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.

AIM: To analyze the existing literature on the effects of TRE with different eating durations on anthropometrics and cardiometabolic health markers in adults with excessive weight and obesity-related metabolic diseases.

METHODS: We reviewed a series of prominent scientific databases, including Medline, Scopus, Web of Science, Academic Search Complete, and Cochrane Library articles to identify published clinical trials on daily TRE in adults with excessive weight and obesity-related metabolic diseases. Randomized controlled trials were assessed for methodological rigor and risk of bias using version 2 of the Cochrane risk-of-bias tool for randomized trials (RoB-2). Outcomes of interest include body weight, waist circumference, fat mass, lean body mass, fasting glucose, insulin, HbA1c, homeostasis model assessment for insulin resistance (HOMA-IR), lipid profiles, C-reactive protein, blood pressure, and heart rate.

RESULTS: Fifteen studies were included in our systematic review. TRE significantly reduces body weight, waist circumference, fat mass, lean body mass, blood glucose, insulin, and triglyceride. However, no significant changes were observed in HbA1c, HOMA-IR, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, heart rate, systolic and diastolic blood pressure. Furthermore, subgroup analyses based on the duration of the eating window revealed significant variation in the effects of TRE intervention depending on the length of the eating window.

DESIGN: This was a single-center prospective observational study that compared resting energy expenditure estimated by 15 commonly used predictive equations against resting energy expenditure measured by indirect calorimetry at different phases. Degree of agreement between resting energy expenditure calculated by predictive equations and resting energy expenditure measured by indirect calorimetry was analyzed using intraclass correlation coefficient and Bland-Altman analyses. Resting energy expenditure values calculated from predictive equations differing by ± 10% from resting energy expenditure measured by indirect calorimetry was used to assess accuracy. A score ranking method was developed to determine the best predictive equations.

SETTING: General Intensive Care Unit, University of Malaya Medical Centre.

PATIENTS: Mechanically ventilated critically ill patients.

INTERVENTIONS: None.

MEASUREMENTS AND MAIN RESULTS: Indirect calorimetry was measured thrice during acute, late, and chronic phases among 305, 180, and 91 ICU patients, respectively. There were significant differences (F= 3.447; p = 0.034) in mean resting energy expenditure measured by indirect calorimetry among the three phases. Pairwise comparison showed mean resting energy expenditure measured by indirect calorimetry in late phase (1,878 ± 517 kcal) was significantly higher than during acute phase (1,765 ± 456 kcal) (p = 0.037). The predictive equations with the best agreement and accuracy for acute phase was Swinamer (1990), for late phase was Brandi (1999) and Swinamer (1990), and for chronic phase was Swinamer (1990). None of the resting energy expenditure calculated from predictive equations showed very good agreement or accuracy.

METHODS: Using indirect calorimetry, REE was measured at acute (≤5 days; n = 294) and late (≥6 days; n = 180) phases of intensive care unit admission. PEs were developed by multiple linear regression. A multi-fold cross-validation approach was used to validate the PEs. The best PEs were selected based on the highest coefficient of determination (R2), the lowest root mean square error (RMSE) and the lowest standard error of estimate (SEE). Two PEs developed from paired 168-patient data were compared with measured REE using mean absolute percentage difference.

RESULTS: Mean absolute percentage difference between predicted and measured REE was <20%, which is not clinically significant. Thus, a single PE was developed and validated from data of the larger sample size measured in the acute phase. The best PE for REE (kcal/day) was 891.6(Height) + 9.0(Weight) + 39.7(Minute Ventilation)-5.6(Age) - 354, with R2 = 0.442, RMSE = 348.3, SEE = 325.6 and mean absolute percentage difference with measured REE was: 15.1 ± 14.2% [acute], 15.0 ± 13.1% [late].

CONCLUSIONS: Separate PEs for acute and late phases may not be necessary. Thus, we have developed and validated a PE from acute phase data and demonstrated that it can provide optimal estimates of REE for patients in both acute and late phases.