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Background Carbapenem-resistant gram-negative bacilli (CR-GNB) have been increasingly reported in China. However, dynamic monitoring data on molecular epidemiology of CR-GNB are limited in pediatric patients. Results 300 CR-GNB isolates (200 Carbapenem-resistant K. pneumoniae (CRKP), 50 carbapenem-resistant A.baumannii (CRAB) and 50 carbapenem-resistant P. aeruginosa (CRPA)) were investigated. The predominant carbapenemase gene was blaNDM−1 (73%) and blaKPC−2 (65%) in neonates and non-neonates. Meanwhile, the predominant STs were ST11 (54%) in neonates and ST17 (27.0%) and ST278 (20.0%) in non-neonates. Notably, a shift in the dominant sequence type of CRKP infections from ST17 /ST278-NDM-1 to ST11-KPC-2 was observed during the years 2017–2021 and KPC-KP showed relatively higher resistance to aminoglycosides and quinolones than NDM-KP.BlaOXA−23 was isolated from all the CRAB isolates while only one isolate expressing blaBIC and 2 isolates expressing blaVIM−2 were found in CRPA isolates. ST195 (22.0%) and ST244 (24.0%) were the most common in CRAB and CRPA isolates and all the STs of CRAB belonged to CC92 while CRPA presents ST types with diversity distribution. Conclusion CRKP showed different molecular phenotypes in neonates and non-neonates and was changing dynamically and high-risk clone of ST11 KPC-KP should be paid more attention. Most CRKP and CRAB strains shared the same CCs, suggesting that intrahospital transmission may occur, and large-scale screening and more effective measures are urgently needed.

Purpose Pediatric patients with inborn errors of immunity (IEI) undergoing umbilical cord blood transplantation (UCBT) are at risk of early mortality. Our aim was to develop and validate a prediction model for early mortality after UCBT in pediatric IEI patients based on pretransplant factors. Methods Data from 230 pediatric IEI patients who received their first UCBT between 2014 and 2021 at a single center were analyzed retrospectively. Data from 2014–2019 and 2020–2021 were used as training and validation sets, respectively. The primary outcome of interest was early mortality. Machine learning algorithms were used to identify risk factors associated with early mortality and to build predictive models. The model with the best performance was visualized using a nomogram. Discriminative ability was measured using the area under the curve (AUC) and decision curve analysis. Results Fifty days was determined as the cutoff for distinguishing early mortality in pediatric IEI patients undergoing UCBT. Of the 230 patients, 43 (18.7%) suffered early mortality. Multivariate logistic regression with pretransplant albumin, CD4 (absolute count), elevated C-reactive protein, and medical history of sepsis showed good discriminant AUC values of 0.7385 (95% CI, 0.5824–0.8945) and 0.827 (95% CI, 0.7409–0.9132) in predicting early mortality in the validation and training sets, respectively. The sensitivity and specificity were 0.5385 and 0.8154 for validation and 0.7667 and 0.7705 for training, respectively. The final model yielded net benefits across a reasonable range of risk thresholds. Conclusion The developed nomogram can predict early mortality in pediatric IEI patients undergoing UCBT.