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To investigate the characteristics and drug resistance patterns of Klebsiella pneumoniae (K. pneumoniae) infection in pediatric intensive care unit (PICU). Methods: K. pneumoniae strains from 17 domestic PICUs were analyzed for overall condition and drug resistance using WHO-NET software. Results: From 2016 to 2022, there was a linear increase in the detection rate of K. pneumoniae (P< 0.05), with a total of 2591 (9.7%) strains detected. The primary sites of K. pneumoniae detection were the respiratory tract (71.1%), blood (8.6%), and urinary tract (7.1%). K. pneumoniae's resistance to penicillin drugs exceeded 90%, and are over 50% to cephalosporins. Resistance to cefoperazone-sulbactam decreased from 51.7% to 25.7%, and ranged from 9.1% to 20.8% for ceftolozane-tazobactam. Carbapenem-resistant K. pneumoniae strains constituted 32.3%. Resistance to imipenem and meropenem have decreased to 33.8% and 40.2%, while increased to 35.2% for ertapenem. Levofloxacin and amikacin resistance rates have decreased to 25.7% and 9.1%, but remain high at 63.8% for moxifloxacin and 44.6% for ciprofloxacin. K. pneumoniae demonstrated the lowest resistance rates to polymyxin B (0.9%), tigecycline (2.2%), and polymyxin E (3.1%). No strain of K. pneumoniae was resistant to both polymyxin B and meropenem. However, some strains showed co-resistance to meropenem with other antibiotics, including tigecycline (2%), imipenem (16%), amikacin (27%), colistin (37%), and levofloxacin (41%). Conclusion: The rates of isolation and drug resistance of K. pneumoniae in PICU have significantly increased over 7 years. Careful antibiotic use, infection control strategies, and appropriate antibiotic combinations are crucial in addressing this problem. [ABSTRACT FROM AUTHOR]

Aiming at a kind of dual-rate Hammerstein system, based on the auxiliary model identification idea, using the maximum likelihood principle and recursive identification technology, this paper proposes a maximum likelihood recursive least squares algorithm. The main method is to construct an auxiliary model for the unknown output in the model, and use the output of the auxiliary model to predict the unknown output. This method can directly identify parameters based on the dual-rate input and output data. The simulation experiments show that the proposed algorithm can effectively identify the dual-rate Hammerstein system, and the final error tends to be about 1%.

Objectives:. Sepsis is responsible for a substantial proportion of global childhood morbidity and mortality. However, evidence demonstrates major inaccuracies in the use of the term “sepsis” in clinical practice, coding, and research. Current and previous definitions of sepsis have been developed using expert consensus but the specific criteria used to identify children with sepsis have not been rigorously evaluated. Therefore, as part of the Society of Critical Care Medicine’s Pediatric Sepsis Definition Taskforce, we will conduct a systematic review to synthesize evidence on individual factors, clinical criteria, or illness severity scores that may be used to identify children with infection who have or are at high risk of developing sepsis-associated organ dysfunction and separately those factors, criteria, and scores that may be used to identify children with sepsis who are at high risk of progressing to multiple organ dysfunction or death. Data Sources:. We will identify eligible studies by searching the following databases: MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials. Study Selection:. We will include all randomized trials and cohort studies published between January 1, 2004, and March 16, 2020. Data Extraction:. Data extraction will include information related to study characteristics, population characteristics, clinical criteria, and outcomes. Data Synthesis:. We will calculate sensitivity and specificity of each criterion for predicting sepsis and conduct a meta-analysis if the data allow. We will also provide pooled estimates of overall hospital mortality. Conclusions:. The potential risk factors, clinical criteria, and illness severity scores from this review which identify patients with infection who are at high risk of developing sepsis-associated organ dysfunction and/or progressing to multiple organ dysfunction or death will be used to inform the next steps of the Pediatric Sepsis Definition Taskforce.