Association Between Enteral Feeding, Weight Status, and Mortality in a Medical Intensive Care Unit.

BACKGROUND: Clinical practice guidelines recommend enteral nutrition for most patients receiving mechanical ventilation. However, recently published evidence on the effect of enteral nutrition on mortality, particularly for patients who are well nourished, is conflicting. OBJECTIVES: To examine the association between enteral feeding and hospital mortality in critically ill patients receiving mechanical ventilation and to determine if body mass index mediates this relationship. METHODS: A retrospective cohort study of patients receiving mechanical ventilation admitted to a medical intensive care unit in 2013. Demographic and clinical variables were collected. Cox proportional hazards regression was used to examine the relationship between an enteral feeding order and hospital mortality and to determine if the relationship was mediated by body mass index. RESULTS: Of 777 patients who had 811 hospitalizations requiring mechanical ventilation, 182 (23.4%) died in the hospital. A total of 478 patients (61.5%) received an order for enteral tube feeding, which was associated with a lower risk of death (hazard ratio, 0.41; 95% CI, 0.29-0.59). Body mass index did not mediate the relationship between mortality and receipt of an order for enteral feeding. Median stay in the unit was 3.6 days. Most deaths (72.0%) occurred more than 48 hours after admission. CONCLUSION: The finding of a positive association between an order for enteral feeding and survival supports enteral feeding of patients in medical intensive care units. Furthermore, the beneficial effect of enteral feeding appears to apply to patients regardless of body mass index.

Characteristics and Outcomes of Adult Inpatients With Malnutrition.

BACKGROUND: The diagnosis of malnutrition remains controversial. Furthermore, it is unknown if physician diagnosis of malnutrition impacts outcomes. We sought to compare outcomes of patients with physician diagnosed malnutrition to patients recognized as malnourished by registered dietitians (RDs), but not physicians, and to describe the impact of each of 6 criteria on the diagnosis of malnutrition. METHODS: We conducted a retrospective cohort study of adult patients identified as meeting criteria for malnutrition. Pediatric, psychiatric, maternity, and rehabilitation patients were excluded. Patient demographics, clinical data, malnutrition type and criteria, nutrition interventions, and outcomes were abstracted from the electronic medical record. RESULTS: RDs identified malnutrition for 291 admissions during our study period. This represents 4.1% of hospital discharges. Physicians only diagnosed malnutrition on 93 (32%) of these cases. Physicians diagnosed malnutrition in 43% of patients with a body mass index <18.5 but only 26% of patients with body mass index higher than 18.5. Patients with a physician diagnosis had a longer length of stay (mean 14.9 days vs 7.1 days) and were more likely to receive parenteral nutrition (PN) (20.4% vs 4.6%). Of the patients, 62% had malnutrition due to chronic illness. Of the 6 criteria used to identify malnourished patients, weight loss and reduced energy intake were the most common. CONCLUSIONS: Malnutrition is underrecognized by physicians. However, further research is needed to determine if physician recognition and treatment of malnutrition can improve outcomes. The most important criteria for identifying malnourished patients in our cohort were weight loss and reduced energy intake.

Impact of universal gowning and gloving on health care worker clothing contamination.

OBJECTIVE: To determine whether gowning and gloving for all patient care reduces contamination of healthcare worker (HCW) clothing, compared to usual practice. DESIGN: Cross-sectional surveys. SETTING: Five study sites were recruited from intensive care units (ICUs) randomized to the intervention arm of the Benefits of Universal Gown and Glove (BUGG) study. PARTICIPANTS: All HCWs performing direct patient care in the study ICUs were eligible to participate. METHODS: Surveys were performed first during the BUGG intervention study period (July-September 2012) with universal gowning/gloving and again after BUGG study conclusion (October-December 2012), with resumption of usual care. During each phase, HCW clothing was sampled at the beginning and near the end of each shift. Cultures were performed using broth enrichment followed by selective media. Acquisition was defined as having a negative clothing culture for samples taken at the beginning of a shift and positive clothing culture at for samples taken at the end of the shift. RESULTS: A total of 348 HCWs participated (21-92 per site), including 179 (51%) during the universal gowning/gloving phase. Overall, 51 (15%) HCWs acquired commonly pathogenic bacteria on their clothing: 13 (7.1%) HCWs acquired bacteria during universal gowning/gloving, and 38 (23%) HCWs acquired bacteria during usual care (odds ratio [OR], 0.3; 95% confidence interval [CI], 0.2-0.6). Pathogens identified included S. aureus (25 species, including 7 methicillin-resistant S. aureus [MRSA]), Enterococcus spp. (25, including 1 vancomycin-resistant Enterococcus [VRE]), Pseudomonas spp. (4), Acinetobacter spp. (4), and Klebsiella (2). CONCLUSION: Nearly 25% of HCWs practicing usual care (gowning and gloving only for patients with known resistant bacteria) contaminate their clothing during their shift. This contamination was reduced by 70% by gowning and gloving for all patient interactions.

Using the AUDIT-PC to predict alcohol withdrawal in hospitalized patients.

BACKGROUND: Alcohol withdrawal syndrome (AWS) occurs when alcohol-dependent individuals abruptly reduce or stop drinking. Hospitalized alcohol-dependent patients are at risk. Hospitals need a validated screening tool to assess withdrawal risk, but no validated tools are currently available. OBJECTIVE: To examine the admission Alcohol Use Disorders Identification Test-(Piccinelli) Consumption (AUDIT-PC) ability to predict the subsequent development of AWS among hospitalized medical-surgical patients admitted to a non-intensive care setting. DESIGN: Retrospective case­control study of patients discharged from the hospital with a diagnosis of AWS. All patients with AWS were classified as presenting with AWS or developing AWS later during admission. Patients admitted to an intensive care setting and those missing AUDIT-PC scores were excluded from analysis. A hierarchical (by hospital unit) logistic regression was performed and receiver-operating characteristics were examined on those developing AWS after admission and randomly selected controls. Because those diagnosing AWS were not blinded to the AUDIT-PC scores, a sensitivity analysis was performed. PARTICIPANTS: The study cohort included all patients age ≥18 years admitted to any medical or surgical units in a single health care system from 6 October 2009 to 7 October 2010. KEY RESULTS: After exclusions, 414 patients were identified with AWS. The 223 (53.9 %) who developed AWS after admission were compared to 466 randomly selected controls without AWS. An AUDIT-PC score ≥4 at admission provides 91.0 % sensitivity and 89.7 % specificity (AUC=0.95; 95 % CI, 0.94­0.97) for AWS, and maximizes the correct classification while resulting in 17 false positives for every true positive identified. Performance remained excellent on sensitivity analysis (AUC=0.92; 95 % CI, 0.90­0.93). Increasing AUDIT-PC scores were associated with an increased risk of AWS (OR=1.68, 95 % CI 1.55­1.82, p<0.001). CONCLUSIONS: The admission AUDIT-PC score is an excellent discriminator of AWS and could be an important component of future clinical prediction rules. Calibration and further validation on a large prospectivecohort is indicated.

Early data from Project Engage: a program to identify and transition medically hospitalized patients into addictions treatment.

BACKGROUND: Patients with untreated substance use disorders (SUDs) are at risk for frequent emergency department visits and repeated hospitalizations. Project Engage, a US pilot program at Wilmington Hospital in Delaware, was conducted to facilitate entry of these patients to SUD treatment after discharge. Patients identified as having hazardous or harmful alcohol consumption based on results of the Alcohol Use Disorders Identification Test-Primary Care (AUDIT-PC), administered to all patients at admission, received bedside assessment with motivational interviewing and facilitated referral to treatment by a patient engagement specialist (PES). This program evaluation provides descriptive information on self-reported rates of SUD treatment initiation of all patients and health-care utilization and costs for a subset of patients. METHODS: Program-level data on treatment entry after discharge were examined retrospectively. Insurance claims data for two small cohorts who entered treatment after discharge (2009, n = 18, and 2010, n = 25) were reviewed over a six-month period in 2009 (three months pre- and post-Project Engage), or over a 12-month period in 2010 (six months pre- and post-Project Engage). These data provided descriptive information on health-care utilization and costs. (Data on those who participated in Project Engage but did not enter treatment were unavailable). RESULTS: Between September 1, 2008, and December 30, 2010, 415 patients participated in Project Engage, and 180 (43%) were admitted for SUD treatment. For a small cohort who participated between June 1, 2009, and November 30, 2009 (n = 18), insurance claims demonstrated a 33% ($35,938) decrease in inpatient medical admissions, a 38% ($4,248) decrease in emergency department visits, a 42% ($1,579) increase in behavioral health/substance abuse (BH/SA) inpatient admissions, and a 33% ($847) increase in outpatient BH/SA admissions, for an overall decrease of $37,760. For a small cohort who participated between June 1, 2010, and November 30, 2010 (n = 25), claims demonstrated a 58% ($68,422) decrease in inpatient medical admissions; a 13% ($3,308) decrease in emergency department visits; a 32% ($18,119) decrease in BH/SA inpatient admissions, and a 32% ($963) increase in outpatient BH/SA admissions, for an overall decrease of $88,886. CONCLUSIONS: These findings demonstrate that a large percentage of patients entered SUD treatment after participating in Project Engage, a novel intervention with facilitated referral to treatment. Although the findings are limited by the retrospective nature of the data and the small sample sizes, they do suggest a potentially cost-effective addition to existing hospital services if replicated in prospective studies with larger samples and controls.