Objective This study aimed to investigate the antimicrobial resistance profiles of bacterial strains isolated from pediatric intensive care units(PICU)in China for better antimicrobial therapy.Methods Clinical isolates were collected from 17 institutions,including tertiary care children's hospitals and pediatric department of tertiary general hospitals in China from January 1,2020 to December 31,2022.Antimicrobial susceptibility testing was carried out according to a unified protocol using Kirby-Bauer method or automated systems.Results were interpreted according to the breakpoints released by the Clinical and Laboratory Standards Institute(CLSI)in 2020.Results A total of 10 688 isolates were collected,including gram-positive organisms(39.2％)and gram-negative organisms(60.8％).The top three organisms were S.aureus(13.6％,1 453/10 688),A.baumannii(10.0％,1 067/10 688),and coagulase-negative Staphylococcus(9.9％,1 058/10 688).Multi-drug resistant organisms(MDROs)were very common in children.The prevalence of methicillin-resistant Staphylococcus aureus(MRSA),carbapenem-resistant Enterobacterales(CRE),carbapenem-resistant E.coli,carbapenem-resistant K.pneumoniae(CRKP),carbapenem-resistant A.baumannii(CRAB),and carbapenem-resistant P.aeruginosa(CRPA)was 41.1％,19.4％,8.8％,30.9％,67.4％,and 28.8％,respectively.Overall,more than 50％of Enterobacteriales isolates were resistant to cephalosporins,while nearly 25％of Enterobacteriales isolates were resistant to carbapenems.MDROs were highly resistant to commonly used antibiotics.More than 80％of CRE and CRAB strains were resistant to all beta-lactam antibiotics.CRE and CRAB showed low resistance rates to tigecycline and polymyxin.CRPA showed lower resistance rates to piperacillin,beta-lactamase inhibitor combinations than the resistance rates to third and fourth generation cephalosporins.All of the Staphylococcus and Enterococcus isolates were susceptible to vancomycin and tigecycline.None of PRSP strains isolated from meningitis and nonmeningitis samples were resistant to rifampicin,vancomycin,or linezolid.The prevalence of β-lactamase-negative ampicillin-resistant(BLNAR)strains was 43.3％in Haemophilus influenzae.Conclusions MDROs were prevalent in PICU.It is necessary to establish an effective multidisciplinary team(MDT)to control the antimicrobial resistance.

Objective This study aimed to investigate the antimicrobial resistance profiles of bacterial strains isolated from pediatric intensive care units(PICU)in China for better antimicrobial therapy.Methods Clinical isolates were collected from 17 institutions,including tertiary care children's hospitals and pediatric department of tertiary general hospitals in China from January 1,2020 to December 31,2022.Antimicrobial susceptibility testing was carried out according to a unified protocol using Kirby-Bauer method or automated systems.Results were interpreted according to the breakpoints released by the Clinical and Laboratory Standards Institute(CLSI)in 2020.Results A total of 10 688 isolates were collected,including gram-positive organisms(39.2％)and gram-negative organisms(60.8％).The top three organisms were S.aureus(13.6％,1 453/10 688),A.baumannii(10.0％,1 067/10 688),and coagulase-negative Staphylococcus(9.9％,1 058/10 688).Multi-drug resistant organisms(MDROs)were very common in children.The prevalence of methicillin-resistant Staphylococcus aureus(MRSA),carbapenem-resistant Enterobacterales(CRE),carbapenem-resistant E.coli,carbapenem-resistant K.pneumoniae(CRKP),carbapenem-resistant A.baumannii(CRAB),and carbapenem-resistant P.aeruginosa(CRPA)was 41.1％,19.4％,8.8％,30.9％,67.4％,and 28.8％,respectively.Overall,more than 50％of Enterobacteriales isolates were resistant to cephalosporins,while nearly 25％of Enterobacteriales isolates were resistant to carbapenems.MDROs were highly resistant to commonly used antibiotics.More than 80％of CRE and CRAB strains were resistant to all beta-lactam antibiotics.CRE and CRAB showed low resistance rates to tigecycline and polymyxin.CRPA showed lower resistance rates to piperacillin,beta-lactamase inhibitor combinations than the resistance rates to third and fourth generation cephalosporins.All of the Staphylococcus and Enterococcus isolates were susceptible to vancomycin and tigecycline.None of PRSP strains isolated from meningitis and nonmeningitis samples were resistant to rifampicin,vancomycin,or linezolid.The prevalence of β-lactamase-negative ampicillin-resistant(BLNAR)strains was 43.3％in Haemophilus influenzae.Conclusions MDROs were prevalent in PICU.It is necessary to establish an effective multidisciplinary team(MDT)to control the antimicrobial resistance.

Objective:To develop and validate a 30-day readmission risk prediction model for patients with chronic obstructive pulmonary disease (COPD) using multiple machine learning algorithms.Methods:Convenience sampling was used to select 1 450 COPD patients hospitalized at Tianjin Medical University General Hospital from January 2017 to December 2023 as study subjects. Twenty-nine variables associated with readmission were included. LASSO was used to screen for primary characteristic variables associated with 30-day readmission. The 1 450 patients were divided into a training set ( n=870) and a test set ( n=580) in a 6∶4 ratio. Ten machine learning methods, including random forest, AdaBoost, extreme radient Boosting (XGB), decision tree and so on, were used for model training and testing to identify the optimal prediction model. The optimal model and SHAP were employed to analyze key features and rank characteristic importance. Results:Among 1 450 COPD patients, the 30-day readmission rate was 24.48% (355/1 450). There were no significant differences in the general information between patients in test set and training set ( P>0.05). LASSO regression analysis identified seven variables with the highest predictive value, namely regular weekly exercise, hospital stay, mean arterial pressure, forced expiratory volume in one second/forced vital capacity (FEV1/FVC), C-reactive protein, body mass index, and medication adherence. Machine learning showed that in the training set, XGB had the highest area under the receiver operating characteristic curve ( AUC), sensitivity, and F1 score of 0.943, 0.926, and 0.930, respectively. In the test set, the AUC and accuracy of XGB were 0.882 and 0.858, respectively, and XGB's various scores showed that it had good generalization and predictive performance. XGB analysis showed that medication adherence, FEV1/FVC, and regular weekly exercise were negatively correlated with the 30-day readmission risk, while body mass index, C-reactive protein, mean arterial pressure, and hospital stay were positively correlated. The characteristics ranked in order of importance were medication adherence, body mass index, C-reactive protein, mean arterial pressure, FEV1/FVC, hospital stay and regular weekly exercise. Conclusions:The XGB model has strong predictive performance and good generalization ability, which can effectively predict the 30-day readmission risk of COPD patients, assist in clinical identification of high-risk patients, implement nursing interventions, and reduce readmission rates.

Objective:To develop and validate a 30-day readmission risk prediction model for patients with chronic obstructive pulmonary disease (COPD) using multiple machine learning algorithms.Methods:Convenience sampling was used to select 1 450 COPD patients hospitalized at Tianjin Medical University General Hospital from January 2017 to December 2023 as study subjects. Twenty-nine variables associated with readmission were included. LASSO was used to screen for primary characteristic variables associated with 30-day readmission. The 1 450 patients were divided into a training set ( n=870) and a test set ( n=580) in a 6∶4 ratio. Ten machine learning methods, including random forest, AdaBoost, extreme radient Boosting (XGB), decision tree and so on, were used for model training and testing to identify the optimal prediction model. The optimal model and SHAP were employed to analyze key features and rank characteristic importance. Results:Among 1 450 COPD patients, the 30-day readmission rate was 24.48% (355/1 450). There were no significant differences in the general information between patients in test set and training set ( P>0.05). LASSO regression analysis identified seven variables with the highest predictive value, namely regular weekly exercise, hospital stay, mean arterial pressure, forced expiratory volume in one second/forced vital capacity (FEV1/FVC), C-reactive protein, body mass index, and medication adherence. Machine learning showed that in the training set, XGB had the highest area under the receiver operating characteristic curve ( AUC), sensitivity, and F1 score of 0.943, 0.926, and 0.930, respectively. In the test set, the AUC and accuracy of XGB were 0.882 and 0.858, respectively, and XGB's various scores showed that it had good generalization and predictive performance. XGB analysis showed that medication adherence, FEV1/FVC, and regular weekly exercise were negatively correlated with the 30-day readmission risk, while body mass index, C-reactive protein, mean arterial pressure, and hospital stay were positively correlated. The characteristics ranked in order of importance were medication adherence, body mass index, C-reactive protein, mean arterial pressure, FEV1/FVC, hospital stay and regular weekly exercise. Conclusions:The XGB model has strong predictive performance and good generalization ability, which can effectively predict the 30-day readmission risk of COPD patients, assist in clinical identification of high-risk patients, implement nursing interventions, and reduce readmission rates.

Objective:To construct and verify a risk prediction model for pulmonary infection in patients with aneurysmal subarachnoid hemorrhage (aSAH) after craniotomy and clipping, providing theoretical basis and practical guidance for improving the quality of postoperative care.Methods:Using the convenience sampling method, a retrospective selection was made of 397 patients with aSAH after craniotomy and clipping who were hospitalized in the Department of Neurosurgery of Shanxi Bethune Hospital (Shanxi Academy of Medical Sciences) from January 2019 to December 2023 as the modeling group. They were randomly divided into the training set and the test set at a ratio of 7:3, with 278 cases in the training set and 119 cases in the test set. Patients were divided into the infection group and the non-infection group based on whether they developed pulmonary infection. Univariate analysis was used to model the risk factors of pulmonary infection after aSAH craniotomy and clamping in the group, and Lasso regression was used to construct a predictive model. A total of 119 patients with aSAH admitted to the neurosurgery department of the same hospital from January to April 2024 were selected for the external validation of the model. The predictive effect of the model was evaluated through the receiver operating characteristic (ROC) curve.Results:In the modeling group, there were 216 male patients and 181 female patients. The incidence of pulmonary infection was 38.54% (153/397). Finally, five influencing factors, namely stroke, Hunt-Hess classification, mechanical ventilation, indwelling nasogastric tube and the timing of initiating enteral nutrition, were included to construct a predictive model. The areas under the ROC curves of the nomogram prediction models of this model in the training set, test set, and external validation group were 0.859(95% CI 0.791-0.928), 0.843(95% CI 0.796-0.890), and 0.800(95% CI 0.711-0.889), respectively. The calibration curve shows that the model's prediction fits well with the actual situation and has a high degree of calibration. Decision curve analysis indicates that this model has high clinical application value under different risk thresholds. Conclusions:The risk prediction model for pulmonary infection in patients after craniotomy and clipping with aSAH has good discrimination and calibration, which can provide reference for medical staff to identify high-risk patients at an early stage and take preventive intervention measures.

Objective:To construct and verify a risk prediction model for pulmonary infection in patients with aneurysmal subarachnoid hemorrhage (aSAH) after craniotomy and clipping, providing theoretical basis and practical guidance for improving the quality of postoperative care.Methods:Using the convenience sampling method, a retrospective selection was made of 397 patients with aSAH after craniotomy and clipping who were hospitalized in the Department of Neurosurgery of Shanxi Bethune Hospital (Shanxi Academy of Medical Sciences) from January 2019 to December 2023 as the modeling group. They were randomly divided into the training set and the test set at a ratio of 7:3, with 278 cases in the training set and 119 cases in the test set. Patients were divided into the infection group and the non-infection group based on whether they developed pulmonary infection. Univariate analysis was used to model the risk factors of pulmonary infection after aSAH craniotomy and clamping in the group, and Lasso regression was used to construct a predictive model. A total of 119 patients with aSAH admitted to the neurosurgery department of the same hospital from January to April 2024 were selected for the external validation of the model. The predictive effect of the model was evaluated through the receiver operating characteristic (ROC) curve.Results:In the modeling group, there were 216 male patients and 181 female patients. The incidence of pulmonary infection was 38.54% (153/397). Finally, five influencing factors, namely stroke, Hunt-Hess classification, mechanical ventilation, indwelling nasogastric tube and the timing of initiating enteral nutrition, were included to construct a predictive model. The areas under the ROC curves of the nomogram prediction models of this model in the training set, test set, and external validation group were 0.859(95% CI 0.791-0.928), 0.843(95% CI 0.796-0.890), and 0.800(95% CI 0.711-0.889), respectively. The calibration curve shows that the model's prediction fits well with the actual situation and has a high degree of calibration. Decision curve analysis indicates that this model has high clinical application value under different risk thresholds. Conclusions:The risk prediction model for pulmonary infection in patients after craniotomy and clipping with aSAH has good discrimination and calibration, which can provide reference for medical staff to identify high-risk patients at an early stage and take preventive intervention measures.

Objective:To explore the effectiveness of limb motor rehabilitation program based on patient health engagement (PHE) model in patients with hemorrhagic stroke, and to provide reference for the limb motor rehabilitation management of hemorrhagic stroke patients.Methods:Through literature review and Delphi expert correspondence, a limb motor rehabilitation program for hemorrhagic stroke patients based on the PHE model was constructed. A non-contemporaneous controlled study was conducted, 45 hemorrhagic stroke patients hospitalized in the Department of Neurosurgery of Shanxi Bethune Hospital from March to August 2022 were selected by convenience sampling method as the control group, and routine exercise rehabilitation measure was given, 45 hemorrhagic stroke patients from September 2022 to February 2023 were selected as the intervention group, a limb motor rehabilitation program based on PHE model was implemented on the basis of control group. The functional exercise compliance, limb motor function, daily activity ability, emotional and social dysfunction of patients in the two groups were observed before intervention, 1 and 3 months after intervention respectively.Results:A total of 85 patients with hemorrhagic stroke were included. There were 42 patients in the intervention group, 25 males and 17 females, aged (52.07 ± 9.91) years old, and 43 patients in the control group, 21 males and 22 females, aged (53.93 ± 10.52) years old. There were no significant differences in the functional exercise compliance, limb motor function, daily activity ability, emotional and social dysfunction of patients before intervention between the two groups. At 3 months after intervention, the functional exercise compliance score in the intervention group was (40.83 ± 7.92) points, higher than that in the control group (37.14 ± 6.44) points, and the difference was statistically significant ( t = 2.36, P<0.05). At 1 and 3 months after intervention, the scores of limb motor function and daily activity ability in the intervention group were (27.12 ± 6.74), (33.67 ± 6.54) points and (61.31 ± 6.72), (74.40 ± 8.71) points, which were higher than (24.91 ± 6.03), (27.02 ± 6.59) points and (52.33 ± 9.78), (60.12 ± 10.03) points of the control group, the differences were statistically significant ( t values were 2.06-7.01, all P<0.05), the scores of emotional and social dysfunction were (75.52 ± 22.09) and (58.33 ± 18.88) points, which were lower than (86.02 ± 23.04), (78.51 ± 21.67) points of the control group, and the differences were statistically significant ( t = - 2.14, - 4.57, both P<0.05). Conclusions:The limb motor rehabilitation program based on the PHE model could improve the exercise compliance of patients with hemorrhagic stroke, improve the limb motor function and daily activity ability of patients, alleviate negative emotions, and reduce the level of social dysfunction.

Objective:To investigate the value of systemic inflammatory response syndrome (SIRS), pediatric sequential organ failure assessment (pSOFA) and pediatric critical illness score (PCIS) in predicting mortality of pediatric sepsis in pediatric intensive care units (PICU) from Southwest China.Methods:This was a prospective multicenter observational study. A total of 447 children with sepsis admitted to 12 PICU in Southwest China from April 2022 to March 2023 were enrolled. Based on the prognosis, the patients were divided into survival group and non-survival group. The physiological parameters of SIRS, pSOFA and PCIS were recorded and scored within 24 h after PICU admission. The general clinical data and some laboratory results were recorded. The area under the curve (AUC) of the receiver operating characteristic curve was used to compare the predictive value of SIRS, pSOFA and PCIS in mortality of pediatric sepsis.Results:Amongst 447 children with sepsis, 260 patients were male and 187 patients were female, aged 2.5 (0.8, 7.0) years, 405 patients were in the survival group and 42 patients were in the non-survival group. 418 patients (93.5%) met the criteria of SIRS, and 440 patients (98.4%) met the criteria of pSOFA≥2. There was no significant difference in the number of items meeting the SIRS criteria between the survival group and the non-survival group (3(2, 4) vs. 3(3, 4) points, Z=1.30, P=0.192). The pSOFA score of the non-survival group was significantly higher than that of the survival group (9(6, 12) vs. 4(3, 7) points, Z=6.56, P<0.001), and the PCIS score was significantly lower than that of the survival group (72(68, 81) vs. 82(76, 88) points, Z=5.90, P<0.001). The predictive value of pSOFA (AUC=0.82) and PCIS (AUC=0.78) for sepsis mortality was significantly higher than that of SIRS (AUC=0.56) ( Z=6.59, 4.23, both P<0.001). There was no significant difference between pSOFA and PCIS ( Z=1.35, P=0.176). Platelet count, procalcitonin, lactic acid, albumin, creatinine, total bilirubin, activated partial thromboplastin time, prothrombin time and international normalized ratio were all able to predict mortality of sepsis to a certain degree (AUC=0.64, 0.68, 0.80, 0.64, 0.68, 0.60, 0.77, 0.75, 0.76, all P<0.05). Conclusion:Compared with SIRS, both pSOFA and PCIS had better predictive value in the mortality of pediatric sepsis in PICU.

Aim To study the distribution of CYP2C19,ABCB1,and PON1 genotypes and their correlation with clopidogrel resistance in patients with coronary heart disease in Tai'an.Methods A total of 594 patients with coronary heart disease who were treated with clopidogrel during hospitalization in Tai'an Central Hospital from January 2019 to March 2020 were selected.Fluorescence in situ hybridization was used to detect CYP2C19*2(rs4244285),CYP2C19*3(rs4986893),CYP2C19*17(rs12248560),ABCB1(rs1045642)and PON1(rs662)gene types.Results CYP2C19*2,CYP2C19*3,CYP2C19*17 genotypes in patients with coronary heart disease in Tai'an were mainly with homozygous(GG).The frequencies of CYP2C19*2 GG,CYP2C19*3 GG,CYP2C19*17 CC,ABCB1 CT and PON1 AG were 48.0％,89.6％,97.0％,46.8％and 47.1％respectively.There was no significant difference in CYP2C19*2,CYP2C19*3,CYP2C19*17,ABCB1,PON1 genotype distribution and allele distribution between male and female patients(P＞0.05).Significant regional differences in the frequency of CYP2C19 alleles and the distribution of metabolic types were found in patients with coronary heart disease in Tai'an.Among 594 patients included in the study,there were 287 patients with a risk level of clopidogrel resistance ≥ 2 in the composite evaluation of patients,approximately 48.3％of the total number of patients.This indicated that clopidogrel resistance was present in 48.3％of patients on the regular dose of clopidogrel.Of the 287 people with a risk level ≥2,46 had a normal CYP2C19 metabolic type,representing approxi-mately 7.7％of the total number of patients.Conclusion There were gene polymorphisms observed in CYP2C19*2,CYP2C19*3,CYP2C19*17,ABCB1 and PON1 distribution in patients with coronary heart disease in Tai'an,and ABCB1 and PON1 gene polymorphisms would had an impact on the outcome of medication guidance in approximately 7.7％.

Objective To establish the framework of a prediagnostic risk assessment system for outpatient dental care and to provide references for ensuring patient safety and improving the quality of medical services.Methods A total of 15 medical workers in a tertiary-care stomatology hospital in Sichuan Province were selected for qualitative interviews between October 2019 and December 2019.On the basis of the results of literature analysis and the interviews,an expert consultation form for prediagnostic risk assessment system for outpatient dental care was developed.Then,consultation of 10 experts in the field of oral health care and nursing was conducted between June 2020 and December 2020.Eventually,the framework of prediagnostic risk assessment system for outpatient dental care was formulated.Results Four themes emerged from the qualitative interviews.Firstly,prediagnostic risks of dental outpatients involved mainly syncope,cardiovascular emergencies,and other emergency medical risks.Secondly,medical risks came from three sources,patients,healthcare professionals,and the environment.Thirdly,medical professionals of outpatient dental care had limited awareness of the prediagnostic medical risks of patient.Fourthly,medical professionals of outpatient dental care showed inadequate response to and management of the prediagnostic medical risks of patient.The expert consultation helped finalize the Dental Outpatient Prediagnostic Risk Assessment Questionnaire,which included 3 primary indicators(namely,general information,medical history[including history of allergy],and medication assessment),12 secondary indicators(including patient demographics,patients'status upon admission,oral hygiene habits and special lifestyle habits,sensory disorders,special periods for female patients[ie,menstruation,pregnancy,and breastfeeding],allergy history[history of oral treatment-related allergies],past/present medical history,types of medication,route of medication administration,duration of medication administration,accuracy of medication administration,and adverse drug reactions),and 39 tertiary indicators.The effective recall rate of the expert consultation form was 100%,expert positivity was 100%,and the authority coefficient was 0.83.Kendall's harmony coefficient ranged from 0.808 to 0.839,which was statistically significant(P<0.001).Conclusion The framework of prediagnosis risk assessment system for outpatient dental care is found to be scientific and specific,but its applicability still needs further validation in clinical practice.