OBJECTIVE: To develop and validate a predictive model for the risk of bloodstream infection (BSI) caused by carbapenem-resistant Klebsiella pneumoniae (CRKP). METHODS: A literature search was conducted in PubMed, Cochrane Library, and Embase databases from inception to July 2022 to identify studies reporting statistically significant risk factors for CRKP-BSI. Relative risks (RR) were extracted and pooled. Based on factor weights, a risk-scoring model was established. For external validation, hospitalized CRKP-infected patients from January 2016 to January 2022 at Shanghai First People's Hospital were included. Clinical data were used to calculate individual risk scores. The predictive accuracy was assessed using receiver operator characteristic curve (ROC curve). Patients were stratified into low-to-intermediate-risk and high-risk groups based on the optimal cut-off, and CRKP BSI incidence was compared between groups. RESULTS: The literatures related to the risk factors of CRKP-BSI published from database inception to July 2022 was retrieved and screened from PubMed, Cochrane Library, and Embase. Fourteen risk factors were included in the scoring model: cardiovascular disease, severe neutropenia or immunosuppression, intensive care unit (ICU) stay history, prior hospitalization, carbapenem exposure, aminoglycoside exposure, antifungal exposure, endotracheal intubation or tracheostomy, mechanical ventilation, hemodialysis, central venous catheter, indwelling urinary catheter, CRKP colonization, and Klebsiella pneumoniae positivity at non infection sites. The total score ranged from 0 to 173.5 points. In the validation cohort of 230 CRKP-infected patients, 41 developed CRKP BSI. The model yielded an area under the curve (AUC) of 0.783 (95%CI was 0.689-0.876). The optimal cut off was 81.25 points, with sensitivity of 75.6% and specificity of 81.0%. Based on this cut off, 163 patients were categorized as low-to-intermediate risk and 67 patients as high risk. The incidence of CRKP BSI in the high-risk group was significantly higher than in the low-to-intermediate-risk group [64.2% (43/67) vs. 4.9% (8/163); RR = 13.175 (95%CI was 5.920-29.319), P < 0.001]. CONCLUSIONS The model, based on 14 routinely available clinical parameters, demonstrated good performance in predicting CRKP BSI risk and may assist clinicians in early identification of high risk patients.
Humans ; Klebsiella pneumoniae/drug effects* ; Klebsiella Infections/microbiology* ; Carbapenems/pharmacology* ; Risk Factors ; Bacteremia/microbiology* ; ROC Curve ; Carbapenem-Resistant Enterobacteriaceae

OBJECTIVETo evaluate the predictive value of a combination of anatomy scoring system and physiological scoring system for the diagnosis of multiple organ dysfunction syndrome (MODS) in patients with severe trauma.

METHODSThe clinical data of 539 patients with severe trauma hospitalized in ICUs of the First Affiliated Hospital of Chengdu Medical College, Daping Hospital of the Third Military Medical University, and the Affiliated Hospital of Zunyi Medical College from January 2010 to December 2014, conforming to the study criteria, were retrospectively analyzed. The patients were divided into MODS group (n=361) and non-MODS group (n=178) according to the diagnostic criteria of MODS. The data of Injury Severity Score (ISS), New Injury Severity Score (NISS), Acute Physiology and Chronic Health Evaluation II (APACHE II) score, ISS+ APACHE II score, and NISS+ APACHE II score of patients on the first day of ICU admission were calculated and compared between the two groups. Data were processed with t test, chi-square test, and receiver operating characteristic (ROC) curves of the various scoring systems in 539 patients with severe trauma were plotted, and DeLong-DeLong non parametric test was used to evaluate the predictive ability for MODS of the various scoring systems.

RESULTSThe scores of ISS, NISS, APACHE II, ISS+ APACHE II, and NISS+ APACHE II of patients in MODS group were respectively (26±8), (36±12), (21±7), (47±10), and (56±14) points, and they were significantly higher than those of patients in non-MODS group [respectively (24±6), (28±7), (16±5), (39±8), and (44±9) points, with t values from 4.970 to 12.120, P values below 0.01]. The total areas under ROC curves (95% confidence interval) of ISS, NISS, APACHE II score, ISS+ APACHE II score, and NISS+ APACHE II score in MODS prediction of patients with severe trauma were respectively 0.611 (0.569-0.653), 0.693 (0.652-0.731), 0.719 (0.679-0.756), 0.727 (0.687-0.764), and 0.764 (0.726-0.799). The total areas under ROC curves of NISS, APACHE II score, ISS+ APACHE II score, and NISS+ APACHE II score were significantly larger than the total area under ROC curve of ISS (with Z values from 3.505 to 7.179, P values below 0.001). The total areas under ROC curves of APACHE II score and ISS+ APACHE II score were larger than the total area under ROC curve of NISS, but there were no significant differences (with Z values respectively 0.931 and 1.657, P values above 0.05), while the total area under ROC curve of NISS+ APACHE II score was significantly larger than that of NISS (Z=5.478, P<0.001). The total area under ROC curve of ISS+ APACHE II score was larger than that of APACHEⅡ score, but there was no significant difference (Z=0.450, P=0.653), and the total area under ROC curve of NISS+ APACHE II score was significantly larger than that of APACHE II score (Z=2.554, P<0.05). The total area under ROC curve of NISS+ APACHE II score was significantly larger than that of ISS+ APACHE II score (Z=2.989, P<0.01).

CONCLUSIONSThe combination of NISS and APACHE II score is more significant than ISS, NISS, APACHE II score, and ISS+ APACHE II score in prediction of MODS in patients with severe trauma.

APACHE ; Humans ; Injury Severity Score ; Intensive Care Units ; Multiple Organ Failure ; diagnosis ; physiopathology ; Organ Dysfunction Scores ; Outcome Assessment (Health Care) ; Predictive Value of Tests ; ROC Curve ; Retrospective Studies ; Sensitivity and Specificity

ObjectiveTo compare the predictive value of anatomic scoring system, physiological scoring system, and the combination of two systems in death prediction of patients with severe trauma in intensive care unit (ICU). Methods A retrospective analysis of patients with severe trauma admitted to department of critical care medicine of Daping Hospital, the Third Military Medical University, and Zunyi Medical University from January 2011 to December 2014 was conducted. The patients meeting the following criteria were enrolled: over 16 years old, admitted to hospital shorter than 24 hours after trauma, length of ICU stay≥48 hours, and injury severity score (ISS)≥16. Patients were divided into two groups: survivors and non-survivors. The data of anatomic scoring system, including ISS and new injury severity score (NISS), and physiological scoring system, including acute physiology and chronic health evaluationⅡ(APACHEⅡ) score were collected. The predictive power for death of the scoring system alone or combination in patients with severe trauma was evaluated.Results A total of 614 patients with severe trauma were enrolled, and there were 153 deaths with a mortality rate of 24.9%. ISS, NISS, APACHEⅡ, ISS+ APACHEⅡ, NISS+ APACHEⅡ of non-survivors were significantly higher than those of survivors (ISS: 29.15±7.75 vs. 24.31±6.50, NISS: 41.96±12.01 vs. 29.64±8.19, APACHEⅡ: 23.71±6.58 vs. 17.02±5.49, ISS+ APACHEⅡ: 52.86±10.00 vs. 41.33±8.70, NISS+ APACHEⅡ: 65.67±13.46 vs. 46.66±10.43, allP< 0.01). The area under receiver operating characteristic curve (AUC) of ISS, NISS, APACHEⅡ, ISS+ APACHEⅡ, NISS+ APACHEⅡ was 0.687, 0.792, 0.782, 0.809, and 0.860, respectively. Both of ISS+ APACHEⅡ and NISS+ APACHEⅡ had higher AUC than that of ISS, NISS or APACHEⅡ alone; and the AUC of NISS+ APACHEⅡ was significantly larger than that of ISS+ APACHEⅡ(allP< 0.05). NISS+ APACHEⅡ showed the largest AUC in death prediction of severe trauma patients. The cut-off value, sensitivity, specificity, positive predict value (+PV), negative predict value (-PV), positive likelihood ratio (+LR), negative likelihood ratio (-LR), and Youden index of NISS+ APACHEⅡ, which had the greatest AUC, were 56, 75.2%, 82.0%, 58.1%, 90.9%, 4.17, 0.30, and 0.572, respectively.Conclusion The combination of anatomic scoring system and physiological scoring system is better than single scoring system for death prediction in patients with severe trauma in ICU, and it may be considered to be a new method for early identification of death risk in patients with severe trauma.

Objective To provide better emergency and patient services in well-equipped comprehensive hospitals, the organization and wisdom therapeutic strategy are of great importance for the recovery of injured patients from the earthquake zone. Method From 12 May 2008, following the 8.0 Magnitude earthquake in Wenchuan county of Sichuan Province, six Chongqing hospitals with third class in grade A were involved in the rescue of the injured patients with great effort. A total of 533 patients were retreated and followed up from quake zone. All the patients were scored with ISS and AIS system. The profiles of the patients examined, operated and clinical infection investigation were documented. Results Of 533 patients, the number of the patients whose ISS is below 16 is 456 (83.6%), the number between 16 and 25 is 65 (12.2%), and the humor above 25 is 12 (2.3%). The patients were classfled based on their fracture parts as follows: head and neck (n = 42), face (n = 7), chest (n = 114), abdominal and cavitas pelvis (n =81), limb and pelvis (n =314), body surface (n =205), with 180 single fracture site, 139 of them being two combined fracture sites, and 114 of them being above three combined fracture sites. Thirty-two of the patients were suffered from amputation. The number of patients suffered from crushing syndrome reached 21, with 281 surgical operations in hospitals. Seventy-nine patients were suffered from infections including 87.3% of pre-hespital infections. The results from bacteria culture and antibiotic susceptibility showed that the infected bacteria mainly involved in Escherichia coli, Staphylococcus anreus, Staphylococcus haemolyticns, Klebsiella pneumoniae, Baumanii, Aerobacter cloacae, Pseudomonas aeruginosa, C type chain coccus, Bacillus aerogenes capsulatus. The antibiotic susceptibility to diverse bacteria has no obvious changes and exists partial overlapping, and infected patients should be given the treatment of cephalosporin, macrolide antibiotic and so on. Conclusions For the emergency conditions after the catastrophe, the comprehensive hospitals must be prepared to meet large quantities of severe trauma and infection therapy. The scientific selection of antibiotics in the combinative therapy is of great importance to the enhancement of early specific treatment, prevention of severe trauma complications and rehabilitation of patients.

BACKGROUNDDecorin is a member of the small proteglycans in extracellular matrix of tumor microenvironment, which is known to relate to the initiation, progression and growth of the tumor. The aim of this study is to investigate the effects and mechanism of decorin on the proliferation of A549 lung adenocarcinoma cell line in vitro.

METHODSLung adenocarcinoma cell line A549 was cultured with decorin in a wide range of concentration for different time. Cell activities were studied by MTT. The changes of cell cycle and apoptosis were analyzed by FCM. Decorin mRNA expression was detected by RT-PCR. P21 expression was determined by Western blot. TGF-β concentration in the culture supernatants was determined by ELISA.

RESULTSThe proliferation of A549 cell could be inhibited by decorin in vitro and the inhibition effect was the time- and dose-dependent relationship. Apoptosis of adenocarcinoma cell could be efficiently induced by decorin in a time/dose-dependent manner. Decorin could upregulate the intrinsic decorin mRNA and P21 protein expression, downregulate the TGF-β, and block cell cycle at G1 phase.

CONCLUSIONSDecorin can inhibit adenocarcinoma cell proliferation and induce apoptosis of adenocarcinoma cells in vitro. The proliferation of A549 cell could be inhibited in vitro by decorin through the mechanism of increasing decorin mRNA, decreasing TGF-β, increasing P21 protein expression, inhibiting cell cycle and inducing cell apoptosis.

The biodiversity of the taxol-producing endophytic fungi,advantage of taxol production by microbe fermentation,isolation of endophytic fungi,and the separation,purification and determination of taxol in fermentation liquid were reviewed.The pathways to improve the production of taxol by endophytic fungi were also comprehensively discussed.