Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 128
Filter
1.
Front Cell Infect Microbiol ; 12: 838749, 2022.
Article in English | MEDLINE | ID: covidwho-1822355

ABSTRACT

The Coronavirus Disease 2019 (COVID-19) has spread all over the world and impacted many people's lives. The characteristics of COVID-19 and other types of pneumonia have both similarities and differences, which confused doctors initially to separate and understand them. Here we presented a retrospective analysis for both COVID-19 and other types of pneumonia by combining the COVID-19 clinical data, eICU and MIMIC-III databases. Machine learning models, including logistic regression, random forest, XGBoost and deep learning neural networks, were developed to predict the severity of COVID-19 infections as well as the mortality of pneumonia patients in intensive care units (ICU). Statistical analysis and feature interpretation, including the analysis of two-level attention mechanisms on both temporal and non-temporal features, were utilized to understand the associations between different clinical variables and disease outcomes. For the COVID-19 data, the XGBoost model obtained the best performance on the test set (AUROC = 1.000 and AUPRC = 0.833). On the MIMIC-III and eICU pneumonia datasets, our deep learning model (Bi-LSTM_Attn) was able to identify clinical variables associated with death of pneumonia patients (AUROC = 0.924 and AUPRC = 0.802 for 24-hour observation window and 12-hour prediction window). The results highlighted clinical indicators, such as the lymphocyte counts, that may help the doctors to predict the disease progression and outcomes for both COVID-19 and other types of pneumonia.


Subject(s)
COVID-19 , Pneumonia , COVID-19/diagnosis , Humans , Intensive Care Units , Machine Learning , Pneumonia/diagnosis , Retrospective Studies
2.
BMC Pulm Med ; 22(1): 121, 2022 Apr 01.
Article in English | MEDLINE | ID: covidwho-1822183

ABSTRACT

BACKGROUND: The respiratory rate-oxygenation (ROX) index has been increasingly applied to predict the outcome of high-flow nasal cannula (HFNC) in pneumonia patients with acute hypoxemic respiratory failure (AHRF). However, its diagnostic accuracy for the HFNC outcome has not yet been systematically assessed. This meta-analysis sought to evaluate the predictive performance of the ROC index for the successful weaning from HFNC in pneumonia patients with AHRF. METHODS: A literature search was conducted on electronic databases through February 12, 2022, to retrieve studies that investigated the diagnostic accuracy of the ROC index for the outcome of HFNC application in pneumonia patients with AHRF. The area under the hierarchical summary receiver operating characteristic curve (AUHSROC) was estimated as the primary measure of diagnostic accuracy due to the varied cutoff values of the index. We observed the distribution of the cutoff values and estimated the optimal threshold with corresponding 95% confidential interval (CI). RESULTS: Thirteen observational studies comprising 1751 patients were included, of whom 1003 (57.3%) successfully weaned from HFNC. The ROC index exhibits good performance for predicting the successful weaning from HFNC in pneumonia patients with AHRF, with an AUHSROC of 0.81 (95% CI 0.77-0.84), a pooled sensitivity of 0.71 (95% CI 0.64-0.78), and a pooled specificity of 0.78 (95% CI 0.70-0.84). The cutoff values of the ROX index were nearly conically symmetrically distributed; most data were centered between 4.5 and 6.0, and the mean and median values were 4.8 (95% CI 4.2-5.4) and 5.3 (95% CI 4.2-5.5), respectively. Moreover, the AUHSROC in the subgroup of measurement within 6 h after commencing HFNC was comparable to that in the subgroup of measurement during 6-12 h. The stratified analyses also suggested that the ROC index was a reliable predictor of HFNC success in pneumonia patients with coronavirus disease 2019. CONCLUSIONS: In pneumonia patients with AHRF, the ROX index measured within 12 h after HFNC initiation is a good predictor of successful weaning from HFNC. The range of 4.2-5.4 may represent the optimal confidence interval for the prediction of HFNC outcome.


Subject(s)
COVID-19 , Pneumonia , Respiratory Insufficiency , Cannula , Humans , Pneumonia/complications , Pneumonia/diagnosis , Pneumonia/therapy , Respiratory Insufficiency/therapy , Respiratory Rate
3.
JAAPA ; 35(4): 29-33, 2022 Apr 01.
Article in English | MEDLINE | ID: covidwho-1806587

ABSTRACT

ABSTRACT: Acute respiratory distress syndrome (ARDS) is a severe, often fatal, lung condition frequently seen in patients in the ICU. ARDS is triggered by an inciting event such as pneumonia or sepsis, which is followed by an inappropriate host inflammatory response that results in pulmonary edema and impaired gas exchange, and may progress to fibrosis. With the increased spotlight and discussion focused on ARDS during the COVID-19 pandemic, healthcare providers must be able to identify and manage symptoms based on evidence-based research.


Subject(s)
COVID-19 , Pneumonia , Pulmonary Edema , Respiratory Distress Syndrome , Humans , Pandemics , Pneumonia/diagnosis , Pulmonary Edema/etiology , Pulmonary Edema/therapy , Respiratory Distress Syndrome/diagnosis , Respiratory Distress Syndrome/etiology , Respiratory Distress Syndrome/therapy
4.
Med Biol Eng Comput ; 60(6): 1763-1774, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1803060

ABSTRACT

Although some studies tried to identify risk factors for COVID-19, the evidence comparing COVID-19 and community-acquired pneumonia (CAP) is inconclusive, and CAP is the most common pneumonia with similar symptoms as COVID-19. We conducted a case-control study with 35 routine-collected clinical indicators and demographic factors to identify predictors for COVID-19 with CAP as controls. We randomly split the dataset into a training set (70%) and testing set (30%). We built Explainable Boosting Machine to select the important factors and built a decision tree on selected variables to interpret their relationships. The top five individual predictors of COVID-19 are albumin, total bilirubin, monocyte count, alanine aminotransferase, and percentage of monocyte with the importance scores ranging from 0.078 to 0.567. The top systematic predictors for COVID-19 are liver function, monocyte increasing, plasma protein, granulocyte, and renal function (importance scores ranging 0.009-0.096). We identified five combinations of important indicators to screen COVID-19 patients from CAP patients with differentiating abilities ranging 83.3-100%. An online predictive tool for our model was published. Certain clinical indicators collected routinely from most hospitals could help screen and distinguish COVID-19 from CAP. While further verification is needed, our findings and predictive tool could help screen suspected COVID-19 cases.


Subject(s)
COVID-19 , Pneumonia , COVID-19/diagnosis , Case-Control Studies , Humans , Machine Learning , Pneumonia/diagnosis , Risk Factors
5.
Ann Intern Med ; 175(4): ITC49-ITC64, 2022 04.
Article in English | MEDLINE | ID: covidwho-1786250

ABSTRACT

Community-acquired pneumonia is an important cause of morbidity and mortality. It can be caused by bacteria, viruses, or fungi and can be prevented through vaccination with pneumococcal, influenza, and COVID-19 vaccines. Diagnosis requires suggestive history and physical findings in conjunction with radiographic evidence of infiltrates. Laboratory testing can help guide therapy. Important issues in treatment include choosing the proper venue, timely initiation of the appropriate antibiotic or antiviral, appropriate respiratory support, deescalation after negative culture results, switching to oral therapy, and short treatment duration.


Subject(s)
COVID-19 , Community-Acquired Infections , Pneumonia , COVID-19 Vaccines , Community-Acquired Infections/diagnosis , Community-Acquired Infections/drug therapy , Humans , Pneumococcal Vaccines , Pneumonia/diagnosis , Pneumonia/drug therapy
6.
Clin Infect Dis ; 73(3): e524-e530, 2021 08 02.
Article in English | MEDLINE | ID: covidwho-1769204

ABSTRACT

BACKGROUND: Proadrenomedullin (proADM), a vasodilatory peptide with antimicrobial and anti-inflammatory properties, predicts severe outcomes in adults with community-acquired pneumonia (CAP) to a greater degree than C-reactive protein and procalcitonin. We evaluated the ability of proADM to predict disease severity across a range of clinical outcomes in children with suspected CAP. METHODS: We performed a prospective cohort study of children 3 months to 18 years with CAP in the emergency department. Disease severity was defined as mild (discharged home), mild-moderate (hospitalized but not moderate-severe or severe), moderate-severe (eg, hospitalized with supplemental oxygen, broadening of antibiotics, complicated pneumonia), and severe (eg, vasoactive infusions, chest drainage, severe sepsis). Outcomes were examined using proportional odds logistic regression within the cohort with suspected CAP and in a subset with radiographic CAP. RESULTS: Among 369 children, median proADM increased with disease severity (mild: median [IQR], 0.53 [0.43-0.73]; mild-moderate: 0.56 [0.45-0.71]; moderate-severe: 0.61 [0.47-0.77]; severe: 0.70 [0.55-1.04] nmol/L) (P = .002). ProADM was significantly associated with increased odds of developing severe outcomes (suspected CAP: OR, 1.68; 95% CI, 1.2-2.36; radiographic CAP: OR, 2.11; 95% CI, 1.36-3.38) adjusted for age, fever duration, antibiotic use, and pathogen. ProADM had an AUC of 0.64 (95% CI, .56-.72) in those with suspected CAP and an AUC of 0.77 (95% CI, .68-.87) in radiographic CAP. CONCLUSIONS: ProADM was associated with severe disease and discriminated moderately well children who developed severe disease from those who did not, particularly in radiographic CAP.


Subject(s)
Adrenomedullin , Community-Acquired Infections , Pneumonia , Biomarkers , Child , Community-Acquired Infections/diagnosis , Humans , Pneumonia/diagnosis , Prognosis , Prospective Studies , Protein Precursors , Severity of Illness Index
7.
BMJ Open Respir Res ; 9(1)2022 03.
Article in English | MEDLINE | ID: covidwho-1736079

ABSTRACT

INTRODUCTION: COVID-19 sequelae are numerous and multisystemic, and how to evaluate those symptomatic patients is a timely issue. Klok et al proposed the Post-COVID-19 Functional Status (PCFS) Scale as an easy tool to evaluate limitations related to persistent symptoms. Our aim was to analyse PCFS Scale ability to detect functional limitations and its correlation with quality of life in a cohort of patients, 2-9 months after hospitalisation for COVID-19 hypoxemic pneumonia. METHODS: PCFS Scale was evaluated in 121 patients together with quality of life and dyspnoea questionnaires, pulmonary function tests and CT scans. RESULTS: We observed a high correlation with multiple questionnaires (Short Form-36, Hospital Anxiety and Depression Scale, modified Medical Research Council, end Borg Six-Minute Walk Test), making the PCFS Scale a quick and global tool to evaluate functional limitations related to various persistent symptoms following COVID-19 pneumonia. DISCUSSION: The PCFS Scale seems to be a suitable instrument to screen for patients who will require careful follow-up after COVID-19 hypoxemic pneumonia even in the absence of pulmonary sequelae.


Subject(s)
COVID-19 , Pneumonia , COVID-19/complications , Functional Status , Humans , Pneumonia/diagnosis , Quality of Life , SARS-CoV-2
8.
Chest ; 161(4): 927-936, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1650215

ABSTRACT

BACKGROUND: The Confusion, Urea > 7 mM, Respiratory Rate ≥ 30 breaths/min, BP < 90 mm Hg (Systolic) or < 60 mm Hg (Diastolic), Age ≥ 65 Years (CURB-65) score and the Pneumonia Severity Index (PSI) are well-established clinical prediction rules for predicting mortality in patients hospitalized with community-acquired pneumonia (CAP). SARS-CoV-2 has emerged as a new etiologic agent for CAP, but the role of CURB-65 score and PSI have not been established. RESEARCH QUESTION: How effective are CURB-65 score and PSI at predicting in-hospital mortality resulting from SARS-CoV-2 CAP compared with non-SARS-CoV-2 CAP? Can these clinical prediction rules be optimized to predict mortality in SARS-CoV-2 CAP by addition of procalcitonin and D-dimer? STUDY DESIGN AND METHODS: Secondary analysis of two prospective cohorts of patients with SARS-CoV-2 CAP or non-SARS-CoV-2 CAP from eight adult hospitals in Louisville, Kentucky. RESULTS: The in-hospital mortality rate was 19% for patients with SARS-CoV-2 CAP and 6.5% for patients with non-SARS-CoV-2 CAP. For the PSI score, receiver operating characteristic (ROC) curve analysis resulted in an area under the ROC curve (AUC) of 0.82 (95% CI, 0.78-0.86) and 0.79 (95% CI, 0.77-0.80) for patients with SARS-CoV-2 CAP and non-SARS-CoV-2 CAP, respectively. For the CURB-65 score, ROC analysis resulted in an AUC of 0.79 (95% CI, 0.75-0.84) and 0.75 (95% CI, 0.73-0.77) for patients with SARS-CoV-2 CAP and non-SARS-CoV-2 CAP, respectively. In SARS-CoV-2 CAP, the addition of D-dimer (optimal cutoff, 1,813 µg/mL) and procalcitonin (optimal cutoff, 0.19 ng/mL) to PSI and CURB-65 score provided negligible improvement in prognostic performance. INTERPRETATION: PSI and CURB-65 score can predict in-hospital mortality for patients with SARS-CoV-2 CAP and non-SARS-CoV-2 CAP comparatively. In patients with SARS-CoV-2 CAP, the inclusion of either D-dimer or procalcitonin to PSI or CURB-65 score did not improve the prognostic performance of either score. In patients with CAP, regardless of cause, PSI and CURB-65 score remain adequate for predicting mortality in clinical practice.


Subject(s)
COVID-19 , Community-Acquired Infections , Pneumonia , Adult , Aged , Hospital Mortality , Humans , Pneumonia/diagnosis , Procalcitonin , Prognosis , Prospective Studies , ROC Curve , Retrospective Studies , SARS-CoV-2 , Severity of Illness Index
9.
Am J Case Rep ; 23: e932999, 2022 Jan 24.
Article in English | MEDLINE | ID: covidwho-1648164

ABSTRACT

BACKGROUND This report describes a 63-year-old Polish man presenting with COVID-19 (Coronavirus Disease 2019) pneumonia in early 2020, before vaccines to prevent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection were available. Nine weeks following recovery from the initial infection, he tested positive again for SARS-CoV-2. CASE REPORT Man, age 63, was admitted to the Military Institute of Medicine on March 12, 2020, with body temperature 40°C, a cough, and breathlessness. On March 12, 2020, SARS-CoV-2 RNA was found in a nasopharynx smear. A chest X-ray (RTG) showed discrete areas of interstitial densities. On June 13, 2020, after 32 days of hospitalization and 2 negative real-time polymerase chain rection (RT-PCR) test results, patient was released home in good general condition. On July 23, 2020 he reported to the emergency room with fever of 39°C and general weakness. A nasopharynx smear confirmed SARS-CoV-2 infection. On admission, the patient was in moderately good condition with auscultatory changes typical for pneumonia on both sides of the chest. On the seventh day of hospitalization, the patient was transported to the Intensive Care Unit (ICU) due to drastic deterioration in respiratory function. Respiratory support with non-invasive high-flow oxygen therapy (Opti-Flow) was used. On August 20, 2020, after negative RT-PCR test results, he was discharged in good general condition. CONCLUSIONS This case of COVID-19 pneumonia presented early in the COVID-19 pandemic of 2020, and the laboratory diagnosis of the initial and subsequent SARS-CoV-2 infection relied on the laboratory methods available at that time. However, several cases of repeat SARS-CoV-2 infection have been described before the development of vaccines in late 2020.


Subject(s)
COVID-19 , Pneumonia , Hospitalization , Hospitals , Humans , Male , Middle Aged , Pandemics , Pneumonia/diagnosis , RNA, Viral , Reinfection , SARS-CoV-2 , United States
10.
Clin Immunol ; 235: 108929, 2022 02.
Article in English | MEDLINE | ID: covidwho-1629722

ABSTRACT

Toll-like receptor 3 (TLR3) and TLR7 genes are involved in the host immune response against viral infections including SARS-COV-2. This study aimed to investigate the association between the TLR3(rs3775290) and TLR7(rs179008) polymorphisms with the prognosis and susceptibility to COVID-19 pneumonia accompanying SARS-COV-2 infection. This case-control study included 236 individuals: 136 COVID-19 pneumonia patients and 100 age and sex-matched controls. Two polymorphisms (TLR3 rs3775290 and TLR7 rs179008) were genotyped by allelic discrimination through TaqMan real-time PCR. This study also investigated predictors of mortality in COVID-19 pneumonia through logistic regression. The mutant 'T/T' genotypes and the 'T' alleles of TLR3(rs3775290) and TLR7(rs179008) polymorphisms were significantly associated with increased risk of COVID-19 pneumonia. This study did not report association between the mutant 'T/T' genotypes of TLR3(rs3775290) and TLR7(rs179008) and the disease outcome. In multivariate analysis, the independent predictors of mortality in COVID-19 pneumonia were male sex, SPO2 ≤ 82%, INR > 1, LDH ≥ 1000 U/l, and lymphocyte count<900/mm3 (P < 0.05).


Subject(s)
COVID-19/genetics , Genetic Predisposition to Disease/genetics , Pneumonia/genetics , Polymorphism, Single Nucleotide , Toll-Like Receptor 3/genetics , Toll-Like Receptor 7/genetics , Aged , Alleles , COVID-19/diagnosis , COVID-19/virology , Case-Control Studies , Female , Gene Frequency , Genotype , Humans , Male , Middle Aged , Pneumonia/diagnosis , Pneumonia/virology , Prognosis , ROC Curve , Risk Factors , SARS-CoV-2/physiology
11.
J Healthc Eng ; 2021: 3514821, 2021.
Article in English | MEDLINE | ID: covidwho-1595649

ABSTRACT

The World Health Organization (WHO) recognized COVID-19 as the cause of a global pandemic in 2019. COVID-19 is caused by SARS-CoV-2, which was identified in China in late December 2019 and is indeed referred to as the severe acute respiratory syndrome coronavirus-2. The whole globe was hit within several months. As millions of individuals around the world are infected with COVID-19, it has become a global health concern. The disease is usually contagious, and those who are infected can quickly pass it on to others with whom they come into contact. As a result, monitoring is an effective way to stop the virus from spreading further. Another disease caused by a virus similar to COVID-19 is pneumonia. The severity of pneumonia can range from minor to life-threatening. This is particularly hazardous for children, people over 65 years of age, and those with health problems or immune systems that are affected. In this paper, we have classified COVID-19 and pneumonia using deep transfer learning. Because there has been extensive research on this subject, the developed method concentrates on boosting precision and employs a transfer learning technique as well as a model that is custom-made. Different pretrained deep convolutional neural network (CNN) models were used to extract deep features. The classification accuracy was used to measure performance to a great extent. According to the findings of this study, deep transfer learning can detect COVID-19 and pneumonia from CXR images. Pretrained customized models such as MobileNetV2 had a 98% accuracy, InceptionV3 had a 96.92% accuracy, EffNet threshold had a 94.95% accuracy, and VGG19 had a 92.82% accuracy. MobileNetV2 has the best accuracy of all of these models.


Subject(s)
COVID-19 , Deep Learning , Pneumonia , Child , Humans , Pandemics , Pneumonia/diagnosis , SARS-CoV-2
12.
PLoS One ; 16(11): e0259910, 2021.
Article in English | MEDLINE | ID: covidwho-1581787

ABSTRACT

BACKGROUND: Clinical observations have shown that there is a relationship between coronavirus disease 2019 (COVID-19) and atypical lymphocytes in the peripheral blood; however, knowledge about the time course of the changes in atypical lymphocytes and the association with the clinical course of COVID-19 is limited. OBJECTIVE: Our purposes were to investigate the dynamics of atypical lymphocytes in COVID-19 patients and to estimate their clinical significance for diagnosis and monitoring disease course. MATERIALS AND METHODS: We retrospectively identified 98 inpatients in a general ward at Kashiwa Municipal Hospital from May 1st, 2020, to October 31st, 2020. We extracted data on patient demographics, symptoms, comorbidities, blood test results, radiographic findings, treatment after admission and clinical course. We compared clinical findings between patients with and without atypical lymphocytes, investigated the behavior of atypical lymphocytes throughout the clinical course of COVID-19, and determined the relationships among the development of pneumonia, the use of supplemental oxygen and the presence of atypical lymphocytes. RESULTS: Patients with atypical lymphocytes had a significantly higher prevalence of pneumonia (80.4% vs. 42.6%, p < 0.0001) and the use of supplemental oxygen (25.5% vs. 4.3%, p = 0.0042). The median time to the appearance of atypical lymphocytes after disease onset was eight days, and atypical lymphocytes were observed in 16/98 (16.3%) patients at the first visit. Atypical lymphocytes appeared after the confirmation of lung infiltrates in 31/41 (75.6%) patients. Of the 13 oxygen-treated patients with atypical lymphocytes, approximately two-thirds had a stable or improved clinical course after the appearance of atypical lymphocytes. CONCLUSION: Atypical lymphocytes frequently appeared in the peripheral blood of COVID-19 patients one week after disease onset. Patients with atypical lymphocytes were more likely to have pneumonia and to need supplemental oxygen; however, two-thirds of them showed clinical improvement after the appearance of atypical lymphocytes.


Subject(s)
COVID-19/diagnosis , Leukocyte Disorders/diagnosis , Pneumonia/diagnosis , Respiratory Tract Infections/diagnosis , Adult , COVID-19/complications , COVID-19/epidemiology , COVID-19/virology , Female , Hospitalization , Humans , Intensive Care Units , Leukocyte Disorders/complications , Leukocyte Disorders/epidemiology , Leukocyte Disorders/virology , Leukocytes, Mononuclear/pathology , Lymphocytes/pathology , Male , Middle Aged , Oxygen/blood , Pneumonia/blood , Pneumonia/epidemiology , Pneumonia/virology , Respiratory Tract Infections/complications , Respiratory Tract Infections/epidemiology , Respiratory Tract Infections/virology , SARS-CoV-2/pathogenicity
13.
Emerg Med J ; 39(3): 199-205, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1583083

ABSTRACT

PURPOSE: Early diagnosis of COVID-19 has a crucial role in confining the spread among the population. Lung ultrasound (LUS) was included in the diagnostic pathway for its high sensitivity, low costs, non-invasiveness and safety. We aimed to test the sensitivity of LUS to rule out COVID-19 pneumonia (COVIDp) in a population of patients with suggestive symptoms. METHODS: Multicentre prospective observational study in three EDs in Northeastern Italy during the first COVID-19 outbreak. A convenience sample of 235 patients admitted to the ED for symptoms suggestive COVIDp (fever, cough or shortness of breath) from 17 March 2020 to 26 April 2020 was enrolled. All patients underwent a sequential assessment involving: clinical examination, LUS, CXR and arterial blood gas. The index test under investigation was a standardised protocol of LUS compared with a pragmatic composite reference standard constituted by: clinical gestalt, real-time PCR test, radiological and blood gas results. Of the 235 enrolled patients, 90 were diagnosed with COVIDp according to the reference standard. RESULTS: Among the patients with suspected COVIDp, the prevalence of SARS-CoV-2 was 38.3%. The sensitivity of LUS for diagnosing COVIDp was 85.6% (95% CI 76.6% to 92.1%); the specificity was 91.7% (95% CI 86.0% to 95.7%). The positive predictive value and the negative predictive value were 86.5% (95%CI 78.8% to 91.7%) and 91.1% (95% CI 86.1% to 94.4%) respectively. The diagnostic accuracy of LUS for COVIDp was 89.4% (95% CI 84.7% to 93.0%). The positive likelihood ratio was 10.3 (95% CI 6.0 to 17.9), and the negative likelihood ratio was 0.16 (95% CI 0.1 to 0.3). CONCLUSION: In a population with high SARS-CoV-2 prevalence, LUS has a high sensitivity (and negative predictive value) enough to rule out COVIDp in patients with suggestive symptoms. The role of LUS in diagnosing patients with COVIDp is perhaps even more promising. Nevertheless, further research with adequately powered studies is needed. TRIAL REGISTRATION NUMBER: NCT04370275.


Subject(s)
COVID-19 , Pneumonia , Humans , Lung/diagnostic imaging , Pneumonia/diagnosis , Prospective Studies , SARS-CoV-2 , Ultrasonography/methods
14.
J Med Internet Res ; 23(2): e23390, 2021 02 22.
Article in English | MEDLINE | ID: covidwho-1574113

ABSTRACT

BACKGROUND: The initial symptoms of patients with COVID-19 are very much like those of patients with community-acquired pneumonia (CAP); it is difficult to distinguish COVID-19 from CAP with clinical symptoms and imaging examination. OBJECTIVE: The objective of our study was to construct an effective model for the early identification of COVID-19 that would also distinguish it from CAP. METHODS: The clinical laboratory indicators (CLIs) of 61 COVID-19 patients and 60 CAP patients were analyzed retrospectively. Random combinations of various CLIs (ie, CLI combinations) were utilized to establish COVID-19 versus CAP classifiers with machine learning algorithms, including random forest classifier (RFC), logistic regression classifier, and gradient boosting classifier (GBC). The performance of the classifiers was assessed by calculating the area under the receiver operating characteristic curve (AUROC) and recall rate in COVID-19 prediction using the test data set. RESULTS: The classifiers that were constructed with three algorithms from 43 CLI combinations showed high performance (recall rate >0.9 and AUROC >0.85) in COVID-19 prediction for the test data set. Among the high-performance classifiers, several CLIs showed a high usage rate; these included procalcitonin (PCT), mean corpuscular hemoglobin concentration (MCHC), uric acid, albumin, albumin to globulin ratio (AGR), neutrophil count, red blood cell (RBC) count, monocyte count, basophil count, and white blood cell (WBC) count. They also had high feature importance except for basophil count. The feature combination (FC) of PCT, AGR, uric acid, WBC count, neutrophil count, basophil count, RBC count, and MCHC was the representative one among the nine FCs used to construct the classifiers with an AUROC equal to 1.0 when using the RFC or GBC algorithms. Replacing any CLI in these FCs would lead to a significant reduction in the performance of the classifiers that were built with them. CONCLUSIONS: The classifiers constructed with only a few specific CLIs could efficiently distinguish COVID-19 from CAP, which could help clinicians perform early isolation and centralized management of COVID-19 patients.


Subject(s)
COVID-19/diagnosis , Community-Acquired Infections/diagnosis , Machine Learning , Pneumonia/diagnosis , SARS-CoV-2/pathogenicity , Area Under Curve , COVID-19/blood , COVID-19/virology , Community-Acquired Infections/blood , Female , Humans , Laboratories , Leukocyte Count , Logistic Models , Male , Middle Aged , Pneumonia/blood , Procalcitonin/blood , ROC Curve , Retrospective Studies
15.
J Med Microbiol ; 70(12)2021 Dec.
Article in English | MEDLINE | ID: covidwho-1570171

ABSTRACT

Introduction. During the early days of coronavirus disease 2019 (COVID-19) in Singapore, Tan Tock Seng Hospital implemented an enhanced pneumonia surveillance (EPS) programme enrolling all patients who were admitted from the Emergency Department (ED) with a diagnosis of pneumonia but not meeting the prevalent COVID-19 suspect case definition.Hypothesis/Gap Statement. There is a paucity of data supporting the implementation of such a programme.Aims. To compare and contrast our hospital-resource utilization of an EPS programme for COVID-19 infection detection with a suitable comparison group.Methodology. We enrolled all patients admitted under the EPS programme from TTSH's ED from 7 February 2020 (date of EPS implementation) to 20 March 2020 (date of study ethics application) inclusive. We designated a comparison cohort over a similar duration the preceding year. Relevant demographic and clinical data were extracted from the electronic medical records.Results. There was a 3.2 times higher incidence of patients with an admitting diagnosis of pneumonia from the ED in the EPS cohort compared to the comparison cohort (P<0.001). However, there was no significant difference in the median length of stay of 7 days (P=0.160). Within the EPS cohort, stroke and fluid overload occur more frequently as alternative primary diagnoses.Conclusions. Our study successfully evaluated our hospital-resource utilization demanded by our EPS programme in relation to an appropriate comparison group. This helps to inform strategic use of hospital resources to meet the needs of both COVID-19 related services and essential 'peace-time' healthcare services concurrently.


Subject(s)
COVID-19 , Epidemiological Monitoring , Health Resources/organization & administration , Pneumonia , Emergency Service, Hospital , Hospitalization , Hospitals , Humans , Pandemics , Pneumonia/diagnosis , Pneumonia/epidemiology , Retrospective Studies , Singapore
16.
Expert Rev Med Devices ; 19(1): 97-106, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1569460

ABSTRACT

BACKGROUND: The sudden outbreak of COVID-19 pneumonia has brought a heavy disaster to individuals globally. Facing this new virus, the clinicians have no automatic tools to assess the severity of pneumonia patients. METHODS: In the current work, a COVID-19 DET-PRE network with two pipelines was proposed. Firstly, the lungs in X-rays were detected and segmented through the improved YOLOv3 Dense network to remove redundant features. Then, the VGG16 classifier was pre-trained on the source domain, and the severity of the disease was predicted on the target domain by means of transfer learning. RESULTS: The experiment results demonstrated that the COVID-19 DET-PRE network can effectively detect the lungs from X-rays and accurately predict the severity of the disease. The mean average precisions (mAPs) of lung detection in patients with mild and severe illness were 0.976 and 0.983 respectively. Moreover, the accuracy of severity prediction of COVID-19 pneumonia can reach 86.1%. CONCLUSIONS: The proposed neural network has high accuracy, which is suitable for the clinical diagnosis of COVID-19 pneumonia.


Subject(s)
COVID-19 , Deep Learning , Pneumonia , COVID-19/diagnosis , DEET , Humans , Lung/diagnostic imaging , Pneumonia/diagnosis , SARS-CoV-2
18.
Turk J Med Sci ; 51(5): 2274-2284, 2021 10 21.
Article in English | MEDLINE | ID: covidwho-1566690

ABSTRACT

Background/aim: COVID-19 patients have a wide spectrum of disease severity. Several biomarkers were evaluated as predictors for progression towards severe disease. IL-21 is a member of common γ-chain cytokine family and creates some specific effects during programming and maintenance of antiviral immunity. We aimed to assess IL-21 as a biomarker for diagnosis and outcome prediction in patients hospitalized with COVID-19. Materials and methods: Patients with a preliminary diagnosis of COVID-19 and pneumonia other than COVID-19 admitted to a tertiary care hospital were included consecutively in this comparative study. Results: The study population consisted of 51 patients with COVID-19 and 11 patients with non-COVID-19 pneumonia. Serum IL-21 concentration was markedly higher, and serum CRP concentration was significantly lower in COVID-19 patients compared to non-COVID-19 pneumonia patients. Within COVID-19 patients, 10 patients showed radiological and clinical progression. Patients with clinical worsening had lower lymphocyte count and haemoglobin. In addition to that, deteriorating patients had higher urea, LDH levels, and elevated concentration of both IL-6 and IL-21. The cut-off value of 106 ng/L for IL-21 has 80.0% sensitivity, %60.9 specificity for discriminating patients with clinical worsening. Multivariable analysis performed to define risk factors for disease progression identified IL-6 and IL-21 as independent predictors. Odds ratio for serum IL-6 concentrations ≥ 3.2 pg/mL was 8.07 (95% CI: 1.37-47.50, p = 0.04) and odds ratio for serum IL-21 concentrations ≥ 106 ng/L was 6.24 (95% CI: 1.04 ­ 37.3, p = 0.02). Conclusion: We identified specific differences in serum IL-21 between COVID-19 and non-COVID-19 pneumonia patients. Serum IL-21 measurement has promising predictive value for disease progression in COVID-19 patients. High serum IL-6 and IL-21 levels obtained upon admission are independent risk factors for clinical worsening.


Subject(s)
COVID-19/diagnosis , Interleukins/blood , Adult , Aged , Biomarkers/blood , COVID-19/blood , Diagnosis, Differential , Female , Humans , Male , Middle Aged , Pneumonia/blood , Pneumonia/diagnosis , Prognosis
19.
Stud Health Technol Inform ; 285: 112-117, 2021 Oct 27.
Article in English | MEDLINE | ID: covidwho-1566635

ABSTRACT

Today pneumonia is one of the main problems of all countries around the world. This disease can lead to early disability, serious complications, and severe cases of high probabilities of lethal outcomes. A big part of cases of pneumonia are complications of COVID-19 disease. This type of pneumonia differs from ordinary pneumonia in symptoms, clinical course, and severity of complications. For optimal treatment of disease, humans need to study specific features of providing 19 pneumonia in comparison with well-studied ordinary pneumonia. In this article, the authors propose a new approach to identifying these specific features. This method is based on creating dynamic disease models for COVID and non-COVID pneumonia based on Bayesian Network design and Hidden Markov Model architecture and their comparison. We build models using real hospital data. We created a model for automatically identifying the type of pneumonia (COVID-19 or ordinary pneumonia) without special COVID tests. And we created dynamic models for simulation future development of both types of pneumonia. All created models showed high quality. Therefore, they can be used as part of decision support systems for medical specialists who work with pneumonia patients.


Subject(s)
COVID-19 , Pneumonia , Bayes Theorem , COVID-19/diagnosis , Forecasting , Humans , Pneumonia/diagnosis
20.
Ann Saudi Med ; 41(6): 327-335, 2021.
Article in English | MEDLINE | ID: covidwho-1555174

ABSTRACT

BACKGROUND: SARS-CoV2/COVID-19 emerged in China and caused a global pandemic in 2020. The mortality rate has been reported to be between 0% and 14.6% in all patients. In this study, we determined the clinical and laboratory parameters of COVID-19 related morbidity and mortality in our hospital. OBJECTIVES: Investigate the relationship between demographic, clinical, and laboratory parameters on COVID-19-related morbidity and mortality. DESIGN: Retrospective observational study. SETTINGS: Tertiary care hospital. PATIENTS AND METHODS: Patients diagnosed with COVID-19 pneumonia from March until the end of December were included in the study. MAIN OUTCOME MEASURES: The relationship between demographic, clinical, and laboratory parameters and the morbidity and mortality rates of patients diagnosed with COVID-19. SAMPLE SIZE: 124 patients RESULTS: The mortality rate was 9.6% (12/124). Coronary artery disease (P<.0001) diabetes mellitus (P=.04) fever (>38.3°C) at presentation (P=.04) hypertension (P<.0001), and positive smoking history (P<.0001) were significantly associated with mortality. Patients who died were older, had a higher comorbid disease index, pneumonia severity index, fasting blood glucose, baseline serum creatinine, D-dimer, and had lower baseline haemoglobin, SaO2, percentage of lymphocyte counts and diastolic blood pressure. Patients admitted to the ICU were older, had a higher comorbidity disease index, pneumonia severity index, C-reactive protein, WBC, D-dimer, creatinine, number of antibiotics used, longer O2 support duration, lower hemoglobin, lymphocyte (%), and baseline SaO2 (%). CONCLUSIONS: Our results were consistent with much of the reported data. We suggest that the frequency, dosage, and duration of steroid treatment should be limited. LIMITATIONS: Low patient number, uncertain reason of mortality, no standard treatment regimen, limited treatment options, like ECMO. CONFLICT OF INTEREST: None.


Subject(s)
COVID-19 , Pneumonia , Humans , Pneumonia/diagnosis , Pneumonia/epidemiology , Prognosis , RNA, Viral , SARS-CoV-2 , Tertiary Care Centers , Turkey/epidemiology
SELECTION OF CITATIONS
SEARCH DETAIL