A Machine Learning Model for Predicting Hospitalization in Patients with Respiratory Symptoms during the COVID-19 Pandemic

Freitas, Victor Muniz de; Chiloff, Daniela Mendes; Bosso, Giulia Gabriella; Teixeira, Janaina Oliveira Pires; Hernandes, Isabele Cristina de Godói; Padilha, Maira do Patrocínio; Moura, Giovanna Corrêa; Andrade, Luís Gustavo Modelli de; Mancuso, Frederico José Neves; Finamor, Francisco E.; Serodio, Aluísio Marçal de Barros; Ota-­Arakaki, Jaquelina Sonoe; Sartori, Marair Grácio Ferreira; Ferreira, Paulo Roberto Abrão; Rangel, Érika B.

doi:10.3390/jcm11154574

Cited by 7 publications

(3 citation statements)

References 65 publications

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“…The conclusion of this study was that elevated cardiac injury biomarkers may improve the identification of patients with high risk of mortality and severity. In de Freitas et al (55)' , the main symptoms, signs and demographic data that were most often associated to the hospitalization in patients with respiratory problems, have been defined by means of a ML model. In Sun et al (56), researchers employed a Support Vector Machine (SVM) model where input data Classification scheme and results: green and red rectangles represent the asy/pauci and severe classes respectively; the two numbers are the number of asy/pauci and severe patients, respectively, falling within the class.…”

Section: Discussionmentioning

confidence: 99%

Decision trees for early prediction of inadequate immune response to coronavirus infections: a pilot study on COVID-19

Pisano,

Cannas,

Fanni

et al. 2023

Front. Med.

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IntroductionFew artificial intelligence models exist to predict severe forms of COVID-19. Most rely on post-infection laboratory data, hindering early treatment for high-risk individuals.MethodsThis study developed a machine learning model to predict inherent risk of severe symptoms after contracting SARS-CoV-2. Using a Decision Tree trained on 153 Alpha variant patients, demographic, clinical and immunogenetic markers were considered. Model performance was assessed on Alpha and Delta variant datasets. Key risk factors included age, gender, absence of KIR2DS2 gene (alone or with HLA-C C1 group alleles), presence of 14-bp polymorphism in HLA-G gene, presence of KIR2DS5 gene, and presence of KIR telomeric region A/A.ResultsThe model achieved 83.01% accuracy for Alpha variant and 78.57% for Delta variant, with True Positive Rates of 80.82 and 77.78%, and True Negative Rates of 85.00% and 79.17%, respectively. The model showed high sensitivity in identifying individuals at risk.DiscussionThe present study demonstrates the potential of AI algorithms, combined with demographic, epidemiologic, and immunogenetic data, in identifying individuals at high risk of severe COVID-19 and facilitating early treatment. Further studies are required for routine clinical integration.

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Section: Discussionmentioning

confidence: 99%

Decision trees for early prediction of inadequate immune response to coronavirus infections: a pilot study on COVID-19

Pisano,

Cannas,

Fanni

et al. 2023

Front. Med.

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“…To note, identifying the impact of being overweight/obesity in a high-risk group of an immunosuppressed population is of paramount importance for defining therapeutic decisions, patient flow management, and the allocation of resources in the COVID-19 setting [ 9 ]. Therefore, we sought to investigate the risk factors associated with oxygen requirements in overweight/obese KTRs with COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Oxygen Requirement in Overweight/Obese Kidney Transplant Recipients with COVID-19: An Observational Cohort Study

et al. 2023

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Introduction: Obesity is one of the components of the cardiometabolic syndrome that contributes to COVID-19 progression and mortality. Immunosuppressed individuals are at greater risk of the COVID-19 burden. Therefore, we sought to investigate the impact of the combination of overweight/obesity and kidney transplant on oxygen (O2) requirements in the COVID-19 setting. Methods: Retrospective analysis of 284 kidney transplant recipients (KTRs) from March/2020 to August/2020 in a single center. We investigated the risk factors associated with O2 requirements in overweight/obese KTRs. Results: Overall, 65.1% had a BMI (body mass index) ≥ 25 kg/m2, 52.4% were male, the mean age was 53.3 ± 11 years old, 78.4% had hypertension, and 41.1% had diabetes mellitus. BMI was an independent risk factor for O2 requirements (OR = 1.07, p = 0.02) alongside age, lymphopenia, and hyponatremia. When overweight/obese KTRs were older, smokers, they presented higher levels of lactate dehydrogenase (LDH), and lower levels of estimated glomerular filtration rate (eGFR), lymphocytes, and sodium at admission, and they needed O2 more often. Conclusion: Being overweight/obese is associated with greater O2 requirements in KTRs, in particular in older people and smokers, with worse kidney allograft functions, more inflammation, and lower sodium levels. Therefore, the early identification of factors that predict a worse outcome in overweight/obese KTRs affected by COVID-19 contributes to risk stratification and therapeutic decisions.

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“…Viral shedding is a major consideration for patients to end isolation because a higher viral load (lower Ct values) means these patients are more contagious ( Li et al, 2022 ). Thus, early identification of whether patients clear the virus in a short period of time will contribute to therapeutic decisions, management measures, patient flow management, and resource allocation ( De Freitas et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Predictive value of immunoglobulin G, activated partial thromboplastin time, platelet, and indirect bilirubin for delayed viral clearance in patients infected with the Omicron variant

Zhang

Xie

Lyu

et al. 2023

PeerJ

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Background Omicron is the recently emerged highly transmissible severe acute respiratory syndrome coronavirus 2 variant that has caused a dramatic increase in coronavirus disease-2019 infection cases worldwide. This study was to investigate the association between demographic and laboratory findings, and the duration of Omicron viral clearance. Methods Approximately 278 Omicron cases at the Ruijin Hospital Luwan Branch, Shanghai Jiaotong University School of Medicine were retrospectively analyzed between August 11 and August 31, 2022. Demographic and laboratory data were also collected. The association between demographics, laboratory findings, and duration of Omicron viral clearance was analyzed using Pearson correlation analysis and univariate and multivariate logistic regression. Results Univariate logistic regression analyses showed that a prolonged viral clearance time was significantly associated with older age and lower immunoglobulin (Ig) G and platelet (PLT) levels. Using multinomial logistic regression analyses, direct bilirubin, IgG, activated partial thromboplastin time (APTT), and PLT were independent factors for longer viral shedding duration. The model combining direct bilirubin, IgG, APTT, and PLT identifies patients infected with Omicron whose viral clearance time was ≥7 days with 62.7% sensitivity and 83.4% specificity. Conclusion These findings suggest that direct bilirubin, IgG, PLT, and APTT are significant risk factors for a longer viral shedding duration in patients infected with Omicron. Measuring levels of direct bilirubin, IgG, PLT, and APTT is advantageous to identify patients infected with Omicron with longer viral shedding duration.

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A Machine Learning Model for Predicting Hospitalization in Patients with Respiratory Symptoms during the COVID-19 Pandemic

Cited by 7 publications

References 65 publications

Decision trees for early prediction of inadequate immune response to coronavirus infections: a pilot study on COVID-19

Decision trees for early prediction of inadequate immune response to coronavirus infections: a pilot study on COVID-19

Oxygen Requirement in Overweight/Obese Kidney Transplant Recipients with COVID-19: An Observational Cohort Study

Predictive value of immunoglobulin G, activated partial thromboplastin time, platelet, and indirect bilirubin for delayed viral clearance in patients infected with the Omicron variant

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