Adherence to Antimicrobial Prophylaxis Guidelines for Elective Surgeries Across 825 US Hospitals, 2019-2020.

BACKGROUND: There are limited US data assessing adherence to surgical antimicrobial prophylaxis guidelines, particularly across a large, nationwide sample. Moreover, commonly prescribed inappropriate antimicrobial prophylaxis regimens remain unknown, hindering improvement initiatives. METHODS: We conducted a retrospective cohort study of adults who underwent elective craniotomy, hip replacement, knee replacement, spinal procedure, or hernia repair in 2019-2020 at hospitals in the PINC AI (Premier) Healthcare Database. We evaluated adherence of prophylaxis regimens, with respect to antimicrobial agents endorsed in the American Society of Health-System Pharmacist guidelines, accounting for patient antibiotic allergy and methicillin-resistant Staphylococcus aureus colonization status. We used multivariable logistic regression with random effects by hospital to evaluate associations between patient, procedural, and hospital characteristics and guideline adherence. RESULTS: Across 825 hospitals and 521 091 inpatient elective surgeries, 308 760 (59%) were adherent to prophylaxis guidelines. In adjusted analysis, adherence varied significantly by US Census division (adjusted OR [aOR] range: .61-1.61) and was significantly lower in 2020 compared with 2019 (aOR: .92; 95% CI: .91-.94; P < .001). The most common reason for nonadherence was unnecessary vancomycin use. In a post hoc analysis, controlling for patient age, comorbidities, other nephrotoxic agent use, and patient and procedure characteristics, patients receiving cefazolin plus vancomycin had 19% higher odds of acute kidney injury (AKI) compared with patients receiving cefazolin alone (aOR: 1.19; 95% CI: 1.11-1.27; P < .001). CONCLUSIONS: Adherence to antimicrobial prophylaxis guidelines remains suboptimal, largely driven by unnecessary vancomycin use, which may increase the risk of AKI. Adherence decreased in the first year of the COVID-19 pandemic.

Patterns, Predictors, and Intercenter Variability in Empiric Gram-Negative Antibiotic Use Across 928 United States Hospitals.

BACKGROUND: Empiric antibiotic use among hospitalized adults in the United States (US) is largely undescribed. Identifying factors associated with broad-spectrum empiric therapy may inform antibiotic stewardship interventions and facilitate benchmarking. METHODS: We performed a retrospective cohort study of adults discharged in 2019 from 928 hospitals in the Premier Healthcare Database. "Empiric" gram-negative antibiotics were defined by administration before day 3 of hospitalization. Multivariable logistic regression models with random effects by hospital were used to evaluate associations between patient and hospital characteristics and empiric receipt of broad-spectrum, compared to narrow-spectrum, gram-negative antibiotics. RESULTS: Of 8 017 740 hospitalized adults, 2 928 657 (37%) received empiric gram-negative antibiotics. Among 1 781 306 who received broad-spectrum therapy, 30% did not have a common infectious syndrome present on admission (pneumonia, urinary tract infection, sepsis, or bacteremia), surgery, or an intensive care unit stay in the empiric window. Holding other factors constant, males were 22% more likely (adjusted odds ratio [aOR], 1.22 [95% confidence interval, 1.22-1.23]), and all non-White racial groups 6%-13% less likely (aOR range, 0.87-0.94), to receive broad-spectrum therapy. There were significant prescribing differences by region, with the highest adjusted odds of broad-spectrum therapy in the US West South Central division. Even after model adjustment, there remained substantial interhospital variability: Among patients receiving empiric therapy, the probability of receiving broad-spectrum antibiotics varied as much as 34+ percentage points due solely to the admitting hospital (95% interval of probabilities: 43%-77%). CONCLUSIONS: Empiric gram-negative antibiotic use is highly variable across US regions, and there is high, unexplained interhospital variability. Sex and racial disparities in the receipt of broad-spectrum therapy warrant further investigation.

Considerations for the Use of Phage Therapy in Clinical Practice.

Increasing antimicrobial resistance and medical device-related infections have led to a renewed interest in phage therapy as an alternative or adjunct to conventional antimicrobials. Expanded access and compassionate use cases have risen exponentially but have varied widely in approach, methodology, and clinical situations in which phage therapy might be considered. Large gaps in knowledge contribute to heterogeneity in approach and lack of consensus in many important clinical areas. The Antibacterial Resistance Leadership Group (ARLG) has convened a panel of experts in phage therapy, clinical microbiology, infectious diseases, and pharmacology, who worked with regulatory experts and a funding agency to identify questions based on a clinical framework and divided them into three themes: potential clinical situations in which phage therapy might be considered, laboratory testing, and pharmacokinetic considerations. Suggestions are provided as answers to a series of questions intended to inform clinicians considering experimental phage therapy for patients in their clinical practices.

Impact of Sex and Metabolic Comorbidities on Coronavirus Disease 2019 (COVID-19) Mortality Risk Across Age Groups: 66 646 Inpatients Across 613 U.S. Hospitals.

BACKGROUND: The relationship between common patient characteristics, such as sex and metabolic comorbidities, and mortality from coronavirus disease 2019 (COVID-19) remains incompletely understood. Emerging evidence suggests that metabolic risk factors may also vary by age. This study aimed to determine the association between common patient characteristics and mortality across age-groups among COVID-19 inpatients. METHODS: We performed a retrospective cohort study of patients discharged from hospitals in the Premier Healthcare Database between April-June 2020. Inpatients were identified using COVID-19 ICD-10-CM diagnosis codes. A priori-defined exposures were sex and present-on-admission hypertension, diabetes, obesity, and interactions between age and these comorbidities. Controlling for additional confounders, we evaluated relationships between these variables and in-hospital mortality in a log-binomial model. RESULTS: Among 66 646 (6.5%) admissions with a COVID-19 diagnosis, across 613 U.S. hospitals, 12 388 (18.6%) died in-hospital. In multivariable analysis, male sex was independently associated with 30% higher mortality risk (aRR, 1.30, 95% CI: 1.26-1.34). Diabetes without chronic complications was not a risk factor at any age (aRR 1.01, 95% CI: 0.96-1.06), and hypertension without chronic complications was a risk factor only in 20-39 year-olds (aRR, 1.68, 95% CI: 1.17-2.40). Diabetes with chronic complications, hypertension with chronic complications, and obesity were risk factors in most age-groups, with highest relative risks among 20-39 year-olds (respective aRRs 1.79, 2.33, 1.92; P-values ≤ .002). CONCLUSIONS: Hospitalized men with COVID-19 are at increased risk of death across all ages. Hypertension, diabetes with chronic complications, and obesity demonstrated age-dependent effects, with the highest relative risks among adults aged 20-39.

Significant Regional Differences in Antibiotic Use Across 576 US Hospitals and 11 701 326 Adult Admissions, 2016-2017.

BACKGROUND: Quantifying the amount and diversity of antibiotic use in United States hospitals assists antibiotic stewardship efforts but is hampered by limited national surveillance. Our study aimed to address this knowledge gap by examining adult antibiotic use across 576 hospitals and nearly 12 million encounters in 2016-2017. METHODS: We conducted a retrospective study of patients aged ≥ 18 years discharged from hospitals in the Premier Healthcare Database between 1 January 2016 and 31 December 2017. Using daily antibiotic charge data, we mapped antibiotics to mutually exclusive classes and to spectrum of activity categories. We evaluated relationships between facility and case-mix characteristics and antibiotic use in negative binomial regression models. RESULTS: The study included 11 701 326 admissions, totaling 64 064 632 patient-days, across 576 hospitals. Overall, patients received antibiotics in 65% of hospitalizations, at a crude rate of 870 days of therapy (DOT) per 1000 patient-days. By class, use was highest among ß-lactam/ß-lactamase inhibitor combinations, third- and fourth-generation cephalosporins, and glycopeptides. Teaching hospitals averaged lower rates of total antibiotic use than nonteaching hospitals (834 vs 957 DOT per 1000 patient-days; P < .001). In adjusted models, teaching hospitals remained associated with lower use of third- and fourth-generation cephalosporins and antipseudomonal agents (adjusted incidence rate ratio [95% confidence interval], 0.92 [.86-.97] and 0.91 [.85-.98], respectively). Significant regional differences in total and class-specific antibiotic use also persisted in adjusted models. CONCLUSIONS: Adult inpatient antibiotic use remains high, driven predominantly by broad-spectrum agents. Better understanding reasons for interhospital usage differences, including by region and teaching status, may inform efforts to reduce inappropriate antibiotic prescribing.

A Multicenter Evaluation of Probiotic Use for the Primary Prevention of Clostridioides difficile Infection.

BACKGROUND: Primary prevention of Clostridioides difficile infection (CDI) is a priority for hospitals. Probiotics have the potential to interfere with colonization and CDI. In this study, we evaluated the impact of a computerized clinical decision support (CCDS) tool to prescribe probiotics for primary prevention of CDI among adult hospitalized patients. METHODS: A CCDS tool was implemented into the electronic medical record at 4 hospitals to prompt prescription of a probiotic preparation at the time of antibiotic prescription in high-risk patients in May 2019. Interrupted time series using segmented regression analysis was conducted to evaluate hospital-wide CDI incidence for the year pre- and post-CCDS implementation. In addition, multivariable logistic regression was used to evaluate CDI incidence in patients who qualified for probiotics in the pre- vs post-intervention periods, adjusting for potential confounders. To adjust for potential differences in patients who received probiotics in the post-intervention period, propensity score-matched pairs were developed to evaluate CDI risk by receipt of probiotics. RESULTS: Quarterly CDI incidence increased over time post-intervention relative to baseline trends (slope change, 1.4; 95% confidence interval [CI], .9-1.9). The odds ratio (OR) of CDI was 1.41 in eligible patients post-intervention compared with pre-intervention (adjusted OR, 1.41; 95% CI, 1.11-1.79). Propensity score-matched analysis showed that patients who received probiotics did not have lower rates of CDI compared with those who did not receive probiotics (OR, 1.46; 95% CI, .87-2.45). CONCLUSIONS: Use of probiotics for primary prevention of CDI among adult inpatients receiving antibiotics is not supported.

Electronically Available Patient Claims Data Improve Models for Comparing Antibiotic Use Across Hospitals: Results From 576 US Facilities.

BACKGROUND: The Centers for Disease Control and Prevention (CDC) uses standardized antimicrobial administration ratios (SAARs)-that is, observed-to-predicted ratios-to compare antibiotic use across facilities. CDC models adjust for facility characteristics when predicting antibiotic use but do not include patient diagnoses and comorbidities that may also affect utilization. This study aimed to identify comorbidities causally related to appropriate antibiotic use and to compare models that include these comorbidities and other patient-level claims variables to a facility model for risk-adjusting inpatient antibiotic utilization. METHODS: The study included adults discharged from Premier Database hospitals in 2016-2017. For each admission, we extracted facility, claims, and antibiotic data. We evaluated 7 models to predict an admission's antibiotic days of therapy (DOTs): a CDC facility model, models that added patient clinical constructs in varying layers of complexity, and an external validation of a published patient-variable model. We calculated hospital-specific SAARs to quantify effects on hospital rankings. Separately, we used Delphi Consensus methodology to identify Elixhauser comorbidities associated with appropriate antibiotic use. RESULTS: The study included 11 701 326 admissions across 576 hospitals. Compared to a CDC-facility model, a model that added Delphi-selected comorbidities and a bacterial infection indicator was more accurate for all antibiotic outcomes. For total antibiotic use, it was 24% more accurate (respective mean absolute errors: 3.11 vs 2.35 DOTs), resulting in 31-33% more hospitals moving into bottom or top usage quartiles postadjustment. CONCLUSIONS: Adding electronically available patient claims data to facility models consistently improved antibiotic utilization predictions and yielded substantial movement in hospitals' utilization rankings.

StenoSCORE: Predicting Stenotrophomonas maltophilia Bloodstream Infections in the Hematologic Malignancy Population.

Stenotrophomonas maltophilia bloodstream infections (BSI) are associated with considerable mortality in the hematologic malignancy population. Trimethoprim-sulfamethoxazole (TMP-SMX) is the treatment of choice; however, it is not routinely included in empirical treatment regimens, both because of its adverse event profile and the relative rarity of S. maltophilia infections. We developed a risk score to predict hematologic malignancy patients at increased risk for S. maltophilia BSI to guide early (TMP-SMX) therapy. Patients ≥12 years of age admitted to five hospitals between July 2016 and December 2019 were included. Two separate risk scores were developed, (i) a "knowledge-driven" risk score based upon previously identified risk factors in the literature in addition to variables identified by regression analysis using the current cohort, and (ii) a risk score based upon automatic variable selection. For both scores, discrimination (receiver operator characteristic [ROC] curves and C statistics) and calibration (Hosmer-Lemeshow goodness-of-fit test and graphical calibration plots) were assessed. Internal validation was assessed using leave-one-out cross-validation. In total, 337 unique patients were included; 21 (6.2%) had S. maltophilia BSI. The knowledge-driven risk score (acute leukemia, absolute neutrophil count category, mucositis, central line, and ≥3 days of carbapenem therapy) had superior performance (C statistic = 0.75; 0.71 after cross-validation) compared to that of the risk score utilizing automatic variable selection (C statistic = 0.63; 0.38 after cross-validation). A user-friendly risk score incorporating five variables easily accessible to clinicians performed moderately well to predict hematologic malignancy patients at increased risk for S. maltophilia BSI. External validation using a larger cohort is necessary to create a refined risk score before broad clinical application.

Antibiotic Use and Bacterial Infection among Inpatients in the First Wave of COVID-19: a Retrospective Cohort Study of 64,691 Patients.

Hospitalized patients with SARS-CoV-2 infection (COVID-19) often receive antibiotics for suspected bacterial coinfection. We estimated the incidence of bacterial coinfection and secondary infection in COVID-19 using clinical diagnoses to determine how frequently antibiotics are administered when bacterial infection is absent. We performed a retrospective cohort study of inpatients with COVID-19 present on admission to hospitals in the Premier Healthcare Database between April and June 2020. Bacterial infections were defined using ICD-10-CM diagnosis codes and associated "present on admission" coding. Coinfections were defined by bacterial infection present on admission, while secondary infections were defined by bacterial infection that developed after admission. Coinfection and secondary infection were not mutually exclusive. A total of 18.5% of 64,961 COVID-19 patients (n = 12,040) presented with bacterial infection at admission, 3.8% (n = 2,506) developed secondary infection after admission, and 0.9% (n = 574) had both; 76.3% (n = 49,551) received an antibiotic while hospitalized, including 71% of patients who had no diagnosis of bacterial infection. Secondary bacterial infection occurred in 5.7% of patients receiving steroids in the first 2 days of hospitalization, 9.9% receiving tocilizumab in the first 2 days of hospitalization, and 10.3% of patients receiving both. After adjusting for patient and hospital characteristics, bacterial coinfection (adjusted relative risk [aRR], 1.15; 95% confidence interval [CI], 1.11 to 1.20) and secondary infection (aRR 1.93; 95% CI, 1.82 to 2.04) were both independently associated with increased mortality. Although 1 in 5 inpatients with COVID-19 presents with bacterial infection, secondary infections in the hospital are uncommon. Most inpatients with COVID-19 receive antibiotic therapy, including 71% of those not diagnosed with bacterial infection.

What is Machine Learning? A Primer for the Epidemiologist.

Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on "Big Data," it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. Here, we provide an overview of the concepts and terminology used in machine learning literature, which encompasses a diverse set of tools with goals ranging from prediction to classification to clustering. We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods.

The Likelihood of Developing a Carbapenem-Resistant Enterobacteriaceae Infection during a Hospital Stay.

Of 1,455 unique patients in U.S. intensive care units (ICUs), 4% were rectally colonized with CRE on admission. A total of 297 patients were initially negative for carbapenem-resistant Enterobacteriaceae (CRE) and remained in the ICU long enough to contribute additional swabs; 22% of these patients had a subsequent CRE-positive swab, with a median time to CRE colonization of 13 days (interquartile range, 7 to 21 days). Patients colonized with carbapenemase-producing CRE were more likely than those colonized with non-carbapenemase-producing CRE to develop CRE infections during their hospitalizations (36% versus 3%; P < 0.05).

AI-Generated Clinical Summaries Require More Than Accuracy.

This Viewpoint discusses AI-generated clinical summaries and the necessity of transparent development of standards for their safe rollout.

Clinical Algorithms, Antidiscrimination Laws, and Medical Device Regulation.

This Viewpoint discusses recent legal directives by the DHHS and FDA that could increase health care entities' liability for possible discriminatory biases of clinical algorithms and the need for additional legal clarity to avoid adverse effects on algorithm development and use.

Comparing the Outcomes of Patients With Carbapenemase-Producing and Non-Carbapenemase-Producing Carbapenem-Resistant Enterobacteriaceae Bacteremia.

BACKGROUND: Carbapenem-resistant Enterobacteriaceae (CRE) are associated with considerable mortality. As mechanisms of carbapenem resistance are heterogeneous, it is unclear if mortality differs based on resistance mechanisms. We sought to determine whether CRE resistance mechanism determination is prognostically informative. METHODS: We conducted an observational study comparing 14-day mortality between patients with carbapenemase-producing (CP)-CRE compared with non-CP-CRE bacteremia. Clinical data were collected on all patients. A comprehensive DNA microarray-based assay was performed on all isolates to identify ß-lactamase-encoding genes. RESULTS: There were 83 unique episodes of monomicrobial CRE bacteremia during the study period: 37 (45%) CP-CRE and 46 (55%) non-CP-CRE. The majority of CP-CRE isolates were bla KPC (92%), followed by bla NDM (5%) and bla OXA-48-type (3%). CP-CRE isolates were more likely to have meropenem minimum inhibitory concentrations (MICs) ≥16 µg/mL, while non-CP-CRE isolates were more likely to have meropenem MICs ≤1 µg/mL (P value < .001). A total of 18 (22%) patients died within 14 days, including 12 (32%) in the CP-CRE group and 6 (13%) in the non-CP-CRE group. Adjusting for severity of illness on day 1 of bacteremia, underlying medical conditions, and differences in antibiotic treatment administered, the odds of dying within 14 days were more than 4 times greater for CP-CRE compared with non-CP-CRE bacteremic patients (adjusted odds ratio, 4.92; 95% confidence interval, 1.01-24.81). CONCLUSION: Our findings suggest that CP-CRE may be more virulent than non-CP-CRE and are associated with poorer outcomes. This underscores the added importance of delineating underlying resistance mechanisms of CRE to direct antibiotic treatment decisions.

Adverse Maternal and Delivery Outcomes in Children and Very Young (Age ≤13 Years) US Adolescents Compared With Older Adolescents and Adults.

This study uses a US claims database to compare morbidity and delivery outcomes among pregnant 10- to 13-year-olds vs 14- to 17-year-olds and 18- to 19-year-olds.

A Clinical Decision Tree to Predict Whether a Bacteremic Patient Is Infected With an Extended-Spectrum ß-Lactamase-Producing Organism.

BACKGROUND: Timely identification of extended-spectrum ß-lactamase (ESBL) bacteremia can improve clinical outcomes while minimizing unnecessary use of broad-spectrum antibiotics, including carbapenems. However, most clinical microbiology laboratories currently require at least 24 additional hours from the time of microbial genus and species identification to confirm ESBL production. Our objective was to develop a user-friendly decision tree to predict which organisms are ESBL producing, to guide appropriate antibiotic therapy. METHODS: We included patients ≥18 years of age with bacteremia due to Escherichia coli or Klebsiella species from October 2008 to March 2015 at Johns Hopkins Hospital. Isolates with ceftriaxone minimum inhibitory concentrations ≥2 µg/mL underwent ESBL confirmatory testing. Recursive partitioning was used to generate a decision tree to determine the likelihood that a bacteremic patient was infected with an ESBL producer. Discrimination of the original and cross-validated models was evaluated using receiver operating characteristic curves and by calculation of C-statistics. RESULTS: A total of 1288 patients with bacteremia met eligibility criteria. For 194 patients (15%), bacteremia was due to a confirmed ESBL producer. The final classification tree for predicting ESBL-positive bacteremia included 5 predictors: history of ESBL colonization/infection, chronic indwelling vascular hardware, age ≥43 years, recent hospitalization in an ESBL high-burden region, and ≥6 days of antibiotic exposure in the prior 6 months. The decision tree's positive and negative predictive values were 90.8% and 91.9%, respectively. CONCLUSIONS: Our findings suggest that a clinical decision tree can be used to estimate a bacteremic patient's likelihood of infection with ESBL-producing bacteria. Recursive partitioning offers a practical, user-friendly approach for addressing important diagnostic questions.