PREVIOUSLY HEALTHY Research Articles

1695. Epidemiology of Positive Blood Cultures with Negative BioFire® Blood Culture Identification Panel Results at a Pediatric Center

Abstract Background The BioFire® Blood Culture Identification (BCID) Panel is a rapid multiplex PCR assay designed to identify common bloodstream infection (BSI) organisms from positive blood cultures (Figure 1). Although a positive BCID result can identify and often guide pathogen-directed treatment, a negative result may lead to uncertainty. Therefore, we aimed to describe the organisms isolated from cultures with negative BCID tests in pediatric patients. Figure 1 Targets on the BioFire Blood Culture Identification Panels 1 vs. 2 Methods In this retrospective study of children (≤ 21 years) in our health system, we extracted all BCID tests performed 01/01/2018 to 03/12/2023 from the electronic medical record (EMR). We excluded positive BCID tests and from the negative tests, excluded duplicates, those with no growth and isolated Gram-positive rod Gram stain. Demographic, clinical, and culture data were abstracted from the EMR. Host status was categorized as previously healthy (PH), chronic condition (CC), and immunocompromised (IC). Descriptive statistics were used to summarize the data. Results Of the 4049 BCID tests (3123 BCID1, 926 BCID2) performed, 387 resulted negative (327 BCID1 [10.4%], 61 BCID2 [6.6%]). After applying exclusion criteria, 351 negative BCID tests (296 BCID1, 55 BCID2) from 325 patients were included in the dataset. Among the 351 negative tests, 81 (23.1%) were in PH hosts, 151 (43.0%) in CC hosts, and 119 (33.9%) in IC hosts. The majority (n=208, 59.3%) of negative test results were from cultures drawn peripherally. Eighteen tests (5.1%) resulted in polymicrobial growth, and 156 (44.4%) were not treated as BSIs by the clinician (Table 1). After the BCID negative result, the median delay in organism identification using conventional methods was 28.9 hours. The 351 cultures from negative BCID tests yielded 372 organisms with the most common Micrococcus spp. (Figure 2). Table 1 Demographic and culture characteristics of patients with negative BioFire BCID test results: a comparison between BCID1 and BCID2 Figure 2 Top 3 organisms isolated from positive cultures with negative BioFire BCID tests, stratified by patient host status and reported Gram stain Conclusion In this large pediatric cohort, the BioFire® BCID1 was negative in 10.4% of tests, decreasing to 6.6% with BCID2. We provide clinicians with a useful resource of organisms isolated from cultures with negative BCID results based on underlying host status. With an identification delay of > 24 hours for the majority of BCID negative tests and many not ultimately treated as BSIs, the impact of negative BCID results on antimicrobial stewardship efforts remains an area of future study. Disclosures Pratik A. Patel, MD, Cardinal Health Inc: Advisor/Consultant

1201: PREVIOUSLY HEALTHY ADULTS AMONG SEPTIC PATIENTS: POPULATION-LEVEL EPIDEMIOLOGY AND OUTCOMES

Introduction: A recent multi-center study has demonstrated that previously healthy (PH) adults are rare among patients with community-acquired sepsis, but have, unexpectedly, higher crude and adjusted short-term mortality than those with comorbidities (PC). It is unclear whether similar outcome associations are present among all septic patients in unselected, statewide populations. Methods: We used a statewide dataset to identify hospitalizations aged ≥18 years with a diagnosis of sepsis in Texas during 2018-2019. Sepsis was defined by “explicit” ICD-10 codes for severe sepsis (R65.20) and septic shock (R65.21). PH was defined as absence of the comorbidities included in the Charlson Comorbidity Index, and of obesity, malnutrition, mental disorders, and substance and alcohol use disorders. Hierarchical models were fitted to estimate the association between PH and short-term mortality (defined as in-hospital death or discharge to hospice) among all sepsis hospitalizations and for sensitivity analyses among the subsets with septic shock, ICU admission, and those undergoing invasive mechanical ventilation (IMV). Results: Among 120,371 sepsis hospitalizations, 6,611 (5.5%) were PH. Compared to those with PC, those PH were younger (aged ≥65 years 36.6% vs 56.3%), and more commonly racial/ethnic minority (51.0% vs 48.6%) and underinsured/uninsured (28.9% vs 17.5%) [p< 0.001 for all comparisons]. Short-term mortality rate was markedly lower among PH than among PC (11.7% vs 28.2%). Following adjustment for confounders, PH remained associated with lower short-term mortality (adjusted odds ratio [aOR] 0.63 [95% CI 0.57-0.69]), with consistent findings on sensitivity analyses among those with septic shock (aOR 0.75 [95% CI 0.68-0.84]) and ICU admission (aOR 0.64 [95% CI 0.57-0.71]). However, PH was no longer associated with short-term mortality among sepsis hospitalization undergoing IMV (aOR 0.99 [95% CI 0.86-1.14]). Conclusions: PH was reported in over 1 in 20 sepsis hospitalizations. The risk of short-term mortality was markedly lower among the PH group, including those with escalating illness severity, except those requiring IMV. Further studies are needed to explore the sources of the contrast with the findings of prior reports.

TIGIT modulates sepsis-induced immune dysregulation in mice with preexisting malignancy.

TIGIT is a recently identified coinhibitory receptor that is upregulated in the setting of cancer and functionally contributes to the impairment of antitumor immunity. However, its role during sepsis is unknown. Because patients with cancer are 10 times more likely to die of sepsis than previously healthy (PH) patients with sepsis, we interrogated the role of TIGIT during sepsis in the context of preexistent malignancy. PH mice or cancer (CA) mice inoculated with lung carcinoma cells were made septic by cecal ligation and puncture (CLP). We found that sepsis induced TIGIT upregulation predominantly on Tregs and NK cells in both PH and CA mice. Anti-TIGIT Ab improved the 7-d survival of CA septic mice but not PH mice after CLP. Treatment of CA septic animals but not PH septic animals with anti-TIGIT mAb significantly reversed sepsis-induced loss of CD4+ T cells, CD8+ T cells, Foxp3+ Treg, and CD19+ B cells in the spleen, which was the result of decreased caspase-3+ apoptotic cells. In sum, we found that anti-TIGIT Ab reversed sepsis-induced T cell apoptosis in CA septic mice and led to a significant survival benefit, suggesting its use as a potential immunotherapy to improve outcomes in septic patients with cancer.

Point-of-Care Complexity Screening Algorithm to Identify Children With Medical Complexity.

For pediatric complex care programs to target enhanced care coordination services to the highest-risk patients, it is critical to accurately identify children with medical complexity (CMC); however, no gold standard definition exists. The aim of this study is to describe a point-of-care screening algorithm to identify CMC with high health care use, a group that may benefit the most from improved care coordination. From July 1, 2015, to June 30, 2016 (fiscal year 2016 [FY16]), a medical complexity screening algorithm was implemented by a pediatric complex care program at a single tertiary care center for hospitalized patients at the time of admission. Using the screening algorithm, we categorized inpatients into 1 of 3 groups: CMC, children with special health care needs (CSHCN), or previously healthy (PH) children. Inpatient resource use for FY16 and FY17 encounters was extracted for children screened in FY16. We categorized 2187 inpatients in FY16 into the 3 complexity groups (CMC = 77; CSHCN = 1437; PH children = 673). CMC had more complex chronic conditions (median = 6; interquartile range [IQR] 4-11) than CSHCN (median = 1; IQR 0-2) and PH children (median = 0; IQR 0-0). CMC had greater per-patient and per-encounter hospital use than CSHCN and PH children. CMC and children with ≥4 complex chronic conditions had comparable levels of resource use. By implementation of a point-of-care screening algorithm, we identified CMC with high health care use. By using this algorithm, it was feasible to identify hospitalized CMC that could benefit from care coordination by a pediatric complex care program.