Outcome and factors of patients with nosocomial meningitis by multi-drug-resistant Gram-negative bacteria in a tertiary hospital in China: a retrospective study

The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review

Background and Aims

This systematic review aimed to evaluating the effectiveness of machine learning (ML) algorithms for the prediction and diagnosis of meningitis.

Methods

On November 12, 2022, a systematic review was carried out using a keyword search in the reliable scientific databases PubMed, EMBASE, Scopus, and Web of Science. The recommendations of Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA) were adhered to. Studies conducted in English that employed ML to predict and identify meningitis were deemed to match the inclusion criteria. The eligibility requirements were used to independently review the titles and abstracts. The whole text was then obtained and independently reviewed in accordance with the eligibility requirements.

Results

After all the research matched the inclusion criteria, a total of 16 studies were added to the systematic review. Studies on the application of ML algorithms in the three categories of disease diagnosis ability (8.16) and disease prediction ability (8.16) (including cases related to identifying patients (50%), risk of death in patients (25%), the consequences of the disease in childhood (12.5%), and its etiology [12.5%]) were placed. Among the ML algorithms used in this study, logistic regression (LR) (4.16, 25%) and multiple logistic regression (MLR) (4.16, 25%) were the most used. All the included studies indicated improvements in the processes of diagnosis, prediction, and disease outbreak with the help of ML algorithms.

Conclusion

The results of the study showed that in all included studies, ML algorithms were an effective approach to facilitate diagnosis, predict consequences for risk classification, and improve resource utilization by predicting the volume of patients or services as well as discovering risk factors. The role of ML algorithms in improving disease diagnosis was more significant than disease prediction and prevalence. Meanwhile, the use of combined methods can optimize differential diagnoses and facilitate the decision-making process for healthcare providers.

Clinical Feature, Therapy, Antimicrobial Resistance Gene Distribution, and Outcome of Nosocomial Meningitis Induced by Multidrug-Resistant Enterobacteriaceae-A Longitudinal Cohort Study From Two Neurosurgical Centers in Northern China

Objectives

This is a comparative cohort study aiming to evaluate the mortality risk factors for patients with nosocomial meningitis (NM) induced by multidrug-resistant Enterobacteriaceae (MDRE) in China. The clinical features and therapies of patients and the resistance mechanisms of MDRE pathogens were also assessed.

Methods

MDRE-NM patients from two neurosurgical centers in China from 2014 to 2019 were included in this study. Clinical features were extracted from the medical record databases of the two centers. The molecular mechanisms underlying the microbiological resistance mechanisms of each MDRE pathogen were determined, Kaplan-Meier survival analysis was conducted, and multivariable analyses were performed using a Cox proportional hazard model.

Results

Ninety MDRE-NM patients were included in this study. Klebsiella pneumoniae accounted for the highest proportion of causative pathogens (46/90, 51.1%), and 40 causative pathogens (44.4%) were meropenem-resistant. blaKPC (27/40, 67.5%) was the predominant carbapenem resistance gene. Multivariate Cox analysis showed that external ventricular drainage (EVD) [hazard ratio (HR) = 2.524, 95% confidence interval (CI) = 1.101-5.787, p = 0.029] and a Glasgow Coma Scale (GCS) score ≤;8 (HR = 4.033, 95% CI = 1.526-10.645, p = 0.005) were mortality risk factors for patients with MDRE-NM. A total of 90.0%, 94.4%, and 97.8% of MDRE-NM patients received antibiotic prophylaxis (AP), antibiotic empirical therapy (AET), and antibiotic definitive therapy (ADT), respectively.

Conclusions

NM caused by MDRE is an important sign of the failure of neurosurgery. MDRE possesses multiple drug resistance genotypes, and EVD and a GCS score ≤;8 are independent mortality risk factors for patients with MDRE-NM, which deserve the attention of microbiologists and neurosurgical clinicians.

Ability of Procalcitonin and C-Reactive Protein for Discriminating between Bacterial and Enteroviral Meningitis in Children Using Decision Tree

Bacterial meningitis (BM) is a public health burden in developing countries, including Central Asia. This disease is characterized by a high mortality rate and serious neurological complications. Delay with the start of adequate therapy is associated with an increase in mortality for patients with acute bacterial meningitis. Cerebrospinal fluid culture, as a gold standard in bacterial meningitis diagnosis, is time-consuming with modest sensitivity, and this is unsuitable for timely decision-making. It has been shown that bacterial meningitis differentiation from viral meningitis could be done through different parameters such as clinical signs and symptoms, laboratory values, such as PCR, including blood and cerebrospinal fluid (CSF) analysis. In this study, we proposed the method for distinguishing the bacterial form of meningitis from enteroviral one. The method is based on the machine learning process deriving making decision rules. The proposed fast-and-frugal trees (FFTree) decision tree approach showed an ability to determine procalcitonin and C-reactive protein (CRP) with cut-off values for distinguishing between bacterial and enteroviral meningitis (EVM) in children. Such a method demonstrated 100% sensitivity, 96% specificity, and 98% accuracy in the differentiation of all cases of bacterial meningitis in this study. These findings and proposed method may be useful for clinicians to facilitate the decision-making process and optimize the diagnostics of meningitis.

Phenotype, molecular characterisation and risk factors for postoperative meningitis caused by ESBL-producing-Enterobacteriaceae: a six years multi-Centre comparative cohort study

BACKGROUND

To determine the phenotype, molecular characterisation and risk factors of postoperative meningitis induced by Extended-spectrum β-lactamase (ESBL)-producing Enterobacteriaceae (EPE) in China.

METHODS

We performed a multi-centre comparative cohort study of postoperative meningitis patients infected with Enterobacteriaceae in 4 neurosurgical centres in China from January 2014 to December 2019. Phenotype and molecular characteristics of the isolates were reviewed and tested, and independent risk factors of the EPE meningitis were evaluated by binary logistic regression.

RESULTS

In total, 220 Enterobacteriaceae include 78 EPE were available in this study. 85.6% (67/78) ESBL-related genes were tested, and blaSHV (14.9%) and blaSHV + blaTEM + blaCTX-M-9 (20.9%) were found to be the most frequent mono and combined ESBL-related genes harboured by Enterobacteriaceae. On binary logistic analysis, craniotomy (OR. 2.583, 95% C.I. 1.274-5.235, P = 0.008) and malignancy (OR. 2.406, 95% C.I. 1.299-4.456, P = 0.005) were the associated independent risk factors to meningitis induced by EPE.

CONCLUSIONS

To the best of our knowledge, this is the largest series focusing on risk factors of EPE meningitis which has been conducted in China. Craniotomy and malignancy were independent risk factors for EPE meningitis. The risk factors identified may be further utilized in clinical practice and research to avoid and reduce the mortality in future.