Bioinformatics assistance of metabolic and nutrition management in the ICU

Blood glucose control in the intensive care unit: Where is the data?

Blood glucose control, including hyperglycemia correction, maintaining glucose at optimal level and avoiding hypoglycemia, is a challenge clinicians face every day in intensive care units (ICUs). If managed inadequately, its related mortality can increase. Prior to 2001, no relevant data from randomized, controlled studies assessing glucose control in the ICU were available. In the past 18 years, however, many clinical trials have defined criteria for managing abnormal blood glucose levels, as well as provided suggestions for glycemic monitoring. Point-of-care blood glucose monitors have become the preferred bedside technology to aid in glycemic management. In addition, in some institutions, continuous glucose monitoring is now available. Cost-effectiveness of adequate glycemic control in the ICU must be taken into consideration when addressing this complex issue. Newer types of glycemic monitoring may reduce nursing staff fatigue and shorten times for the treatment of hyperglycemia or hypoglycemia. There are a variety of glycemic care protocols available. However, not all ICU clinicians are aware of them. The following minireview describes some of these concepts.

Impact of a computer-assisted decision support system (CDSS) on nutrition management in critically ill hematology patients: the NUTCHOCO study (nutritional care in hematology oncologic patients and critical outcome)

BACKGROUND

Mortality of critically ill hematology (HM) patients has improved over time. Thus, those patients require an extensive diagnostic workup and the optimal use of available treatments. There are no data regarding nutrition strategy for critically ill HM patients, while nutritional support is crucial for both HM and critically ill patients. We hypothesized that the implementation of a computer-assisted decision support system (CDSS), designed to supervise a nutritional intervention by a multidisciplinary team, would be able to increase guidelines adherence and outcomes.

RESULTS

In this before/after study, 275 critically ill hematology patients admitted to the ICU over 5-year period were included. Energy and protein intakes were delivered using standard protocol in the 147 patients (53%) of the 'before group' and using a CDSS in order to reach every day predefined caloric and protein targets accordingly to the catabolic or anabolic status in the 128 patients (47%) of the 'after group.' Using a Poisson regression, we showed that the use of CDSS allowed to reach a relative increase in the rate of days in compliance with caloric (1.57; 95% confidence interval (CI), [1.17-2.10], p = 0.0025) and protein targets (3.86 [2.21-6.73], p < 0.0001) in the 'after group' by more than 50% as compared with the 'before group.' Interestingly, compliance rates were low and only reached 30% after intervention. Hospital mortality, ICU-acquired infection, and hospital, and ICU length of stay were similar in the two groups of patients. Importantly, exploratory analysis showed that hospital mortality was lower in the 'after group' for neutropenic and severely ill patients.

CONCLUSION

For critically ill hematology patients, the use of a nutritional CDSS allowed to increase the days in compliance with caloric and protein targets as compared with no CDSS use. In this context, overall hospital mortality was not affected.

Implementation of a computerized system in pediatric wards to improve nutritional care: a cluster randomized trial

BACKGROUND/OBJECTIVES

Malnutrition occurs frequently in hospitalized children. We aimed to assess whether a computerized system could lead to improved clinical practices in malnourished children.

SUBJECTS/METHODS

Healthcare workers (242) from six departments in a pediatric university hospital participated in a cluster randomized trial, studying 1457 malnourished children hospitalized from September 2009 to August 2011. Following a baseline observational pre-intervention period, all departments were randomized into either intervention or control arms. A computerized malnutrition-screening system was implemented in the intervention group to automatically trigger a dietetic referral in real time. Furthermore, the nutrition support team conducted an awareness campaign with healthcare workers and a leadership-based strategy to reinforce the message during the entire study period. Adherence to practice guidelines (daily weights, investigation of etiology for malnutrition, management by a dietitian and application of refeeding protocols) was compared between pre- and post-intervention periods in both the intervention and trial arms.

RESULTS

When compared with the pre-intervention period, the clinical practices were significantly improved within the intervention arm for every outcome (P<0.01), whereas remained unchanged in the control arm. In addition, during the post-intervention period, malnutrition etiology investigation by physicians (adjusted odds ratio (OR) of 4.4, 95% confidence interval (CI) 1.7-11.8, P=0.003) and management by a dietitian (OR 2.7, 95% CI 1.0-6.9, P=0.046) occurred more frequently in the intervention clusters.

CONCLUSIONS

Implementation of an electronic system to detect malnutrition in real time was associated with a rapid improvement in clinical practices for better care of hospitalized children.

Influence of smart real-time electronic alerting on glucose control in critically ill patients

PURPOSE

Hyperglycemia and hypoglycemia are frequently encountered in critically ill patients and associated with adverse outcomes. We configured a smart glycemia alert (S-GLY alert) with our Intensive Care Information System to decrease the number of hyperglycemic values and increase the proportion of time within the glucose interval of 80 to 150 mg/dL.

MATERIALS AND METHODS

Prospective intervention study in surgical intensive care unit in a tertiary care hospital. An 11-week prealert phase was followed by a 15-week intervention phase where the S-GLY alert was alerting the nurses through the Clinical Notification System of the Intensive Care Information System.

RESULTS

Overall, 2335 S-GLY alerts were recorded. There were less hyperglycemic values and less persistent hyperglycemic episodes in the alert phase (19.5% vs 26.5% [P < .001] and 9.9% vs 15.4% [P < .001], respectively). More time was spent within target glucose interval (82.3% vs 75.0%, P = .009). A lower proportion of patients experienced a new-onset hypoglycemic event (<70 mg/dL) in the alert phase (9.2% vs 15.2%, P = .016). The Sequential Organ Failure Assessment score was significantly reduced (5.2 vs 4.2, P < .001).

CONCLUSIONS

The implementation of a real-time smart electronic glycemia alert resulted in significantly less episodes of persistent hyperglycemia and a higher proportion of time with normoglycemia, while decreasing the number of hypoglycemic events.

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.

Monitoring in the intensive care

In critical care, the monitoring is essential to the daily care of ICU patients, as the optimization of patient's hemodynamic, ventilation, temperature, nutrition, and metabolism is the key to improve patients' survival. Indeed, the decisive endpoint is the supply of oxygen to tissues according to their metabolic needs in order to fuel mitochondrial respiration and, therefore, life. In this sense, both oxygenation and perfusion must be monitored in the implementation of any resuscitation strategy. The emerging concept has been the enhancement of macrocirculation through sequential optimization of heart function and then judging the adequacy of perfusion/oxygenation on specific parameters in a strategy which was aptly coined “goal directed therapy.” On the other hand, the maintenance of normal temperature is critical and should be regularly monitored. Regarding respiratory monitoring of ventilated ICU patients, it includes serial assessment of gas exchange, of respiratory system mechanics, and of patients' readiness for liberation from invasive positive pressure ventilation. Also, the monitoring of nutritional and metabolic care should allow controlling nutrients delivery, adequation between energy needs and delivery, and blood glucose. The present paper will describe the physiological basis, interpretation of, and clinical use of the major endpoints of perfusion/oxygenation adequacy and of temperature, respiratory, nutritional, and metabolic monitorings.

A preliminary report on the functioning and data collection capabilities of a web-based nutrition algorithm for patients with chronic kidney disease

OBJECTIVE

The purpose of this study was to test the functional and data collection capabilities of an online nutrition algorithm for patients with chronic kidney disease by comparing dietitian-selected nutrition diagnoses, etiologies, and interventions in hemodialysis (HD) patients with and without diabetes mellitus (DM).

DESIGN

Data were collected using an online nutrition screening tool and algorithm for HD patients based on the American Dietetic Association's Nutrition Care Process.

SETTING

Data were collected by dietitians in the United States, New Zealand, Australia, and Brazil.

PATIENTS

Patients undergoing HD under the care of a participating dietitian and who were deemed at nutrition risk at visit 1 were eligible to participate. Other inclusion criteria included age >19 years, able to speak and write English, and not receiving hospice care or the international equivalent. Data were available on 26 patients (50% males, 39% with DM). Mean baseline values were as follows: age, 56.3 years; body mass index, 28.2 kg/m(2); and serum albumin (bromocresol green), 36.8 g/L (3.68 g/dL). There were no statistically significant differences between DM and non-DM patients except in mean hemoglobin A1C.

MAIN OUTCOME MEASURE

Differences in the frequency of selection of diagnoses, etiology, and intervention categories were compared.

RESULTS

The algorithm is under continuous development using input from participating dietitians, but its use was generally considered feasible. The initial data analysis showed that the algorithm is an effective method for collecting data on HD patients. In this small cohort, patients with and without DM had similar dietitian-selected nutrition diagnoses and etiologies, but had statistically significant differences in the dietitian-selected nutrition interventions that were selected most frequently. Health Care Team Referral was selected more often in DM patients (P < .003) and Recommendation of Specific Foods was selected more often in non-DM patients (P < .0170).

CONCLUSION

This preliminary analysis shows that the algorithm can be used as both a clinical and a data collection tool. The test analysis, although small in sample size, showed interesting differences in the care of DM and non-DM HD patients.