A Trial of Automated Outbreak Detection to Reduce Hospital Pathogen Spread.

BACKGROUND: Detection and containment of hospital outbreaks currently depend on variable and personnel-intensive surveillance methods. Whether automated statistical surveillance for outbreaks of health care-associated pathogens allows earlier containment efforts that would reduce the size of outbreaks is unknown. METHODS: We conducted a cluster-randomized trial in 82 community hospitals within a larger health care system. All hospitals followed an outbreak response protocol when outbreaks were detected by their infection prevention programs. Half of the hospitals additionally used statistical surveillance of microbiology data, which alerted infection prevention programs to outbreaks. Statistical surveillance was also applied to microbiology data from control hospitals without alerting their infection prevention programs. The primary outcome was the number of additional cases occurring after outbreak detection. Analyses assessed differences between the intervention period (July 2019 to January 2022) versus baseline period (February 2017 to January 2019) between randomized groups. A post hoc analysis separately assessed pre-coronavirus disease 2019 (Covid-19) and Covid-19 pandemic intervention periods. RESULTS: Real-time alerts did not significantly reduce the number of additional outbreak cases (intervention period versus baseline: statistical surveillance relative rate [RR]=1.41, control RR=1.81; difference-in-differences, 0.78; 95% confidence interval [CI], 0.40 to 1.52; P=0.46). Comparing only the prepandemic intervention with baseline periods, the statistical outbreak surveillance group was associated with a 64.1% reduction in additional cases (statistical surveillance RR=0.78, control RR=2.19; difference-in-differences, 0.36; 95% CI, 0.13 to 0.99). There was no similarly observed association between the pandemic versus baseline periods (statistical surveillance RR=1.56, control RR=1.66; difference-in-differences, 0.94; 95% CI, 0.46 to 1.92). CONCLUSIONS: Automated detection of hospital outbreaks using statistical surveillance did not reduce overall outbreak size in the context of an ongoing pandemic. (Funded by the Centers for Disease Control and Prevention; ClinicalTrials.gov number, NCT04053075. Support for HCA Healthcare's participation in the study was provided in kind by HCA.).

Stewardship Prompts to Improve Antibiotic Selection for Urinary Tract Infection: The INSPIRE Randomized Clinical Trial.

Importance: Urinary tract infection (UTI) is the second most common infection leading to hospitalization and is often associated with gram-negative multidrug-resistant organisms (MDROs). Clinicians overuse extended-spectrum antibiotics although most patients are at low risk for MDRO infection. Safe strategies to limit overuse of empiric antibiotics are needed. Objective: To evaluate whether computerized provider order entry (CPOE) prompts providing patient- and pathogen-specific MDRO risk estimates could reduce use of empiric extended-spectrum antibiotics for treatment of UTI. Design, Setting, and Participants: Cluster-randomized trial in 59 US community hospitals comparing the effect of a CPOE stewardship bundle (education, feedback, and real-time and risk-based CPOE prompts; 29 hospitals) vs routine stewardship (n = 30 hospitals) on antibiotic selection during the first 3 hospital days (empiric period) in noncritically ill adults (≥18 years) hospitalized with UTI with an 18-month baseline (April 1, 2017-September 30, 2018) and 15-month intervention period (April 1, 2019-June 30, 2020). Interventions: CPOE prompts recommending empiric standard-spectrum antibiotics in patients ordered to receive extended-spectrum antibiotics who have low estimated absolute risk (<10%) of MDRO UTI, coupled with feedback and education. Main Outcomes and Measures: The primary outcome was empiric (first 3 days of hospitalization) extended-spectrum antibiotic days of therapy. Secondary outcomes included empiric vancomycin and antipseudomonal days of therapy. Safety outcomes included days to intensive care unit (ICU) transfer and hospital length of stay. Outcomes were assessed using generalized linear mixed-effect models to assess differences between the baseline and intervention periods. Results: Among 127 403 adult patients (71 991 baseline and 55 412 intervention period) admitted with UTI in 59 hospitals, the mean (SD) age was 69.4 (17.9) years, 30.5% were male, and the median Elixhauser Comorbidity Index count was 4 (IQR, 2-5). Compared with routine stewardship, the group using CPOE prompts had a 17.4% (95% CI, 11.2%-23.2%) reduction in empiric extended-spectrum days of therapy (rate ratio, 0.83 [95% CI, 0.77-0.89]; P < .001). The safety outcomes of mean days to ICU transfer (6.6 vs 7.0 days) and hospital length of stay (6.3 vs 6.5 days) did not differ significantly between the routine and intervention groups, respectively. Conclusions and Relevance: Compared with routine stewardship, CPOE prompts providing real-time recommendations for standard-spectrum antibiotics for patients with low MDRO risk coupled with feedback and education significantly reduced empiric extended-spectrum antibiotic use among noncritically ill adults admitted with UTI without changing hospital length of stay or days to ICU transfers. Trial Registration: ClinicalTrials.gov Identifier: NCT03697096.

Stewardship Prompts to Improve Antibiotic Selection for Pneumonia: The INSPIRE Randomized Clinical Trial.

Importance: Pneumonia is the most common infection requiring hospitalization and is a major reason for overuse of extended-spectrum antibiotics. Despite low risk of multidrug-resistant organism (MDRO) infection, clinical uncertainty often drives initial antibiotic selection. Strategies to limit empiric antibiotic overuse for patients with pneumonia are needed. Objective: To evaluate whether computerized provider order entry (CPOE) prompts providing patient- and pathogen-specific MDRO infection risk estimates could reduce empiric extended-spectrum antibiotics for non-critically ill patients admitted with pneumonia. Design, Setting, and Participants: Cluster-randomized trial in 59 US community hospitals comparing the effect of a CPOE stewardship bundle (education, feedback, and real-time MDRO risk-based CPOE prompts; n = 29 hospitals) vs routine stewardship (n = 30 hospitals) on antibiotic selection during the first 3 hospital days (empiric period) in non-critically ill adults (≥18 years) hospitalized with pneumonia. There was an 18-month baseline period from April 1, 2017, to September 30, 2018, and a 15-month intervention period from April 1, 2019, to June 30, 2020. Intervention: CPOE prompts recommending standard-spectrum antibiotics in patients ordered to receive extended-spectrum antibiotics during the empiric period who have low estimated absolute risk (<10%) of MDRO pneumonia, coupled with feedback and education. Main Outcomes and Measures: The primary outcome was empiric (first 3 days of hospitalization) extended-spectrum antibiotic days of therapy. Secondary outcomes included empiric vancomycin and antipseudomonal days of therapy and safety outcomes included days to intensive care unit (ICU) transfer and hospital length of stay. Outcomes compared differences between baseline and intervention periods across strategies. Results: Among 59 hospitals with 96 451 (51 671 in the baseline period and 44 780 in the intervention period) adult patients admitted with pneumonia, the mean (SD) age of patients was 68.1 (17.0) years, 48.1% were men, and the median (IQR) Elixhauser comorbidity count was 4 (2-6). Compared with routine stewardship, the group using CPOE prompts had a 28.4% reduction in empiric extended-spectrum days of therapy (rate ratio, 0.72 [95% CI, 0.66-0.78]; P < .001). Safety outcomes of mean days to ICU transfer (6.5 vs 7.1 days) and hospital length of stay (6.8 vs 7.1 days) did not differ significantly between the routine and CPOE intervention groups. Conclusions and Relevance: Empiric extended-spectrum antibiotic use was significantly lower among adults admitted with pneumonia to non-ICU settings in hospitals using education, feedback, and CPOE prompts recommending standard-spectrum antibiotics for patients at low risk of MDRO infection, compared with routine stewardship practices. Hospital length of stay and days to ICU transfer were unchanged. Trial Registration: ClinicalTrials.gov Identifier: NCT03697070.

A Case-Control Study of Distinguishing Between Stroke Mimics and True Strokes.

This study was conducted with the primary aim to distinguish patients with a true stroke versus a stroke mimic based on clinical features and imaging. We conducted a retrospective case-control study on 116 adult patients who received alteplase (tPA) to treat acute stroke at our hospital. We further analyzed 79 patients with a normal computed tomography angiography (CTA). Based on their magnetic resonance imaging (MRI) of the brain, they were divided into cases (stroke mimics) and controls (true strokes). Data were collected retrospectively by reviewing individual medical charts on the electronic medical record (EMR), including age, gender, history of stroke, seizure, hypertension, diabetes, atrial fibrillation, hyperlipidemia, presenting NIH Stroke Scale/Score, hemorrhagic conversion, history of migraine, history of depression, sidedness of symptoms and aphasia. Data were categorized to separate those who were later diagnosed to be stroke mimics by being-postictal, encephalopathic, in acute migraine, suffered post-stroke recrudescence (PSR) due to metabolic insult, or had conversion disorder when symptoms could not be attributed to any medical condition or mental illness. Of the 79 study subjects, 48 (60%) were stroke mimics. The mean age of the cohort was 68.67 years, and 46.8% of the study subjects were females. Based on the multivariate logistic regression analysis, factors associated with being a stroke mimic were older age, history of migraine, and a history of prior stroke. In conclusion, increased attention to history and clinical examination as the first step can aid in the proper diagnosis of strokes versus stroke mimics. Identifying stroke mimics early could help expedite hospital workup and prevent inadvertent investigations, reducing hospital occupancy during the ongoing COVID-19 pandemic. We could potentially avoid the administration of tPA to such patients, reducing both the cost and adverse effects of it. Every stroke can cause neurological deficits, but every deficit need not be a stroke.

The Impact of Coronavirus Disease 2019 (COVID-19) on Healthcare-Associated Infections.

BACKGROUND: The profound changes wrought by coronavirus disease 2019 (COVID-19) on routine hospital operations may have influenced performance on hospital measures, including healthcare-associated infections (HAIs). We aimed to evaluate the association between COVID-19 surges and HAI and cluster rates. METHODS: In 148 HCA Healthcare-affiliated hospitals, from 1 March 2020 to 30 September 2020, and a subset of hospitals with microbiology and cluster data through 31 December 2020, we evaluated the association between COVID-19 surges and HAIs, hospital-onset pathogens, and cluster rates using negative binomial mixed models. To account for local variation in COVID-19 pandemic surge timing, we included the number of discharges with a laboratory-confirmed COVID-19 diagnosis per staffed bed per month. RESULTS: Central line-associated blood stream infections (CLABSI), catheter-associated urinary tract infections (CAUTI), and methicillin-resistant Staphylococcus aureus (MRSA) bacteremia increased as COVID-19 burden increased. There were 60% (95% confidence interval [CI]: 23-108%) more CLABSI, 43% (95% CI: 8-90%) more CAUTI, and 44% (95% CI: 10-88%) more cases of MRSA bacteremia than expected over 7 months based on predicted HAIs had there not been COVID-19 cases. Clostridioides difficile infection was not significantly associated with COVID-19 burden. Microbiology data from 81 of the hospitals corroborated the findings. Notably, rates of hospital-onset bloodstream infections and multidrug resistant organisms, including MRSA, vancomycin-resistant enterococcus, and Gram-negative organisms, were each significantly associated with COVID-19 surges. Finally, clusters of hospital-onset pathogens increased as the COVID-19 burden increased. CONCLUSIONS: COVID-19 surges adversely impact HAI rates and clusters of infections within hospitals, emphasizing the need for balancing COVID-related demands with routine hospital infection prevention.

Automated outbreak detection of hospital-associated pathogens: Value to infection prevention programs.

OBJECTIVE: To assess the utility of an automated, statistically-based outbreak detection system to identify clusters of hospital-acquired microorganisms. DESIGN: Multicenter retrospective cohort study. SETTING: The study included 43 hospitals using a common infection prevention surveillance system. METHODS: A space-time permutation scan statistic was applied to hospital microbiology, admission, discharge, and transfer data to identify clustering of microorganisms within hospital locations and services. Infection preventionists were asked to rate the importance of each cluster. A convenience sample of 10 hospitals also provided information about clusters previously identified through their usual surveillance methods. RESULTS: We identified 230 clusters in 43 hospitals involving Gram-positive and -negative bacteria and fungi. Half of the clusters progressed after initial detection, suggesting that early detection could trigger interventions to curtail further spread. Infection preventionists reported that they would have wanted to be alerted about 81% of these clusters. Factors associated with clusters judged to be moderately or highly concerning included high statistical significance, large size, and clusters involving Clostridioides difficile or multidrug-resistant organisms. Based on comparison data provided by the convenience sample of hospitals, only 9 (18%) of 51 clusters detected by usual surveillance met statistical significance, and of the 70 clusters not previously detected, 58 (83%) involved organisms not routinely targeted by the hospitals' surveillance programs. All infection prevention programs felt that an automated outbreak detection tool would improve their ability to detect outbreaks and streamline their work. CONCLUSIONS: Automated, statistically-based outbreak detection can increase the consistency, scope, and comprehensiveness of detecting hospital-associated transmission.

Risk Factors for Shoulder Dystocia: the Impact of Mother's Race and Ethnicity.

Shoulder dystocia is a rare but severe birth trauma where the neonate's shoulders fail to deliver after delivery of the head. Failure to deliver the shoulders quickly can lead to severe, long-term injury to the infant, including nerve injury, skeletal fractures, and potentially death. This observational study examined shoulder dystocia risk factors by race and ethnicity using a sample of 19,236 pregnant women who presented for labor and delivery from July 1, 2010 until June 30, 2013 at five locations. Multivariate analyses were used to identify risk factors associated with shoulder dystocia occurrence in racial/ethnic groups with high incidence rates. For White non-Hispanic mothers, the strongest risk factors were delivering past 40 weeks' gestation (odds ratio [OR] = 2.4; 95% confidence interval [CI] = 1.5, 3.9; p < .01) and use of epidural anesthesia during delivery (OR = 4.4; 95% CI = 3.0, 6.4; p < .01). Among Black non-Hispanic mothers, the risk factors with the greatest impact were use of epidural (OR = 5.3; 95% CI = 3.2, 8.7; p < .01) and having gestational diabetes and controlling the condition with insulin (OR = 4.6; 95% CI = 1.5, 13.8; p < .01). Additionally, among Hispanic mothers, having Spanish as primary language increased shoulder dystocia likelihood compared to those who did not cite it as their primary language (OR = 2.3; 95% CI = 1.1, 4.6; p < .05). This study provides evidence that risk factors for a labor and delivery condition can vary significantly across racial and ethnic subgroups. These differences emphasize the importance of evaluating risk by population subgroups and might provide a basis for labor and delivery clinicians to enhance personalized medicine to reduce adverse events.

A Novel Oral Fluid Assay (LC-QTOF-MS) for the Detection of Fentanyl and Clandestine Opioids in Oral Fluid After Reported Heroin Overdose.

INTRODUCTION: The adulteration of heroin with non-pharmaceutical fentanyl and other high-potency opioids is one of the factors contributing to striking increases in overdose deaths. To fully understand the magnitude of this problem, accurate detection methods for fentanyl and other novel opioid adulterant exposures are urgently required. The objective of this work was to compare the detection of fentanyl in oral fluid and urine specimens using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) in a population of heroin users presenting to the Emergency Department after overdose. METHODS: This was a prospective observational study of adult Emergency Department patients who presented after a reported heroin overdose requiring naloxone administration. Participants provided paired oral fluid and urine specimens, which were prepared, extracted, and analyzed using a dual LC-QTOF-MS workflow for the identification of traditional and emerging drugs of abuse. Analytical instrumentation included SCIEX TripleTOF® 5600+ and Waters Xevo® G2-S QTOF systems. RESULTS: Thirty participants (N = 30) were enrolled during the study period. Twenty-nine participants had fentanyl detected in their urine, while 27 had fentanyl identified in their oral fluid (overall agreement 93.3%, positive percent agreement 93.1%). Cohen's Kappa (k) was calculated and demonstrated moderately, significant agreement (k = 0.47; p value 0.002) in fentanyl detection between oral fluid and urine using this LC-QTOF-MS methodology. Additional novel opioids and metabolites, including norfentanyl, acetylfentanyl, and U-47700, were detected during this study. CONCLUSION: In this study of individuals presenting to the ED after reported heroin overdose, a strikingly high proportion had a detectable fentanyl exposure. Using LC-QTOF-MS, the agreement between paired oral fluid and urine testing for fentanyl detection indicates a role for oral fluid testing in surveillance for nonpharmaceutical fentanyl. Additionally, the use of LC-QTOF-MS allowed for the detection of other clandestine opioids (acetylfentanyl and U-47700) in oral fluid.