Impact of a multifaceted, outpatient antimicrobial stewardship intervention bundle on unnecessary antimicrobial prescribing in upper respiratory tract infections (URI)

Background. URIs are the most common indication for outpatient antibiotic prescribing. Given high rates of unnecessary prescribing, these indications have been identified as a high-priority target for outpatient antimicrobial stewardship programs (ASP). Our primary objective was to evaluate the impact of a system-wide, multifaceted, outpatient ASP intervention bundle on unnecessary antibiotic prescribing for URI. Methods. This quasi-experimental study was conducted from 2019 to 2021. ICD-10 codes for URIs were grouped into 3 tiers (i.e., tier I = antibiotics always indicated, tier II = sometimes, tier III = never). Encounters from 5 care specialties (i.e., family medicine, community internal medicine, express care, pediatrics, and emergency department) with a tier III URI primary ICD-10 code but without a secondary tier I or tier II code were included. COVID-19 ICD-10 codes were excluded. Interventions included construction of a prescribing data model, dissemination of clinician prescribing data and education, promotion of symptom management strategies, a patient-facing commitment poster, and a pre-populated URI order panel. Tools were designed at a system level and implemented by regional champions beginning in the 3rd quarter of 2020. The primary outcome was the rate of antibiotic prescribing, and the secondary outcome and counterbalance measure was the rate of repeat URI-related healthcare contact within 14 days. Outcomes were analyzed with chi-square with an alpha level of 0.05. Results. A total of 147403 encounters were included. The overall antibiotic prescribing rate decreased from 24.1% to 12.3% between 2019 and 2021 (p< 0.01). Significant reductions in tier III antibiotic prescribing were demonstrated for each region, care specialty, and syndrome evaluated (Table 1). A reduction in repeat healthcare contact was seen across the total cohort (9.5% in 2019 vs. 8.3% in 2021, p< 0.01);decreases in repeat contact rates were observed in those not initially receiving an antibiotic (10.3% vs. 8.6%, p< 0.01), but not in those who initially received an antibiotic (6.8% vs. 6.8%, p = 0.94). Tier III URI encounter level antimicrobial prescribing rates by region, care specialty, and syndrome Conclusion. A multifaceted, outpatient ASP intervention bundle decreased rates of unnecessary antimicrobial prescribing without increasing rates of 14-day repeat URI-related healthcare contact.

Automated risk assessment of covid-19 patients at diagnosis using electronic healthcare records

COVID-19 causes significant morbidity and mortality and early intervention is key to minimizing deadly complications. Available treatments, such as monoclonal antibody therapy, may limit complications, but only when given soon after symptom onset. Unfortunately, these treatments are often expensive, in limited supply, require administration within a hospital setting, and should be given before the onset of severe symptoms. These challenges have created the need for early triage of patients likely to develop life-threatening complications. To meet this need, we developed an automated patient risk assessment model using a real-world hospital system dataset with over 17,000 COVID-positive patients. Specifically, for each COVID-positive patient, we generate a separate risk score for each of four clinical outcomes including death within 30 days, mechanical ventilator use, ICU admission, and any catastrophic event (a superset of dangerous outcomes). We hypothesized that a deep learning binary classification approach can generate these four risk scores from electronic healthcare records data at the time of diagnosis. Our approach achieves significant performance on the four tasks with an area under receiver operating curve (AUROC) for any catastrophic outcome, death within 30 days, ventilator use, and ICU admission of 86.7%, 88.2%, 86.2%, and 87.8%, respectively. In addition, we visualize the sensitivity and specificity of these risk scores to allow clinicians to customize their usage within different clinical outcomes. We believe this work fulfills a clear clinical need for early detection of objective clinical outcomes and can be used for early screening for treatment intervention. © 2021 IEEE

Otolaryngology consultations for COVID-19: Determining occupational exposure and inpatient interventions

Introduction: The novel coronavirus SARS-CoV-2 has ravaged the United States and transformed the way medical care is delivered. As specialists in upper airway anatomy, otolaryngology (ENT) services may be called upon to manage various head-and-neck complaints for patients with COVID-19. While ear, nose, and throat (ENT) consults may benefit critically ill patients, they also expose physicians to the transmission of COVID-19. We sought to identify the reasons for ENT intervention and examine trends in testing through the pandemic. Method: Records for all ENT consults from May 1 to September 29, 2020, were retrospectively reviewed. Demographic information, admission diagnoses, length of stay, COVID status, and ENT interventions were recorded. Univariate analysis was performed. Results: Of 1343 distinct consults, 965 (72%) were tested for COVID-19, with 62 (4.6%) positive. In May 200 (70%) of 287 consults were tested with 2 (0.7%) positive, while in September, 251 (78.5%) of 320 consults were tested with 22 (6.9%) positive. The most common ENT consultation for COVID-positive patients was nasal and oropharyngeal bleeding (n = 19, 30.6%), followed by facial trauma (n = 15, 24.2%). Other reasons included respiratory distress, tracheostomy, and foreign body (retained COVID swab). Of 96 interventions for patients with COVID-19, 49 (51%) were for management of bleeding, 24 (25%) were for upper airway evaluation (UAE), and 8 (8.3%) were for tracheostomy or trach management. Conclusion: Although patients with COVID-19 necessitated various otolaryngologic interventions, management of bleeding was the most common complaint, which may be associated with therapeutic anticoagulation as well as coagulopathy from the disease process. Bleeding control was followed by UAE and trach management, 2 aerosol-generating procedures that may increase the risk of COVID transmission. The proportion of consults tested and confirmed positive for COVID- 19 at our institution increased from May to September, possibly assisting otolaryngologists to take appropriate preventive precautions.

Early platelet counts distinguish viral and bacterial pulmonary sepsis

Rationale: Respiratory viruses are commonly detected pathogens in pulmonary sepsis. Prior studies have demonstrated that patients with respiratory viral infections may have transient lymphocytopenia and thrombocytopenia. Leukocyte parameters including lymphocyte to monocyte ratio (LMR) and neutrophil to lymphocyte ratio (NLR) have been reported as screening tools for viral infections. Platelet counts and dynamics have been described as quantitative traits for ARDS risk and mortality. Therefore, we hypothesized that early hematologic parameters including lymphocyte count, monocyte count, platelet count, NLR, and LMR may distinguish viral from bacterial pulmonary sepsis. Methods: We enrolled 1,158 critically ill patients with pulmonary sepsis from 2009 to 2020 and measured lymphocyte count, monocyte count, platelet count, NLR, and LMR on ICU admission and at 24-hrs. Respiratory viruses were detected via PCR panel on nasopharyngeal swabs. Pulmonary sepsis was adjudicated by a physician panel. APACHE III scores were collected during the first 24-hrs. Shock was assessed by vasopressor use or mean arterial pressure <65mmHg despite 30cc/kg fluid resuscitation. ARDS was defined per Berlin criteria. We assessed mortality at 30 days. We used multivariable linear regression to test the association between each of the laboratory studies and a positive respiratory pathogen panel (RVP) adjusting for APACHE III score, age, sex, malignancy, and race. We used multivariable logistic regression to assess for associations between a positive RVP and outcomes. Results: The incidence of respiratory virus detection was 33.9%. The incidence of ARDS and mortality were 52.7% and 49.0%, respectively. The most commonly detected pathogens were SARS-CoV-2 and rhinovirus (Table 1). Lower platelet counts at 24-hrs were significantly associated with respiratory virus detection (β-41.59 × 109/L [95%CI-79.03,-4.15], p=0.03), whereas admission platelet counts were not significantly associated (β-22.38 × 109/L [95%CI-63.26, 20.49], p=0.32). The significant association at 24-hrs was also present on sensitivity analyses excluding patients with SARS-CoV-2. There were no statistically significant differences between the populations with respect to lymphocyte count, monocyte count, NLR, LMR, ARDS, shock, and mortality. Conclusion: Lower early platelet counts were identified in patients with viral pulmonary sepsis. Although LMR and NLR have been reported as screening tools for viral infections in non-critically ill populations, we did not detect significant associations between lymphocyte count, monocyte count, NLR or LMR and viral detection in pulmonary sepsis. Our findings suggest that platelet counts in combination with other validated parameters may warrant further investigation for the early discrimination of viral versus bacterial pulmonary sepsis.

Plasma resistin levels are associated with acute kidney injury in hospitalized COVID-19 patients

Rationale: Obesity is a strong risk factor for acute kidney injury (AKI) in patients with COVID-19, but underlying mechanisms are unknown. Resistin is an immunomodulatory adipokine with elevated circulating levels in obese outpatients that could contribute to inflammatory kidney injury. We hypothesized that plasma resistin levels would be associated with AKI and BMI, and correlated with the inflammatory markers IL6 and MCP1 in hospitalized COVID-19 patients. Methods: We conducted a prospective cohort study of 134 patients admitted to the Hospital of the University of Pennsylvania with a primary diagnosis of COVID-19. Plasma samples were collected within 48 hours of admission and analyzed using the Olink Proximity Extension Assay, with biomarker levels expressed using normalized protein expression (NPX) values relative to common pooled control plasma. We tested the association of each biomarker with AKI, defined by Kidney Disease Improving Global Outcomes creatinine and dialysis criteria, using the Wilcoxon rank-sum test as well as multivariable logistic regression to adjust for confounders. Spearman's rho and correlation coefficients were calculated for the correlation of biomarker levels with each other. We used causal mediation models to investigate effects of BMI on AKI mediated by plasma resistin. Results: Of 134 patients enrolled, 43 (32.1%) developed AKI: 25 with stage 1, 5 with stage 2, and 13 with stage 3. Plasma resistin levels ranged from 5.26-13.01 NPX units and were strongly associated with AKI: odds ratio 2.13 (95% CI 1.43-3.17) per NPX unit. This association was diminished but remained significant after adjustment for age and APACHE III score (OR 1.69 (1.09-2.63)). Body mass index was higher in patients with AKI than without (median 31.4 (IQR 27.1-37.6) kg/m2 v. 28.3 (25.1-34.9) kg/m2, respectively), but the difference was not statistically significant (p=0.082). There was no significant correlation of BMI with resistin levels (rho 0.05, p=0.562), and causal mediation models failed to detect significant mediation of BMI-AKI association through resistin. Plasma IL6 and MCP1 were associated with AKI (p=0.044 and p=0.003, respectively) and correlated with resistin levels (rho=0.32, p<0.001 and rho=0.40, p<0.001, respectively). Conclusion: In patients hospitalized with COVID-19, plasma levels of the adipokine resistin were strongly associated with the development of AKI, and correlated with circulating inflammatory markers IL6 and MCP1. We did not detect a mediation effect of the obesity-AKI association by plasma resistin but had limited sample size to adequately power this analysis.

Proteomic analysis of COVID-19 plasma reveals dysregulated trem-1, I-17, and tumor microenvironment pathways associated with disease severity

Rationale: To utilize high-dimensional proteomic data to identify dysregulated pathways that are associated with COVID-19 disease severity and suggest potential therapeutic targets. Methods: We enrolled 161 COVID-19 inpatients admitted at two tertiary care hospitals. Plasma samples collected within 48 hours of admission were analyzed with the Olink Proximity Extension Assay;713 unique proteins were assayed. The WHO COVID-19 ordinal severity scale at enrollment was dichotomized into moderate (levels 3-4) and severe (levels 5-7). Normalized protein expression (NPX) values were generated in relation to a common pooled control plasma on each plate. The association between NPX values and disease severity on admission was estimated with logistic regression (LR) after adjustment for age, sex, race, and select comorbidities. Ingenuity Pathway Analysis (IPA) was employed after application of the Benjamini-Hochberg procedure with a false discovery rate of 5% to all proteins for which the NPX difference was +/-0.8 between groups. Predictive models of disease severity on hospital day 7 using all proteins as potential features were fit using elastic net LR (ENLR) and gradient boosting (GBM). Performance was estimated on a held-out test set (40% of the data) with area under the receiveroperator characteristic curve (AUROC). Results: Of 161 subjects, 85 (53%) were classified as having severe COVID-19. A total of 552 proteins were differentially expressed (Figure 1), and 31 of these proteins met criteria for inclusion in pathway analysis. IPA identified the triggering receptor expressed on myeloid cells 1 (TREM-1) signaling pathway (4 members, p=3.8E-3), the tumor microenvironment (TME) pathway (5 members, p=4.1E-3), and the interleukin 17 (IL-17) signaling pathway (4 members, p=1.8E-2). Interleukin 1 receptor-like 1, a member of the TREM-1 pathway, was the protein most associated with disease severity (OR=3.18, p=1.82E-08). Tumor necrosis factor ligand superfamily member 11 (TNFSF11), a member of the IL-17 signaling pathway was the only factor whose enrichment was associated with less severe disease (OR=0.39, p=2.3E-05). ENLR and GBM predicted disease severity on day 7 with AUROC values of 0.908 (0.828, 0.968) and 0.882 (0.788, 0.957), respectively. Conclusion: We identified pathways differentially expressed between patients with severe and nonsevere COVID-19 associated with immune function and angiogenesis. Several agents currently being investigated to treat severe COVID-19 act on these dysregulated pathways, and future investigations could test whether these proteins act as enrichment markers or response indicators. Integrating protein expression with cellular immune phenotype may help explain COVID-19 pathophysiology.