Development and validation of nomograms for predicting the risk probability of carbapenem resistance and 28-day all-cause mortality in gram-negative bacteremia among patients with hematological diseases.

Objectives: Gram-negative bacteria (GNB) bloodstream infections (BSIs) are the most widespread and serious complications in hospitalized patients with hematological diseases. The emergence and prevalence of carbapenem-resistant (CR) pathogens has developed into a considerable challenge in clinical practice. Currently, nomograms have been extensively applied in the field of medicine to facilitate clinical diagnosis and treatment. The purpose of this study was to explore risk indicators predicting mortality and carbapenem resistance in hematological (HM) patients with GNB BSI and to construct two nomograms to achieve personalized prediction. Methods: A single-center retrospective case-control study enrolled 244 hospitalized HM patients with GNB-BSI from January 2015 to December 2019. The least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate logistic regression analysis were conducted to select potential characteristic predictors of plotting nomograms. Subsequently, to evaluate the prediction performance of the models, the prediction models were internally validated using the bootstrap approach (resampling = 1000) and 10-fold cross validation. Results: Of all 244 eligible patients with BSI attributed to GNB in this study, 77 (31.6%) were resistant to carbapenems. The rate of carbapenem resistance exhibited a growing tendency year by year, from 20.4% in 2015 to 42.6% in 2019 (p = 0.004). The carbapenem resistance nomogram constructed with the parameters of hypoproteinemia, duration of neutropenia ≥ 6 days, previous exposure to carbapenems, and previous exposure to cephalosporin/ß-lactamase inhibitors indicated a favorable discrimination ability with a modified concordance index (C-index) of 0.788 and 0.781 in both the bootstrapping and 10-fold cross validation procedures. The 28-day all-cause mortality was 28.3% (68/240). The prognosis nomogram plotted with the variables of hypoproteinemia, septic shock, isolation of CR-GNB, and the incomplete remission status of underlying diseases showed a superior discriminative ability of poorer clinical prognosis. The modified C-index of the prognosis nomogram was 0.873 with bootstrapping and 0.887 with 10-fold cross validation. The decision curve analysis (DCA) for two nomogram models both demonstrated better clinical practicality. Conclusions: For clinicians, nomogram models were effective individualized risk prediction tools to facilitate the early identification of HM patients with GNB BSI at high risk of mortality and carbapenem resistance.

Distribution and resistance of pathogens in infected patients within 1 year after heart transplantation.

BACKGROUND: Infection is a major cause of morbidity and mortality after heart transplantation (HT). However, there have been few data on clinical manifestation, distribution, and resistance of pathogens in the infected population of heart transplant recipients. METHODS: We conducted a single-center retrospective study on patients who underwent HT in Wuhan Union Hospital from August 3, 2012 to July 30, 2016. Risk factors for infections that occur within 1 year after HT were investigated by multivariable logistic regression analysis. RESULTS: Among 299 patients, 147 patients (49.2%) confirmed infection. The most common site of infection was the respiratory system. A total of 259 pathogens were detected in 147 patients (49.2%) with infection after HT. In all, 64 multidrug-resistant (MDR) bacteria were detected in infected patients within 1 year after HT, the most common MDR bacteria were extended-spectrum ß-lactamases (ESBL) Klebsiella pneumonia and methicillin-resistant Staphylococcus aureus (MRSA). In the multivariable model, diabetes (OR 3.273 [95%CI, 1.748-6.130], and p < .001) and antibiotics treatment within 1 month before transplant (OR 1.860 [95%CI, 1.093-3.166], and p = .022) were significantly associated with infections within 1 year after HT. CONCLUSIONS: This study confirmed the high rate of infections within 1 year after HT. Diabetes and antibiotics treatment within 1 month before transplant were independent risk factors for infections within 1 year after HT.

Coinfection of SARS-CoV-2 and Other Respiratory Pathogens.

PURPOSE: To differentiate between respiratory infections caused by SARS-CoV-2 and other respiratory pathogens during the COVID-19 outbreak in Wuhan, we simultaneously tested for SARS-CoV-2 and pathogens associated with CAP to determine the incidence and impact of respiratory coinfections in COVID-19 patients. PATIENTS AND METHODS: We included 250 patients who were diagnosed with COVID-19. RT-PCR was used to detect influenza A, influenza B and respiratory syncytial viruses. Chemiluminescence immunoassays were used to detect IgM antibodies for adenovirus, Chlamydia pneumoniae and Mycoplasma pneumoniae in the serum of patients. Based on these results, we divided the patients into two groups, the simple SARS-CoV-2-infected group and the coinfected SARS-COV-2 group. Coinfected patients were then further categorized as having a coinfection of viral pathogen (CoIV) or coinfection of atypical bacterial pathogen (CoIaB). RESULTS: No statistically significant differences were found in age, gender, the time taken to return negative SARS-CoV-2 nucleic acid test results, length of hospital stays, and mortality between the simple SARS-CoV-2 infection group and the coinfection group. Of the 250 hospitalized COVID-19 patients, 39 (15.6%) tested positive for at least one respiratory pathogen in addition to SARS-CoV-2. A third of these pathogens were detected as early as the 1st week after symptom onset and another third were identified after more than three weeks. The most detected CAP pathogen was C. pneumoniae (5.2%), followed by the respiratory syncytial virus (4.8%), M. pneumoniae (4.4%) and adenovirus (2.8%). Patients coinfected with viral pathogens (CoIV) (n=18) had longer hospital stays when compared to patients coinfected with atypical bacterial pathogens (CoIaB) (n=21). Except for one fatality, the remaining 38 coinfected patients all recovered with favourable outcomes. CONCLUSION: Coinfections in COVID-19 patients are common. The coinfecting pathogens can be detected at variable intervals during COVID-19 disease course and remain an important consideration in targeted treatment strategies for COVID-19 patients.

TXNIP Gene Single Nucleotide Polymorphisms Associated with the Risk of Type 2 Diabetes Mellitus in a Chinese Han Population.

Thioredoxin-interacting protein (TXNIP) plays important roles in the pathogenesis of type 2 diabetes mellitus (T2DM). This study was aimed to investigate the association between T2DM and polymorphisms of TXNIP genes in a Chinese Han population. To our knowledge, there were no studies on this aspect in China. In our research, genotyping was performed by direct sequencing in 161 T2DM patients and 146 healthy controls. The relationships between the genotypes of single nucleotide polymorphism and serum-specific clinical variables were analyzed. TXNIP protein expression was detected by western blotting. We found that the frequencies of G allele, the CG, and CG/GG genotypes of TXNIP rs7212 were all significantly higher in T2DM patients than the controls. G allele, CG, and CG/GG genotypes of rs7212 were associated with a higher risk of T2DM. Furthermore, the CG and CG/GG genotypes of rs7212 in T2DM patients with a longer duration of disease were more frequent compared with the CC genotype diabetes. For rs7212, T2DM patients with higher glucose (GLU) level or/and HbA1c level more frequently carried G allele. In addition, the protein level of TXNIP in the peripheral leukocytes with T2DM was significantly higher than in the controls. Our study indicates that the TXNIP polymorphisms are associated with the susceptibility to T2DM in a Chinese Han population from central China.

Spreading of extended-spectrum ß-lactamase-producing Escherichia coli ST131 and Klebsiella pneumoniae ST11 in patients with pneumonia: a molecular epidemiological study.

BACKGROUND: Extended-spectrum ß-lactamase (ESBL)-producing Escherichia coli (E. coli) and Klebsiella pneumoniae (K. pneumoniae) are the important pathogens causing pneumonia. This study aimed to investigate the clinical characteristics and molecular epidemiology of ESBL-producing E. coli and K. pneumoniae causing pneumonia at a large teaching hospital in China. METHODS: We collected patient's clinical data and ESBL-producing E. coli and K. pneumoniae strains causing pneumonia (from December 2015 to June 2016) at a hospital in Wuhan. The susceptibilities, multi-locus sequence typing, homologous analysis, ESBL genes by polymerase chain reaction and sequencing were determined. RESULTS: A total of 59 ESBL-producing strains (31 E. coli and 28 K. pneumoniae) isolated from patients with pneumonia were analyzed. The majority of strains were isolated from patients were with hospital-acquired pneumonia (37/59, 62.7%), followed by community-acquired pneumonia (13/59, 22.0%), and ventilator-related pneumonia (9/59, 15.3%). The E. coli ST131 (9 isolates, 29.0%) and K. pneumoniae ST11 (5 isolates, 17.9%) were the predominant sub-types. The most prevalent ESBL gene was CTX-M-14, followed by SHV-77, CTX-M-3, SHV-11, and CTX-M-27. At least 33 (55.9%) of the ESBL-producing strains carried two or more ESBL genes. The ISEcp1 and IS26 were found upstream of all blaCTX-M (CTX-Ms) and of most blaSHV (SHVs) (57.6%), respectively. Moreover, three ESBL-producing K. pneumoniae ST11 strains which were resistant to carbapenems carried the blaNDM-1 and blaKPC-2, two of which also bearing blaOXA-48 were resistant to all antibiotics (including Tigecycline). CONCLUSIONS: Hospital-acquired pneumonia is more likely correlated with ESBL-producing E. coli and K. pneumoniae. ESBL-producing E. coli ST131 and multi-drug resistance ESBL-producing, as well as New Delhi metallo-ß-lactamase-1 (NDM-1) and Klebsiella pneumoniae carbapenemases-2 (KPC-2) bearing K. pneumoniae ST11 are spreading in patients with pneumonia in hospital.

The Simplified Carbapenem Inactivation Method (sCIM) for Simple and Accurate Detection of Carbapenemase-Producing Gram-Negative Bacilli.

This study reports the simplified carbapenem inactivation method (sCIM) to detect carbapenemase-producing gram-negative bacilli in a simple and accurate manner. This method is based on the modified carbapenem inactivation method (mCIM) with the improvement of experimental procedures. Instead of incubating the antibiotic disk in the organism culture media, the organism to be tested was smeared directly onto the antibiotic disk in the sCIM. For evaluating the sensitivity and specificity of the method, a total of 196 Enterobacteriaceae, 73 Acinetobacter baumannii, and 158 Pseudomonas aeruginosa isolates were collected. Polymerase chain reaction (PCR) was used to detect the carbapenemase genes. Phenotypic evaluations were performed using both the sCIM and the mCIM. PCR results showed that, of the 196 Enterobacteriaceae strains, 147 expressed the carbapenemase genes blaKPC-2 (58.5%), blaIMP-4 (21.8%), blaIMP-2 (2.0%), blaVIM-1 (6.1%), blaNDM-1 (10.2%), and blaOXA-48 (1.4%). sCIM results had high concordance with PCR results (99.5%) and mCIM results (100%) with the exception of one Klebsiella pneumoniae strain, which had an minimal inhibitory concentration (MIC) for imipenem of 0.25 mg/L. PCR demonstrated that 53 of the 73 A. baumannii isolates expressed the carbapenemase genes blaOXA-23 (98.1%) and blaVIM-2 (1.8%). sCIM and PCR results corresponded but all A. baumannii isolates were carbapenemase negative by the mCIM. PCR demonstrated that 25 of the 158 P. aeruginosa isolates expressed carbapenemase genes blaVIM-1 (52%) , blaVIM-2 (8%) , blaVIM-4 (36%), and blaIMP-4 (4%). sCIM results had high concordance with PCR results (100%) and the mCIM results (99.4%) with the exception of one P. aeruginosa isolate that expressed the blaVIM-4 gene. The sCIM offers specificity and sensitivity comparable to PCR but has the advantage of being more user-friendly. This method is suitable for routine use in most clinical microbiology laboratories for the detection of carbapenemase-producing gram-negative bacilli.