A single-center prospective observational study comparing resting energy expenditure in different phases of critical illness: indirect calorimetry versus predictive equations

Objectives: Several predictive equations have been developed for estimation of resting energy expenditure, but no study has been done to compare predictive equations against indirect calorimetry among critically ill patients at different phases of critical illness. This study aimed to determine the degree of agreement and accuracy of predictive equations among ICU patients during acute phase (≤ 5 d), late phase (6–10 d), and chronic phase (≥ 11 d). 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. Conclusions: Predictive equations tend to either over- or underestimate resting energy expenditure at different phases. Predictive equations with “dynamic” variables and respiratory data had better agreement with resting energy expenditure measured by indirect calorimetry compared with predictive equations developed for healthy adults or predictive equations based on “static” variables. Although none of the resting energy expenditure calculated from predictive equations had very good agreement, Swinamer (1990) appears to provide relatively good agreement across three phases and could be used to predict resting energy expenditure when indirect calorimetry is not available

Do we need different predictive equations for the acute and late phases of critical illness? A prospective observational study with repeated indirect calorimetry measurements

BACKGROUND: Predictive equations (PEs) for estimating resting energy expenditure (REE) that have been developed from acute phase data may not be applicable in the late phase and vice versa. This study aimed to assess whether separate PEs are needed for acute and late phases of critical illness and to develop and validate PE(s) based on the results of this assessment. 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

Effect of a Hospital-based Case Management Approach on Treatment Outcome of Patients with Tuberculosis

Background/PurposeTuberculosis (TB) continues to pose a heavy public health burden in Taiwan. This prospective study analyzed the factors influencing treatment outcome in patients with TB treated with and without a hospital-based case management (HBCM) approach in a referral center in Taipei.MethodsA register-based cohort study design was used to enroll all new cases of pulmonary or extra-pulmonary TB from February 2003 to January 2004. The case manager served as the coordinator among patients, physicians and public health nurses, to facilitate compliance with anti-TB treatment. Treatment outcomes were assessed according to the consensus recommendations of the World Health Organization and the International Union Against Tuberculosis and Lung Disease.ResultsSuspected or confirmed pulmonary or extrapulmonary TB was diagnosed in 524 patients in our hospital from February 2003 to January 2004. Fifty-two of these patients were excluded due to duplicate reporting, previous treatment or death before enrollment. Out of 472 patients enrolled, 103 whose original diagnosis was revised were further excluded, leaving 369 cases eligible for analysis. Patients with case management had a significantly higher rate of successful treatment (cured plus completed treatment) compared to patients without case management, (240/277, 86.6% vs. 67/92, 72.8%; p = 0.002). The overall successful treatment rate including both case and non-case management was 83.2% (307/369), which was higher than the nationwide surveillance data of 78.3% in 2002 and 69.4% in 2003.ConclusionTreatment of TB patients by a HBCM approach provides improved treatment outcomes compared to those without case management

Validity of predictive equations for estimation of resting energy expenditure among mechanically ventilated critically ill patients: Preliminary findings

Background: Several predictive equations (PEs) have been developed for estimation of energy requirement but very few has been validated among mechanically ventilated critically ill patients in Asian population. Objectives: This study aimed at determining the validity of 14 PEs for energy requirement and identifying metabolic determinants that influence resting energy expenditure (REE). Methods: REE was measured among 90 ventilated critically ill patients by using IndirectCalorimetry (IC). 14 PEs used to estimate patients’ energy requirement was validated against IC using intraclass correlation coefficient (ICC) test. Metabolic determinants assessed were sex, body mass index (BMI), age, patient condition, mNUTRIC score and body cell mass (BCM) status. Recruitment is on-going until sample size of 314 is achieved. Results: In the early phase (≤5days), mean REE for all critically ill patients was 1677±403kcal whereas for obese patients was 1926±438kcal. Penn State equation [PSU(m),2003b] shows highest correlation (ICC=0.635), 95%CI(0.49,0.75), p<0.001 with IC in estimating REE among all critically ill patients. Meanwhile, Harris Benedict Equation (variants) [HBEa(50)x1.25] shows highest correlation (ICC= 0.581), 95%CI(0.12,0.84), p=0.010 in estimating REE among obese patients. There was significant difference in REE by sex, BMI and BCM status during early and late phase (6-10days). During chronic phase (>10days), significant difference in REE was observed in patient condition and BCM status. Conclusion: These preliminary results show that most available validated equations had poor to fair agreement with IC measurement. As such, we opine that it is crucial to determine a reliable PE for assessing energy requirement of Asian critically ill patients