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11 pages, 786 KiB

Open AccessArticle

TLRs Gene Polymorphisms Associated with Pneumonia before and during COVID-19 Pandemic

by Svetlana Salamaikina, Maria Karnaushkina, Vitaly Korchagin, Maria Litvinova, Konstantin Mironov and Vasily Akimkin

Diagnostics 2023, 13(1), 121; https://doi.org/10.3390/diagnostics13010121 - 30 Dec 2022

Cited by 4 | Viewed by 1495

Abstract

Background: The progression of infectious diseases depends on the characteristics of a patient’s innate immunity, and the efficiency of an immune system depends on the patient’s genetic factors, including SNPs in the TLR genes. In this pilot study, we determined the frequency of [...] Read more.

Background: The progression of infectious diseases depends on the characteristics of a patient’s innate immunity, and the efficiency of an immune system depends on the patient’s genetic factors, including SNPs in the TLR genes. In this pilot study, we determined the frequency of alleles in these SNPs in a subset of patients with pneumonia. Methods: This study assessed six SNPs from TLR genes: rs5743551 (TLR1), rs5743708, rs3804100 (TLR2), rs4986790 (TLR4), rs5743810 (TLR6), and rs3764880 (TLR8). Three groups of patients participated in this study: patients with pneumonia in 2019 (76 samples), patients with pneumonia caused by SARS-CoV-2 in 2021 (85 samples), and the control group (99 samples). Results: The allele and genotype frequencies obtained for each group were examined using four genetic models. Significant results were obtained when comparing the samples obtained from individuals with pneumonia before the spread of SARS-CoV-2 and from the controls for rs5743551 (TLR1) and rs3764880 (TLR8). Additionally, the comparison of COVID-19-related pneumonia cases and the control group revealed a significant result for rs3804100-G (TLR2). Conclusions: Determining SNP allele frequencies and searching for their associations with the course of pneumonia are important for personalized patient management. However, our results need to be comprehensively assessed in consideration of other clinical parameters. Full article

(This article belongs to the Special Issue Advances in the Diagnosis of Pneumonia)

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9 pages, 269 KiB

Open AccessArticle

Evaluation and Clinical Impact of Biofire FilmArray Pneumonia Panel Plus in ICU-Hospitalized COVID-19 Patients

by Dolores Escudero, Jonathan Fernández-Suarez, Lorena Forcelledo, Salvador Balboa, Javier Fernández, Ivan Astola, Brigida Quindos, Rainer Campos, Fernando Vázquez and José Antonio Boga

Diagnostics 2022, 12(12), 3134; https://doi.org/10.3390/diagnostics12123134 - 12 Dec 2022

Cited by 4 | Viewed by 1673

Abstract

Microbiological diagnosis by using commercial multiplex quantitative PCR systems provides great advantages over the conventional culture. In this work, the Biofire FilmArray Pneumonia Panel Plus (FAPP+) was used to test 144 low respiratory tract samples from 105 COVID-19 patients admitted to an Intensive [...] Read more.

Microbiological diagnosis by using commercial multiplex quantitative PCR systems provides great advantages over the conventional culture. In this work, the Biofire FilmArray Pneumonia Panel Plus (FAPP+) was used to test 144 low respiratory tract samples from 105 COVID-19 patients admitted to an Intensive Care Unit (ICU), detecting 78 pathogens in 59 (41%) samples. The molecular panel was evaluated by using the conventional culture (CC) as comparator, which isolated 42 pathogens in 40 (27.7%) samples. The overall percentage of agreement was 82.6%. Values of sensitivity (93%), specificity (62%), positive predictive value (50%), and negative predictive value (96%) were obtained. The mean time elapsed from sample extraction to modification of antibiotic treatment was 7.6 h. A change in antimicrobial treatment after the FAPP+ results was performed in 27% of patients. The FAPP+ is a highly sensitive diagnostic method that can be used to significantly reduce diagnostic time and that allows an early optimization of antimicrobial treatment. Full article

(This article belongs to the Special Issue Advances in the Diagnosis of Pneumonia)

10 pages, 1187 KiB

Open AccessArticle

Post-COVID-19 Syndrome Based on Disease Form and Associated Comorbidities

by Ramona Cioboata, Dragos Nicolosu, Costin Teodor Streba, Corina Maria Vasile, Madalina Olteanu, Alexandra Nemes, Andreea Gheorghe, Cristina Calarasu and Adina Andreea Turcu

Diagnostics 2022, 12(10), 2502; https://doi.org/10.3390/diagnostics12102502 - 15 Oct 2022

Cited by 3 | Viewed by 1387

Abstract

(1) Background: SARS-CoV-2 has infected more than 97 million people worldwide and caused the death of more than 6 million. (2) Methods: Between 1 October and 31 December 2020, 764 patients diagnosed with SARS-CoV-2 infection were selected based on RT-PCR test results. The [...] Read more.

(1) Background: SARS-CoV-2 has infected more than 97 million people worldwide and caused the death of more than 6 million. (2) Methods: Between 1 October and 31 December 2020, 764 patients diagnosed with SARS-CoV-2 infection were selected based on RT-PCR test results. The following parameters were noted: age, gender, origin, days of hospitalization, COVID-19 experienced form, radiographic imaging features, associated comorbidities, and recommended treatment at discharge. (3) Results: The mean age at the time of COVID-19 infection was 55.2 years for men and 55.3 years for women. There was a similar age distribution among patients, regardless of gender. There was a substantial difference between the average lengths of hospitalization and those with residual symptoms—most patients who reported symptoms after discharge had been admitted with moderately severe forms of illness. Fatigue was the main remaining symptom (36%). (4) Conclusions: In conclusion, to clarify the impact of SARS-CoV-2 infection on patients in the long term, further studies are needed to investigate the elements assessed. Well-designed recovery programs will be needed to effectively manage these patients, with multidisciplinary collaboration and a team of professionals involved in all aspects of post-COVID patient health. Full article

(This article belongs to the Special Issue Advances in the Diagnosis of Pneumonia)

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16 pages, 730 KiB

Open AccessArticle

Oropharyngeal Candidiasis among Egyptian COVID-19 Patients: Clinical Characteristics, Species Identification, and Antifungal Susceptibility, with Disease Severity and Fungal Coinfection Prediction Models

by Mahmoud A. F. Khalil, Mahmoud R. M. El-Ansary, Rasha H. Bassyouni, Eman E. Mahmoud, Inas A. Ali, Tarek I. Ahmed, Essam A. Hassan and Tamer M. Samir

Diagnostics 2022, 12(7), 1719; https://doi.org/10.3390/diagnostics12071719 - 15 Jul 2022

Cited by 5 | Viewed by 2223

Abstract

The study aimed to investigate the causative species, antifungal susceptibility, and factors associated with oropharyngeal candidiasis (OPC) among Egyptian COVID-19 patients. This is an observational, case-controlled, single-center study that included three groups: COVID-19 patients (30), COVID-19 patients with OPC (39), and healthy individuals [...] Read more.

The study aimed to investigate the causative species, antifungal susceptibility, and factors associated with oropharyngeal candidiasis (OPC) among Egyptian COVID-19 patients. This is an observational, case-controlled, single-center study that included three groups: COVID-19 patients (30), COVID-19 patients with OPC (39), and healthy individuals (31). Patients’ demographic data (age, sex), laboratory tests, comorbidities, treatment, and outcomes were included. Candida species were isolated from COVID-OPC patient’s oropharyngeal swabs by convenient microbiological methods. Isolated strains were tested for antimicrobial susceptibility, biofilm production, aspartyl protease, and phospholipase activities. The most common respiratory symptoms reported were dyspnea (36/39; 92.4%) and cough (33/39; 84.7%). Candida albicans was the most common isolated species, accounting for 74.36% (29/39), followed by Candida tropicalis and Candida glabrata (15.38% and 10.26%, respectively). Amphotericin was effective against all isolates, while fluconazole was effective against 61.5%. A total of 53.8% of the isolates were biofilm producers. The phospholipase activity of C. albicans was detected among 58.6% (17/29) of the isolates. Significant variables from this study were used to create two equations from a regression model that can predict the severity of disease course and liability to fungal infection, with a stativity of 87% and 91%, respectively. According to our findings, COVID-19 patients with moderate to severe infection under prolonged use of broad-spectrum antibiotics and corticosteroids should be considered a high-risk group for developing OPC, and prophylactic measures are recommended to be included in the treatment protocols. In addition, due to the increased rate of fluconazole resistance, other new antifungals should be considered. Full article

(This article belongs to the Special Issue Advances in the Diagnosis of Pneumonia)

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16 pages, 5414 KiB

Open AccessArticle

Co-Infections and Superinfections in COVID-19 Critically Ill Patients Are Associated with CT Imaging Abnormalities and the Worst Outcomes

by Nicolò Brandi, Federica Ciccarese, Caterina Balacchi, Maria Rita Rimondi, Cecilia Modolon, Camilla Sportoletti, Chiara Capozzi, Matteo Renzulli, Alexandro Paccapelo, Andrea Castelli and Rita Golfieri

Diagnostics 2022, 12(7), 1617; https://doi.org/10.3390/diagnostics12071617 - 03 Jul 2022

Cited by 16 | Viewed by 1980

Abstract

Background: Bacterial and fungal co-infections and superinfections have a critical role in the outcome of the COVID-19 patients admitted to the Intensive Care Unit (ICU). Methods: The present study is a retrospective analysis of 95 patients admitted to the ICU for COVID-19-related ARDS [...] Read more.

Background: Bacterial and fungal co-infections and superinfections have a critical role in the outcome of the COVID-19 patients admitted to the Intensive Care Unit (ICU). Methods: The present study is a retrospective analysis of 95 patients admitted to the ICU for COVID-19-related ARDS during the first (February–May 2020) and second waves of the pandemic (October 2020–January 2021). Demographic and clinical data, CT imaging features, and pulmonary and extra-pulmonary complications were recorded, as well as the temporal evolution of CT findings when more than one scan was available. The presence of co-infections and superinfections was registered, reporting the culprit pathogens and the specimen type for culture. A comparison between patients with and without bacterial and/or co-infections/superinfections was performed. Results: Sixty-three patients (66.3%) developed at least one confirmed co-infection/superinfection, with 52 (82.5%) developing pneumonia and 43 (68.3%) bloodstream infection. Gram-negative bacteria were the most common co-pathogens identified and Aspergillus spp. was the most frequent pulmonary microorganism. Consolidations, cavitations, and bronchiectasis were significantly associated with the presence of co-infections/superinfections (p = 0.009, p = 0.010 and p = 0.009, respectively); when considering only patients with pulmonary co-pathogens, only consolidations remained statistically significative (p = 0.004). Invasive pulmonary aspergillosis was significantly associated with the presence of cavitations and bronchiectasis (p < 0.001). Patients with co-infections/superinfections presented a significantly higher mortality rate compared to patients with COVID-19 only (52.4% vs. 25%, p = 0.016). Conclusions: Bacterial and fungal co-infections and superinfections are frequent in COVID-19 patients admitted to ICU and are associated with worse outcomes. Imaging plays an important role in monitoring critically ill COVID-19 patients and may help detect these complications, suggesting further laboratory investigations. Full article

(This article belongs to the Special Issue Advances in the Diagnosis of Pneumonia)

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17 pages, 740 KiB

Open AccessArticle

Diagnostic Coding Intensity among a Pneumonia Inpatient Cohort Using a Risk-Adjustment Model and Claims Data: A U.S. Population-Based Study

by Ruchi Mishra, Himadri Verma, Venkata Bhargavi Aynala, Paul R. Arredondo, John Martin, Michael Korvink and Laura H. Gunn

Diagnostics 2022, 12(6), 1495; https://doi.org/10.3390/diagnostics12061495 - 19 Jun 2022

Cited by 3 | Viewed by 1880

Abstract

Hospital payments depend on the Medicare Severity Diagnosis-Related Group’s estimated cost and the set of diagnoses identified during inpatient stays. However, over-coding and under-coding diagnoses can occur for different reasons, leading to financial and clinical consequences. We provide a novel approach to measure [...] Read more.

Hospital payments depend on the Medicare Severity Diagnosis-Related Group’s estimated cost and the set of diagnoses identified during inpatient stays. However, over-coding and under-coding diagnoses can occur for different reasons, leading to financial and clinical consequences. We provide a novel approach to measure diagnostic coding intensity, built on commonly available administrative claims data, and demonstrated through a 2019 pneumonia acute inpatient cohort (N = 182,666). A Poisson additive model (PAM) is proposed to model risk-adjusted additional coded diagnoses. Excess coding intensity per patient visit was estimated as the difference between the observed and PAM-based expected counts of secondary diagnoses upon risk adjustment by patient-level characteristics. Incidence rate ratios were extracted for patient-level characteristics and further adjustments were explored by facility-level characteristics to account for facility and geographical differences. Facility-level factors contribute substantially to explain the remaining variability in excess diagnostic coding, even upon adjusting for patient-level risk factors. This approach can provide hospitals and stakeholders with a tool to identify outlying facilities that may experience substantial differences in processes and procedures compared to peers or general industry standards. The approach does not rely on the availability of clinical information or disease-specific markers, is generalizable to other patient cohorts, and can be expanded to use other sources of information, when available. Full article

(This article belongs to the Special Issue Advances in the Diagnosis of Pneumonia)

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16 pages, 930 KiB

Open AccessArticle

Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network

by Muhammad Mujahid, Furqan Rustam, Roberto Álvarez, Juan Luis Vidal Mazón, Isabel de la Torre Díez and Imran Ashraf

Diagnostics 2022, 12(5), 1280; https://doi.org/10.3390/diagnostics12051280 - 21 May 2022

Cited by 38 | Viewed by 5337

Abstract

Pneumonia is one of the leading causes of death in both infants and elderly people, with approximately 4 million deaths each year. It may be a virus, bacterial, or fungal, depending on the contagious pathogen that damages the lung’s tiny air sacs (alveoli). [...] Read more.

Pneumonia is one of the leading causes of death in both infants and elderly people, with approximately 4 million deaths each year. It may be a virus, bacterial, or fungal, depending on the contagious pathogen that damages the lung’s tiny air sacs (alveoli). Patients with underlying disorders such as asthma, a weakened immune system, hospitalized babies, and older persons on ventilators are all at risk, particularly if pneumonia is not detected early. Despite the existing approaches for its diagnosis, low accuracy and efficiency require further research for more accurate systems. This study is a similar endeavor for the detection of pneumonia by the use of X-ray images. The dataset is preprocessed to make it suitable for transfer learning tasks. Different pre-trained convolutional neural network (CNN) variants are utilized, including VGG16, Inception-v3, and ResNet50. Ensembles are made by incorporating CNN with Inception-V3, VGG-16, and ResNet50. Besides the common evaluation metrics, the performance of the pre-trained and ensemble deep learning models is measured with Cohen’s kappa as well as the area under the curve (AUC). Experimental results show that Inception-V3 with CNN attained the highest accuracy and recall score of 99.29% and 99.73%, respectively. Full article

(This article belongs to the Special Issue Advances in the Diagnosis of Pneumonia)

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Review

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22 pages, 4099 KiB

Open AccessReview

An Imaging Overview of COVID-19 ARDS in ICU Patients and Its Complications: A Pictorial Review

by Nicolò Brandi, Federica Ciccarese, Maria Rita Rimondi, Caterina Balacchi, Cecilia Modolon, Camilla Sportoletti, Matteo Renzulli, Francesca Coppola and Rita Golfieri

Diagnostics 2022, 12(4), 846; https://doi.org/10.3390/diagnostics12040846 - 29 Mar 2022

Cited by 27 | Viewed by 3535

Abstract

A significant proportion of patients with COVID-19 pneumonia could develop acute respiratory distress syndrome (ARDS), thus requiring mechanical ventilation, and resulting in a high rate of intensive care unit (ICU) admission. Several complications can arise during an ICU stay, from both COVID-19 infection [...] Read more.

A significant proportion of patients with COVID-19 pneumonia could develop acute respiratory distress syndrome (ARDS), thus requiring mechanical ventilation, and resulting in a high rate of intensive care unit (ICU) admission. Several complications can arise during an ICU stay, from both COVID-19 infection and the respiratory supporting system, including barotraumas (pneumothorax and pneumomediastinum), superimposed pneumonia, coagulation disorders (pulmonary embolism, venous thromboembolism, hemorrhages and acute ischemic stroke), abdominal involvement (acute mesenteric ischemia, pancreatitis and acute kidney injury) and sarcopenia. Imaging plays a pivotal role in the detection and monitoring of ICU complications and is expanding even to prognosis prediction. The present pictorial review describes the clinicopathological and radiological findings of COVID-19 ARDS in ICU patients and discusses the imaging features of complications related to invasive ventilation support, as well as those of COVID-19 itself in this particularly fragile population. Radiologists need to be familiar with COVID-19’s possible extra-pulmonary complications and, through reliable and constant monitoring, guide therapeutic decisions. Moreover, as more research is pursued and the pathophysiology of COVID-19 is increasingly understood, the role of imaging must evolve accordingly, expanding from the diagnosis and subsequent management of patients to prognosis prediction. Full article

(This article belongs to the Special Issue Advances in the Diagnosis of Pneumonia)

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