A multi-center validation of the electronic health record admission source and discharge location fields against the clinical notes for identifying inpatients with long-term care facility exposure.

Identifying long-term care facility (LTCF)-exposed inpatients is important for infection control research and practice, but ascertaining LTCF exposure is challenging. Across a large validation study, electronic health record data fields identified 76% of LTCF-exposed patients compared to manual chart review. OBJECTIVE: Residence or recent stay in a long-term care facility (LTCF) is an important risk factor for antibiotic-resistant bacterial colonization. However, absent dedicated intake questionnaires or resource-intensive chart review, ascertaining LTCF exposure in inpatients is challenging. We aimed to validate the electronic health record (EHR) admission and discharge location fields against the clinical notes for identifying LTCF-exposed inpatients. METHODS: We conducted a retrospective study of 1020 randomly sampled adult admissions between 2016 and 2021 across 12 University of Maryland Medical System hospitals. Using study-developed guidelines, we categorized the following data for LTCF exposure: each admission's history & physical (H&P) note, each admission's EHR-extracted "Admission Source," and (3) the EHR-extracted admission and discharge locations for previous admissions (≤90 days). We estimated sensitivities, with 95% CIs, of H&P notes and of EHR admission/discharge location fields for detecting "current" and "any recent" (≤90 days, including current) LTCF exposure. RESULTS: For detecting current LTCF exposure, the sensitivity of the index admission's EHR-extracted "Admission Source" was 46% (95% CI: 35%­58%) and of the H&P note was 92% (83%­97%). For detecting any recent LTCF exposure, the sensitivity of "Admission Source" across the index and previous admissions was 32% (24%­41%), "Discharge Location" across previous admission(s) was 57% (47%­66%), and of the H&P note was 68% (59%­76%). The combined sensitivity of admission source and discharge location for detecting any recent LTCF exposure was 76% (67%­83%). CONCLUSIONS: The EHR-obtained admission source and discharge location fields identified 76% of LTCF-exposed patients compared to chart review but disproportionately missed currently exposed patients.

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.

Delivery outcomes in a cohort of pregnant patients with COVID-19 with and without viral pneumonia.

BACKGROUND: Among pregnant people, COVID-19 can lead to adverse outcomes, but the specific pregnancy outcomes that are affected by the disease are unclear. In addition, the effect of the severity of COVID-19 on pregnancy outcomes has not been clearly identified. OBJECTIVE: This study aimed to evaluate the associations between COVID-19 with and without viral pneumonia and cesarean delivery, preterm delivery, preeclampsia, and stillbirth. STUDY DESIGN: We conducted a retrospective cohort study (April 2020-May 2021) of deliveries between 20 and 42 weeks of gestation from US hospitals in the Premier Healthcare Database. The primary outcomes were cesarean delivery, preterm delivery, preeclampsia, and stillbirth. We used a viral pneumonia diagnosis (International Classification of Diseases -Tenth-Clinical Modification codes J12.8 and J12.9) to categorize patients by severity of COVID-19. Pregnancies were categorized into 3 groups: NOCOVID (no COVID-19), COVID (COVID-19 without viral pneumonia), and PNA (COVID-19 with viral pneumonia). Groups were balanced for risk factors by propensity-score matching. RESULTS: A total of 814,649 deliveries from 853 US hospitals were included (NOCOVID: n=799,132; COVID: n=14,744; PNA: n=773). After propensity-score matching, the risks of cesarean delivery and preeclampsia were similar in the COVID group compared with the NOCOVID group (matched risk ratio, 0.97; 95% confidence interval, 0.94-1.00; and matched risk ratio, 1.02; 95% confidence interval, 0.96-1.07; respectively). The risks of preterm delivery and stillbirth were greater in the COVID group than in the NOCOVID group (matched risk ratio, 1.11; 95% confidence interval, 1.05-1.19; and matched risk ratio, 1.30; 95% confidence interval, 1.01-1.66; respectively). The risks of cesarean delivery, preeclampsia, and preterm delivery were higher in the PNA group than in the COVID group (matched risk ratio, 1.76; 95% confidence interval, 1.53-2.03; matched risk ratio, 1.37; 95% confidence interval, 1.08-1.74; and matched risk ratio, 3.33; 95% confidence interval, 2.56-4.33; respectively). The risk of stillbirth was similar in the PNA and COVID group (matched risk ratio, 1.17; 95% confidence interval, 0.40-3.44). CONCLUSION: Within a large national cohort of hospitalized pregnant people, we found that the risk of some adverse delivery outcomes was elevated in people with COVID-19 with and without viral pneumonia, with much higher risks in the group with viral pneumonia.

Clinical, contextual and hospital-level factors associated with escalation and de-escalation of empiric Gram-negative antibiotics among US inpatients.

Background: Empiric Gram-negative antibiotics are frequently changed in response to new information. To inform antibiotic stewardship, we sought to identify predictors of antibiotic changes using information knowable before microbiological test results. Methods: We performed a retrospective cohort study. Survival-time models were used to evaluate clinical factors associated with antibiotic escalation and de-escalation (defined as an increase or decrease, respectively, in the spectrum or number of Gram-negative antibiotics within 5 days of initiation). Spectrum was categorized as narrow, broad, extended or protected. Tjur's D statistic was used to estimate the discriminatory power of groups of variables. Results: In 2019, 2 751 969 patients received empiric Gram-negative antibiotics at 920 study hospitals. Antibiotic escalation occurred in 6.5%, and 49.2% underwent de-escalation; 8.8% were changed to an equivalent regimen. Escalation was more likely when empiric antibiotics were narrow-spectrum (HR 19.0 relative to protected; 95% CI: 17.9-20.1), broad-spectrum (HR 10.3; 95% CI: 9.78-10.9) or extended-spectrum (HR 3.49; 95% CI: 3.30-3.69). Patients with sepsis present on admission (HR 1.94; 95% CI: 1.91-1.96) and urinary tract infection present on admission (HR 1.36; 95% CI: 1.35-1.38) were more likely to undergo antibiotic escalation than patients without these syndromes. De-escalation was more likely with combination therapy (HR 2.62 per additional agent; 95% CI: 2.61-2.63) or narrow-spectrum empiric antibiotics (HR 1.67 relative to protected; 95% CI: 1.65-1.69). Choice of empiric regimen accounted for 51% and 74% of the explained variation in antibiotic escalation and de-escalation, respectively. Conclusions: Empiric Gram-negative antibiotics are frequently de-escalated early in hospitalization, whereas escalation is infrequent. Changes are primarily driven by choice of empiric therapy and presence of infectious syndromes.

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.

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.

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.

Comorbidities associated with 30-day readmission following index coronavirus disease 2019 (COVID-19) hospitalization: A retrospective cohort study of 331,136 patients in the United States.

OBJECTIVE: Hospital readmission is unsettling to patients and caregivers, costly to the healthcare system, and may leave patients at additional risk for hospital-acquired infections and other complications. We evaluated the association between comorbidities present during index coronavirus disease 2019 (COVID-19) hospitalization and the risk of 30-day readmission. DESIGN, SETTING, AND PARTICIPANTS: We used the Premier Healthcare database to perform a retrospective cohort study of COVID-19 hospitalized patients discharged between April 2020 and March 2021 who were followed for 30 days after discharge to capture readmission to the same hospital. RESULTS: Among the 331,136 unique patients in the index cohort, 36,827 (11.1%) had at least 1 all-cause readmission within 30 days. Of the readmitted patients, 11,382 (3.4%) were readmitted with COVID-19 as the primary diagnosis. In the multivariable model adjusted for demographics, hospital characteristics, coexisting comorbidities, and COVID-19 severity, each additional comorbidity category was associated with an 18% increase in the odds of all-cause readmission (adjusted odds ratio [aOR], 1.18; 95% confidence interval [CI], 1.17-1.19) and a 10% increase in the odds of readmission with COVID-19 as the primary readmission diagnosis (aOR, 1.10; 95% CI, 1.09-1.11). Lymphoma (aOR, 1.86; 95% CI, 1.58-2.19), renal failure (aOR, 1.32; 95% CI, 1.25-1.40), and chronic lung disease (aOR, 1.29; 95% CI, 1.24-1.34) were most associated with readmission for COVID-19. CONCLUSIONS: Readmission within 30 days was common among COVID-19 survivors. A better understanding of comorbidities associated with readmission will aid hospital care teams in improving postdischarge care. Additionally, it will assist hospital epidemiologists and quality administrators in planning resources, allocating staff, and managing bed-flow issues to improve patient care and safety.

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.

Appropriate Antibiotic Use for Group B Streptococcus Prophylaxis Among Penicillin-Allergic Patients in Academic and Nonacademic Hospitals.

This study estimated prophylactic antibiotic usage rates for the prevention of early-onset invasive neonatal group B Streptococcus infection among patients with penicillin allergy. Undertreatment (no antibiotics, underuse of cefazolin, overuse of clindamycin inconsistent with resistance patterns) and overtreatment (vancomycin use) were common. Academic hospitals were marginally more adherent to guidelines than nonacademic hospitals.

Real-world Antimicrobial Stewardship Experience in a Large Academic Medical Center: Using Statistical and Machine Learning Approaches to Identify Intervention "Hotspots" in an Antibiotic Audit and Feedback Program.

Background: Prospective audit with feedback (PAF) is an impactful strategy for antimicrobial stewardship program (ASP) activities. However, because PAF requires reviewing large numbers of antimicrobial orders on a case-by-case basis, PAF programs are highly resource intensive. The current study aimed to identify predictors of ASP intervention (ie, feedback) and to build models to identify orders that can be safely bypassed from review, to make PAF programs more efficient. Methods: We performed a retrospective cross-sectional study of inpatient antimicrobial orders reviewed by the University of Maryland Medical Center's PAF program between 2017 and 2019. We evaluated the relationship between antimicrobial and patient characteristics with ASP intervention using multivariable logistic regression models. Separately, we built prediction models for ASP intervention using statistical and machine learning approaches and evaluated performance on held-out data. Results: Across 17 503 PAF reviews, 4219 (24%) resulted in intervention. In adjusted analyses, a clinical pharmacist on the ordering unit or receipt of an infectious disease consult were associated with 17% and 56% lower intervention odds, respectively (adjusted odds ratios [aORs], 0.83 and 0.44; P ≤ .001 for both). Fluoroquinolones had the highest adjusted intervention odds (aOR, 3.22 [95% confidence interval, 2.63-3.96]). A machine learning classifier (C-statistic 0.76) reduced reviews by 49% while achieving 78% sensitivity. A "workflow simplified" regression model that restricted to antimicrobial class and clinical indication variables, 2 strong machine learning-identified predictors, reduced reviews by one-third while achieving 81% sensitivity. Conclusions: Prediction models substantially reduced PAF review caseloads while maintaining high sensitivities. Our results and approach may offer a blueprint for other ASPs.

Pregnancy and the Risk of In-Hospital Coronavirus Disease 2019 (COVID-19) Mortality.

OBJECTIVE: To evaluate whether pregnancy is an independent risk factor for in-hospital mortality among patients of reproductive age hospitalized with coronavirus disease 2019 (COVID-19) viral pneumonia. METHODS: We conducted a retrospective cohort study (April 2020-May 2021) of 23,574 female inpatients aged 15-45 years with an International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis code for COVID-19 discharged from 749 U.S. hospitals in the Premier Healthcare Database. We used a viral pneumonia diagnosis to select for patients with symptomatic COVID-19. The associations between pregnancy and in-hospital mortality, intensive care unit (ICU) admission, and mechanical ventilation were analyzed using propensity score-matched conditional logistic regression. Models were matched for age, marital status, race and ethnicity, Elixhauser comorbidity score, payer, hospital number of beds, season of discharge, hospital region, obesity, hypertension, diabetes mellitus, chronic pulmonary disease, deficiency anemias, depression, hypothyroidism, and liver disease. RESULTS: In-hospital mortality occurred in 1.1% of pregnant patients and 3.5% of nonpregnant patients hospitalized with COVID-19 and viral pneumonia (propensity score-matched odds ratio [OR] 0.39, 95% CI 0.25-0.63). The frequency of ICU admission for pregnant and nonpregnant patients was 22.0% and 17.7%, respectively (OR 1.34, 95% CI 1.15-1.55). Mechanical ventilation was used in 8.7% of both pregnant and nonpregnant patients (OR 1.05, 95% CI 0.86-1.29). Among patients who were admitted to an ICU, mortality was lower for pregnant compared with nonpregnant patients (OR 0.33, 95% CI 0.20-0.57), though mechanical ventilation rates were similar (35.7% vs 38.3%, OR 0.90, 95% CI 0.70-1.16). Among patients with mechanical ventilation, pregnant patients had a reduced risk of in-hospital mortality compared with nonpregnant patients (0.26, 95% CI 0.15-0.46). CONCLUSION: Despite a higher frequency of ICU admission, in-hospital mortality was lower among pregnant patients compared with nonpregnant patients with COVID-19 viral pneumonia, and these findings persisted after propensity score matching.

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.

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.

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.

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.