Post-admission glucose levels are associated with healthcare-associated bloodstream infections and pneumonia in hospitalized patients with diabetes

The occurrence of Hospital-Acquired Pneumonia is independently associated with elevated Stress Hyperglycaemia Ratio at admission but not elevated blood glucose

BACKGROUND

The association between stress-induced hyperglycaemia (SIH) and increased infection rates in hospitalised subjects is well-known. It is less clear if SIH at admission independently drives new-onset infections. We assessed the relationship between early exposure at admission to both the Stress Hyperglycaemia Ratio (SHR) and Blood Glucose (BG) with Hospital-Acquired Pneumonia (HAP).

METHODS

This observational retrospective study included those with length-of-stay > 1 day, BG within 24 h of admission and recent haemoglobin A1c. SIH was defined as BG ≥ 10 mmol/L, or SHR ≥ 1.1, measured at both admission and as a 24-hour maximum. Multivariable analyses were adjusted for length-of-stay, age, mechanical ventilation, and chronic respiratory disease.

RESULTS

Of 5,339 eligible subjects, 110 (2.1%) experienced HAP. Admission SHR ≥ 1.1 was independently associated with HAP (OR 3.04, 95% CI 1.98-4.68, p < 0.0001) but not BG ≥ 10 mmol/L (OR 0.65, 95% CI 0.41-1.03, p = 0.0675). The association with SHR strengthened using maximum 24-hour values (OR 3.37, 95% CI 2.05-5.52, p < 0.0001) while BG ≥ 10 mmol/L remained insignificant (OR 0.96, 95% CI 0.63-1.46, p = 0.86). Of those experiencing HAP 40 (36.4%) occurred in subjects with no recorded BG ≥ 10 mmol/L but SHR ≥ 1.1.

CONCLUSION

SIH at admission defined as SHR ≥ 1.1, but not the conventional marker of BG ≥ 10 mmol/L, was independently associated with the subsequent onset of HAP, commonly at BG < 10 mmol/L.

Systematic Review of Literature Examining Bacterial Urinary Tract Infections in Diabetes

This systematic review addresses the central research question, "what is known from the published, peer-reviewed literature about the impact of diabetes on the risk of bacterial urinary tract infections (UTI)?" We examine the results from laboratory studies where researchers have successfully adapted mouse models of diabetes to study the pathophysiology of ascending UTI. These studies have identified molecular and cellular effectors shaping immune defenses against infection of the diabetic urinary tract. In addition, we present evidence from clinical studies that in addition to diabetes, female gender, increased age, and diabetes-associated hyperglycemia, glycosuria, and immune impairment are important risk factors which further increase the risk of UTI in diabetic individuals. Clinical studies also show that the uropathogenic genera causing UTI are largely similar between diabetic and nondiabetic individuals, although diabetes significantly increases risk of UTI by drug-resistant uropathogenic bacteria.

Prediction of Pancreatic Cancer in Diabetes Patients with Worsening Glycemic Control

BACKGROUND

Worsening glycemic control indicates elevated risk of pancreatic ductal adenocarcinoma (PDAC). We developed prediction models for PDAC among those with worsening glycemic control after diabetes diagnosis.

METHODS

In 2000-2016 records within the Veterans Affairs Health System (VA), we identified three cohorts with progression of diabetes: (i) insulin initiation (n = 449,685), (ii) initiation of combination oral hypoglycemic medication (n = 414,460), and (iii) hemoglobin A1c (HbA1c) ≥8% with ≥Δ1% within 15 months (n = 593,401). We computed 12-, 36-, and 60-month incidence of PDAC and developed prediction models separately for males and females, with consideration of >30 demographic, behavioral, clinical, and laboratory variables. Models were selected to optimize Akaike's Information Criterion, and performance for predicting 12-, 36-, and 60-month incident PDAC was evaluated by bootstrap.

RESULTS

Incidence of PDAC was highest for insulin initiators and greater in males than in females. Optimism-corrected c-indices of the models for predicting 36-month incidence of PDAC in the male population were: (i) 0.72, (ii) 0.70, and (iii) 0.71, respectively. Models performed better for predicting 12-month incident PDAC [c-index (i) 0.78, (ii) 0.73, (iii) 0.76 for males], and worse for predicting 60-month incident PDAC [c-index (i) 0.69, (ii) 0.67, (iii) 0.68 for males]. Model performance was lower among females. For subjects whose model-predicted 36-month PDAC risks were ≥1%, the observed incidences were (i) 1.9%, (ii) 2.2%, and (iii) 1.8%.

CONCLUSIONS

Sex-specific models for PDAC can estimate risk of PDAC at the time of progression of diabetes.

IMPACT

Our models can identify diabetes patients who would benefit from PDAC screening.

A New Prognostic Index PDPI for the Risk of Pneumonia Among Patients With Diabetes

Objective

Risk factors for the development of pneumonia among patients with diabetes mellitus are unclear. The aim of our study was to elucidate the potential risk factors and attempt to predict the probability of pneumonia based on the history of diabetes.

Methods

We performed a population-based, prospective multicenter cohort study of 1,043 adult patients with diabetes in China during 2017–2019. Demographic information, comorbidities, or laboratory examinations were collected.

Results

The study included 417 diabetic patients with pneumonia and 626 no-pneumonia-onset diabetic patients. The predictive risk factors were chosen on the basis of a multivariate logistic regression model to predict pneumonia among patients with diabetes including male sex [odds ratio (OR) = 1.72, 95% confidence interval (CI): 1.27–2.33, p < 0.001], age ≥ 75 years (OR = 2.31, 95% CI: 1.61–3.31, p < 0.001), body mass index < 25 (OR = 2.59, 95% CI: 1.92–3.50, p < 0.001), chronic obstructive pulmonary disease (OR = 6.58, 95% CI: 2.09–20.7, p = 0.001), hypertension (OR = 4.27, 95% CI: 3.12–5.85, p < 0.001), coronary heart disease (OR = 2.98, 95% CI: 1.61–5.52, p < 0.001), renal failure (OR = 1.82, 95% CI: 1.002–3.29, p = 0.049), cancer (OR = 3.57, 95% CI: 1.80–7.06, p < 0.001), use of insulin (OR = 2.28, 95% CI: 1.60–3.25, p < 0.001), and hemoglobin A1c ≥ 9% (OR = 2.70, 95% CI: 1.89–3.85, p < 0.001). A predictive nomogram was established. This model showed c-statistics of 0.811, and sensitivity and specificity were 0.717 and 0.780, respectively, under cut-off of 125 score.

Conclusion

We designed a clinically predictive tool for assessing the risk of pneumonia among adult patients with diabetes. This tool stratifies patients into relevant risk categories and may provide a basis for individually tailored intervention for the purpose of early prevention.

The pathogenic characteristics and influencing factors of health care-associated infection in elderly care center under the mode of integration of medical care and elderly care service: A cross-sectional study

The aim of this study was to analyze the distribution of pathogenic bacteria in hospitalized patients in elderly care centers under the mode of integration of medical care and elderly care service, and explore the influencing factors to reduce the health care-associated infection rate of hospitalized patients.A total of 2597 inpatients admitted to elderly care centers from April 2018 to December 2019 were included in the study. The etiology characteristics of health care-associated infections (HCAI) was statistically analyzed, univariate analysis, and multivariate logistic regression analysis method were used to analyze the influencing factors of HCAI.A total of 98 of 2597 inpatients in the elderly care centers had HCAI, and the infection rate was 3.77%. The infection sites were mainly in the lower respiratory tract and urinary tract, accounting for 53.92% and 18.63%, respectively. A total of 53 pathogenic bacteria were isolated, 43 of which (81.13%) were Gram-negative, mainly Escherichia coli, Pseudomonas aeruginosa, and Klebsiella pneumoniae, which respectively accounted for 24.53, 16.98, and 13.21%. 9 (16.98%) strains were Gram-positive, mainly Staphylococcus aureus and Enterococcus faecium, respectively accounting for 7.55 and 5.66%. Only 1 patient (1.89%) had a fungal infection. Multivariate logistic regression analysis indicated that total hospitalization days, antibiotic agents used, days of central line catheter, use of urinary catheter and diabetes were independent risk factors of nosocomial infection in elderly care centers (P < .05).Many factors can lead to nosocomial infections in elderly care centers. Medical staff should take effective intervention measures according to the influencing factors to reduce the risk of infection in elderly care facilities.

Non-ventilator health care-associated pneumonia (NV-HAP): Best practices for prevention of NV-HAP

Although the latest research and data show decreases in many health care-associated infections, recent publications highlight the understated but significant burden of nonventilator hospital-acquired pneumonia (NV-HAP). This section presents best practices to prevent NV-HAP. Many of the tools and interventions address basic nursing care such as oral care, oral and nonoral alimentation, patient positioning and mobility, pharmacologic and immunologic controls. The section stresses the importance of working with an interdisciplinary caregiver team to address fundamental activities of daily living that mitigate risk of developing NV-HAP.

The Predictive Value of Glycated Hemoglobin and Albumin for the Clinical Course Following Hospitalization of Patients with Febrile Urinary Tract Infection

Background

Diabetes is considered a risk factor for acquisition of febrile urinary tract infection (f-UTI), but information on the association of diabetes with subsequent course of the disease is lacking. Thus, we investigated the clinical variables including diabetic status which determined the clinical course in patients with community-acquired f-UTI.

Materials and Methods

Patients hospitalized consecutively for f-UTI between February 2016 and January 2018 were used for this single center study. The routine laboratory tests including blood glucose and glycated hemoglobin (HbA1c) were done and empiric treatment with parenteral antibiotics was commenced on admission. The clinical course such as duration of fever (DOF) and length of hospital stay (LOS) were compared among groups classified by the clinical variables.

Results

Among the101 patients admitted for f-UTI, 15 patients with diabetes (14.9%) experienced significantly longer febrile period and hospitalization compared to those with hyperglycemia (n = 18, 17.8%) or those without diabetes and hyperglycemia (n = 68, 67.3%). Of the laboratory parameters tested on admission and several clinical factors, the presence of diabetes and risk factors for severe complicated infection (hydronephrosis, urosepsis, and disseminated intravascular coagulopathy) as well as HbA1c and albumin were identified as predictors for LOS by univariate analysis, whereas none of the variables failed to predict DOF. In the subsequent multivariate analysis, HbA1c levels and albumin levels were isolated as independent predictors of LOS.

Conclusion

Patients with higher HbA1c and lower albumin levels required the longest period of hospitalization. Thus, an evaluation of diabetic and nutritional status on admission will be feasible to foretell the clinical course and better manage the subset of patients at risk of prolonged hospitalization.

The effectiveness of UV-C radiation for facility-wide environmental disinfection to reduce health care-acquired infections

BACKGROUND

Health care-acquired infections (HAIs) constitute an increasing threat for patients worldwide. Potential contributors of HAIs include environmental surfaces in health care settings, where ultraviolet-C radiation (UV-C) is commonly used for disinfection. This UV-C intervention-based pilot study was conducted in a hospital setting to identify any change in the incidence of HAIs before and after UV-C intervention, and to determine the effectiveness of UV-C in reducing pathogens.

METHODS

In a hospital in Culver City, CA, during 2012-2013, bactericidal doses of UV-C radiation (254 nm) were delivered through a UV-C-based mobile environmental decontamination unit. The UV-C dosing technology and expertise of the specifically trained personnel were provided together as a dedicated service model by a contracted company. The incidence of HAIs before and after the intervention period were determined and compared.

RESULTS

The dedicated service model dramatically reduced HAIs (incidence difference, 1.3/1000 patient-days, a 34.2% reduction). Reductions in the total number and incidence proportions (28.8%) of HAIs were observed after increasing and maintaining the coverage of UV-C treatments.

CONCLUSION

The dedicated service model was found to be effective in decreasing the incidence of HAIs, which could reduce disease morbidity and mortality in hospitalized patients. This model provides a continuously monitored and frequently UV-C-treated patient environment. This approach to UV-C disinfection was associated with a decreased incidence of HAIs.

Challenges Associated With Using Large Data Sets for Quality Assessment and Research in Clinical Settings

The rapidly expanding use of electronic records in health-care settings is generating unprecedented quantities of data available for clinical, epidemiological, and cost-effectiveness research. Several challenges are associated with using these data for clinical research, including issues surrounding access and information security, poor data quality, inconsistency of data within and across institutions, and a paucity of staff with expertise to manage and manipulate large clinical data sets. In this article, we describe our experience with assembling a data-mart and conducting clinical research using electronic data from four facilities within a single hospital network in New York City. We culled data from several electronic sources, including the institution's admission-discharge-transfer system, cost accounting system, electronic health record, clinical data warehouse, and departmental records. The final data-mart contained information for more than 760,000 discharges occurring from 2006 through 2012. Using categories identified by the National Institutes of Health Big Data to Knowledge initiative as a framework, we outlined challenges encountered during the development and use of a domain-specific data-mart and recommend approaches to overcome these challenges.

Effects of hospital-acquired conditions on length of stay for patients with diabetes

BACKGROUND

Inpatients with diabetes have longer length of stays (LOS). Understanding patterns of in-hospital complications between patients with diabetes and others may reveal measures to improve patient welfare and minimise LOS.

AIM

This study evaluates the rates and types of hospital-acquired conditions among patients with and without diabetes and assesses any effects on LOS.

METHODS

A total of 47 615 admission episodes from The Northern Hospital over 12 months was reviewed. Episodes were divided into four groups: (i) patients without diabetes; (ii) patients with diabetes without end-organ sequelae (EOS); (iii) patients with diabetes with EOS; and (iv) a subset of non-diabetic patients with a Charlson Co-morbidity score ≥1 (comparison group). The Classification of Hospital Acquired Diagnoses (CHADx) was applied to the groups to compare rates and types of inpatient complications. Linear regression was used to analyse the impact of the number of CHADx on LOS.

RESULTS

Almost 30% of admissions of patients with diabetes and EOS had at least one CHADx, compared with 13% for non-diabetes patients and 17% for the comparison group. The types of CHADx experienced by diabetes patients with EOS were similar to the comparison group. However, rates were 10 times higher. Linear regression demonstrated diabetes patients with EOS have increased LOS and each CHADx per episode has a larger effect on LOS.

CONCLUSION

We demonstrate that diabetes patients have consistently higher rates of CHADx and longer LOS than similar patients with complex and chronic conditions. This provides a foundation for future studies to investigate preventative practices for this high-risk patient population.