Escherichia coli (E. coli) is a predominant pathogen of urinary tract infections (UTIs) in the United States. We analyzed resistance patterns by geographic location in Washington State to assess the need for regional antibiograms. The study included urinary E. coli antibiotic susceptibility tests performed by Quest Diagnostics on Washington outpatient isolates from 2013 to 2019. We conducted logistic regressions with robust standard errors for five antibiotics (ceftriaxone, ciprofloxacin, gentamicin, trimethoprim-sulfamethoxazole), with isolates classified as “susceptible” or “resistant” for each antibiotic tested. Analyses were adjusted for sex, year of isolate collection, and age group (0–18, 19–50, >50). The state’s nine Public Health Emergency Preparedness Regions (PHEPRs) were used as the geographic level for the analysis. The analysis included 40,217 isolates (93% from females, mean age 47 years). Compared to the Central PHEPR (containing Seattle), most other regions had significantly lower adjusted prevalence ratios (aPORs) of antimicrobial resistance (AMR), with aPORs as low as 0.20 (95% CI: 0.06–0.63) for ceftriaxone in the North Central region. Additionally, no regions had significantly higher aPOR of resistance for any antibiotic. Differences in resistance between the Central and other regions varied by antibiotic with the largest difference for ceftriaxone and smallest for ampicillin. The finding of regional variation of E. coli AMR calls for more specific community antibiograms to enable a precise approach to antibiotic prescribing and stewardship.

This study included deidentified antibiotic susceptibility results from outpatient urinary Escherichia coli isolates from Washington state which were tested at a large clinical laboratory during 2013–2019. Isolates were categorized as representing the first, second, third, or fourth-or-greater occurrence of infection in data from individual patients. We used logistic regression with the outcome of resistance, adjusting for year of antimicrobial susceptibility test, patient sex, patient age, and facility type. In cases of subsequent infection, we found a significant risk of resistance to levofloxacin, ciprofloxacin, ceftriaxone, trimethoprim-sulfa, nitrofurantoin, ampicillin, gentamicin, and amoxicillin-clavulanate. Our findings suggest that Escherichia coli isolates from recurrent urinary tract infections have a higher rate of resistance to most tested antibiotics than isolates from the first urinary tract infection in a given year. However, susceptibility frequencies did not differ significantly between antibiograms constructed using only the first occurrence in a patient and those constructed using all subsequent occurrences. These findings suggest that the traditional approach of including only the first occurrence of urinary Escherichia coli in a patient may underestimate levels of antibiotic resistance in a community. Such underestimation could negatively affect empiric therapeutic choices, health outcomes, and treatment costs.

Transplant patients are at risk of infections due to long-term immunosuppression contributing to morbidity and mortality in this population. Post-transplant testing guidelines were established to monitor and guide therapeutic interventions in transplant recipients. We hypothesize that there are gaps in adherence to the recommended frequency of laboratory testing in post-transplant patients. We analyzed national reference laboratory data to compare viral post-transplant infection (PTI) testing frequency with their respective published guidelines to understand patient uptake and compliance. We evaluated the ordering patterns, positivity rates, and frequency of molecular infectious disease tests (MIDTs). We included 345 patients with International Classification of Diseases (ICD)-10 codes for transplant (Z940-Z942, Z944, Z9481, Z9483, Z9484) with at least two tests (within 7 days) in January 2019 and at least one test in December 2020 to find patients in the post-transplant period. We analyzed two cohorts: kidney transplant recipients (KTRs; 40%) and non-KTR (60%) then followed them longitudinally for the study period. In KTR cohort, high-to-low proportion of ordered MIDT was blood BK virus (bBKV) followed by cytomegalovirus (CMV); in non-KTR cohort, CMV was followed by Epstein–Barr virus (EBV). KTR cohort positivity was highest for urine BK virus (uBKV; 58%) followed by EBV (46%), bBKV (40%), and CMV (31%). Non-KTR cohort positivity was highest for uBKV (64%), EBV (51%), CMV (30%), bBKV (8%), and adenovirus (7%). All patients were tested at progressively longer intervals from the date of the first post-transplant ICD-10-coded test. More than 40% of the KTR cohort were tested less frequently for EBV and bBKV, and more than 20% of the non-KTR cohort were tested for EBV less frequently than published guidelines 4 months after transplant. Despite regular testing, the results of MIDT testing for KTR and non-KTR patients in the post-transplant period are not aligned with published guidelines. IMPORTANCE: Guidance for post-transplant infectious disease testing is established, however, for certain infections it allows for clinician discretion. This leads to transplant center policies developing their own testing/surveillance strategies based on their specific transplant patient population (kidney, stem cell, etc.). The Organ Procurement and Transplant Network (OPTN) has developed a strategic plan to improve and standardize the transplant process in the US to improve outcomes of living donors and recipients. Publishing national reference lab data on the testing frequency and its alignment with the recommended guidelines for post-transplant infectious diseases can inform patient uptake and compliance for these strategic OPTN efforts.

Context.—: Disease courses in COVID-19 patients vary widely. Prediction of disease severity on initial diagnosis would aid appropriate therapy, but few studies include data from initial diagnosis. Objective.—: To develop predictive models of COVID-19 severity based on demographic, clinical, and laboratory data collected at initial patient contact after diagnosis of COVID-19. Design.—: We studied demographic data and clinical laboratory biomarkers at time of diagnosis, using backward logistic regression modeling to determine severe and mild outcomes. We used deidentified data from 14 147 patients who were diagnosed with COVID-19 by polymerase chain reaction SARS-CoV-2 testing at Montefiore Health System, from March 2020 to September 2021. We generated models predicting severe disease (death or more than 90 hospital days) versus mild disease (alive and fewer than 2 hospital days), starting with 58 variables, by backward stepwise logistic regression. Results.—: Of the 14 147 patients, including Whites, Blacks, and Hispanics, 2546 (18%) patients had severe outcomes and 3395 (24%) had mild outcomes. The final number of patients per model varied from 445 to 755 because not all patients had all available variables. Four models (inclusive, receiver operating characteristic, specific, and sensitive) were identified as proficient in predicting patient outcomes. The parameters that remained in all models were age, albumin, diastolic blood pressure, ferritin, lactic dehydrogenase, socioeconomic status, procalcitonin, B-type natriuretic peptide, and platelet count. Conclusions.—: These findings suggest that the biomarkers found within the specific and sensitive models would be most useful to health care providers on their initial severity evaluation of COVID-19.