World Pneumonia Day during a global pneumonia pandemic: 12 November 2020

Comparative performance of biofire pneumonia panel and standard culture-based methods for diagnosing pneumonia in critically ill patients: Impact on antibiotic stewardship

INTRODUCTION

Lower respiratory tract infections (LRTIs) are a common cause of morbidity and mortality worldwide. Accurate identification of the pathogens causing LRTIs is crucial for ensuring of diagnostic and antibiotic stewardship. The Biofire Pneumonia Panel (BFPP) is a molecular diagnostic test that allows rapid detection of various bacterial and viral pathogens. In this study, we compared the performance of BFPP with standard culture methods for the detection of pathogens.

MATERIALS AND METHODS

Respiratory samples from 70 patient with suspected LRTIs were tested using both BFPP and standard culture methods. The distribution of isolated bacterial pathogens was analyzed, and the sensitivity and specificity of BFPP were calculated. Additionally, the performance of BFPP in detecting antimicrobial resistance genes was evaluated. The results were compared with those obtained from VITEK-2 antimicrobial susceptibility testing and culture-based methods.

RESULTS

Among the suspected LRTI cases, BFPP identified a single pathogen in 32.8% of cases and multiple pathogens in 40% of cases. The standard culture method detected a single pathogen in 47.1% of cases. BFPP showed a sensitivity of 93.9% and a specificity of 45.9% for the total sample. The performance of BFPP in detecting antimicrobial resistance genes varied for different pathogens with overall sensitivity of 40.1% and specificity of 95.9%.

CONCLUSION

The Biofire Pneumonia Panel (BFPP) demonstrated high sensitivity for several bacterial pathogens, indicating its potential as a rapid diagnostic tool. However, its performance varied for different microorganisms, and it had limitations in detecting certain pathogens and antimicrobial resistance genes for which still required more further studies to explore different resistance gene mechanism that can be incorporated in this panel in future. The BFPP can complement standard culture methods as a rapid tool in the diagnosis of LRTIs.

Increased risk of death from pneumonia among cancer survivors: A propensity score‐matched cohort analysis

BACKGROUND

The repeated global pandemic of the new virus has led to interest in the possibility of severe pneumonia among cancer patients and survivors. Here, we aimed to assess the association between incident cancer and risk of death from pneumonia in Japanese in a large population-based cohort study.

METHODS

We used the data from The Japan Public Health Center-based Prospective Study (JPHC Study), which enrolled subjects aged 40 to 69 between 1990 and 1994 and followed their cancer incidence and mortality until 2013. After identifying 103,757 eligible subjects for analysis and imputing missing data on covariates by the chained equations approach, we conducted propensity score-matched analysis for 1:4 matching, leaving 14,520 cases diagnosed with cancer and 48,947 controls without cancer during the study period for final analysis. A Cox proportional hazards regression model was used to estimate the hazard ratio (HR) and corresponding confidence interval (CI) for the risk of death from pneumonia with comparison of cancer cases and cancer-free controls.

RESULTS

Compared to cancer-free individuals, risk of death from pneumonia was significantly higher among those who had any diagnosed cancer (HR, 1.41; 95%CI, 1.08-1.84); those within 1 year of diagnosis (HR, 23.0; 95% CI, 2.98-177.3); within 1 to <2 years (HR, 3.66; 95% CI, 1.04-12.9); and those with regional spread or distant metastatic cancer at initial diagnosis (HR, 2.01; 95% CI, 1.26-3.21). A history of lung, oesophageal, and head and neck cancer conferred the higher risk among site-specific cancers.

CONCLUSION

We found a positive association between incident cancer and risk of death from pneumonia in this study. These results imply the possibility that the immunocompromised status and respiratory failure due to antitumor treatment may have resulted in a more severe outcome from pneumonia among cancer survivors than the general population.

Evaluation of the BioFire FilmArray Pneumonia Panel Plus to the Conventional Diagnostic Methods in Determining the Microbiological Etiology of Hospital-Acquired Pneumonia

Hospital-acquired pneumonia (HAP) is a substantial public health issue that is associated with high mortality rates and is complicated by an arsenal of microbial etiologies, expressing multidrug-resistant phenotypes, rendering relatively limited therapeutic options. BioFire FilmArray Pneumonia Panel plus (BFPP) is a simple multiplexed PCR system that integrates sample preparation, nucleic acid extraction, amplification, and analysis of microbial etiology, with a turnaround time of about one hour. In comparison to standard culture methods, BFPP is simpler, easier to perform, and can simultaneously detect the most common pathogens involved in lower respiratory tract infections (34 targets). Accordingly, we evaluated the diagnostic performance of the multiplexed BFPP for the rapid detection of 27 clinically relevant respiratory pathogens and 7 genetic markers among 50 HAP cases admitted to the intensive care unit (ICU), who submitted mini-bronchoalveolar (mBAL) specimens. In comparison to standard culture methods, BFPP showed an overall sensitivity of 100% [95% CI; 90-100] and overall specificity of 90% [95% CI; 87.4-92.5] among all the tested bacterial targets. BFPP identified 11 viral targets (22%) among the tested specimens. The BFPP semi-quantitative analysis showed a concordance rate of 47.4% among positive culture specimens. For the investigation of the antibiotic resistance genes, BFPP showed a positive percent agreement (PPA), a negative percent agreement (NPA), and an overall percent agreement (OPA), reaching 97% [95% CI; 90-100], 95% [95% CI; 91.5-97], and 95% [95% CI; 93-97], respectively, with standard antibiotic sensitivity testing. In conclusion, BFPP has the potential to enhance the rapid microbiological diagnosis of HAP cases, and could aid in tailoring appropriate antibiotic therapies.

Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model

This article presents non-invasive sensing-based diagnoses of pneumonia disease, exploiting a deep learning model to make the technique non-invasive coupled with security preservation. Sensing and securing healthcare and medical images such as X-rays that can be used to diagnose viral diseases such as pneumonia is a challenging task for researchers. In the past few years, patients’ medical records have been shared using various wireless technologies. The wireless transmitted data are prone to attacks, resulting in the misuse of patients’ medical records. Therefore, it is important to secure medical data, which are in the form of images. The proposed work is divided into two sections: in the first section, primary data in the form of images are encrypted using the proposed technique based on chaos and convolution neural network. Furthermore, multiple chaotic maps are incorporated to create a random number generator, and the generated random sequence is used for pixel permutation and substitution. In the second part of the proposed work, a new technique for pneumonia diagnosis using deep learning, in which X-ray images are used as a dataset, is proposed. Several physiological features such as cough, fever, chest pain, flu, low energy, sweating, shaking, chills, shortness of breath, fatigue, loss of appetite, and headache and statistical features such as entropy, correlation, contrast dissimilarity, etc., are extracted from the X-ray images for the pneumonia diagnosis. Moreover, machine learning algorithms such as support vector machines, decision trees, random forests, and naive Bayes are also implemented for the proposed model and compared with the proposed CNN-based model. Furthermore, to improve the CNN-based proposed model, transfer learning and fine tuning are also incorporated. It is found that CNN performs better than other machine learning algorithms as the accuracy of the proposed work when using naive Bayes and CNN is 89% and 97%, respectively, which is also greater than the average accuracy of the existing schemes, which is 90%. Further, K-fold analysis and voting techniques are also incorporated to improve the accuracy of the proposed model. Different metrics such as entropy, correlation, contrast, and energy are used to gauge the performance of the proposed encryption technology, while precision, recall, F1 score, and support are used to evaluate the effectiveness of the proposed machine learning-based model for pneumonia diagnosis. The entropy and correlation of the proposed work are 7.999 and 0.0001, respectively, which reflects that the proposed encryption algorithm offers a higher security of the digital data. Moreover, a detailed comparison with the existing work is also made and reveals that both the proposed models work better than the existing work.