Strategies to reduce hospital-associated bloodstream infections in a limited resource setting: Preventing Infections in Neonates (PIN) collaborative

Clinical characteristics, antimicrobial resistance, and mortality of neonatal bloodstream infections in Northern Tanzania, 2022-2023

Neonatal bloodstream infections (BSI) make a substantial contribution to morbidity and mortality in low- and middle-income countries (LMICs), but data on the epidemiology and antimicrobial resistance (AMR) in Tanzania are limited. We describe the prevalence, resistance patterns, and associated factors of neonatal BSI at the Kilimanjaro Christian Medical Centre (KCMC), a large referral hospital in northern Tanzania. We conducted a prospective, observational study involving infants aged 0-60 days with perinatal risk factors or clinical signs of sepsis. Aerobic blood cultures were obtained at enrollment and monitored using a continuously monitored blood culture instrument. Antimicrobial susceptibility testing was performed using standard phenotypic methods. Vital status was obtained on days 2, 7, and 28 post-enrollment. BSI was defined as the isolation of established neonatal pathogens, including yeast and coagulase-negative Staphylococcus spp. (CoNS). Early-onset BSI occurred on day of life (DOL) 0-2, while late-onset BSI occurred on DOL 3 or later. Among 236 enrolled infants, blood culture was obtained in 233. BSI occurred in 106 (45.5%) of 233 infants, 50 (47.2%) were early-onset, and 56 (52.8%) were late-onset BSI. The isolated pathogens included 58 (54.7%) Gram-positive bacteria, 40 (37.7%) Gram-negative bacteria, and 8 (7.5%) yeast. CoNS (n = 55, 51.9%) and Klebsiella pneumoniae (n = 35, 33.0%) were the most common pathogens. Notably, all K. pneumoniae isolates were extended-spectrum beta-lactamase producers, resistant to ampicillin and ceftriaxone. Among the 56 infants who died, 29 (51.8%) had BSI; 11 (19.6%) infants with EO-BSI, and 18 (32.1%) with LO-BSI. Infants requiring respiratory support at admission had a 1.89-fold increased adjusted odds of BSI (95% CI, 1.05-3.44). We found high prevalence of neonatal BSI due to bacteria with a high prevalence of AMR, and BSI was associated with high mortality. There is an urgent need for effective preventive, diagnostic, and therapeutic interventions to address BSI among hospitalized infants in northern Tanzania.

Assessment of the CLOT (children's likelihood of thrombosis) real-time risk prediction model of hospital-associated venous thromboembolism in children with congenital heart disease

BACKGROUND

Children with congenital heart disease (CHD) are at high risk for hospital-associated venous thromboembolism (HA-VTE). The children's likelihood of thrombosis (CLOT) trial validated a real-time predictive model for HA-VTE using data extracted from the EHR for pediatric inpatients. We tested the hypothesis that addition of CHD specific data would improve model prediction in the CHD population.

METHODS

Model performance in CHD patients from 2010 to 2022, was assessed using 3 iterations of the CLOT model: 1) the original CLOT model, 2) the original model refit using only data from the CHD cohort, and 3) the model updated with the addition of cardiopulmonary bypass time, STAT Mortality Category, height, and weight as covariates. The discrimination of the three models was quantified and compared using AUROC.

RESULTS

Our CHD cohort included 1457 patient encounters (median 2.0 IQR [0.5-5.2] years-old). HA-VTE was present in 5% of our CHD cohort versus 1% in the general pediatric population. Several features from the original model were associated with thrombosis in the CHD cohort including younger age, thrombosis history, infectious disease consultation, and EHR coding of a central venous line. Lower height and weight were associated with thrombosis. HA-VTE rate was 12% (18/149) amongst those with STAT Category 4-5 operation versus 4% (49/1256) with STAT Category 1-3 operation (P < .001). Longer cardiopulmonary bypass time (124 [92-205] vs. 94 [65-136] minutes, P < .001) was associated with thrombosis. The AUROC for the original (0.80 95% CI [0.75-0.85]), refit (0.85 [0.81-0.89]), and updated (0.86 [0.81-0.90]) models demonstrated excellent discriminatory ability within the CHD cohort.

CONCLUSION

The automated approach with EHR data extraction makes the applicability of such models appealing for ease of clinical use. The addition of cardiac specific features improved model discrimination; however, this benefit was marginal compared to refitting the original model to the CHD cohort. This suggests strong predictive generalized models, such as CLOT, can be optimized for cohort subsets without additional data extraction, thus reducing cost of model development and deployment.