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已发表论文
中国北方地区（2018 - 2024 年）儿童大肠杆菌感染的七年监测及基于人工智能的耐药性预测
Authors Chen Y, Song Z , Di R, Zhao Q, Liu J, Song H, Wang J, Chen Y
Received 1 October 2025
Accepted for publication 3 January 2026
Published 9 January 2026 Volume 2026:19 566930
DOI https://doi.org/10.2147/IDR.S566930
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Hemant Joshi
Yanyan Chen,1,2,* Ziqi Song,1,* Ruihua Di,1,* Qing Zhao,1,2 Jia Liu,1 Haobin Song,1,2 Jingya Wang,1 Yingnan Chen3
1Department of Laboratory Medicine, Baoding Hospital of Beijing Children’s Hospital, Capital Medical University, Baoding, Hebei, 071000, People’s Republic of China; 2Hebei Key Laboratory of Infectious Diseases Pathogenesis and Precise Diagnosis and Treatment, Baoding Hebei, 071000, People’s Republic of China; 3The Third Special Care Hospital for Disabled Soldiers of Hebei Province, Baoding, 071000, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Yingnan Chen, Email yingnan090905@163.com
Background: Pediatric Escherichia coli infections are a major cause of morbidity, and increasing antimicrobial resistance (AMR) complicates empirical treatment. Long-term local surveillance combined with trend forecasting may support rational antimicrobial use and stewardship in northern China.
Methods: We retrospectively analyzed 2021 pediatric E. coli isolates collected at Baoding Hospital of Beijing Children’s Hospital, Capital Medical University (2018– 2024), calculated annual resistance rates to 14 commonly used antibiotics and the prevalence of extended-spectrum β-lactamase (ESBL) producers, defined an overall resistance indicator as the mean annual resistance across these agents, and used χ2-tests, linear regression, and autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) models to evaluate temporal trends and generate exploratory forecasts for 2025– 2027.
Results: Nearly half of isolates were from children < 2 years and about two-thirds from boys; pus, sputum, and urine were the predominant specimen types. Resistance to ampicillin and trimethoprim–sulfamethoxazole remained high, whereas resistance to several β-lactams, including ampicillin/sulbactam and third-generation cephalosporins, declined significantly over time. ESBL-producing isolates accounted for 45.93% of all strains, with annual detection rates > 50% in 2018– 2020 and around 40% thereafter, while carbapenems and amikacin maintained very low resistance rates. Both ARIMA and LSTM models suggested a modest further decline in the overall resistance indicator through 2027, with LSTM showing slightly better fit and lower prediction errors.
Conclusion: Pediatric E. coli isolates in our center exhibited high resistance to several common oral agents but encouraging declines in ESBL prevalence and cephalosporin resistance, likely reflecting local antimicrobial stewardship. Exploratory AI-based time-series models may help anticipate resistance trajectories and support pediatric antibiotic policies and stewardship, although forecasts from a short annual series should be interpreted cautiously.
Keywords: Escherichia coli, pediatric infections, antimicrobial resistance, extended-spectrum β-lactamase, ESBL, time-series forecasting, LSTM
1Department of Laboratory Medicine, Baoding Hospital of Beijing Children’s Hospital, Capital Medical University, Baoding, Hebei, 071000, People’s Republic of China; 2Hebei Key Laboratory of Infectious Diseases Pathogenesis and Precise Diagnosis and Treatment, Baoding Hebei, 071000, People’s Republic of China; 3The Third Special Care Hospital for Disabled Soldiers of Hebei Province, Baoding, 071000, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Yingnan Chen, Email yingnan090905@163.com
Background: Pediatric Escherichia coli infections are a major cause of morbidity, and increasing antimicrobial resistance (AMR) complicates empirical treatment. Long-term local surveillance combined with trend forecasting may support rational antimicrobial use and stewardship in northern China.
Methods: We retrospectively analyzed 2021 pediatric E. coli isolates collected at Baoding Hospital of Beijing Children’s Hospital, Capital Medical University (2018– 2024), calculated annual resistance rates to 14 commonly used antibiotics and the prevalence of extended-spectrum β-lactamase (ESBL) producers, defined an overall resistance indicator as the mean annual resistance across these agents, and used χ2-tests, linear regression, and autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) models to evaluate temporal trends and generate exploratory forecasts for 2025– 2027.
Results: Nearly half of isolates were from children < 2 years and about two-thirds from boys; pus, sputum, and urine were the predominant specimen types. Resistance to ampicillin and trimethoprim–sulfamethoxazole remained high, whereas resistance to several β-lactams, including ampicillin/sulbactam and third-generation cephalosporins, declined significantly over time. ESBL-producing isolates accounted for 45.93% of all strains, with annual detection rates > 50% in 2018– 2020 and around 40% thereafter, while carbapenems and amikacin maintained very low resistance rates. Both ARIMA and LSTM models suggested a modest further decline in the overall resistance indicator through 2027, with LSTM showing slightly better fit and lower prediction errors.
Conclusion: Pediatric E. coli isolates in our center exhibited high resistance to several common oral agents but encouraging declines in ESBL prevalence and cephalosporin resistance, likely reflecting local antimicrobial stewardship. Exploratory AI-based time-series models may help anticipate resistance trajectories and support pediatric antibiotic policies and stewardship, although forecasts from a short annual series should be interpreted cautiously.
Keywords: Escherichia coli, pediatric infections, antimicrobial resistance, extended-spectrum β-lactamase, ESBL, time-series forecasting, LSTM