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1.
Pharmacol Res Perspect ; 10(2): e00931, 2022 04.
Article in English | MEDLINE | ID: covidwho-1782680

ABSTRACT

The aim of this study was to estimate healthcare costs and mortality associated with serious fluoroquinolone-related adverse reactions in Finland from 2008 to 2019. Serious adverse reaction types were identified from the Finnish Pharmaceutical Insurance Pool's pharmaceutical injury claims and the Finnish Medicines Agency's Adverse Reaction Register. A decision tree model was built to predict costs and mortality associated with serious adverse drug reactions (ADR). Severe clostridioides difficile infections, severe cutaneous adverse reactions, tendon ruptures, aortic ruptures, and liver injuries were included as serious adverse drug reactions in the model. Direct healthcare costs of a serious ADR were based on the number of reimbursed fluoroquinolone prescriptions from the Social Insurance Institution of Finland's database. Sensitivity analyses were conducted to address parameter uncertainty. A total of 1 831 537 fluoroquinolone prescriptions were filled between 2008 and 2019 in Finland, with prescription numbers declining 40% in recent years. Serious ADRs associated with fluoroquinolones lead to estimated direct healthcare costs of 501 938 402 €, including 11 405 ADRs and 3,884 deaths between 2008 and 2019. The average mortality risk associated with the use of fluoroquinolones was 0.21%. Severe clostridioides difficile infections were the most frequent, fatal, and costly serious ADRs associated with the use of fluoroquinolones. Although fluoroquinolones continue to be generally well-tolerated antimicrobials, serious adverse reactions cause long-term impairment to patients and high healthcare costs. Therefore, the risks and benefits should be weighed carefully in antibiotic prescription policies, as well as with individual patients.


Subject(s)
Anti-Bacterial Agents/adverse effects , Fluoroquinolones/adverse effects , Health Care Costs/statistics & numerical data , Adverse Drug Reaction Reporting Systems/statistics & numerical data , Anti-Bacterial Agents/economics , Databases, Factual/statistics & numerical data , Decision Trees , Drug-Related Side Effects and Adverse Reactions/economics , Drug-Related Side Effects and Adverse Reactions/epidemiology , Drug-Related Side Effects and Adverse Reactions/mortality , Finland , Fluoroquinolones/economics , Humans , Retrospective Studies
2.
Lancet Oncol ; 23(2): 270-278, 2022 02.
Article in English | MEDLINE | ID: covidwho-1616869

ABSTRACT

BACKGROUND: Endoscopic surveillance is recommended for patients with Barrett's oesophagus because, although the progression risk is low, endoscopic intervention is highly effective for high-grade dysplasia and cancer. However, repeated endoscopy has associated harms and access has been limited during the COVID-19 pandemic. We aimed to evaluate the role of a non-endoscopic device (Cytosponge) coupled with laboratory biomarkers and clinical factors to prioritise endoscopy for Barrett's oesophagus. METHODS: We first conducted a retrospective, multicentre, cross-sectional study in patients older than 18 years who were having endoscopic surveillance for Barrett's oesophagus (with intestinal metaplasia confirmed by TFF3 and a minimum Barrett's segment length of 1 cm [circumferential or tongues by the Prague C and M criteria]). All patients had received the Cytosponge and confirmatory endoscopy during the BEST2 (ISRCTN12730505) and BEST3 (ISRCTN68382401) clinical trials, from July 7, 2011, to April 1, 2019 (UK Clinical Research Network Study Portfolio 9461). Participants were divided into training (n=557) and validation (n=334) cohorts to identify optimal risk groups. The biomarkers evaluated were overexpression of p53, cellular atypia, and 17 clinical demographic variables. Endoscopic biopsy diagnosis of high-grade dysplasia or cancer was the primary endpoint. Clinical feasibility of a decision tree for Cytosponge triage was evaluated in a real-world prospective cohort from Aug 27, 2020 (DELTA; ISRCTN91655550; n=223), in response to COVID-19 and the need to provide an alternative to endoscopic surveillance. FINDINGS: The prevalence of high-grade dysplasia or cancer determined by the current gold standard of endoscopic biopsy was 17% (92 of 557 patients) in the training cohort and 10% (35 of 344) in the validation cohort. From the new biomarker analysis, three risk groups were identified: high risk, defined as atypia or p53 overexpression or both on Cytosponge; moderate risk, defined by the presence of a clinical risk factor (age, sex, and segment length); and low risk, defined as Cytosponge-negative and no clinical risk factors. The risk of high-grade dysplasia or intramucosal cancer in the high-risk group was 52% (68 of 132 patients) in the training cohort and 41% (31 of 75) in the validation cohort, compared with 2% (five of 210) and 1% (two of 185) in the low-risk group, respectively. In the real-world setting, Cytosponge results prospectively identified 39 (17%) of 223 patients as high risk (atypia or p53 overexpression, or both) requiring endoscopy, among whom the positive predictive value was 31% (12 of 39 patients) for high-grade dysplasia or intramucosal cancer and 44% (17 of 39) for any grade of dysplasia. INTERPRETATION: Cytosponge atypia, p53 overexpression, and clinical risk factors (age, sex, and segment length) could be used to prioritise patients for endoscopy. Further investigation could validate their use in clinical practice and lead to a substantial reduction in endoscopy procedures compared with current surveillance pathways. FUNDING: Medical Research Council, Cancer Research UK, Innovate UK.


Subject(s)
Adenocarcinoma/pathology , Barrett Esophagus/pathology , COVID-19 , Esophageal Neoplasms/pathology , Patient Selection , Watchful Waiting/methods , Adenocarcinoma/diagnostic imaging , Adenocarcinoma/metabolism , Aged , Barrett Esophagus/diagnostic imaging , Barrett Esophagus/metabolism , Barrett Esophagus/therapy , Biomarkers/metabolism , COVID-19/prevention & control , Clinical Decision-Making , Clinical Trials as Topic , Cross-Sectional Studies , Decision Trees , Disease Progression , Esophageal Neoplasms/diagnostic imaging , Esophageal Neoplasms/metabolism , Esophagoscopy , Feasibility Studies , Female , Humans , Male , Middle Aged , Pilot Projects , Prospective Studies , Retrospective Studies , Risk Assessment , Risk Factors , SARS-CoV-2 , Trefoil Factor-3/metabolism , Tumor Suppressor Protein p53/metabolism
3.
CMAJ Open ; 9(4): E1223-E1231, 2021.
Article in English | MEDLINE | ID: covidwho-1593829

ABSTRACT

BACKGROUND: The COVID-19 pandemic has led to an increased demand for health care resources and, in some cases, shortage of medical equipment and staff. Our objective was to develop and validate a multivariable model to predict risk of hospitalization for patients infected with SARS-CoV-2. METHODS: We used routinely collected health records in a patient cohort to develop and validate our prediction model. This cohort included adult patients (age ≥ 18 yr) from Ontario, Canada, who tested positive for SARS-CoV-2 ribonucleic acid by polymerase chain reaction between Feb. 2 and Oct. 5, 2020, and were followed up through Nov. 5, 2020. Patients living in long-term care facilities were excluded, as they were all assumed to be at high risk of hospitalization for COVID-19. Risk of hospitalization within 30 days of diagnosis of SARS-CoV-2 infection was estimated via gradient-boosting decision trees, and variable importance examined via Shapley values. We built a gradient-boosting model using the Extreme Gradient Boosting (XGBoost) algorithm and compared its performance against 4 empirical rules commonly used for risk stratifications based on age and number of comorbidities. RESULTS: The cohort included 36 323 patients with 2583 hospitalizations (7.1%). Hospitalized patients had a higher median age (64 yr v. 43 yr), were more likely to be male (56.3% v. 47.3%) and had a higher median number of comorbidities (3, interquartile range [IQR] 2-6 v. 1, IQR 0-3) than nonhospitalized patients. Patients were split into development (n = 29 058, 80.0%) and held-out validation (n = 7265, 20.0%) cohorts. The gradient-boosting model achieved high discrimination (development cohort: area under the receiver operating characteristic curve across the 5 folds of 0.852; validation cohort: 0.8475) and strong calibration (slope = 1.01, intercept = -0.01). The patients who scored at the top 10% captured 47.4% of hospitalizations, and those who scored at the top 30% captured 80.6%. INTERPRETATION: We developed and validated an accurate risk stratification model using routinely collected health administrative data. We envision that modelling such risk stratification based on routinely collected health data could support management of COVID-19 on a population health level.


Subject(s)
COVID-19/epidemiology , Decision Trees , Hospitalization/statistics & numerical data , Risk Assessment , Adult , Aged , COVID-19/therapy , Female , Humans , Male , Middle Aged , Models, Statistical , Ontario/epidemiology , Risk Assessment/methods , Risk Factors
4.
PLoS One ; 16(3): e0248438, 2021.
Article in English | MEDLINE | ID: covidwho-1574763

ABSTRACT

OBJECTIVES: Accurate and reliable criteria to rapidly estimate the probability of infection with the novel coronavirus-2 that causes the severe acute respiratory syndrome (SARS-CoV-2) and associated disease (COVID-19) remain an urgent unmet need, especially in emergency care. The objective was to derive and validate a clinical prediction score for SARS-CoV-2 infection that uses simple criteria widely available at the point of care. METHODS: Data came from the registry data from the national REgistry of suspected COVID-19 in EmeRgency care (RECOVER network) comprising 116 hospitals from 25 states in the US. Clinical variables and 30-day outcomes were abstracted from medical records of 19,850 emergency department (ED) patients tested for SARS-CoV-2. The criterion standard for diagnosis of SARS-CoV-2 required a positive molecular test from a swabbed sample or positive antibody testing within 30 days. The prediction score was derived from a 50% random sample (n = 9,925) using unadjusted analysis of 107 candidate variables as a screening step, followed by stepwise forward logistic regression on 72 variables. RESULTS: Multivariable regression yielded a 13-variable score, which was simplified to a 13-point score: +1 point each for age>50 years, measured temperature>37.5°C, oxygen saturation<95%, Black race, Hispanic or Latino ethnicity, household contact with known or suspected COVID-19, patient reported history of dry cough, anosmia/dysgeusia, myalgias or fever; and -1 point each for White race, no direct contact with infected person, or smoking. In the validation sample (n = 9,975), the probability from logistic regression score produced an area under the receiver operating characteristic curve of 0.80 (95% CI: 0.79-0.81), and this level of accuracy was retained across patients enrolled from the early spring to summer of 2020. In the simplified score, a score of zero produced a sensitivity of 95.6% (94.8-96.3%), specificity of 20.0% (19.0-21.0%), negative likelihood ratio of 0.22 (0.19-0.26). Increasing points on the simplified score predicted higher probability of infection (e.g., >75% probability with +5 or more points). CONCLUSION: Criteria that are available at the point of care can accurately predict the probability of SARS-CoV-2 infection. These criteria could assist with decisions about isolation and testing at high throughput checkpoints.


Subject(s)
COVID-19/diagnosis , COVID-19/epidemiology , Emergency Service, Hospital/trends , Adult , Aged , Clinical Decision Rules , Coronavirus Infections/diagnosis , Cough , Databases, Factual , Decision Trees , Emergency Service, Hospital/statistics & numerical data , Female , Fever , Humans , Male , Mass Screening , Middle Aged , Registries , SARS-CoV-2/pathogenicity , United States/epidemiology
5.
Comput Math Methods Med ; 2021: 5514220, 2021.
Article in English | MEDLINE | ID: covidwho-1518177

ABSTRACT

A vast amount of data is generated every second for microblogs, content sharing via social media sites, and social networking. Twitter is an essential popular microblog where people voice their opinions about daily issues. Recently, analyzing these opinions is the primary concern of Sentiment analysis or opinion mining. Efficiently capturing, gathering, and analyzing sentiments have been challenging for researchers. To deal with these challenges, in this research work, we propose a highly accurate approach for SA of fake news on COVID-19. The fake news dataset contains fake news on COVID-19; we started by data preprocessing (replace the missing value, noise removal, tokenization, and stemming). We applied a semantic model with term frequency and inverse document frequency weighting for data representation. In the measuring and evaluation step, we applied eight machine-learning algorithms such as Naive Bayesian, Adaboost, K-nearest neighbors, random forest, logistic regression, decision tree, neural networks, and support vector machine and four deep learning CNN, LSTM, RNN, and GRU. Afterward, based on the results, we boiled a highly efficient prediction model with python, and we trained and evaluated the classification model according to the performance measures (confusion matrix, classification rate, true positives rate...), then tested the model on a set of unclassified fake news on COVID-19, to predict the sentiment class of each fake news on COVID-19. Obtained results demonstrate a high accuracy compared to the other models. Finally, a set of recommendations is provided with future directions for this research to help researchers select an efficient sentiment analysis model on Twitter data.


Subject(s)
Algorithms , COVID-19 , Deep Learning , Bayes Theorem , Computational Biology , Databases, Factual , Decision Trees , Humans , Logistic Models , Models, Statistical , Natural Language Processing , Neural Networks, Computer , SARS-CoV-2 , Social Media , Social Networking , Support Vector Machine
6.
Surg Clin North Am ; 102(1): 169-180, 2022 Feb.
Article in English | MEDLINE | ID: covidwho-1517479

ABSTRACT

Mass casualty incidents are increasingly common. They are defined by large numbers of patients arriving nearly simultaneously, overwhelming available resources needed for optimal care. They require rapid mobilization of resources to provide optimal outcomes and limit disability and death. Because the mechanism of injury in a mass casualty incident is often traumatic in nature, surgeons should be aware of the critical role they play in planning and response. The coronavirus disease 2019 pandemic is a notable, resulting in a sustained surge of critically ill patients. Initial response requires local mobilization of resources; large-scale events potentially require a national response.


Subject(s)
Civil Defense , Emergency Medical Services , Health Resources , Mass Casualty Incidents , COVID-19/epidemiology , COVID-19/prevention & control , Decision Trees , Humans , Triage
7.
Western Pac Surveill Response J ; 12(3): 56-64, 2021.
Article in English | MEDLINE | ID: covidwho-1497708

ABSTRACT

OBJECTIVE: The aim of this study was to create a decision tree model with machine learning to predict the outcomes of COVID-19 cases from data publicly available in the Philippine Department of Health (DOH) COVID Data Drop. METHODS: The study design was a cross-sectional records review of the DOH COVID Data Drop for 25 August 2020. Resolved cases that had either recovered or died were used as the final data set. Machine learning processes were used to generate, train and validate a decision tree model. RESULTS: A list of 132 939 resolved COVID-19 cases was used. The notification rates and case fatality rates were higher among males (145.67 per 100 000 and 2.46%, respectively). Most COVID-19 cases were clustered among people of working age, and older cases had higher case fatality rates. The majority of cases were from the National Capital Region (590.20 per 100 000), and the highest case fatality rate (5.83%) was observed in Region VII. The decision tree model prioritized age and history of hospital admission as predictors of mortality. The model had high accuracy (81.42%), sensitivity (81.65%), specificity (81.41%) and area under the curve (0.876) but a poor F-score (16.74%). DISCUSSION: The model predicted higher case fatality rates among older people. For cases aged > 51 years, a history of hospital admission increased the probability of COVID-19-related death. We recommend that more comprehensive primary COVID-19 data sets be used to create more robust prognostic models.


Subject(s)
COVID-19 , Aged , Cross-Sectional Studies , Decision Trees , Humans , Machine Learning , Male , Philippines/epidemiology , SARS-CoV-2
8.
Med Gas Res ; 12(2): 60-66, 2022.
Article in English | MEDLINE | ID: covidwho-1481083

ABSTRACT

The coronavirus disease 2019 (COVID-19) epidemic went down in history as a pandemic caused by corona-viruses that emerged in 2019 and spread rapidly around the world. The different symptoms of COVID-19 made it difficult to understand which variables were more influential on the diagnosis, course and mortality of the disease. Machine learning models can accurately assess hidden patterns among risk factors by analyzing large-datasets to quickly predict diagnosis, prognosis and mortality of diseases. Because of this advantage, the use of machine learning models as decision support systems in health services is increasing. The aim of this study is to determine the diagnosis and prognosis of COVID-19 disease with blood-gas data using the Chi-squared Automatic Interaction Detector (CHAID) decision-tree-model, one of the machine learning methods, which is a subfield of artificial intelligence. This study was carried out on a total of 686 patients with COVID-19 (n = 343) and non-COVID-19 (n = 343) treated at Erzincan-Mengücek-Gazi-Training and Research-Hospital between April 1, 2020 and March 1, 2021. Arterial blood gas values of all patients were obtained from the hospital registry system. While the total-accuracyratio of the decision-tree-model was 65.0% in predicting the prognosis of the disease, it was 68.2% in the diagnosis of the disease. According to the results obtained, the low ionized-calcium value (< 1.10 mM) significantly predicted the need for intensive care of COVID-19 patients. At admission, low-carboxyhemoglobin (< 1.00%), high-pH (> 7.43), low-sodium (< 135.0 mM), hematocrit (< 40.0%), and methemoglobin (< 1.30%) values are important biomarkers in the diagnosis of COVID-19 and the results were promising. The findings in the study may aid in the early-diagnosis of the disease and the intensive-care treatment of patients who are severe. The study was approved by the Ministry of Health and Erzincan University Faculty of Medicine Clinical Research Ethics Committee.


Subject(s)
Artificial Intelligence , COVID-19 , Decision Trees , Humans , Machine Learning , Prognosis , SARS-CoV-2
9.
Stroke ; 53(2): 578-585, 2022 02.
Article in English | MEDLINE | ID: covidwho-1450645

ABSTRACT

BACKGROUND AND PURPOSE: The ARAT (Action Research Arm Test) has been used to classify upper limb motor outcome after stroke in 1 of 3, 4, or 5 categories. The COVID-19 pandemic has encouraged the development of assessments that can be performed quickly and remotely. The aim of this study was to derive and internally validate decision trees for categorizing upper limb motor outcomes at the late subacute and chronic stages of stroke using a subset of ARAT tasks. METHODS: This study retrospectively analyzed ARAT scores obtained in-person at 3 months poststroke from 333 patients. In-person ARAT scores were used to categorize patients' 3-month upper limb outcome using classification systems with 3, 4, and 5 outcome categories. Individual task scores from in-person assessments were then used in classification and regression tree analyses to determine subsets of tasks that could accurately categorize upper limb outcome for each of the 3 classification systems. The decision trees developed using 3-month ARAT data were also applied to in-person ARAT data obtained from 157 patients at 6 months poststroke. RESULTS: The classification and regression tree analyses produced decision trees requiring 2 to 4 ARAT tasks. The overall accuracy of the cross-validated decision trees ranged from 87.7% (SE, 1.0%) to 96.7% (SE, 2.0%). Accuracy was highest when classifying patients into one of 3 outcome categories and lowest for 5 categories. The decision trees are referred to as FOCUS (Fast Outcome Categorization of the Upper Limb After Stroke) assessments and they remained accurate for 6-month poststroke ARAT scores (overall accuracy range 83.4%-91.7%). CONCLUSIONS: A subset of ARAT tasks can accurately categorize upper limb motor outcomes after stroke. Future studies could investigate the feasibility and accuracy of categorizing outcomes using the FOCUS assessments remotely via video call.


Subject(s)
Stroke Rehabilitation , Stroke/physiopathology , Upper Extremity/physiopathology , Activities of Daily Living , Adolescent , Adult , Aged , Aged, 80 and over , Arm/physiopathology , COVID-19/complications , Decision Trees , Female , Hemiplegia/etiology , Hemiplegia/rehabilitation , Humans , Male , Middle Aged , New Zealand , Pandemics , Recovery of Function , Reproducibility of Results , Retrospective Studies , Stroke/etiology , Treatment Outcome , Young Adult
10.
BMC Infect Dis ; 21(1): 783, 2021 Aug 09.
Article in English | MEDLINE | ID: covidwho-1350140

ABSTRACT

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death. METHODS: A total of 2169 adult COVID-19 patients were enrolled from Wuhan, China, from February 10th to April 15th, 2020. Difference analyses of medical records were performed between severe and non-severe groups, as well as between survivors and non-survivors. In addition, we developed a decision tree model to predict death outcome in severe patients. RESULTS: Of the 2169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed as severe illness, and 75 patients died. An older median age and a higher proportion of male patients were found in severe group or non-survivors compared to their counterparts. Significant differences in clinical characteristics and laboratory examinations were found between severe and non-severe groups, as well as between survivors and non-survivors. A decision tree, including three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase, was developed to predict death outcome in severe patients. This model performed well both in training and test datasets. The accuracy of this model were 0.98 in both datasets. CONCLUSION: We performed a comprehensive analysis of COVID-19 patients from the outbreak in Wuhan, China, and proposed a simple and clinically operable decision tree to help clinicians rapidly identify COVID-19 patients at high risk of death, to whom priority treatment and intensive care should be given.


Subject(s)
COVID-19 , Adult , China/epidemiology , Decision Trees , Humans , Infant, Newborn , Male , Retrospective Studies , Risk Factors , SARS-CoV-2
11.
Comput Math Methods Med ; 2021: 4602465, 2021.
Article in English | MEDLINE | ID: covidwho-1309865

ABSTRACT

Dementia interferes with the individual's motor, behavioural, and intellectual functions, causing him to be unable to perform instrumental activities of daily living. This study is aimed at identifying the best performing algorithm and the most relevant characteristics to categorise individuals with HIV/AIDS at high risk of dementia from the application of data mining. Principal component analysis (PCA) algorithm was used and tested comparatively between the following machine learning algorithms: logistic regression, decision tree, neural network, KNN, and random forest. The database used for this study was built from the data collection of 270 individuals infected with HIV/AIDS and followed up at the outpatient clinic of a reference hospital for infectious and parasitic diseases in the State of Ceará, Brazil, from January to April 2019. Also, the performance of the algorithms was analysed for the 104 characteristics available in the database; then, with the reduction of dimensionality, there was an improvement in the quality of the machine learning algorithms and identified that during the tests, even losing about 30% of the variation. Besides, when considering only 23 characteristics, the precision of the algorithms was 86% in random forest, 56% logistic regression, 68% decision tree, 60% KNN, and 59% neural network. The random forest algorithm proved to be more effective than the others, obtaining 84% precision and 86% accuracy.


Subject(s)
AIDS Dementia Complex/diagnosis , Acquired Immunodeficiency Syndrome/complications , Algorithms , Dementia/etiology , AIDS Dementia Complex/epidemiology , AIDS Dementia Complex/etiology , Aged , Brazil/epidemiology , Computational Biology , Data Mining/methods , Data Mining/statistics & numerical data , Databases, Factual , Decision Trees , Female , Follow-Up Studies , Humans , Logistic Models , Machine Learning , Male , Middle Aged , Neural Networks, Computer , Risk Factors
12.
BJS Open ; 5(4)2021 07 06.
Article in English | MEDLINE | ID: covidwho-1297380

ABSTRACT

BACKGROUND: COVID-19 has brought an unprecedented challenge to healthcare services. The authors' COVID-adapted pathway for suspected bowel cancer combines two quantitative faecal immunochemical tests (qFITs) with a standard CT scan with oral preparation (CT mini-prep). The aim of this study was to estimate the degree of risk mitigation and residual risk of undiagnosed colorectal cancer. METHOD: Decision-tree models were developed using a combination of data from the COVID-adapted pathway (April-May 2020), a local audit of qFIT for symptomatic patients performed since 2018, relevant data (prevalence of colorectal cancer and sensitivity and specificity of diagnostic tools) obtained from literature and a local cancer data set, and expert opinion for any missing data. The considered diagnostic scenarios included: single qFIT; two qFITs; single qFIT and CT mini-prep; two qFITs and CT mini-prep (enriched pathway). These were compared to the standard diagnostic pathway (colonoscopy or CT virtual colonoscopy (CTVC)). RESULTS: The COVID-adapted pathway included 422 patients, whereas the audit of qFIT included more than 5000 patients. The risk of missing a colorectal cancer, if present, was estimated as high as 20.2 per cent with use of a single qFIT as a triage test. Using both a second qFIT and a CT mini-prep as add-on tests reduced the risk of missed cancer to 6.49 per cent. The trade-off was an increased rate of colonoscopy or CTVC, from 287 for a single qFIT to 418 for the double qFIT and CT mini-prep combination, per 1000 patients. CONCLUSION: Triage using qFIT alone could lead to a high rate of missed cancers. This may be reduced using CT mini-prep as an add-on test for triage to colonoscopy or CTVC.


Subject(s)
COVID-19 , Colorectal Neoplasms/diagnosis , Diagnostic Errors/statistics & numerical data , Occult Blood , Triage/organization & administration , Clinical Audit , Colonoscopy , Decision Trees , Early Detection of Cancer/methods , Humans , Scotland , Sensitivity and Specificity , Tomography, X-Ray Computed
13.
Lab Med ; 52(4): e104-e114, 2021 Jul 01.
Article in English | MEDLINE | ID: covidwho-1294755

ABSTRACT

OBJECTIVE: This research aims to develop a laboratory model that can accurately distinguish pneumonia from nonpneumonia in patients with COVID-19 and to identify potential protective factors against lung infection. METHODS: We recruited 50 patients diagnosed with COVID-19 infection with or without pneumonia. We selected candidate predictors through group comparison and punitive least absolute shrinkage and selection operator (LASSO) analysis. A stepwise logistic regression model was used to distinguish patients with and without pneumonia. Finally, we used a decision-tree method and randomly selected 50% of the patients 1000 times from the same specimen to verify the effectiveness of the model. RESULTS: We found that the percentage of eosinophils, a high-fluorescence-reticulocyte ratio, and creatinine had better discriminatory power than other factors. Age and underlying diseases were not significant for discrimination. The model correctly discriminated 77.1% of patients. In the final validation step, we observed that the model had an overall predictive rate of 81.3%. CONCLUSION: We developed a laboratory model for COVID-19 pneumonia in patients with mild to moderate symptoms. In the clinical setting, the model will be able to predict and differentiate pneumonia vs nonpneumonia before any lung computed tomography findings. In addition, the percentage of eosinophils, a high-fluorescence-reticulocyte ratio, and creatinine were considered protective factors against lung infection in patients without pneumonia.


Subject(s)
COVID-19 , Models, Statistical , Adult , Blood Cell Count , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19 Testing , Creatinine/analysis , Decision Trees , Female , Humans , Laboratories , Male , Middle Aged , Predictive Value of Tests , Reticulocytes/cytology , Tomography, X-Ray Computed , Young Adult
14.
Emerg Med Clin North Am ; 39(3): 453-465, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1263258

ABSTRACT

The role of the emergency provider lies at the forefront of recognition and treatment of novel and re-emerging infectious diseases in children. Familiarity with disease presentations that might be considered rare, such as vaccine-preventable and non-endemic illnesses, is essential in identifying and controlling outbreaks. As we have seen thus far in the novel coronavirus pandemic, susceptibility, severity, transmission, and disease presentation can all have unique patterns in children. Emergency providers also have the potential to play a public health role by using lessons learned from the phenomena of vaccine hesitancy and refusal.


Subject(s)
Communicable Diseases, Emerging/epidemiology , Pediatrics , COVID-19/diagnosis , COVID-19/therapy , COVID-19/transmission , Chickenpox/diagnosis , Chickenpox/therapy , Chickenpox/transmission , Chikungunya Fever/diagnosis , Chikungunya Fever/therapy , Chikungunya Fever/transmission , Child , Communicable Diseases, Emerging/immunology , Decision Trees , Dengue/diagnosis , Dengue/therapy , Dengue/transmission , Emergency Medicine , Hemorrhagic Fever, Ebola/diagnosis , Hemorrhagic Fever, Ebola/therapy , Hemorrhagic Fever, Ebola/transmission , Humans , Incidence , Malaria/diagnosis , Malaria/therapy , Malaria/transmission , Measles/diagnosis , Measles/therapy , Measles/transmission , Physician's Role , Public Health , SARS-CoV-2 , Systemic Inflammatory Response Syndrome , Travel-Related Illness , Vaccination , Vaccination Refusal , Whooping Cough/diagnosis , Whooping Cough/therapy , Whooping Cough/transmission , Zika Virus Infection/diagnosis , Zika Virus Infection/therapy , Zika Virus Infection/transmission
15.
Int J Environ Res Public Health ; 18(11)2021 05 30.
Article in English | MEDLINE | ID: covidwho-1256531

ABSTRACT

BACKGROUND: A growing body of scientific literature indicates that risk factors for COVID-19 contribute to a high level of psychological distress. However, there is no consensus on which factors contribute more to predicting psychological health. OBJECTIVES: The present study quantifies the importance of related risk factors on the level of psychological distress and further explores the threshold effect of each rick factor on the level of psychological distress. Both subjective and objective measures of risk factors are considered in the model. METHODS: We sampled 937 individual items of data obtained from an online questionnaire between 20 January and 13 February 2020 in China. Objective risk factors were measured in terms of direct distance from respondents' housing to the nearest COVID-19 hospital, direct distance from respondents' housing to the nearest park, and the air quality index (AQI). Perceived risk factors were measured in regard to perceived distance to the nearest COVID-19 hospital, perceived air quality, and perceived environmental quality. Psychological distress was measured with the Kessler psychological distress scale K6 score. The following health risk factors and sociodemographic factors were considered: self-rated health level, physical health status, physical activity, current smoker or drinker, age, gender, marital status, educational attainment level, residence location, and household income level. A gradient boosting decision tree (GBDT) was used to analyse the data. RESULTS: Health risk factors were the greatest contributors to predicting the level of psychological distress, with a relative importance of 42.32% among all influential factors. Objective risk factors had a stronger predictive power than perceived risk factors (23.49% vs. 16.26%). Furthermore, it was found that there was a dramatic rise in the moderate level of psychological distress regarding the threshold of AQI between 40 and 50, and 110 and 130, respectively. Gender-sensitive analysis revealed that women and men responded differently to psychological distress based on different risk factors. CONCLUSION: We found evidence that perceived indoor air quality played a more important role in predicting psychological distress compared to ambient air pollution during the COVID-19 pandemic.


Subject(s)
COVID-19 , Psychological Distress , China/epidemiology , Decision Trees , Female , Humans , Male , Pandemics , Risk Factors , SARS-CoV-2 , Stress, Psychological/epidemiology
16.
Dis Markers ; 2021: 5522729, 2021.
Article in English | MEDLINE | ID: covidwho-1202046

ABSTRACT

Reverse Transcription Polymerase Chain Reaction (RT-PCR) used for diagnosing COVID-19 has been found to give low detection rate during early stages of infection. Radiological analysis of CT images has given higher prediction rate when compared to RT-PCR technique. In this paper, hybrid learning models are used to classify COVID-19 CT images, Community-Acquired Pneumonia (CAP) CT images, and normal CT images with high specificity and sensitivity. The proposed system in this paper has been compared with various machine learning classifiers and other deep learning classifiers for better data analysis. The outcome of this study is also compared with other studies which were carried out recently on COVID-19 classification for further analysis. The proposed model has been found to outperform with an accuracy of 96.69%, sensitivity of 96%, and specificity of 98%.


Subject(s)
COVID-19/diagnostic imaging , Lung/diagnostic imaging , Machine Learning , Tomography, X-Ray Computed/methods , Bayes Theorem , Case-Control Studies , Community-Acquired Infections/diagnostic imaging , Decision Trees , Humans , Models, Statistical , Pneumonia/diagnostic imaging , Sensitivity and Specificity
17.
Br J Radiol ; 94(1122): 20201007, 2021 Jun 01.
Article in English | MEDLINE | ID: covidwho-1197360

ABSTRACT

OBJECTIVES: To develop and validate a radiomic model to predict the rapid progression (defined as volume growth of pneumonia lesions > 50% within seven days) in patients with coronavirus disease 2019 (COVID-19). METHODS: Patients with laboratory-confirmed COVID-19 who underwent longitudinal chest CT between January 01 and February 18, 2020 were included. A total of 1316 radiomic features were extracted from the lung parenchyma window for each CT. The least absolute shrinkage and selection operator (LASSO), Relief, Las Vegas Wrapper (LVW), L1-norm-Support Vector Machine (L1-norm-SVM), and recursive feature elimination (RFE) were applied to select the features that associated with rapid progression. Four machine learning classifiers were used for modeling, including Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and Decision Tree (DT). Accordingly, 20 radiomic models were developed on the basis of 296 CT scans and validated in 74 CT scans. Model performance was determined by the receiver operating characteristic curve. RESULTS: A total of 107 patients (median age, 49.0 years, interquartile range, 35-54) were evaluated. The patients underwent a total of 370 chest CT scans with a median interval of 4 days (interquartile range, 3-5 days). The combination methods of L1-norm SVM and SVM with 17 radiomic features yielded the highest performance in predicting the likelihood of rapid progression of pneumonia lesions on next CT scan, with an AUC of 0.857 (95% CI: 0.766-0.947), sensitivity of 87.5%, and specificity of 70.7%. CONCLUSIONS: Our radiomic model based on longitudinal chest CT data could predict the rapid progression of pneumonia lesions, which may facilitate the CT follow-up intervals and reduce the radiation. ADVANCES IN KNOWLEDGE: Radiomic features extracted from the current chest CT have potential in predicting the likelihood of rapid progression of pneumonia lesions on the next chest CT, which would improve clinical decision-making regarding timely treatment.


Subject(s)
COVID-19/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/methods , Adult , Decision Trees , Disease Progression , Female , Humans , Logistic Models , Male , Middle Aged , Pandemics , Pneumonia, Viral/virology , Predictive Value of Tests , SARS-CoV-2 , Sensitivity and Specificity , Support Vector Machine
18.
Eur Rev Med Pharmacol Sci ; 25(6): 2785-2794, 2021 Mar.
Article in English | MEDLINE | ID: covidwho-1173128

ABSTRACT

OBJECTIVE: To develop a deep learning-based decision tree for the primary care setting, to stratify adult patients with confirmed and unconfirmed coronavirus disease 2019 (COVID-19), and to predict the need for hospitalization or home monitoring. PATIENTS AND METHODS: We performed a retrospective cohort study on data from patients admitted to a COVID hospital in Rome, Italy, between 5 March 2020 and 5 June 2020. A confirmed case was defined as a patient with a positive nasopharyngeal RT-PCR test result, while an unconfirmed case had negative results on repeated swabs. Patients' medical history and clinical, laboratory and radiological findings were collected, and the dataset was used to train a predictive model for COVID-19 severity. RESULTS: Data of 198 patients were included in the study. Twenty-eight (14.14%) had mild disease, 62 (31.31%) had moderate disease, 64 (32.32%) had severe disease, and 44 (22.22%) had critical disease. The G2 value assessed the contribution of each collected value to decision tree building. On this basis, SpO2 (%) with a cut point at 92 was chosen for the optimal first split. Therefore, the decision tree was built using values maximizing G2 and LogWorth. After the tree was built, the correspondence between inputs and outcomes was validated. CONCLUSIONS: We developed a machine learning-based tool that is easy to understand and apply. It provides good discrimination in stratifying confirmed and unconfirmed COVID-19 patients with different prognoses in every context. Our tool might allow general practitioners visiting patients at home to decide whether the patient needs to be hospitalized.


Subject(s)
Algorithms , COVID-19/diagnosis , COVID-19/therapy , Decision Trees , Home Care Services/statistics & numerical data , Hospitalization/statistics & numerical data , Aged , COVID-19/epidemiology , COVID-19/virology , COVID-19 Testing , Cohort Studies , Decision Making, Computer-Assisted , Female , Follow-Up Studies , Humans , Italy/epidemiology , Machine Learning , Male , Monitoring, Physiologic , Prognosis , Retrospective Studies , SARS-CoV-2/isolation & purification
19.
Comput Biol Med ; 132: 104335, 2021 05.
Article in English | MEDLINE | ID: covidwho-1163582

ABSTRACT

The sudden outbreak of coronavirus disease 2019 (COVID-19) revealed the need for fast and reliable automatic tools to help health teams. This paper aims to present understandable solutions based on Machine Learning (ML) techniques to deal with COVID-19 screening in routine blood tests. We tested different ML classifiers in a public dataset from the Hospital Albert Einstein, São Paulo, Brazil. After cleaning and pre-processing the data has 608 patients, of which 84 are positive for COVID-19 confirmed by RT-PCR. To understand the model decisions, we introduce (i) a local Decision Tree Explainer (DTX) for local explanation and (ii) a Criteria Graph to aggregate these explanations and portrait a global picture of the results. Random Forest (RF) classifier achieved the best results (accuracy 0.88, F1-score 0.76, sensitivity 0.66, specificity 0.91, and AUROC 0.86). By using DTX and Criteria Graph for cases confirmed by the RF, it was possible to find some patterns among the individuals able to aid the clinicians to understand the interconnection among the blood parameters either globally or on a case-by-case basis. The results are in accordance with the literature and the proposed methodology may be embedded in an electronic health record system.


Subject(s)
COVID-19 , Brazil , Decision Trees , Hematologic Tests , Humans , Machine Learning , SARS-CoV-2
20.
PLoS One ; 16(3): e0247995, 2021.
Article in English | MEDLINE | ID: covidwho-1115307

ABSTRACT

BACKGROUND: Primary care is the major point of access in most health systems in developed countries and therefore for the detection of coronavirus disease 2019 (COVID-19) cases. The quality of its IT systems, together with access to the results of mass screening with Polymerase chain reaction (PCR) tests, makes it possible to analyse the impact of various concurrent factors on the likelihood of contracting the disease. METHODS AND FINDINGS: Through data mining techniques with the sociodemographic and clinical variables recorded in patient's medical histories, a decision tree-based logistic regression model has been proposed which analyses the significance of demographic and clinical variables in the probability of having a positive PCR in a sample of 7,314 individuals treated in the Primary Care service of the public health system of Catalonia. The statistical approach to decision tree modelling allows 66.2% of diagnoses of infection by COVID-19 to be classified with a sensitivity of 64.3% and a specificity of 62.5%, with prior contact with a positive case being the primary predictor variable. CONCLUSIONS: The use of a classification tree model may be useful in screening for COVID-19 infection. Contact detection is the most reliable variable for detecting Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases. The model would support that, beyond a symptomatic diagnosis, the best way to detect cases would be to engage in contact tracing.


Subject(s)
COVID-19/diagnosis , COVID-19/transmission , Disease Transmission, Infectious/statistics & numerical data , Adult , Aged , COVID-19/epidemiology , Cohort Studies , Contact Tracing , Data Mining/methods , Decision Trees , Female , Humans , Male , Mass Screening/methods , Middle Aged , Probability , Retrospective Studies , SARS-CoV-2/pathogenicity , Sensitivity and Specificity
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