Retrospective data analysis of CLABSI rates at Baystate Medical Center during the COVID-19 pandemic
Bibliographic record
Abstract
Background: Central-line–associated bloodstream infections (CLABSIs) are an important public health issue. Recent data from the CDC have shown an increase in healthcare-associated infections (including CLABSI) during the COVID-19 pandemic. Therefore, the main aim of this project was to analyze the epidemiology of central-line–associated bloodstream infection during different periods at the Baystate Medical Center (Springfield, MA) before, during, and after COVID-19 peaks of infection. Methods: Two specific periods were considered during the year (quarter January–March and quarter July–September) to consider potential seasonal variations, and the incidence of CLABSI during those 2 quarters was analyzed for 4 different years: 2019 (prepandemic), 2020–2021 (intrapandemic), and 2022 (postpandemic). An analysis of the microbial pathogens causing line infections was also performed to investigate differences described by other authors. Results: In total, 97 CLABSI (all from different patients) were reported into the NHSN website during the 8 periods considered. The average age of the patients was 55 years, with a male:female ratio of 57%:43%, and 14 renal patients were on dialysis. The CLABSI rates ranged from a minimum of 1.11 in Q1 of 2020 (start of COVID) to a maximum of 2 in Q3 of 2021 (SARS-CoV-2 delta variant) (Table 1). A statistical comparison of the pre–COVID-19 period with the respective quarters during the pandemic years (2020, 2021, and 2022) did not show any significant differences (Table 2). In term of microbiological data, of the 97 patients with CLABSIs, most of the patients (n = 70) had only 1 pathogen isolated, 14 patients had 2 pathogens, and 3 patients had 3 pathogens, bringing the total number of bacteria cultured to 117. Candida spp and Enterococcus spp were the most frequently isolated pathogens at 19% and 13%, respectively (Fig. 1). There was no statistically significant difference between the pre–COVID-19 and intra–COVID-19 periods for Candida spp (rate ratio, 1.391; 95% CI, 0.5477–3.533; P = .48) or Enterococcus spp (rate ratio, 2.385; 95% CI, 0.8365–6.798; P = .09). Conclusions: The COVID-19 pandemic did not seem to have an impact on the local epidemiology at Baystate Medical Center in terms of CLABSI rates or type of pathogens causing infections, but the sample size taken into consideration may not have been powerful enough to detect statistical significance. Note. This project was carried out as part of Dr Satta’s MPH requirements at UMass. Disclosures: None
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".