Prevalence of gram-negative bacteria and their antibiotic resistance in neonatal sepsis in Iran: a systematic review and meta-analysis.

BACKGROUND: Neonatal sepsis, particularly gram-negative (GN) bacteria-induced, is a significant cause of morbidity and mortality in newborns. Healthcare professionals find this issue challenging because of antibiotic resistance. This study aims to combine findings to identify the prevalence of GN bacteria and their antibiotic resistance in Iranian neonates with sepsis. METHODS: This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). The literature search was performed through international databases, including (PubMed/MEDLINE, EMBASE, Scopus, and Web of Science), Iranian local databases (Magiran, Iranmedex, Irandoc, Scimed, and SID), and the first 100 records of Google Scholar. Analytical cross-sectional study checklist from the Joanna Briggs Institute (JBI) was used for the quality assessment of included studies. Comprehensive Meta-Analysis Software Version 2 was used to conduct the meta-analysis. The between-study heterogeneity was investigated by I2 statistics. RESULTS: The prevalence of GN bacteria was estimated to be 53.6% [95% CI: 45.9- 61.1: P = 0.362] in Iranian neonates with sepsis, based on 31 studies with a sample size of 104,566. klebsiella pneumoniae (K.pneumonia) (23.2% [95% CI: 17.5-30.0, P < 0.001]) followed by Escherichia coli (E.coli) (13.5% [95% CI: 9.4-18.9, P < 0.001]) were more prevalent among GN bacteria. The highest resistance in K.pneumoniae was observed in Cefixime (80.6%, [95% CI: 56.3-93.1, P = 0.018]). E.coli showed greater resistance to Ampicillin (61.8%, [95% CI: 44.2-76.5, P = 0.188]. The prevalence of GN bacteria in Iranian neonates with sepsis has a decreasing trend based on the year, as shown by a meta-regression model (P < 0.0004). CONCLUSION: GN pathogens, particularly K.pneumoniae, and E.coli, are the leading cause of neonatal sepsis in Iran. GN bacteria showed the highest resistance to Third-generation cephalosporin and Aminoglycosides.

Clinical Risk Factors for Early-Onset Sepsis in Neonates: An International Delphi Study.

Background: Despite growing evidence, there is still uncertainty about potentially modifiable risk factors for neonatal early-onset sepsis (EOS). This study aimed to identify potential clinical risk factors for EOS based on a literature review and expert opinions. Methods: A literature search was conducted in PubMed (MEDLINE), Cochrane, Embase, and Scopus databases. Articles in English, published up to May 2021, on clinical risk factors for neonatal EOS were included. Initially, a questionnaire on risk factors for EOS was developed and validated. The fuzzy Delphi method (FDM) was used to formulate the final version of the questionnaire. The validity of the risk factors was assessed using the Chi square test. P<0.05 was considered statistically significant. Results: In the review phase, 30 risk factors were approved by two neonatologists and included in the FDM phase. In total, 25 risk factors met the consensus criteria and entered the validation phase. During the observational study, 114 neonates (31 with and 83 without EOS) were evaluated for two months. The results of the Chi square test showed that cesarean section was not a significant risk factor for EOS (P=0.862). The need for mechanical ventilation and feed intolerance was observed in about 70% of neonates with EOS, and therefore considered significant risk factors for EOS (P<0.001). Finally, 26 potential clinical risk factors were determined. Conclusion: Neonatal-related risk factors for EOS were birth weight, one-min Apgar score, and prematurity. Maternal-related risk factors were gestational age and urinary tract infection. Delivery-related risk factors were premature rupture of membranes, chorioamnionitis, and intrapartum fever.

Newfangled Procedures Using X-ray to Determine the Cobb Angle in Patients with Scoliosis: An Updated Systematic Review.

BACKGROUND: Scoliosis is a three-dimensional deformity of the spine. It is usually assessed by measuring Cobb angle. Nowadays, due to increasing effectiveness of image processing and machine vision, willingness to apply these methods has improved considerably in determining scoliosis of Cobb angle. METHODS: In accordance with the PRISMA guideline, a broad electronic search of all English language literature was conducted on the topic through four databases, including MEDLINE, Web of Science, Scopus, and the Cochrane Library from 2012 (last search date from earlier review) to 30 March 2017. RESULTS: Twelve studies were included. 90% labeled high-quality were selected for analysis. Eighty percent of the selected studies were published in the period between 2012 and 2017. Three new procedures were used to measure the Cobb angle. One study used automated procedure (7%), two studies used smartphone procedure (14%), and nine studies used the semiautomatic procedure of Cobb measurement (79%). Seven studies reported reproducibility and repeatability. Reproducibility range was 0.72 to 1 in reporting of ICC. Repeatability has a high range in three separated methods. CONCLUSION: Computerized assessment method (Automatic and Semi-automatic) is most commonly performed in Cobb measurement. Semi-automatic is an effective measurement option for computerized assessment Cobb angle. There is no significant difference between manual, computer- based, and smartphone-based methods in described measures.

Analyzing a Lung Cancer Patient Dataset with the Focus on Predicting Survival Rate One Year after Thoracic Surgery

Background: Data mining, a new concept introduced in the mid-1990s, can help researchers to gain new, profound insights and facilitate access to unanticipated knowledge sources in biomedical datasets. Many issues in the medical field are concerned with the diagnosis of diseases based on tests conducted on individuals at risk. Early diagnosis and treatment can provide a better outcome regarding the survival of lung cancer patients. Researchers can use data mining techniques to create effective diagnostic models. The aim of this study was to evaluate patterns existing in risk factor data of for mortality one year after thoracic surgery for lung cancer. Methods: The dataset used in this study contained 470 records and 17 features. First, the most important variables involved in the incidence of lung cancer were extracted using knowledge discovery and datamining algorithms such as naive Bayes, maximum expectation and then, using a regression analysis algorithm, a questionnaire was developed to predict the risk of death one year after lung surgery. Outliers in the data were excluded and reported using the clustering algorithm. Finally, a calculator was designed to estimate the risk for one-year post-operative mortality based on a scorecard algorithm. Results: The results revealed the most important factor involved in increased mortality to be large tumor size. Roles for type II diabetes and preoperative dyspnea in lower survival were also identified. The greatest commonality in classification of patients was Forced expiratory volume in first second (FEV1), based on levels of which patients could be classified into different categories. Conclusion: Development of a questionnaire based on calculations to diagnose disease can be used to identify and fill knowledge gaps in clinical practice guidelines.

A Step-by-Step Framework on Discrete Events Simulation in Emergency Department; A Systematic Review.

OBJECTIVE: To systematically review the current literature of simulation in healthcare including the structured steps in the emergency healthcare sector by proposing a framework for simulation in the emergency department. METHODS: For the purpose of collecting the data, PubMed and ACM databases were used between the years 2003 and 2013. The inclusion criteria were to select English-written articles available in full text with the closest objectives from among a total of 54 articles retrieved from the databases. Subsequently, 11 articles were selected for further analysis. RESULTS: The studies focused on the reduction of waiting time and patient stay, optimization of resources allocation, creation of crisis and maximum demand scenarios, identification of overcrowding bottlenecks, investigation of the impact of other systems on the existing system, and improvement of the system operations and functions. Subsequently, 10 simulation steps were derived from the relevant studies after an expert's evaluation. CONCLUSION: The 10-steps approach proposed on the basis of the selected studies provides simulation and planning specialists with a structured method for both analyzing problems and choosing best-case scenarios. Moreover, following this framework systematically enables the development of design processes as well as software implementation of simulation problems.

Exploring Complexity of Deliberate Self-Poisoning through Network Analysis.

The purpose of this research was to examine the complexity of circumstances that result in deliberate self-poisoning cases. For the purposes of this paper, the cases were patients that presented for care and were admitted to the specialty hospital in Northwest of Iran. The research examined the problems preceding deliberate self-poisoning and the interrelations among them by applying network analysis methods. The network was scored for degrees of centrality and betweenness centrality. Structural analysis of network also was conducted using block modelling. The results showed that family conflicts had the highest score for degree of centrality among women, while the highest score for degree of centrality among men belonged to those dealing with drug addiction. Analysis for degree of betweenness centrality revealed that drug addiction had the highest score among men, whereas the highest score for women on betweenness centrality was related to physical illness. Structural analysis of the network showed differences in role that various problems played in intentional self-poisoning. The findings from this research can be used by public health authorities to create prevention programs that address the problems leading to deliberate self-poisoning.