A predictive model for hospitalization and survival to COVID-19 in a retrospective population-based study

Cisterna-García, Alejandro; Guillen-Teruel, Antonio; Caracena, Marcos; Pérez, Enrique; Jiménez, Fernando; Francisco-Verdu, Francisco J.; Reina, Gabriel; González-Billalabeitia, Enrique; Palma, José; Sánchez‐Ferrer, Álvaro; Botía, Juan A.

doi:10.1038/s41598-022-22547-9

Cited by 14 publications

(16 citation statements)

References 49 publications

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“…In a predictive model examining the survival of COVID-19 patients, we found OA to be a predictor associated with this model, which drew our attention ( 11 ). Therefore, to investigate the potential link between COVID-19 and OA, we researched all the literature related to COVID-19 and OA and found that most studies focused on changes in treatment, diagnosis, and recovery status of OA patients during COVID-19 isolation ( 20 , 21 ).…”

Section: Discussionmentioning

confidence: 92%

“…In a predictive model examining the survival of COVID-19 patients, we found OA to be a predictor associated with this model, which drew our attention ( 11 ). Interestingly, Paxlovid, a drug widely used to treat COVID-19, has been shown to accelerate cartilage degeneration in patients, thereby triggering or exacerbating osteoarthritis ( 12 ).…”

Section: Introductionmentioning

confidence: 92%

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Identification of 3 key genes as novel diagnostic and therapeutic targets for OA and COVID-19

et al. 2023

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BackgroundCorona Virus Disease 2019 (COVID-19) and Osteoarthritis (OA) are diseases that seriously affect the physical and mental health and life quality of patients, particularly elderly patients. However, the association between COVID-19 and osteoarthritis at the genetic level has not been investigated. This study is intended to analyze the pathogenesis shared by OA and COVID-19 and to identify drugs that could be used to treat SARS-CoV-2-infected OA patients.MethodsThe four datasets of OA and COVID-19 (GSE114007, GSE55235, GSE147507, and GSE17111) used for the analysis in this paper were obtained from the GEO database. Common genes of OA and COVID-19 were identified through Weighted Gene Co-Expression Network Analysis (WGCNA) and differential gene expression analysis. The least absolute shrinkage and selection operator (LASSO) algorithm was used to screen key genes, which were analyzed for expression patterns by single-cell analysis. Finally, drug prediction and molecular docking were carried out using the Drug Signatures Database (DSigDB) and AutoDockToolsResultsFirstly, WGCNA identified a total of 26 genes common between OA and COVID-19, and functional analysis of the common genes revealed the common pathological processes and molecular changes between OA and COVID-19 are mainly related to immune dysfunction. In addition, we screened 3 key genes, DDIT3, MAFF, and PNRC1, and uncovered that key genes are possibly involved in the pathogenesis of OA and COVID-19 through high expression in neutrophils. Finally, we established a regulatory network of common genes between OA and COVID-19, and the free energy of binding estimation was used to identify suitable medicines for the treatment of OA patients infected with SARS-CoV-2.ConclusionIn the present study, we succeeded in identifying 3 key genes, DDIT3, MAFF, and PNRC1, which are possibly involved in the development of both OA and COVID-19 and have high diagnostic value for OA and COVID-19. In addition, niclosamide, ciclopirox, and ticlopidine were found to be potentially useful for the treatment of OA patients infected with SARS-CoV-2.

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Section: Discussionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 92%

Identification of 3 key genes as novel diagnostic and therapeutic targets for OA and COVID-19

et al. 2023

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“…Even though there is considerable heterogeneity among COVID-19 mortality prediction models, our findings regarding asthma as a strongly predictive COVID-19 mortality factor should be interpreted with caution. In fact, in studies where asthma was not predictive of mortality, intravenous corticosteroids, vasopressors, oxygen, and intubation were commonly used in ICU patients (Banoei et al, 2021 ; Cisterna-García et al, 2022 ). Another plausible explanation is that female gender was a significant predictor of mortality in our study, and asthma deaths in the ICU are strongly associated with women, specifically those admitted for COVID-19 in the ICU (Pennington et al, 2019 ; Beurnier et al, 2020 ; Ren et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Machine learning algorithms for predicting determinants of COVID-19 mortality in South Africa

Chimbunde,

Sigwadhi,

Tamuzi

et al. 2023

Front. Artif. Intell.

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BackgroundCOVID-19 has strained healthcare resources, necessitating efficient prognostication to triage patients effectively. This study quantified COVID-19 risk factors and predicted COVID-19 intensive care unit (ICU) mortality in South Africa based on machine learning algorithms.MethodsData for this study were obtained from 392 COVID-19 ICU patients enrolled between 26 March 2020 and 10 February 2021. We used an artificial neural network (ANN) and random forest (RF) to predict mortality among ICU patients and a semi-parametric logistic regression with nine covariates, including a grouping variable based on K-means clustering. Further evaluation of the algorithms was performed using sensitivity, accuracy, specificity, and Cohen's K statistics.ResultsFrom the semi-parametric logistic regression and ANN variable importance, age, gender, cluster, presence of severe symptoms, being on the ventilator, and comorbidities of asthma significantly contributed to ICU death. In particular, the odds of mortality were six times higher among asthmatic patients than non-asthmatic patients. In univariable and multivariate regression, advanced age, PF1 and 2, FiO2, severe symptoms, asthma, oxygen saturation, and cluster 4 were strongly predictive of mortality. The RF model revealed that intubation status, age, cluster, diabetes, and hypertension were the top five significant predictors of mortality. The ANN performed well with an accuracy of 71%, a precision of 83%, an F1 score of 100%, Matthew's correlation coefficient (MCC) score of 100%, and a recall of 88%. In addition, Cohen's k-value of 0.75 verified the most extreme discriminative power of the ANN. In comparison, the RF model provided a 76% recall, an 87% precision, and a 65% MCC.ConclusionBased on the findings, we can conclude that both ANN and RF can predict COVID-19 mortality in the ICU with accuracy. The proposed models accurately predict the prognosis of COVID-19 patients after diagnosis. The models can be used to prioritize COVID-19 patients with a high mortality risk in resource-constrained ICUs.

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“…Multiple comorbidities and advanced age are independent risk factors for mortality in COVID-19 patients [ 19 ]. The CCI is a well-validated, simple, easy-to-use index for assessing the prognosis and survival of COVID-19 patients [ 8 ].…”

Section: Discussionmentioning

confidence: 99%

Identifying Predominant Causes of Death Among Hospitalized COVID-19 Patients During Poland\'s Second and Third Waves

Niecwietajewa,

Frączek,

Mroczkowska

et al. 2023

Med Sci Monit

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Background Number of confirmed COVID-19 deaths per million population in Poland between November 2020 and May 2021 was one of the largest in Europe. This retrospective study was conducted at a single center in Poland between November 2020 and May 2021to evaluate the morbidity and mortality rates in 581 patients hospitalized with COVID-19. Material/Methods A retrospective single-center study was conducted in a dedicated COVID-19 hospital from November, 2020 to May, 2021. The data of 581 hospitalized patients were analyzed. Multimorbidity was assessed using the Charlson Comorbidity Index, including chronic kidney, respiratory, cardiovascular diseases, diabetes mellitus, cancer, and dementia. The observation period covered admission to the hospital for severe COVID-19 until discharge or death. Diagnosis of COVID-19 was confirmed by quantitative reverse transcription polymerase chain reaction test. Statistical analysis was carried out in the IBM SPSS Statistics program. Results The mortality rate was 35% of all admitted patients. Lung damage was the cause of death in 60%, bacterial superinfection in 26%, arterial thrombosis or thromboembolism in 9%, and heart failure in 5% of patients. The chi-square test showed a significant relationship between sex and the cause of death related to COVID-19 pneumonia and bacterial ventilator-associated pneumonia (VAP). Conclusions The findings from this study supports findings from other countries that between November 2020 and May 2021, before SARS-CoV-2 vaccination programs were fully implemented and before effective medications and antiviral agents were developed, patients with severe COVID-19 had high rates of morbidity and mortality.

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A predictive model for hospitalization and survival to COVID-19 in a retrospective population-based study

Cited by 14 publications

References 49 publications

Identification of 3 key genes as novel diagnostic and therapeutic targets for OA and COVID-19

Identification of 3 key genes as novel diagnostic and therapeutic targets for OA and COVID-19

Machine learning algorithms for predicting determinants of COVID-19 mortality in South Africa

Identifying Predominant Causes of Death Among Hospitalized COVID-19 Patients During Poland\'s Second and Third Waves

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