High Dose Steroids as First-Line Treatment Increased the Risk of In-Hospital Infections in Patients With Anti-NMDAR Encephalitis

ObjectiveTo address the effects of high dose steroids on in-hospital infection and neurologic outcome in anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis patients.MethodsWe retrospectively reviewed the clinical data of anti-NMDAR encephalitis patients in West China Hospital, the Third Hospital of Mianyang and Mianyang Central Hospital between October 2011 and August 2020. The development of infections, inflammatory factors, neurologic outcome at discharge and risk factors for in-hospital infection were assessed in patients with and without high dose steroid therapy before and after immunotherapy. Least absolute shrinkage and selection operator (LASSO) regression and logistic regression models were established to assess risk factors for in-hospital infection.ResultsA total of 278 patients with anti-NMDAR encephalitis were included in the study. Thirty-four patients received high dose methylprednisolone (IVMP) therapy only, 84 patients received intravenous immunoglobulin (IVIG) therapy, and 160 patients received IVIG and IVMP therapy. Compared with the IVIG group, IVIG + IVMP group had a higher infection rate (64.38% vs 39.29%, P < 0.001), a higher incidence of noninfectious complications (76.25% vs 61.90%, P = 0.018) and a higher modified Rankin Scale (mRS) score at discharge from the hospital (3 vs 2, P < 0.001). Inflammatory indicators, including white blood cell (WBC) count, neutrophil-to-lymphocyte ratio (NLR) and systemic immune-inflammation index (SII), were higher (9.93 vs 5.65, 6.94 vs 3.47 and 1.47 vs 0.70, respectively, P < 0.001) in the IVIG + IVMP group than in the IVIG group. Moreover, lymphocyte-to-monocyte ratio (LMR) was lower (2.20 vs 2.54, P = 0.047) in the IVIG + IVMP group. The LASSO model showed that mRS score on admission, seizure, body temperature, uric acid (URIC), cerebrospinal fluid immunoglobulin G (CSF IgG), NLR and LMR were risk factors for in-hospital infection. The prediction model exhibited an area under the curve (AUC) of 0.885.ConclusionsHigh dose steroids therapy was significantly associated with higher in-hospital infectious complication rates and a poor short-term prognosis in relatively severe anti-NMDAR encephalitis patients. The established prediction model might be helpful to reduce the risk of in-hospital infection.

Using Whole Genome Sequencing to Trace, Control and Characterize a Hospital Infection of IMP-4-Producing Klebsiella pneumoniae ST2253 in a Neonatal Unit in a Tertiary Hospital, China

Background: The purpose of this study is to use whole genome sequencing (WGS) combined with epidemiological data to track a hospital infection of the carbapenem-resistant Klebsiella pneumoniae (CRKP), which affected 3 neonatal patients in the neonatal intensive care unit (NICU).Methods: The minimum inhibitory concentrations for the antimicrobial agents were determined according to the guidelines of the Clinical and Laboratory Standards Institute. Beta-lactamases were investigated using the polymerase chain reaction and DNA sequencing. The transferability of the plasmid was investigated by a conjugation experiment. The clonal relationships were evaluated using multilocus sequence typing (MLST) and pulsed-field gel electrophoresis (PFGE). WGS and single nucleotide polymorphisms (SNPs) analysis were performed on the CRKP isolates to investigate how the infection might progress.Results: Nine CRKP isolates were obtained from the NICU, seven from three patients, one from a duster cloth and one from the hand of a nurse, they all harbored blaIMP-4. Other resistance genes including blaKPC-2, blaIMP-4, blaSHV-1, blaTEM-1, blaCTX-M-15, and blaDHA-1 were also detected. PFGE analysis showed that IMP-4-producing K. pneumoniae were clonally related, and MLST assigned them to a new sequence type 2253. The SNP variations throughout the genome divided the 9 strains into three clades. Clade 1 comprised 7 strains (K1- K2 and K4-K8), whereas clade 2 and 3 consisted of only one strain each: K3 and K9, respectively.The sputum isolate K3 from patient 3 was the most distinct one differing from the other eight isolates by 239-275 SNPs.Conclusions: This is a report of using WGS to track a hospital infecion of IMP-4-producing K. pneumoniae ST2253 among neonates. Nosocomial surveillance systems are needed to limit the spread of the infection caused by these pathogens resulting from the environmental exposure in NICUs.

The Impact of SARS-COV-2 Response on Hospital Infection Prevention Programs and Practices in Southeastern United States

Initial assessments of SARS-COV-2 preparedness revealed resource shortages and variations in infection prevention policies across US hospitals. Our follow-up survey revealed improvement in resource availability, increase in testing capacity, and uniformity in infection prevention policies. Most importantly, the survey highlighted an increase in staffing shortages and use of travel nursing.

Susceptible-Infected-Removed Mathematical Model under Deep Learning in Hospital Infection Control of Novel Coronavirus Pneumonia

Objective. This research aimed to explore the application of a mathematical model based on deep learning in hospital infection control of novel coronavirus (COVID-19) pneumonia. Methods. First, the epidemic data of Beijing, China, were utilized to make a definite susceptible-infected-removed (SIR) model fitting to determine the estimated value of the COVID-19 removal intensity β, which was then used to do a determined SIR model and a stochastic SIR model fitting for the hospital. In addition, the reasonable β and γ estimates of the hospital were determined, and the spread of the epidemic in hospital was simulated, to discuss the impact of basal reproductive number changes, isolation, vaccination, and so forth on COVID-19. Results. There was a certain gap between the fitting of SIR to the remover and the actual data. The fitting of the number of infections was accurate. The growth rate of the number of infections decreased after measures, such as isolation, were taken. The effect of herd immunity was achieved after the overall immunity reached 70.9%. Conclusion. The SIR model based on deep learning and the stochastic SIR fitting model were accurate in judging the development trend of the epidemic, which can provide basis and reference for hospital epidemic infection control.