To determine the relationship between severe acute respiratory syndrome coronavirus 2 infection, hospital-acquired infections (HAIs), and mortality.

Retrospective cohort.

Three St. Louis, MO hospitals.

Adults admitted ≥48 hours from January 1, 2017 to August 31, 2020.

Hospital-acquired infections were defined as those occurring ≥48 hours after admission and were based on positive urine, respiratory, and blood cultures. Poisson interrupted time series compared mortality trajectory before (beginning January 1, 2017) and during the first 6 months of the pandemic. Multivariable logistic regression models were fitted to identify risk factors for mortality in patients with an HAI before and during the pandemic. A time-to-event analysis considered time to death and discharge by fitting Cox proportional hazards models.

Among 6,447 admissions with subsequent HAIs, patients were predominantly White (67.9%), with more females (50.9% vs 46.1%, P = .02), having slightly lower body mass index (28 vs 29, P = .001), and more having private insurance (50.6% vs 45.7%, P = .01) in the pre-pandemic period. In the pre-pandemic era, there were 1,000 (17.6%) patient deaths, whereas there were 160 deaths (21.3%, P = .01) during the pandemic. A total of 53 (42.1%) coronavirus disease 2019 (COVID-19) patients died having an HAI. Age and comorbidities increased the risk of death in patients with COVID-19 and an HAI. During the pandemic, Black patients with an HAI and COVID-19 were more likely to die than White patients with an HAI and COVID-19.

In three Midwestern hospitals, patients with concurrent HAIs and COVID-19 were more likely to die if they were Black, elderly, and had certain chronic comorbidities.

To compare clinical outcomes over time of inpatients with healthcare-associated coronavirus disease 2019 (HA-COVID-19) versus community-acquired COVID-19 (CA-COVID-19).

We conducted a multicenter, prospective observational cohort study of inpatients with COVID-19.

The study was conducted across 16 acute-care hospitals in Switzerland.

We compared HA-COVID-19 cases, defined as patients with a positive severe acute respiratory coronavirus virus 2 (SARS-CoV-2) test > 5 days after hospital admission, with hospitalized CA-COVID-19 cases, defined as those who tested positive within 5 days of admission. The composite primary outcome was patient transfer to an intensive care unit (ICU) or an intermediate care unit (IMCU) and/or all-cause in-hospital mortality. We used cause-specific Cox regression and Fine-Gray regression to model the time to the composite clinical outcome, adjusting for confounders and accounting for the competing event of discharge from hospital. We compared our results to those from a conventional approach using an adjusted logistic regression model where time-varying effects and competitive risk were ignored.

Between February 19, 2020, and December 31, 2020, we included 1,337 HA-COVID-19 cases and 9,068 CA-COVID-19 cases. HA-COVID-19 patients were significantly older: median, 80 (interquartile range [IQR], 71–87) versus median 70 (IQR, 57–80) (P < .001). A greater proportion of HA-COVID-19 patients had a Charlson comorbidity index ≥ 5 (79% vs 55%; P < .001) than did CA-COVID-19 patients. In time-varying analyses, between day 0 and 8, HA-COVID-19 cases had a decreased risk of death or ICU or IMCU transfer compared to CA-COVID-19 cases (cause-specific hazard ratio [csHR], 0.43; 95% confidence interval [CI], 0.33–0.56). In contrast, from day 8 to 30, HA-COVID-19 cases had an increased risk of death or ICU or IMCU transfer (csHR, 1.49; 95% CI, 1.20–1.85), with no significant effect on the rate of discharge (csHR, 0.83; 95% CI, 0.61–1.14). In the conventional logistic regression model, HA-COVID-19 was protective against transfer to an ICU or IMCU and/or all-cause in-hospital mortality (adjusted odds ratio [aOR], 0.79, 95% CI, 0.67–0.93).

The risk of adverse clinical outcomes for HA-COVID-19 cases increased substantially over time in hospital and exceeded that for CA-COVID-19. Using approaches that do not account for time-varying effects or competing events may not fully capture the true risk of HA-COVID-19 compared to CA-COVID-19.

To investigate clinically relevant microbiological characteristics of uropathogens and to compare patients with catheter-associated urinary tract infections (CAUTIs) to those with non-CAUTIs.

All urine cultures from the calendar year 2019 of the Swiss Centre for Antibiotic Resistance database were analyzed. Group differences in the proportions of bacterial species and antibiotic-resistant isolates from CAUTI and non-CAUTI samples were investigated.

Data from 27,158 urine cultures met the inclusion criteria. Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Proteus mirabilis together represented 70% and 85% of pathogens identified in CAUTI and non-CAUTI samples, respectively. Pseudomonas aeruginosa was significantly more often detected in CAUTI samples. The overall resistance rate for the empirically often-prescribed antibiotics ciprofloxacin (CIP), norfloxacin (NOR), and trimethoprim-sulfamethoxazole (TMP-SMX) was between 13% and 31%. Except for nitrofurantoin, E. coli from CAUTI samples were more often resistant (P ≤ .048) to all classes of antibiotics analyzed, including third-generation cephalosporines used as surrogate for extended-spectrum β-lactamase (ESBL). Significanty higher resistance proportions in CAUTI samples versus non-CAUTI samples were observed for CIP (P = .001) and NOR (P = .033) in K. pneumoniae, for NOR (P = .011) in P. mirabilis, and for cefepime (P = .015), and piperacillin-tazobactam (P = .043) in P. aeruginosa.

CAUTI pathogens were more often resistant to recommended empirical antibiotics than non-CAUTI pathogens. This finding emphasizes the need for urine sampling for culturing before initiating therapy for CAUTI and the importance of considering therapeutic alternatives.

From January 1, 2018, until July 31, 2020, our hospital network experienced an outbreak of vancomycin-resistant enterococci (VRE). The goal of our study was to improve existing processes by applying machine-learning and graph-theoretical methods to a nosocomial outbreak investigation.

We assembled medical records generated during the first 2 years of the outbreak period (January 2018 through December 2019). We identified risk factors for VRE colonization using standard statistical methods, and we extended these with a decision-tree machine-learning approach. We then elicited possible transmission pathways by detecting commonalities between VRE cases using a graph theoretical network analysis approach.

We compared 560 VRE patients to 86,684 controls. Logistic models revealed predictors of VRE colonization as age (aOR, 1.4 (per 10 years), with 95% confidence interval [CI], 1.3–1.5; P < .001), ICU admission during stay (aOR, 1.5; 95% CI, 1.2–1.9; P < .001), Charlson comorbidity score (aOR, 1.1; 95% CI, 1.1–1.2; P < .001), the number of different prescribed antibiotics (aOR, 1.6; 95% CI, 1.5–1.7; P < .001), and the number of rooms the patient stayed in during their hospitalization(s) (aOR, 1.1; 95% CI, 1.1–1.2; P < .001). The decision-tree machine-learning method confirmed these findings. Graph network analysis established 3 main pathways by which the VRE cases were connected: healthcare personnel, medical devices, and patient rooms.

We identified risk factors for being a VRE carrier, along with 3 important links with VRE (healthcare personnel, medical devices, patient rooms). Data science is likely to provide a better understanding of outbreaks, but interpretations require data maturity, and potential confounding factors must be considered.

The incidence of surgical site infections may be underreported if the data are not routinely validated for accuracy. Our goal was to investigate the communicated SSI rate from a large network of Swiss hospitals compared with the results from on-site surveillance quality audits.

Retrospective cohort study.

In total, 81,957 knee and hip prosthetic arthroplasties from 125 hospitals and 33,315 colorectal surgeries from 110 hospitals were included in the study.

Hospitals had at least 2 external audits to assess the surveillance quality. The 50-point standardized score per audit summarizes quantitative and qualitative information from both structured interviews and a random selection of patient records. We calculated the mean National Healthcare Safety Network (NHSN) risk index adjusted infection rates in both surgery groups.

The median NHSN adjusted infection rate per hospital was 1.0% (interquartile range [IQR], 0.6%–1.5%) with median audit score of 37 (IQR, 33–42) for knee and hip arthroplasty, and 12.7% (IQR, 9.0%–16.6%), with median audit score 38 (IQR, 35–42) for colorectal surgeries. We observed a wide range of SSI rates and surveillance quality, with discernible clustering for public and private hospitals, and both lower infection rates and audit scores for private hospitals. Infection rates increased with audit scores for knee and hip arthroplasty (P value for the slope = .002), and this was also the case for planned (P = .002), and unplanned (P = .02) colorectal surgeries.

Surveillance systems without routine evaluation of validity may underestimate the true incidence of SSIs. Audit quality should be taken into account when interpreting SSI rates, perhaps by adjusting infection rates for those hospitals with lower audit scores.