The effects of endotracheal suctioning on the accuracy of oxygen consumption and carbon dioxide production measurements and pulmonary mechanics calculated by a compact metabolic monitor

External Validation with Accuracy Confounders of VCO2-Derived Predicted Energy Expenditure Compared to Resting Energy Expenditure Measured by Indirect Calorimetry in Mechanically Ventilated Children

Optimal energy provision, guided by measured resting energy expenditure (REE) and determined by indirect calorimetry (IC), is fundamental in Intensive Care Units (ICU). Because IC availability is limited, methods to predict REE based on carbon dioxide production (VCO2) measurements (REEVCO2) alone have been proposed as a surrogate for REE measured by IC (REEIC). The study aimed at externally and internally validating the accuracy of the REEVCO2 as an alternative to REEIC in mechanically ventilated children. A ventilator’s integrated gas exchange module (E-COVX) was used to prospectively measure REEIC and predict REEVCO2 on 107 mechanically ventilated children during the first 24 h of admission. The accuracy of the REEVCO2 compared to REEIC was assessed through the calculation of bias and precision, paired median differences, linear regression, and ROC analysis. Accuracy within ±10% of the REEIC was deemed acceptable for the REEVCO2 equation. The calculated REEVCO2 based on respiratory quotient (RQ) 0.89 resulted in a mean bias of −72.7 kcal/day (95% limits of agreement −321.7 to 176.3 kcal/day) and a high coefficient of variation (174.7%), while 51.4% of the calculations fell outside the ±10% accuracy rate. REEVCO2 derived from RQ 0.80 or 0.85 did not improve accuracy. Only measured RQ (Beta 0.73, p < 0.001) and no-recorded neuromuscular blocking agents (Beta −0.13, p = 0.044) were independently associated with the REEVCO2−REEIC difference. Among the recorded anthropometric, metabolic, nutrition, or clinical variables, only measured RQ was a strong predictor of REEVCO2 inaccuracy (p < 0.001). Cutoffs of RQ = 0.80 predicted 89% of underestimated REEIC (sensitivity 0.99; specificity 0.89) and RQ = 0.82 predicted 56% of overestimated REEIC (sensitivity of 0.99; specificity 0.56). REEVCO2 cannot be recommended as an alternative to REEIC in mechanically ventilated children, regardless of the metabolic, anthropometric, or clinical status at the time of the evaluation.

External Validation of Equations to Estimate Resting Energy Expenditure in Critically Ill Children and Adolescents with and without Malnutrition: A Cross-Sectional Study

We evaluated the validity of sixteen predictive energy expenditure equations for resting energy expenditure estimation (eREE) against measured resting energy expenditure using indirect calorimetry (REEIC) in 153 critically ill children. Predictive equations were included based on weight, height, sex, and age. The agreement between eREE and REEIC was analyzed using the Bland−Altman method. Precision was defined by the 95% limits of the agreement; differences > ±10% from REEIC were considered clinically unacceptable. The reliability was assessed by the intraclass correlation coefficient (Cronbach’s alpha). The influence of anthropometric, nutritional, and clinical variables on REEIC was also assessed. Thirty (19.6%) of the 153 enrolled patients were malnourished (19.6%), and fifty-four were overweight (10.5%) or obese (24.8%). All patients received sedation and analgesia. Mortality was 3.9%. The calculated eREE either underestimated (median 606, IQR 512; 784 kcal/day) or overestimated (1126.6, 929; 1340 kcal/day) REEIC compared with indirect calorimetry (928.3, 651; 1239 kcal/day). These differences resulted in significant biases of −342 to 592 kcal (95% limits of agreement (precision)−1107 to 1380 kcal/day) and high coefficients of variation (up to 1242%). Although predicted equations exhibited moderate reliability, the clinically acceptable ±10% accuracy rate ranged from only 6.5% to a maximum of 24.2%, with the inaccuracy varying from −31% to +71.5% of the measured patient’s energy needs. REEIC (p = 0.017) and eREE (p < 0.001) were higher in the underweight compared to overweight and obese patients. Apart from a younger age, malnutrition, clinical characteristics, temperature, vasoactive drugs, neuromuscular blockade, and energy intake did not affect REEIC and thereby predictive equations’ accuracy. Commonly used predictive equations for calculating energy needs are inaccurate for individual patients, either underestimating or overestimating REEIC compared with indirect calorimetry. Altogether these findings underscore the urgency for measuring REEIC in clinical situations where accurate knowledge of energy needs is vital.

Energy Expenditure in Mechanically Ventilated Korean Children: Single-Center Evaluation of a New Estimation Equation

OBJECTIVES

Accurate assessments of energy expenditure are vital for determining optimal nutritional support, especially in critically ill children. We evaluated current methods for energy expenditure prediction, in comparison with indirect calorimetry, and developed a new estimation equation for mechanically ventilated, critically ill Korean children.

DESIGN

Single-center retrospective study.

SETTING

Fourteen-bed pediatric medical ICU in a tertiary care children's hospital.

PATIENTS

Pediatric patients admitted to the PICU between October 2017 and September 2019 with a measured energy expenditure by indirect calorimetry.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A total 95 pediatric patients (70 in derivation cohort for development of a new predictive equation and 25 in validation cohort) were included. Mean measured energy expenditure of group A was 66.20 ± 15.35 kcal/kg/d. All previously established predictive equations underestimated the predicted energy expenditure, compared with the measured energy expenditure, except the Food and Agriculture/World Health Organization/United Nations University equation. The Schofield-Height and Weight equation showed the best performance among the tested predictive equations for the entire cohort (least bias, -68.58 kcal/d; best percentage, 108.46% ± 33.60%) compared with the measured energy expenditure. It was also the best performing predictive equation in subgroup analysis by age, sex, nutritional status, and organ failure. Because some discrepancies remained between the measured energy expenditure and predicted energy expenditures, we developed a new estimation equation using multiple regression analysis and those variables significantly associated with our current measured energy expenditures: Energy expenditure = -321.264 + 72.152 × (body weight, kg)-1.396 × (body weight) + 5.668 × height (cm) + organ dysfunction* (*hematologic, 76.699; neurologic, -87.984). This new estimation equation showed the least bias and best percentage compared with previous predictive equations (least bias, 15.51 kcal/d; best percentage, 102.30% ± 28.10%).

CONCLUSIONS

There are significant disparities between measured and calculated energy expenditures. We developed a new estimation equation based on measured energy expenditure data that shows better performance in mechanically ventilated Korean children than other equations. This new estimation equation requires further prospective validation in pediatric series with a range in body habitus.

Factors Related to the Assessment of Resting Metabolic Rate in Critically Ill Patients

BACKGROUND

Predicting resting metabolic rate (RMR) in mechanically ventilated, critically ill patients is an important part of the nutrition care in such patients.

METHODS

RMR and associated clinical data from various studies of mechanically ventilated, critically ill patients were combined, and the impact of body size, age, reason for admission, and sedation level were analyzed along with prediction methods of RMR (the American Society for Parenteral and Enteral Nutrition [ASPEN] standards and the Penn State equation).

RESULTS

Among 826 measurements, trauma patients had a higher RMR than surgical and medical patients (2077 ± 290 vs 1987 ± 282 kcal/d; P < .0001). RMR was not different in sedated vs unsedated patients. Wide ranges of weight (27-374 kg) and age (18-95 years) were captured. The relationships between weight and RMR and RMR and age were curvilinear. For weight-based ratio methods of RMR prediction, <50% of predictions were within the range in which they were designed to work. The accuracy of the Penn State equation was better in some weight categories than others. New equations based on a wider range of body weights and ages are presented.

CONCLUSIONS

Curvilinear functions exist for weight and age in relation to RMR, but extraordinary levels of each are required for the curve to become apparent. The ASPEN energy standards (kcal/kg body weight) fail to predict RMR because the relationship is more complex than a simple ratio. The Penn State equations are better able to model these relationships. The new versions of the equation presented here await validation.

The Metabolic Response to Stress and Infection in Critically Ill Children: The Opportunity of an Individualized Approach

The metabolic response to stress and infection is closely related to the corresponding requirements of energy and nutrients. On a general level, the response is driven by a complex endocrine network and related to the nature and severity of the insult. On an individual level, the effects of nutritional interventions are highly variable and a possible source of complications. This narrative review aims to discuss the metabolic changes in critically-ill children and the potential of developing personalized nutritional interventions. Through a literature search strategy, we have investigated the importance of blood glucose levels, the nutritional aspects of the different phases of acute stress response, and the reliability of the available tools to assess the energy expenditure. The dynamics of metabolism during stressful events reveal the difficult balance between risk of hypo- or hyperglycemia and under- or overfeeding. Within this context, individualized and accurate measurement of energy expenditure may help in defining the metabolic needs of patients. Given the variability of the metabolic response in critical conditions, randomized clinical studies in ill children are needed to evaluate the effect of individualized nutritional intervention on health outcomes.

Effects of thoracic squeezing on airway secretion removal in mechanically ventilated patients

Background:

Accumulation of secretions in the airways of patients with an endotracheal tube and mechanical ventilation will have serious consequences. One of the most common methods of airway clearance is endotracheal suctioning. In order to facilitate discharge of airway secretion resulting in promotion of gas exchange, chest physiotherapy techniques can be used at the time of expiration before suction.

Materials and Methods:

In this clinical trial with a cross-over design, 50 mechanically ventilated patients admitted to intensive care units (ICUs) were randomly divided into two groups of thoracic squeezing. In each patient, two interventions of endotracheal suctioning were conducted, one with and the other without thoracic squeezing during exhalation, with a 3 h gap between the two interventions and an elapse of three respiratory cycles between the number of compressions. Sputum secreted was collected in a container connected to a suction catheter and weighed. Data were recorded in data gathering forms and analyzed using descriptive and inferential statistics (Wilcoxon and independent t-test, Chi-square) in SPSS version 16.

Results:

Findings showed that the mean weight of the suction secretions removed from airway without thoracic squeezing was 1.35 g and that of suction secretions removed by thoracic squeezing was 1.94 g. Wilcoxon test showed a significant difference regarding the rate of secretion between the two techniques (P = 0.003).

Conclusions:

According to the study findings, endotracheal suction with thoracic squeezing on expiration helps airway secretion discharge more than suction alone in patients on mechanical ventilators and can be used as an effective method.

Nutrition Monitoring in the PICU

The ideal set of variables for nutritional monitoring that may correlate with patient outcomes has not been identified. This is particularly difficult in the PICU patient because many of the standard modes of nutritional monitoring, although well described and available, are fraught with difficulties. Thus, repeated anthropometric and laboratory markers must be jointly analyzed but individually interpreted according to disease and metabolic changes, in order to modify and monitor the nutritional treatment. In addition, isotope techniques are neither clinically feasible nor compatible with the multiple measurements needed to follow progression. On the other hand, indirect alternatives exist but may have pitfalls, of which the clinician must be aware. Risks exist for both overfeeding and underfeeding of PICU patients so that an accurate monitoring of energy expenditure, using targeted indirect calorimetry, is necessary to avoid either extreme. This is very important, since the monitoring of the nutritional status of the critically ill child serves as a guide to early and effective nutritional intervention.

Energy imbalance and the risk of overfeeding in critically ill children

OBJECTIVE

To examine the role of targeted indirect calorimetry in detecting the adequacy of energy intake and the risk of cumulative energy imbalance in a subgroup of critically ill children suspected to have alterations in resting energy expenditure. We examined the accuracy of standard equations used for estimating resting energy expenditure in relation to measured resting energy expenditure in relation to measured resting energy expenditure and cumulative energy balance over 1 week in this cohort.

DESIGN

A prospective cohort study.

SETTING

Pediatric intensive care unit in a tertiary academic center.

INTERVENTIONS

A subgroup of critically ill children in the pediatric intensive care unit was selected using a set of criteria for targeted indirect calorimetry.

MEASUREMENTS

Measured resting energy expenditure from indirect calorimetry and estimated resting energy expenditure from standard equations were obtained. The metabolic state of each patient was assigned as hypermetabolic (measured resting energy expenditure/estimated resting energy expenditure >110%), hypometabolic (measured resting energy expenditure/estimated resting energy expenditure <90%), or normal (measured resting energy expenditure/estimated resting energy expenditure = 90-110%). Clinical variables associated with metabolic state and factors influencing the adequacy of energy intake were examined.

MAIN RESULTS

Children identified by criteria for targeted indirect calorimetry, had a median length of stay of 44 days, a high incidence (72%) of metabolic instability and alterations in resting energy expenditure with a predominance of hypometabolism in those admitted to the medical service. Physicians failed to accurately predict the true metabolic state in a majority (62%) of patients. Standard equations overestimated the energy expenditure and a high incidence of overfeeding (83%) with cumulative energy excess of up to 8000 kcal/week was observed, especially in children <1 yr of age. We did not find a correlation between energy balance and respiratory quotient (RQ) in our study.

CONCLUSIONS

We detected a high incidence of overfeeding in a subgroup of critically ill children using targeted indirect calorimetry The predominance of hypometabolism, failure of physicians to correctly predict metabolic state, use of stress factors, and inaccuracy of standard equations all contributed to overfeeding in this cohort. Critically ill children, especially those with a longer stay in the PICU, are at a risk of unintended overfeeding with cumulative energy excess.

[Monitoring oxygen consumption in energy metabolism in pediatric anesthesia: clinical utility]

OBJECTIVES

To determine changes in oxygen consumption as a marker of energy metabolism during general inhaled anesthesia in pediatric patients and to identify factors that might influence consumption.

MATERIAL AND METHODS

Prospective, observational, double-blind study in children under inhaled anesthesia in spontaneous ventilation. We monitored heart rate electrocardiogram, noninvasive blood pressure, respiratory frequency, carbon dioxide (CO2) end-expiratory pressure, oxygen saturation by pulse oximetry, state entropy, response entropy, esophageal temperature, and (by indirect calorimetry) oxygen consumption and the respiratory quotient. Capillary blood was extracted every 5 minutes to determine lactate concentration.

RESULTS

Thirty-six patients (ASA 1-2) between 5 and 11 years old were included. Mean (SD) oxygen consumption was 0.6 (0.12) mL x kg(-1)min(-1) at baseline, 5.3 (03) mL x kg(-1) min(-1) during maintenance of anesthesia, and 8.1 (1.1) mL x kg(-1) min(-1) on awakening. A progressive increase was detected in lactic acid concentration, from a baseline mean of 0.8 (0.1) mmol/L to 2.2 (0.9) mmol/L half an hour later; the change was unrelated to oxygen consumption. After correcting the flow of normal saline solution to 0.9%, a significant increase in oxygen consumption (P < .05) was detected. Factors that were significantly correlated (P < 0.1 and r of +/- 0.95) were temperature (oxygen consumption decreased > 10% for each degree centigrade decrease), inspired oxygen fraction > 0.8; sharp changes in the expired CO2 fraction exceeding 2 standard deviations (+/- 6), use of nitrous oxide in the gas mix (inspired nitrous oxide fraction > 20%), the length of the sampling line, and increased respiratory frequency. A model with 3 factors was constructed to explain the kinetics of oxygen consumption during anesthesia.

CONCLUSIONS

Oxygen consumption monitoring may provide an indirect indicator of homeostatic changes during surgery. The ideal system for carrying out such monitoring during anesthesia remains to be found, and the values to guide the anesthesiologist in deciding whether or not to intervene immediately still need to be determined.