: A cross-sectional study was conducted among 134 geriatric patients with a mean age of 68.9 ± 8.4 who stayed at acute care wards in Hospital Tuanku Ampuan Rahimah, Klang from July 2017 to August 2017. The SGA, MNA, and GNRI were administered through face-to-face interviews with all the participants who gave their consent. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the GNRI and MNA were analyzed against the SGA. Receiver-operating characteristic (ROC) curve analysis was used to obtain the area under the curve (AUC) and suitable optimal cutoff values for both the GNRI and MNA.
RESULTS: According to the SGA, MNA, and GNRI, 26.9%, 42.5%, and 44.0% of the participants were malnourished, respectively. The sensitivity, specificity, PPV, and NPV for the GNRI were 0.622, 0.977, 0.982, and 0.558, respectively, while those for the MNA were 0.611, 0.909, 0.932, and 0.533, respectively. The AUC of the GNRI was comparable to that of the MNA (0.831 and 0.898, respectively). Moreover, the optimal malnutrition cutoff value for the GNRI was 94.95.
CONCLUSIONS: The prevalence of malnutrition remains high among hospitalized elderly patients. Validity of the GNRI is comparable to that of the MNA, and use of the GNRI to assess the nutritional status of this group is proposed with the new suggested cutoff value (GNRI ≤ 94.95), as it is simpler and more efficient. Underdiagnosis of malnutrition can be prevented, possibly reducing the prevalence of malnourished hospitalized elderly patients and improving the quality of the nutritional care process practiced in Malaysia.
OBJECTIVE: To localize and quantify geometric morphometric differences in facial soft tissue morphology in adults with and without OSA.
MATERIALS AND METHODS: Eighty adult Malays, consisting of 40 patients with OSA and 40 non-OSA controls, were studied. Both groups were evaluated by the attending physician and through ambulatory sleep studies. 3-D stereophotogrammetry was used to capture facial soft tissues of both groups. The 3-D mean OSA and control facial configurations were computed and subjected to principal components analysis (PCA) and finite-element morphometry (FEM).
RESULTS: The body mass index was significantly greater for the OSA group (32.3 kg/m(2) compared to 24.8 kg/m(2), p < 0.001). The neck circumference was greater for the OSA group (42.7 cm compared to 37.1 cm, p < 0.001). Using PCA, significant differences were found in facial shape between the two groups using the first two principal components, which accounted for 50% of the total shape change (p < 0.05). Using FEM, these differences were localized in the bucco-submandibular regions of the face predominantly, indicating an increase in volume of 7-22% (p < 0.05) for the OSA group.
CONCLUSION: Craniofacial obesity in the bucco-submandibular regions is associated with OSA and may provide valuable screening information for the identification of patients with undiagnosed OSA.