Assessment of retrospective collection of EQ-5D-5L in a US COVID-19 population.

BACKGROUND: It is imperative to evaluate health related quality of life (HRQoL) pre-COVID-19, but there is currently no evidence of the retrospective application of the EuroQol 5-Dimension, 5 level version (EQ-5D-5L) for COVID-19 studies. METHODS: Symptomatic patients with SARS-CoV-2 at CVS Health US test sites were recruited between 01/31/2022-04/30/2022. Consented participants completed the EQ-5D-5L questionnaire twice: a modified version where all the questions were past tense to retrospectively assess pre-COVID-19 baseline QoL, and the standard version in present tense to assess current HRQoL. Duncan's new multiple range test was adopted for post analysis of variance pairwise comparisons of EQ visual analog scale (EQ VAS) means between problem levels for each of 5 domains. A linear mixed model was applied to check whether the relationship between EQ VAS and utility index (UI) was consistent pre-COVID-19 and during COVID-19. Matching-adjusted indirect comparison was used to compare pre-COVID-19 UI and EQ VAS scores with those of the US population. Lastly, Cohen's d was used to quantify the magnitude of difference in means between two groups. RESULTS: Of 676 participants, 10.2% were age 65 or more years old, 73.2% female and 71.9% white. Diabetes was reported by 4.7% participants and hypertension by 11.2%. The estimated coefficient for the interaction of UI-by-retrospective collection indicator (0 = standard prospective collection, 1 = retrospective for pre-COVID-19), -4.2 (SE: 3.2), P = 0.197, indicates that retrospective collection does not significantly alter the relationship between EQ VAS and UI. After adjusting for age, gender, diabetes, hypertension, and percent of mobility problems, the predicted means of pre-COVID-19 baseline EQ VAS and UI were 84.6 and 0.866, respectively. Both means were close to published US population norms (80.4 and 0.851) compared to those observed (87.4 and 0.924). After adjusting for age, gender, diabetes, and hypertension, the calculated ES between pre-COVID-19 and COVID-19 for UI and EQ VAS were 0.15 and 0.39, respectively. Without retrospectively collected EQ-5D-5L, using US population norms tended to underestimate the impact of COVID-19 on HRQoL. CONCLUSION: At a group level the retrospectively collected pre-COVID-19 EQ-5D-5L is adequate and makes it possible to directly evaluate the impact of COVID-19 on HRQoL. ( ClinicalTrials.gov NCT05160636).

Impact of COVID-19 and effects of booster vaccination with BNT162b2 on six-month long COVID symptoms, quality of life, work productivity and activity impairment during Omicron.

BACKGROUND: Longitudinal estimates of long COVID burden during Omicron remain limited. This study characterized long-term impacts of COVID-19 and booster vaccination on symptoms, Health-Related Quality of Life (HRQoL), and Work Productivity Activity Impairment (WPAI). METHODS: Outpatients with ≥ 1 self-reported symptom and positive SARS-CoV-2 test at CVS Health United States test sites were recruited between 01/31 and 04/30/2022. Symptoms, EQ-5D and WPAI were collected via online surveys until 6 months following infection. Both observed and model-based estimates were analyzed. Effect sizes based on Cohen's d quantified the magnitude of outcome changes over time, within and between vaccination groups. Mixed models for repeated measures were conducted for multivariable analyses, adjusting for covariates. Logistic regression assessed odds ratio (OR) of long COVID between vaccination groups. RESULTS: At long COVID start (Week 4), 328 participants included 87 (27%) Boosted with BNT162b2, 86 (26%) with a BNT162b2 primary series (Primed), and 155 (47%) Unvaccinated. Mean age was 42.0 years, 73.8% were female, 26.5% had ≥ 1 comorbidity, 36.9% prior infection, and 39.6% reported ≥ 3 symptoms (mean: 3.1 symptoms). At Month 6, among 260 participants, Boosted reported a mean of 1.1 symptoms versus 3.4 and 2.8 in Unvaccinated and Primed, respectively (p < 0.001). Boosted had reduced risks of ≥ 3 symptoms versus Unvaccinated (observed: OR 0.22, 95% CI 0.10-0.47, p < 0.001; model-based: OR 0.36, 95% CI 0.15-0.87, p = 0.019) and Primed (observed: OR 0.29, 95% CI 0.13-0.67, p = 0.003; model-based: OR 0.59, 95% CI 0.21-1.65, p = 0.459). Results were consistent using ≥ 2 symptoms. Regarding HRQoL, among those with long COVID, Boosted had higher EQ-5D Utility Index (UI) than Unvaccinated (observed: 0.922 vs. 0.731, p = 0.014; model-based: 0.910 vs. 0.758, p-value = 0.038) and Primed (0.922 vs. 0.648, p = 0.014; model-based: 0.910 vs. 0.708, p-value = 0.008). Observed and model-based estimates for EQ-VAS and UI among Boosted were comparable with pre-COVID since Month 3. Subjects vaccinated generally reported better WPAI scores. CONCLUSIONS: Long COVID negatively impacted HRQoL and WPAI. The BNT162b2 booster could have a beneficial effect in reducing the risk and burden of long COVID. Boosted participants reported fewer and less durable symptoms, which contributed to improve HRQoL and maintain WPAI levels. Limitations included self-reported data and small sample size for WPAI.

Impact of COVID-19 and effects of BNT162b2 on patient-reported outcomes: quality of life, symptoms, and work productivity among US adult outpatients.

BACKGROUND: Although there is extensive literature on the clinical benefits of COVID-19 vaccination, data on humanistic effects are limited. This study evaluated the impact of SARS-CoV-2 infection on symptoms, Health-Related Quality of Life (HRQoL) and Work Productivity and Impairment (WPAI) prior to and one month following infection between individuals vaccinated with BNT162b2 and those unvaccinated. METHODS: Subjects with ≥ 1 self-reported symptom and positive RT-PCR for SARS-CoV-2 at CVS Health US test sites were recruited between 01/31/2022 and 04/30/2022. Socio-demographics, clinical characteristics and vaccination status were evaluated. Self-reported symptoms, HRQoL, and WPAI outcomes were assessed using questionnaires and validated instruments (EQ-5D-5L, WPAI-GH) across acute COVID time points from pre-COVID to Week 4, and between vaccination groups. Mixed models for repeated measures were conducted for multivariable analyses, adjusting for several covariates. Effect size (ES) of Cohen's d was calculated to quantify the magnitude of outcome changes within and between vaccination groups. RESULTS: The study population included 430 subjects: 197 unvaccinated and 233 vaccinated with BNT162b2. Mean (SD) age was 42.4 years (14.3), 76.0% were female, 38.8% reported prior infection and 24.2% at least one comorbidity. Statistically significant differences in outcomes were observed compared with baseline and between groups. The EQ-Visual analogue scale scores and Utility Index dropped in both cohorts at Day 3 and increased by Week 4 but did not return to pre-COVID levels. The mean changes were statistically lower in the BNT162b2 cohort at Day 3 and Week 4. The BNT162b2 cohort reported lower prevalence and fewer symptoms at index date and Week 4. At Week 1, COVID-19 had a large impact on all WPAI-GH domains: the work productivity time loss among unvaccinated and vaccinated was 65.0% and 53.8%, and the mean activity impairment was 50.2% and 43.9%, respectively. Except for absenteeism at Week 4, the BNT162b2 cohort was associated with statistically significant less worsening in all WPAI-GH scores at both Week 1 and 4. CONCLUSIONS: COVID-19 negatively impacted HRQoL and work productivity among mildly symptomatic outpatients. Compared with unvaccinated, those vaccinated with BNT162b2 were less impacted by COVID-19 infection and recovered faster.

Relationships between creatinine increase and mortality rates in patients given vancomycin in 76 hospitals: The increasing role of infectious disease pharmacists.

PURPOSE: Vancomycin is a commonly used antimicrobial with the potential for renal toxicity. We evaluated vancomycin duration, changes in renal function after vancomycin initiation ("post-vancomycin" renal function changes), and associated mortality risk among hospitalized patients. METHODS: We analyzed data from 76 hospitals and excluded patients with a baseline serum creatinine concentration (SCr) of >3.35 mg/dL. We estimated mortality risk relative to vancomycin duration and the magnitude of post-vancomycin SCr change, controlling for demographics, baseline SCr, underlying diseases, clinical acuity, and comorbidities. RESULTS: Among 128,993 adult inpatients treated with vancomycin, 49.0% did not experience SCr elevation. Among the remaining patients, 26.0%, 11.4%, 8.8% and 4.8% experienced increases in post-vancomycin SCr of 1% to 20%, 21% to 40%, 41% to 100%, and greater than 100%, respectively. Compared to mortality risk among patients with a vancomycin therapy duration between 4 and 5 days (the lowest-mortality group), longer vancomycin therapy duration was not independently associated with higher mortality risk after adjusting for confounders. In contrast, there was a graded relationship between post-vancomycin SCr elevation and mortality. Multivariable adjusted mortality odds ratios ranged from 1.60 to 13.66, corresponding to SCr increases of 10% and greater than 200%, respectively. CONCLUSION: Half of patients given vancomycin did not experience SCr elevation and had the lowest mortality, suggesting that vancomycin can be used safely if renal function is stabilized. In the large study cohort, vancomycin duration itself was not an independent predictor of mortality. Post-vancomycin SCr elevation appeared to be a driver of in-hospital mortality. Even a 10% post-vancomycin SCr increase was associated with an increased mortality risk. This finding stresses the importance of closely monitoring renal function and may support the value of pharmacokinetic dosing.

Predicting hospital readmission in patients with mental or substance use disorders: A machine learning approach.

OBJECTIVE: Mental or substance use disorders (M/SUD) are major contributors of disease burden with high risk for hospital readmissions. We sought to develop and evaluate a readmission model using a machine learning (ML) approach. METHODS: We analyzed patients with continuous enrollment for three years and at least one episode of M/SUD as the primary reason for hospital admission. The outcome was readmission within 30-days from discharge. Model performance was evaluated using the Area under the Receiver Operating Characteristic (AUROC). We compared the AUROCs of an extreme gradient boosted tree (XGBoost) model to generalized linear model with elastic net regularization (GLMNet). RESULTS: We analyzed 65,426 unique patients and 97,688 admissions. Patients with mental disorders accounted for 66 % (13.2 % readmission rate) and substance use disorders, 34 % (22.3 % readmission rate). Among all those who had readmissions, 70.7 %, 17.0 %, and 12.4 % had 1, 2, or 3+ readmissions, respectively. Previous hospitalizations, hospital utilization, discharge disposition, diagnosis category, and comorbidity were among the highest important features in the XGBoost model. The XGBoost model AUROC was 0.737 (95 % CI: 0.732 to 0.742) versus the GLMNet 0.697 (95 % CI: 0.690 to 0.703). The AUROC of the final XGBoost model on the testing set was 0.738 (95 % CI: 0.730 to 0.748), higher than published readmission models for mental health patients. CONCLUSIONS: The XGBoost model has a better performance than GLMNet and previously published models in predicting readmissions in mental health patients. Our model may be further tested to aid targeted demographic initiatives to reduce M/SUDs readmissions and benchmarking.

Contributing Factors to the Clinical and Economic Burden of Patients with Laboratory-Confirmed Carbapenem-Nonsusceptible Gram-Negative Urinary Tract Infections.

PURPOSE: We explored patient- and hospital-level predictor variables for worse clinical and economic outcomes in carbapenem-nonsusceptible urinary tract infections (UTIs). PATIENTS AND METHODS: We used electronic data (January 2013-September 2015; 78 US hospitals) from a large multicenter clinical database. Nonduplicate gram-negative isolates were considered carbapenem-nonsusceptible if they had resistant/intermediate susceptibility. Potential predictors of outcomes (mortality, 30-day readmissions, length of stay [LOS], hospital total cost, and net gain/loss per case) were examined using generalized linear mixed models. Significant predictors were identified based on statistical significance and model goodness-of-fit criteria. RESULTS: A total of 1439 carbapenem-nonsusceptible urine cases were identified. The mortality rate was 5.5%; the hospital readmission rate was 25.0%. Mean (standard deviation [SD]) LOS, total cost, and loss per case were 12 (14) days, $21,502 ($37,172), and $5828 ($26,540), respectively. Hospital-onset (vs community-onset) infection significantly impacted all outcomes: mortality (odds ratio [OR], 2.21; 95% confidence interval [CI], 1.19-4.11; P=.01), 30-day readmissions (OR, 2.35; 95% CI, 1.49-3.71; P<.001), LOS (25.7 vs 10.2 days; P<.001), hospital total cost ($67,810 vs $22,141; P<.001), and loss per case (-$28,054 vs -$10,809; P<.001). Mechanical ventilation/intensive care unit status, neoplasms, and other underlying diseases were also common predictors for worse outcomes overall; polymicrobial infection was significantly associated with worse economic outcomes. Other key predictors were >1 prior hospitalization for 30-day readmissions, high Acute Laboratory Risk of Mortality Score for mortality, LOS, cost, and hospital teaching status for cost. CONCLUSION: Hospital-onset infections, polymicrobial infections, higher clinical severity, and underlying diseases are key predictors for worsened overall burden of carbapenem-nonsusceptible gram-negative UTIs.

Contributing Factors to the Clinical and Economic Burden of Patients with Laboratory-Confirmed Carbapenem-Nonsusceptible Gram-Negative Respiratory Infections.

PURPOSE: This study examined patient- and hospital-level predictor variables that contribute to worse clinical and economic outcomes in patients with carbapenem-nonsusceptible respiratory infections. PATIENTS AND METHODS: Electronic data (January 2013 to September 2015) were from 78 US hospitals. Nonduplicate, gram-negative respiratory isolates were considered carbapenem-nonsusceptible if they tested resistant/intermediate to imipenem, meropenem, doripenem, or ertapenem. Potential predictors of outcomes (in-hospital mortality, 30-day readmission, length of stay [LOS], hospital total cost, and net gain/loss per patient) were examined using univariate analysis and generalized linear mixed models. Statistical significance and model goodness-of-fit criteria were used to identify significant predictors. RESULTS: A total of 1488 carbapenem-nonsusceptible respiratory patients were identified. Overall, the mortality rate was 13.7%, 30-day readmission rate was 20.6%, mean LOS was 20 days, mean total cost was $54,158, and mean net loss was $139 per patient. Our models showed that hospital-onset infection, higher clinical severity, mechanical ventilation/intensive care unit status, polymicrobial infection, and underlying diseases were all significant predictors for mortality, LOS, and total cost. Hospital-onset infections were also associated with a significantly greater net loss (P≤.01), and underlying disease significantly impacted readmissions (P=.03). The number of prior admissions, hospital characteristics, and payer type were also found to significantly impact measured outcomes. CONCLUSION: Carbapenem-nonsusceptible respiratory infections are associated with a considerable clinical and economic burden. The impact of hospital-onset infections on both clinical and economic outcomes highlights the continued need for action on this modifiable risk factor through antimicrobial stewardship and optimal therapy, thereby reducing the burden in this patient population.

Attributable burden in patients with carbapenem-nonsusceptible gram-negative respiratory infections.

OBJECTIVE: We aimed to describe the clinical and economic burden attributable to carbapenem-nonsusceptible (C-NS) respiratory infections. METHODS: This retrospective matched cohort study assessed clinical and economic outcomes of adult patients (aged ≥18 years) who were admitted to one of 78 acute care hospitals in the United States with nonduplicate C-NS and carbapenem-susceptible (C-S) isolates from a respiratory source. A subset analysis of patients with principal diagnosis codes denoting bacterial pneumonia or other diagnoses was also conducted. Isolates were classified as community- or hospital-onset based on collection time. A generalized linear mixed model method was used to estimate the attributable burden for mortality, 30-day readmission, length of stay (LOS), cost, and net gain/loss (payment minus cost) using propensity score-matched C-NS versus C-S cohorts. RESULTS: For C-NS cases, mortality (25.7%), LOS (29.4 days), and costs ($81,574) were highest in the other principal diagnosis, hospital-onset subgroup; readmissions (19.4%) and net loss (-$9522) were greatest in the bacterial pneumonia, hospital-onset subgroup. Mortality and readmissions were not significantly higher for C-NS cases in any propensity score-matched subgroup. Significant C-NS-attributable burden was found for both other principal diagnosis subgroups for LOS (hospital-onset: 3.7 days, P = 0.006; community-onset: 1.5 days, P<0.001) and cost (hospital-onset: $12,777, P<0.01; community-onset: $2681, P<0.001). CONCLUSIONS: Increased LOS and cost burden were observed in propensity score-matched patients with C-NS compared with C-S respiratory infections; the C-NS-attributable burden was significant only for patients with other principal diagnoses.

Hospital-level high-risk antibiotic use in relation to hospital-associated Clostridioides difficile infections: Retrospective analysis of 2016-2017 data from US hospitals.

OBJECTIVE: Antibiotics are widely used by all specialties in the hospital setting. We evaluated previously defined high-risk antibiotic use in relation to Clostridioides difficile infections (CDIs). METHODS: We analyzed 2016-2017 data from 171 hospitals. High-risk antibiotics included second-, third-, and fourth-generation cephalosporins, fluoroquinolones, carbapenems, and lincosamides. A CDI case was a positive stool C. difficile toxin or molecular assay result from a patient without a positive result in the previous 8 weeks. Hospital-associated (HA) CDI cases included specimens collected >3 calendar days after admission or ≤3 calendar days from a patient with a prior same-hospital discharge within 28 days. We used the multivariable Poisson regression model to estimate the relative risk (RR) of high-risk antibiotic use on HA CDI, controlling for confounders. RESULTS: The median days of therapy for high-risk antibiotic use was 241.2 (interquartile range [IQR], 192.6-295.2) per 1,000 days present; the overall HA CDI rate was 33 (IQR, 24-43) per 10,000 admissions. The overall correlation of high-risk antibiotic use and HA CDI was 0.22 (P = .003), and higher correlation was observed in teaching hospitals (0.38; P = .002). For every 100-day (per 1,000 days present) increase in high-risk antibiotic therapy, there was a 12% increase in HA CDI (RR, 1.12; 95% CI, 1.04-1.21; P = .002) after adjusting for confounders. CONCLUSIONS: High-risk antibiotic use is an independent predictor of HA CDI. This assessment of poststewardship implementation in the United States highlights the importance of tracking trends of antimicrobial use over time as it relates to CDI.

Blood Culture Turnaround Time in U.S. Acute Care Hospitals and Implications for Laboratory Process Optimization.

The rapid identification of blood culture isolates and antimicrobial susceptibility test (AST) results play critical roles for the optimal treatment of patients with bloodstream infections. Whereas others have looked at the time to detection in automated culture systems, we examined the overall time from specimen collection to actionable test results. We examined four points of time, namely, blood specimen collection, Gram stain, organism identification (ID), and AST reports, from electronic data from 13 U.S. hospitals for the 11 most common, clinically significant organisms in septic patients. We compared the differences in turnaround times and the times from when specimens were collected and the results were reported in the 24-h spectrum. From January 2015 to June 2016, 165,593 blood specimens were collected, of which, 9.5% gave positive cultures. No matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry was used during the study period. Across the 10 common bacterial isolates (n = 6,412), the overall median (interquartile range) turnaround times were 0.80 (0.64 to 1.08), 1.81 (1.34 to 2.46), and 2.71 (2.46 to 2.99) days for Gram stain, organism ID, and AST, respectively. For all positive cultures, approximately 25% of the specimens were collected between 6:00 a.m. and 11:59 a.m. In contrast, more of the laboratory reporting times were concentrated between 6:00 a.m. and 11:59 a.m. for Gram stain (43%), organism ID (78%), and AST (82%), respectively (P < 0.001). The overall average turnaround times from specimen collection for Gram stain, organism ID, and AST were approximately 1, 2, and 3 days, respectively. The laboratory results were reported predominantly in the morning hours. Laboratory automation and work flow optimization may play important roles in reducing the microbiology result turnaround time.

The Effect of a Piperacillin/Tazobactam Shortage on Antimicrobial Prescribing and Clostridium difficile Risk in 88 US Medical Centers.

BACKGROUND: Anti-infective shortages are a pervasive problem in the United States. The objective of this study was to identify any associations between changes in prescribing of antibiotics that have a high risk for CDI during a piperacillin/tazobactam (PIP/TAZO) shortage and hospital-onset Clostridium difficile infection (HO-CDI) risk in 88 US medical centers. METHODS: We analyzed electronically captured microbiology and antibiotic use data from a network of US hospitals from July 2014 through June 2016. The primary outcome was HO-CDI rate and the secondary outcome was changes in antibiotic usage. We fit a Poisson model to estimate the risk of HO-CDI associated with PIP/TAZO shortage that were associated with increased high-risk antibiotic use while controlling for hospital characteristics. RESULTS: A total of 88 hospitals experienced PIP/TAZO shortage and 72 of them experienced a shift toward increased use of high-risk antibiotics during the shortage period. The adjusted relative risk (RR) of HO-CDI for hospitals experiencing a PIP/TAZO shortage was 1.03 (95% confidence interval [CI], .85-1.26; P = .73). The adjusted RR of HO-CDI for hospitals that both experienced a shortage and also showed a shift toward increased use of high-risk antibiotics was 1.30 (95% CI, 1.03-1.64; P < .05). CONCLUSIONS: Hospitals that experienced a PIP/TAZO shortage and responded to that shortage by shifting antibiotic usage toward antibiotics traditionally known to place patients at greater risk for CDI experienced greater HO-CDI rates; this highlights an important adverse effect of the PIP/TAZO shortage and the importance of antibiotic stewardship when mitigating drug shortages.

Diagnosis of Chronic Pancreatitis Incorporating Endosonographic Features, Demographics, and Behavioral Risk.

OBJECTIVES: Diagnosing chronic pancreatitis remains challenging. Endoscopic ultrasound (EUS) is utilized to evaluate pancreatic disease. Abnormal pancreas function test is considered the "nonhistologic" criterion standard for chronic pancreatitis. We derived a prediction model for abnormal endoscopic pancreatic function test (ePFT) by enriching EUS findings with patient demographic and pancreatitis behavioral risk characteristics. METHODS: Demographics, behavioral risk characteristics, EUS findings, and peak bicarbonate results were collected from patients evaluated for pancreatic disease. Abnormal ePFT was defined as peak bicarbonate of less than 75 mEq/L. We fit a logistic regression model and converted it to a risk score system. The risk score was validated using 1000 bootstrap simulations. RESULTS: A total of 176 patients were included; 61% were female with median age of 48 years (interquartile range, 38-57 years). Abnormal ePFT rate was 39.2% (69/176). Four variables formulated the risk score: alcohol or smoking status, number of parenchymal abnormalities, number of ductal abnormalities, and calcifications. Abnormal ePFT occurred in 10.7% with scores 4 or less versus 92.0% scoring 20 or greater. The model C-statistic was 0.78 (95% confidence interval, 0.71-0.85). CONCLUSIONS: Number of EUS pancreatic duct and parenchymal abnormalities, presence of calcification, and smoking/alcohol status were predictive of abnormal ePFT. This simple model has good discrimination for ePFT results.

Predicting Readmission at Early Hospitalization Using Electronic Clinical Data: An Early Readmission Risk Score.

BACKGROUND: Identifying patients at high risk for readmission early during hospitalization may aid efforts in reducing readmissions. We sought to develop an early readmission risk predictive model using automated clinical data available at hospital admission. METHODS: We developed an early readmission risk model using a derivation cohort and validated the model with a validation cohort. We used a published Acute Laboratory Risk of Mortality Score as an aggregated measure of clinical severity at admission and the number of hospital discharges in the previous 90 days as a measure of disease progression. We then evaluated the administrative data-enhanced model by adding principal and secondary diagnoses and other variables. We examined the c-statistic change when additional variables were added to the model. RESULTS: There were 1,195,640 adult discharges from 70 hospitals with 39.8% male and the median age of 63 years (first and third quartile: 43, 78). The 30-day readmission rate was 11.9% (n=142,211). The early readmission model yielded a graded relationship of readmission and the Acute Laboratory Risk of Mortality Score and the number of previous discharges within 90 days. The model c-statistic was 0.697 with good calibration. When administrative variables were added to the model, the c-statistic increased to 0.722. CONCLUSIONS: Automated clinical data can generate a readmission risk score early at hospitalization with fair discrimination. It may have applied value to aid early care transition. Adding administrative data increases predictive accuracy. The administrative data-enhanced model may be used for hospital comparison and outcome research.

Global Health Education in Gastroenterology Fellowship: A National Survey.

BACKGROUND: Interest in global health (GH) education is increasing across disciplines. AIMS: To assess exposure to and perception of GH training among gastroenterology fellows and program directors across the USA. METHODS: Design: Electronic survey study. SETTING: The questionnaire was circulated to accredited US gastroenterology fellowship programs, with the assistance of the American Gastroenterological Association. PARTICIPANTS: Gastroenterology program directors and fellows. RESULTS: The questionnaire was returned by 127 respondents (47 program directors, 78 fellows) from 55 training programs (36 % of all training programs). 61 % of respondents had prior experience in GH. 17 % of programs offered GH curriculum with international elective (13 %), didactic (9 %), and research activity (7 %) being the most common. Fellows had adequate experience managing hepatitis B (93 %), cholangiocarcinoma (84 %), and intrahepatic duct stones (84 %). 74, 69 and 68 % reported having little to no experience managing hepatitis E, tuberculosis mesenteritis, or epidemic infectious enteritis, respectively. Most fellows would participate in an elective in an underserved area locally (81 %) or a 4-week elective abroad (71 %), if available. 44 % of fellows planned on working or volunteering abroad after fellowship. Barriers to establishing GH curriculum included funding (94 %), scheduling (88 %), and a lack of standardized objectives (78 %). Lack of interest, however, was not a concern. Fellows (49 %), more than faculty (29 %) (χ 2 = 21.9; p = 0.03), believed that GH education should be included in fellowship curriculum. CONCLUSIONS: Program directors and trainees recognize the importance of GH education. However, only 17 % of ACGME-approved fellowship programs offer the opportunity. Global health curriculum may enhance gastroenterology training.

Innovative Use of Existing Public and Private Data Sources for Postmarketing Surveillance of Central Line-Associated Bloodstream Infections Associated With Intravenous Needleless Connectors.

The Centers for Medicare and Medicaid Services (CMS) Hospital Compare central line-associated bloodstream infection (CLABSI) data and private databases containing new-generation intravenous needleless connector (study NC) use at the hospital level were linked. The relative risk (RR) of CLABSI associated with the study NCs was estimated, adjusting for hospital characteristics. Among 3074 eligible hospitals in the 2013 CMS database, 758 (25%) hospitals used the study NCs. The study NC hospitals had a lower unadjusted CLABSI rate (1.03 vs 1.13 CLABSIs per 1000 central line days, P < .0001) compared with comparator hospitals. The adjusted RR for CLABSI was 0.94 (95% confidence interval: 0.86, 1.02; P = .11).

Performance characteristics and associated outcomes for an automated surveillance tool for bloodstream infection.

BACKGROUND: The objective of this study was to evaluate performance metrics and associated patient outcomes of an automated surveillance system, the blood Nosocomial Infection Marker (NIM). METHODS: We reviewed records of 237 patients with and 36,927 patients without blood NIM using the National Healthcare Safety Network (NHSN) definition for laboratory-confirmed bloodstream infection (BSI) as the gold standard. We matched cases with noncases by propensity score and estimated attributable mortality and cost of NHSN-reportable central line-associated bloodstream infections (CLABSIs) and non-NHSN-reportable BSIs. RESULTS: For patients with central lines (CL), the blood NIM had 73.2% positive predictive value (PPV), 99.9% negative predictive value (NPV), 89.2% sensitivity, and 99.7% specificity. For all patients regardless of CL status, the blood NIM had 53.6% PPV, 99.9% NPV, 84.0% sensitivity, and 99.9% specificity. For CLABSI cases compared with noncases, mortality was 17.5% versus 9.4% (P = .098), and median charge was $143,935 (interquartile range [IQR], $89,794-$257,447) versus $115,267 (IQR, $74,937-$173,053) (P < .01). For non-NHSN-reportable BSI cases compared with noncases, mortality was 23.6% versus 6.7% (P < .0001), and median charge was $86,927 (IQR, $54,728-$156,669) versus $62,929 (IQR, $36,743-$115,693) (P < .0001). CONCLUSIONS: The NIM is an effective screening tool for BSI. Both NHSN-reportable and nonreportable BSI cases were associated with increased mortality and cost.

Predicting the risk for hospital-onset Clostridium difficile infection (HO-CDI) at the time of inpatient admission: HO-CDI risk score.

OBJECTIVE: To predict the likelihood of hospital-onset Clostridium difficile infection (HO-CDI) based on patient clinical presentations at admission DESIGN: Retrospective data analysis SETTING: Six US acute care hospitals PATIENTS: Adult inpatients METHODS: We used clinical data collected at the time of admission in electronic health record (EHR) systems to develop and validate a HO-CDI predictive model. The outcome measure was HO-CDI cases identified by a nonduplicate positive C. difficile toxin assay result with stool specimens collected >48 hours after inpatient admission. We fit a logistic regression model to predict the risk of HO-CDI. We validated the model using 1,000 bootstrap simulations. RESULTS: Among 78,080 adult admissions, 323 HO-CDI cases were identified (ie, a rate of 4.1 per 1,000 admissions). The logistic regression model yielded 14 independent predictors, including hospital community onset CDI pressure, patient age ≥65, previous healthcare exposures, CDI in previous admission, admission to the intensive care unit, albumin ≤3 g/dL, creatinine >2.0 mg/dL, bands >32%, platelets ≤150 or >420 109/L, and white blood cell count >11,000 mm3. The model had a c-statistic of 0.78 (95% confidence interval [CI], 0.76-0.81) with good calibration. Among 79% of patients with risk scores of 0-7, 19 HO-CDIs occurred per 10,000 admissions; for patients with risk scores >20, 623 HO-CDIs occurred per 10,000 admissions (P<.0001). CONCLUSION: Using clinical parameters available at the time of admission, this HO-CDI model demonstrated good predictive ability, and it may have utility as an early risk identification tool for HO-CDI preventive interventions and outcome comparisons.

Meta-analysis on central line-associated bloodstream infections associated with a needleless intravenous connector with a new engineering design.

BACKGROUND: Intravenous needleless connectors (NCs) with a desired patient safety design may facilitate effective intravenous line care and reduce the risk for central line-associated bloodstream infection (CLA-BSI). We conducted a meta-analysis to determine the risk for CLA-BSI associated with the use of a new NC with an improved engineering design. METHODS: We reviewed MEDLINE, Cochrane Database of Systematic Reviews, Embase, ClinicalTrials.gov, and studies presented in 2010-2012 at infection control and infectious diseases meetings. Studies reporting the CLA-BSIs in patients using the positive-displacement NC (study NC) compared with negative- or neutral-displacement NCs were analyzed. We estimated the relative risk of CLA-BSIs with the study NC for the pooled effect using the random effects method. RESULTS: Seven studies met the inclusion criteria: 4 were conducted in intensive care units, 1 in a home health setting, and 2 in long-term acute care settings. In the comparator period, total central venous line (CL) days were 111,255; the CLA-BSI rate was 1.5 events per 1,000 CL days. In the study NC period, total CL days were 95,383; the CLA-BSI rate was 0.5 events per 1,000 CL days. The pooled CLA-BSI relative risk associated with the study NC was 0.37 (95% confidence interval, 0.16-0.90). CONCLUSION: The NC with an improved engineering design is associated with lower CLA-BSI risk.