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Sugihara H, Marumo A, Okabe H, Kohama K, Mera T, Morishita E. Platelet and large platelet ratios are useful in predicting severity of COVID-19. Int J Hematol 2024; 119:638-646. [PMID: 38520659 DOI: 10.1007/s12185-024-03737-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 02/05/2024] [Accepted: 02/19/2024] [Indexed: 03/25/2024]
Abstract
The role of platelets in coronavirus disease (COVID-19) severity requires further exploration. To determine whether the platelet index is useful in predicting COVID-19 severity, we compared the platelet index in patients with higher and lower oxygen requirements (≥ 4 L/min vs. < 4 L/min) and patients without COVID-19. We also analyzed the time course of the platelet index in each group. A total of 285 patients with COVID-19 and 36 without COVID-19 who were hospitalized at Fussa Hospital were analyzed. After matching for oxygen requirement at admission, multivariate analysis was performed. Platelets (≤ 16.6 × 104/μL) and platelet-large cell ratio (P-LCR) (≥ 27.8%) were significant factors influencing severity. Based on these factors, we created the Fussa platelet score, and the group with a Fussa platelet score ≥ 2 was significantly more likely to reach the 4 L/min oxygen requirement (event-free survival: Fussa platelet score ≥ 2 versus < 2, P < 0.00000001). Analysis of platelet index by time period showed a significant increase from 6-10 days after onset. The Fussa platelet score can be measured quickly, easily, and inexpensively in a clinic and may be useful in determining need for transfer to a critical care hospital.
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Affiliation(s)
- Hisae Sugihara
- Division of Clinical Laboratory, Fussa Hospital, Fussa, Tokyo, Japan
| | - Atsushi Marumo
- Division of Internal Medicine, Fussa Hospital, 1-6-1 Kamidaira, Fussa, Tokyo, 197-8511, Japan.
- Department of Hematology, Graduate School of Medicine, Nippon Medical School, Tokyo, Japan.
| | - Haruka Okabe
- Division of Internal Medicine, Fussa Hospital, 1-6-1 Kamidaira, Fussa, Tokyo, 197-8511, Japan
| | - Kiyotaka Kohama
- Division of Internal Medicine, Fussa Hospital, 1-6-1 Kamidaira, Fussa, Tokyo, 197-8511, Japan
| | - Takashi Mera
- Division of Clinical Laboratory, Fussa Hospital, Fussa, Tokyo, Japan
| | - Eriko Morishita
- Department of Hematology, Graduate School of Medicine, Nippon Medical School, Tokyo, Japan
- Department of Clinical Laboratory Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Faculty of Health Sciences, Kanazawa University, Kanazawa, Japan
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2
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Gangi-Burton A, Chan N, Jassel I, Ashok AH, Nair A. Temporal evolution of chest radiographic appearances in COVID-19 with clinicoradiological associations: a multicentre United Kingdom resident-led study. Clin Radiol 2024; 79:287-295. [PMID: 38238147 DOI: 10.1016/j.crad.2023.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 09/12/2023] [Accepted: 11/01/2023] [Indexed: 03/09/2024]
Abstract
AIM To describe the (a) frequency of improving, static, and worsening chest radiograph (CXR) appearances; (b) differences in demographic, initial rudimentary haematological and CXR variables and these patterns; and (c) frequency of different trajectories of serial CXR evolution, in COVID-19 patients presenting consecutively. MATERIALS AND METHODS This multicentre retrospective study included all COVID-19 patients admitted from 1-30 April 2020, meeting the inclusion criteria across 24 (blinded) hospitals. Follow-up CXRs on admission, the subsequent (where available), and at 4-8 weeks were scored for the presence of parenchymal opacities across six zones. Three cohorts were defined: improved, static, and/or worsened. The chi-squared and Kruskal-Wallis tests were used to compare demographic, laboratory, and CXR variables. Trajectories of CXR evolution were assessed when all three CXRs were available (226 patients). RESULTS Of 452 included patients (median age 66 years, interquartile range 54.3-79, 262 men), 211 (46.7%) improved, 140 (31%) were static, and 101 (22.3%) worsened. Improving patients were more likely younger, with a classic COVID-19 radiograph and higher initial CXR zonal severity scores (both p<0.001), while worsening patients had lower initial lymphocyte counts (p=0.008). The most frequent trajectory was worsened then improved (n=63, 27.9%) followed by static then improved (n=46, 20.4%) and static (n=42, 18.6%). CONCLUSION Most patients with COVID-19 during the first wave of the pandemic demonstrated radiographic improvement; these patients were more likely younger with classic COVID-19 appearances and initially more extensive abnormality. Conversely, radiographic deterioration was associated with lower lymphocyte counts. The three most common trajectories were worsening then improvement, static then improvement, and static throughout.
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Affiliation(s)
- A Gangi-Burton
- Department of Radiology, Nottingham University Hospitals NHS Trust, Nottingham, UK.
| | - N Chan
- Department of Interventional Neuroradiology, The Royal London Hospital, London, UK
| | - I Jassel
- Department of Radiology, New Cross Hospital, Royal Wolverhampton NHS Trust, Wolverhampton, UK
| | - A H Ashok
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospital NHS Foundation Trust, Cambridge, UK; Department of Radiology, University of Cambridge, Cambridge, UK
| | - A Nair
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK
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3
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De Vito A, Saderi L, Colpani A, Puci MV, Zauli B, Fiore V, Fois M, Meloni MC, Bitti A, Moi G, Maida I, Babudieri S, Sotgiu G, Madeddu G. New score to predict COVID-19 progression in vaccine and early treatment era: the COVID-19 Sardinian Progression Score (CSPS). Eur J Med Res 2024; 29:123. [PMID: 38360688 PMCID: PMC10868088 DOI: 10.1186/s40001-024-01718-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 02/08/2024] [Indexed: 02/17/2024] Open
Abstract
BACKGROUND Several scores aimed at predicting COVID-19 progression have been proposed. As the variables vaccination and early SARS-CoV-2 treatment were systematically excluded from the prognostic scores, the present study's objective was to develop a new model adapted to the current epidemiological scenario. METHODS We included all patients evaluated by the Infectious Disease Unit in Sassari, with SARS-CoV-2 infection and without signs of respiratory failure at the first evaluation (P/F > 300). Disease progression was defined by the prescription of supplemental oxygen. In addition, variables related to demographics, vaccines, comorbidities, symptoms, CT scans, blood tests, and therapies were collected. Multivariate logistic regression modelling was performed to determine factors associated with progression; any variable with significant univariate test or clinical relevance was selected as a candidate for multivariate analysis. Hosmer-Lemeshow (HL) goodness of fit statistic was calculated. Odds ratio values were used to derive an integer score for developing an easy-to-use progression risk score. The discrimination performance of the risk index was determined using the AUC, and the best cut-off point, according to the Youden index, sensitivity, specificity, predictive value, and likelihood ratio, was chosen. RESULTS 1145 patients [median (IQR) age 74 (62-83) years; 53.5% males] were enrolled; 336 (29.3%) had disease progression. Patients with a clinical progression were older and showed more comorbidities; furthermore, they were less vaccinated and exposed to preventive therapy. In the multivariate logistic regression analysis, age ≥ 60 years, COPD, dementia, haematological tumours, heart failure, exposure to no or one vaccine dose, fever, dyspnoea, GGO, consolidation, ferritin, De Ritis ≥ 1.2, LDH, and no exposure to early anti-SARS-CoV-2 treatment were associated with disease progression. The final risk score ranged from 0 to 45. The ROC curve analysis showed an AUC of 0.92 (95% CI 0.90-0.93) with a 93.7% specificity and 72.9% sensitivity. Low risk was defined when the cut-off value was less than 23. Three risk levels were identified: low (0-23 points), medium (24-35), and high (≥ 36). CONCLUSIONS The proportion of patients with progression increases with high scores: the assessment of the risk could be helpful for clinicians to plan appropriate therapeutic strategies.
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Affiliation(s)
- Andrea De Vito
- Unit of Infectious Disease, Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100, Sassari, Italy.
- PhD School in Biomedical Science, Biomedical Science Department, University of Sassari, Sassari, Italy.
| | - Laura Saderi
- Clinical Epidemiology and Medical Statistics Unit, Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100, Sassari, Italy
| | - Agnese Colpani
- Unit of Infectious Disease, Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100, Sassari, Italy
| | - Mariangela V Puci
- Clinical Epidemiology and Medical Statistics Unit, Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100, Sassari, Italy
| | - Beatrice Zauli
- Unit of Infectious Disease, Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100, Sassari, Italy
| | - Vito Fiore
- Unit of Infectious Disease, Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100, Sassari, Italy
| | - Marco Fois
- Unit of Infectious Disease, Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100, Sassari, Italy
| | - Maria Chiara Meloni
- Unit of Infectious Disease, Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100, Sassari, Italy
| | - Alessandra Bitti
- Unit of Infectious Disease, Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100, Sassari, Italy
| | - Giulia Moi
- Unit of Infectious Disease, Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100, Sassari, Italy
| | - Ivana Maida
- Unit of Infectious Disease, Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100, Sassari, Italy
| | - Sergio Babudieri
- Unit of Infectious Disease, Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100, Sassari, Italy
| | - Giovanni Sotgiu
- Clinical Epidemiology and Medical Statistics Unit, Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100, Sassari, Italy
| | - Giordano Madeddu
- Unit of Infectious Disease, Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100, Sassari, Italy
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Buttia C, Llanaj E, Raeisi-Dehkordi H, Kastrati L, Amiri M, Meçani R, Taneri PE, Ochoa SAG, Raguindin PF, Wehrli F, Khatami F, Espínola OP, Rojas LZ, de Mortanges AP, Macharia-Nimietz EF, Alijla F, Minder B, Leichtle AB, Lüthi N, Ehrhard S, Que YA, Fernandes LK, Hautz W, Muka T. Prognostic models in COVID-19 infection that predict severity: a systematic review. Eur J Epidemiol 2023; 38:355-372. [PMID: 36840867 PMCID: PMC9958330 DOI: 10.1007/s10654-023-00973-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 01/28/2023] [Indexed: 02/26/2023]
Abstract
Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.
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Affiliation(s)
- Chepkoech Buttia
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Emergency Department, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, 3010 Bern, Switzerland
- Epistudia, Bern, Switzerland
| | - Erand Llanaj
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany
- ELKH-DE Public Health Research Group of the Hungarian Academy of Sciences, Department of Public Health and Epidemiology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
- Epistudia, Bern, Switzerland
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Hamidreza Raeisi-Dehkordi
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Lum Kastrati
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Mojgan Amiri
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Renald Meçani
- Department of Pediatrics, “Mother Teresa” University Hospital Center, Tirana, University of Medicine, Tirana, Albania
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
| | - Petek Eylul Taneri
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- HRB-Trials Methodology Research Network College of Medicine, Nursing and Health Sciences University of Galway, Galway, Ireland
| | | | - Peter Francis Raguindin
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Swiss Paraplegic Research, Nottwil, Switzerland
- Faculty of Health Sciences, University of Lucerne, Lucerne, Switzerland
| | - Faina Wehrli
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Farnaz Khatami
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
- Department of Community Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Octavio Pano Espínola
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Department of Preventive Medicine and Public Health, University of Navarre, Pamplona, Spain
- Navarra Institute for Health Research, IdiSNA, Pamplona, Spain
| | - Lyda Z. Rojas
- Research Group and Development of Nursing Knowledge (GIDCEN-FCV), Research Center, Cardiovascular Foundation of Colombia, Floridablanca, Santander, Colombia
| | | | | | - Fadi Alijla
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
| | - Beatrice Minder
- Public Health and Primary Care Library, University Library of Bern, University of Bern, Bern, Switzerland
| | - Alexander B. Leichtle
- University Institute of Clinical Chemistry, Inselspital, Bern University Hospital, and Center for Artificial Intelligence in Medicine (CAIM), University of Bern, Bern, Switzerland
| | - Nora Lüthi
- Emergency Department, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, 3010 Bern, Switzerland
| | - Simone Ehrhard
- Emergency Department, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, 3010 Bern, Switzerland
| | - Yok-Ai Que
- Department of Intensive Care Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Laurenz Kopp Fernandes
- Deutsches Herzzentrum Berlin (DHZB), Berlin, Germany
- Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Wolf Hautz
- Emergency Department, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, 3010 Bern, Switzerland
| | - Taulant Muka
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Epistudia, Bern, Switzerland
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5
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Greco S, Salatiello A, Fabbri N, Riguzzi F, Locorotondo E, Spaggiari R, De Giorgi A, Passaro A. Rapid Assessment of COVID-19 Mortality Risk with GASS Classifiers. Biomedicines 2023; 11:831. [DOI: doi.org/10.3390/biomedicines11030831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2023] Open
Abstract
Risk prediction models are fundamental to effectively triage incoming COVID-19 patients. However, current triaging methods often have poor predictive performance, are based on variables that are expensive to measure, and often lead to hard-to-interpret decisions. We introduce two new classification methods that can predict COVID-19 mortality risk from the automatic analysis of routine clinical variables with high accuracy and interpretability. SVM22-GASS and Clinical-GASS classifiers leverage machine learning methods and clinical expertise, respectively. Both were developed using a derivation cohort of 499 patients from the first wave of the pandemic and were validated with an independent validation cohort of 250 patients from the second pandemic phase. The Clinical-GASS classifier is a threshold-based classifier that leverages the General Assessment of SARS-CoV-2 Severity (GASS) score, a COVID-19-specific clinical score that recently showed its effectiveness in predicting the COVID-19 mortality risk. The SVM22-GASS model is a binary classifier that non-linearly processes clinical data using a Support Vector Machine (SVM). In this study, we show that SMV22-GASS was able to predict the mortality risk of the validation cohort with an AUC of 0.87 and an accuracy of 0.88, better than most scores previously developed. Similarly, the Clinical-GASS classifier predicted the mortality risk of the validation cohort with an AUC of 0.77 and an accuracy of 0.78, on par with other established and emerging machine-learning-based methods. Our results demonstrate the feasibility of accurate COVID-19 mortality risk prediction using only routine clinical variables, readily collected in the early stages of hospital admission.
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Affiliation(s)
- Salvatore Greco
- Department of Translational Medicine, University of Ferrara, Via Luigi Borsari 46, 44121 Ferrara, Italy
- Department of Internal Medicine, Ospedale del Delta, Via Valle Oppio 2, 44023 Ferrara, Italy
| | - Alessandro Salatiello
- Section for Computational Sensomotorics, Department of Cognitive Neurology, Centre for Integrative Neuroscience & Hertie Institute for Clinical Brain Research, University Clinic Tübingen, Otfried-Müller-Straße 25, 72076 Tübingen, Germany
| | - Nicolò Fabbri
- Department of General Surgery, Ospedale del Delta, Via Valle Oppio 2, 44023 Ferrara, Italy
| | - Fabrizio Riguzzi
- Department of Mathematics and Informatics, Via Nicolò Machiavelli 30, 44121 Ferrara, Italy
| | - Emanuele Locorotondo
- Radiology Department, University Radiology Unit, Hospital of Ferrara Arcispedale Sant’Anna, Via Aldo Moro 8, 44124 Ferrara, Italy
| | - Riccardo Spaggiari
- Department of Translational Medicine, University of Ferrara, Via Luigi Borsari 46, 44121 Ferrara, Italy
| | - Alfredo De Giorgi
- Clinica Medica Unit, Azienda Ospedaliero-Universitaria S. Anna of Ferrara, Via Aldo Moro 8, 44124 Ferrara, Italy
| | - Angelina Passaro
- Department of Translational Medicine, University of Ferrara, Via Luigi Borsari 46, 44121 Ferrara, Italy
- Medical Department, University Hospital of Ferrara Arcispedale Sant’Anna, Via A. Moro 8, 44124 Ferrara, Italy
- Research and Innovation Section, University Hospital of Ferrara Arcispedale Sant’Anna, Via A. Moro 8, 44124 Ferrara, Italy
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6
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Rapid Assessment of COVID-19 Mortality Risk with GASS Classifiers. Biomedicines 2023; 11:biomedicines11030831. [PMID: 36979810 PMCID: PMC10045158 DOI: 10.3390/biomedicines11030831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/10/2023] [Accepted: 03/03/2023] [Indexed: 03/12/2023] Open
Abstract
Risk prediction models are fundamental to effectively triage incoming COVID-19 patients. However, current triaging methods often have poor predictive performance, are based on variables that are expensive to measure, and often lead to hard-to-interpret decisions. We introduce two new classification methods that can predict COVID-19 mortality risk from the automatic analysis of routine clinical variables with high accuracy and interpretability. SVM22-GASS and Clinical-GASS classifiers leverage machine learning methods and clinical expertise, respectively. Both were developed using a derivation cohort of 499 patients from the first wave of the pandemic and were validated with an independent validation cohort of 250 patients from the second pandemic phase. The Clinical-GASS classifier is a threshold-based classifier that leverages the General Assessment of SARS-CoV-2 Severity (GASS) score, a COVID-19-specific clinical score that recently showed its effectiveness in predicting the COVID-19 mortality risk. The SVM22-GASS model is a binary classifier that non-linearly processes clinical data using a Support Vector Machine (SVM). In this study, we show that SMV22-GASS was able to predict the mortality risk of the validation cohort with an AUC of 0.87 and an accuracy of 0.88, better than most scores previously developed. Similarly, the Clinical-GASS classifier predicted the mortality risk of the validation cohort with an AUC of 0.77 and an accuracy of 0.78, on par with other established and emerging machine-learning-based methods. Our results demonstrate the feasibility of accurate COVID-19 mortality risk prediction using only routine clinical variables, readily collected in the early stages of hospital admission.
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7
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de Paiva BBM, Pereira PD, de Andrade CMV, Gomes VMR, Souza-Silva MVR, Martins KPMP, Sales TLS, de Carvalho RLR, Pires MC, Ramos LEF, Silva RT, de Freitas Martins Vieira A, Nunes AGS, de Oliveira Jorge A, de Oliveira Maurílio A, Scotton ALBA, da Silva CTCA, Cimini CCR, Ponce D, Pereira EC, Manenti ERF, Rodrigues FD, Anschau F, Botoni FA, Bartolazzi F, Grizende GMS, Noal HC, Duani H, Gomes IM, Costa JHSM, di Sabatino Santos Guimarães J, Tupinambás JT, Rugolo JM, Batista JDL, de Alvarenga JC, Chatkin JM, Ruschel KB, Zandoná LB, Pinheiro LS, Menezes LSM, de Oliveira LMC, Kopittke L, Assis LA, Marques LM, Raposo MC, Floriani MA, Bicalho MAC, Nogueira MCA, de Oliveira NR, Ziegelmann PK, Paraiso PG, de Lima Martelli PJ, Senger R, Menezes RM, Francisco SC, Araújo SF, Kurtz T, Fereguetti TO, de Oliveira TC, Ribeiro YCNMB, Ramires YC, Lima MCPB, Carneiro M, Bezerra AFB, Schwarzbold AV, de Moura Costa AS, Farace BL, Silveira DV, de Almeida Cenci EP, Lucas FB, Aranha FG, Bastos GAN, Vietta GG, Nascimento GF, Vianna HR, Guimarães HC, de Morais JDP, Moreira LB, de Oliveira LS, de Deus Sousa L, de Souza Viana L, de Souza Cabral MA, Ferreira MAP, de Godoy MF, de Figueiredo MP, Guimarães-Junior MH, de Paula de Sordi MA, da Cunha Severino Sampaio N, Assaf PL, Lutkmeier R, Valacio RA, Finger RG, de Freitas R, Guimarães SMM, Oliveira TF, Diniz THO, Gonçalves MA, Marcolino MS. Potential and limitations of machine meta-learning (ensemble) methods for predicting COVID-19 mortality in a large inhospital Brazilian dataset. Sci Rep 2023; 13:3463. [PMID: 36859446 PMCID: PMC9975879 DOI: 10.1038/s41598-023-28579-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 01/20/2023] [Indexed: 03/03/2023] Open
Abstract
The majority of early prediction scores and methods to predict COVID-19 mortality are bound by methodological flaws and technological limitations (e.g., the use of a single prediction model). Our aim is to provide a thorough comparative study that tackles those methodological issues, considering multiple techniques to build mortality prediction models, including modern machine learning (neural) algorithms and traditional statistical techniques, as well as meta-learning (ensemble) approaches. This study used a dataset from a multicenter cohort of 10,897 adult Brazilian COVID-19 patients, admitted from March/2020 to November/2021, including patients [median age 60 (interquartile range 48-71), 46% women]. We also proposed new original population-based meta-features that have not been devised in the literature. Stacking has shown to achieve the best results reported in the literature for the death prediction task, improving over previous state-of-the-art by more than 46% in Recall for predicting death, with AUROC 0.826 and MacroF1 of 65.4%. The newly proposed meta-features were highly discriminative of death, but fell short in producing large improvements in final prediction performance, demonstrating that we are possibly on the limits of the prediction capabilities that can be achieved with the current set of ML techniques and (meta-)features. Finally, we investigated how the trained models perform on different hospitals, showing that there are indeed large differences in classifier performance between different hospitals, further making the case that errors are produced by factors that cannot be modeled with the current predictors.
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Affiliation(s)
- Bruno Barbosa Miranda de Paiva
- grid.8430.f0000 0001 2181 4888Computer Science Department, Universidade Federal de Minas Gerais, Av. Presidente Antônio Carlos, 6627, Belo Horizonte, Brazil
| | - Polianna Delfino Pereira
- grid.8430.f0000 0001 2181 4888Universidade Federal de Minas Gerais, Av. Presidente Antônio Carlos, 6627, Belo Horizonte, Brazil ,Institute for Health Technology Assessment (IATS/ CNPq), R. Ramiro Barcelos, 2359, building 21, room 507, Porto Alegre, Brazil
| | - Claudio Moisés Valiense de Andrade
- grid.8430.f0000 0001 2181 4888Computer Science Department, Universidade Federal de Minas Gerais, Av. Presidente Antônio Carlos, 6627, Belo Horizonte, Brazil
| | - Virginia Mara Reis Gomes
- grid.8430.f0000 0001 2181 4888Medical School and University Hospital, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, 190, room 246, Belo Horizonte, Brazil
| | - Maira Viana Rego Souza-Silva
- grid.8430.f0000 0001 2181 4888Medical School and University Hospital, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, 190, room 246, Belo Horizonte, Brazil
| | - Karina Paula Medeiros Prado Martins
- grid.8430.f0000 0001 2181 4888Medical School and University Hospital, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, 190, room 246, Belo Horizonte, Brazil
| | - Thaís Lorenna Souza Sales
- grid.428481.30000 0001 1516 3599Universidade Federal de São João del-Rei, R. Sebastião Gonçalves Coelho, 400, Divinópolis, Brazil
| | | | - Magda Carvalho Pires
- grid.8430.f0000 0001 2181 4888Department of Statistics, Universidade Federal de Minas Gerais, Av. Presidente Antônio Carlos, 6627, ICEx, room 4071, Belo Horizonte, Brazil
| | - Lucas Emanuel Ferreira Ramos
- grid.8430.f0000 0001 2181 4888Department of Statistics, Universidade Federal de Minas Gerais, Av. Presidente Antônio Carlos, 6627, ICEx, room 4071, Belo Horizonte, Brazil
| | - Rafael Tavares Silva
- grid.8430.f0000 0001 2181 4888Department of Statistics, Universidade Federal de Minas Gerais, Av. Presidente Antônio Carlos, 6627, ICEx, room 4071, Belo Horizonte, Brazil
| | | | | | | | | | | | | | | | - Daniela Ponce
- grid.410543.70000 0001 2188 478XFaculdade de Medicina de Botucatu-Universidade Estadual Paulista “Júlio de Mesquita Filho”, Av. Prof. Mário Rubens Guimarães Montenegro, s/n-UNESP-Campus de Botucatu, Botucatu, Brazil
| | | | | | - Fernanda d’Athayde Rodrigues
- grid.414449.80000 0001 0125 3761Hospital de Clínicas de Porto Alegre, R. Ramiro Barcelos, 2350, Porto Alegre, Brazil
| | - Fernando Anschau
- grid.414914.dHospital Nossa Senhora da Conceição and Hospital Cristo Redentor, Av. Francisco Trein, 326, Porto Alegre, Brazil
| | | | - Frederico Bartolazzi
- Hospital Santo Antônio, Pç. Dr. Márcio Carvalho Lopes Filho, 501, Curvelo, Brazil
| | - Genna Maira Santos Grizende
- grid.477816.b0000 0004 4692 337XHospital Santa Casa de Misericórdia de Belo Horizonte, Av. Francisco Sales, 1111, Belo Horizonte, Brazil
| | - Helena Carolina Noal
- grid.411239.c0000 0001 2284 6531Universidade Federal de Santa Maria/Hospital Universitário/EBSERH, Av. Roraima, 1000, building 22, Santa Maria, Brazil
| | - Helena Duani
- grid.8430.f0000 0001 2181 4888Medical School and University Hospital, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, 190, room 246, Belo Horizonte, Brazil
| | - Isabela Moraes Gomes
- grid.8430.f0000 0001 2181 4888Medical School and University Hospital, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, 190, room 246, Belo Horizonte, Brazil
| | | | | | | | - Juliana Machado Rugolo
- grid.410543.70000 0001 2188 478XFaculdade de Medicina de Botucatu-Universidade Estadual Paulista “Júlio de Mesquita Filho”, Av. Prof. Mário Rubens Guimarães Montenegro, s/n-UNESP-Campus de Botucatu, Botucatu, Brazil
| | - Joanna d’Arc Lyra Batista
- grid.440565.60000 0004 0491 0431Universidade Federal da Fronteira Sul, Av. Fernando Machado, 108E, Chapecó, Brazil
| | | | - José Miguel Chatkin
- grid.411379.90000 0001 2198 7041Hospital São Lucas PUCRS, Av. Ipiranga, 6690, Porto Alegre, Brazil
| | - Karen Brasil Ruschel
- grid.414871.f0000 0004 0491 7596Hospital Mãe de Deus, R. José de Alencar, 286, Porto Alegre, Brazil
| | | | | | - Luanna Silva Monteiro Menezes
- Hospital Metropolitano Odilon Behrens, R. Formiga, 50, Belo Horizonte, Brazil ,Hospital Luxemburgo, R. Gentios, 1350, Belo Horizonte, Brazil
| | | | - Luciane Kopittke
- grid.414914.dHospital Nossa Senhora da Conceição and Hospital Cristo Redentor, Av. Francisco Trein, 326, Porto Alegre, Brazil
| | - Luisa Argolo Assis
- grid.412520.00000 0001 2155 6671Pontifícia Universidade Católica de Minas Gerais, Av. Dom José Gaspar, 500, Belo Horizonte, Brazil
| | - Luiza Margoto Marques
- grid.419130.e0000 0004 0413 0953Faculdade de Ciências Médicas de Minas Gerais, Al. Ezequiel Dias, 275, Belo Horizonte, Brazil
| | - Magda Cesar Raposo
- grid.428481.30000 0001 1516 3599Universidade Federal de São João del-Rei, R. Sebastião Gonçalves Coelho, 400, Divinópolis, Brazil
| | - Maiara Anschau Floriani
- grid.414856.a0000 0004 0398 2134Hospital Moinhos de Vento, R. Ramiro Barcelos, 910, Porto Alegre, Brazil ,Moinhos Research Institute, 910 Ramiro Barcelos Street, 5 floor, Porto Alegre, Brazil
| | - Maria Aparecida Camargos Bicalho
- grid.452464.50000 0000 9270 1314Fundação Hospitalar do Estado de Minas Gerais–FHEMIG, Cidade Administrativa de Minas Gerais, Edifício Gerais, 13rd floor, Rod. Papa João Paulo II, 3777, Belo Horizonte, Brazil
| | | | - Neimy Ramos de Oliveira
- grid.452464.50000 0000 9270 1314Hospital Eduardo de Menezes, R. Dr. Cristiano Rezende, 2213, Belo Horizonte, Brazil
| | | | | | | | - Roberta Senger
- grid.411239.c0000 0001 2284 6531Universidade Federal de Santa Maria/Hospital Universitário/EBSERH, Av. Roraima, 1000, building 22, Santa Maria, Brazil
| | | | | | | | - Tatiana Kurtz
- Hospital Santa Cruz, R. Fernando Abott, 174, Santa Cruz do Sul, Brazil
| | - Tatiani Oliveira Fereguetti
- grid.452464.50000 0000 9270 1314Hospital Eduardo de Menezes, R. Dr. Cristiano Rezende, 2213, Belo Horizonte, Brazil
| | | | | | | | | | - Marcelo Carneiro
- Hospital Santa Cruz, R. Fernando Abott, 174, Santa Cruz do Sul, Brazil
| | | | - Alexandre Vargas Schwarzbold
- grid.411239.c0000 0001 2284 6531Universidade Federal de Santa Maria/Hospital Universitário/EBSERH, Av. Roraima, 1000, building 22, Santa Maria, Brazil
| | | | - Barbara Lopes Farace
- grid.490178.3Hospital Risoleta Tolentino Neves, R. das Gabirobas, 01, Belo Horizonte, Brazil
| | | | | | | | | | - Gisele Alsina Nader Bastos
- grid.414856.a0000 0004 0398 2134Hospital Moinhos de Vento, R. Ramiro Barcelos, 910, Porto Alegre, Brazil
| | | | | | | | | | | | - Leila Beltrami Moreira
- grid.414449.80000 0001 0125 3761Hospital de Clínicas de Porto Alegre, R. Ramiro Barcelos, 2350, Porto Alegre, Brazil
| | | | | | | | - Máderson Alvares de Souza Cabral
- grid.8430.f0000 0001 2181 4888Medical School and University Hospital, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, 190, room 246, Belo Horizonte, Brazil
| | - Maria Angélica Pires Ferreira
- grid.414449.80000 0001 0125 3761Hospital de Clínicas de Porto Alegre, R. Ramiro Barcelos, 2350, Porto Alegre, Brazil
| | - Mariana Frizzo de Godoy
- grid.411379.90000 0001 2198 7041Hospital São Lucas PUCRS, Av. Ipiranga, 6690, Porto Alegre, Brazil
| | | | | | - Mônica Aparecida de Paula de Sordi
- grid.410543.70000 0001 2188 478XFaculdade de Medicina de Botucatu-Universidade Estadual Paulista “Júlio de Mesquita Filho”, Av. Prof. Mário Rubens Guimarães Montenegro, s/n-UNESP-Campus de Botucatu, Botucatu, Brazil
| | | | - Pedro Ledic Assaf
- Hospital Metropolitano Doutor Célio de Castro, R. Dona Luiza, 311, Belo Horizonte, Brazil
| | - Raquel Lutkmeier
- grid.414914.dHospital Nossa Senhora da Conceição and Hospital Cristo Redentor, Av. Francisco Trein, 326, Porto Alegre, Brazil
| | | | | | - Rufino de Freitas
- Hospital São João de Deus, R. do Cobre, 800, São João de Deus, Brazil
| | | | | | | | - Marcos André Gonçalves
- grid.8430.f0000 0001 2181 4888Computer Science Department, Universidade Federal de Minas Gerais, Av. Presidente Antônio Carlos, 6627, Belo Horizonte, Brazil
| | - Milena Soriano Marcolino
- Institute for Health Technology Assessment (IATS/ CNPq), R. Ramiro Barcelos, 2359, building 21, room 507, Porto Alegre, Brazil. .,Medical School and University Hospital, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, 190, room 246, Belo Horizonte, Brazil. .,Telehealth Center, University Hospital, Universidade Federal de Minas Gerais, Avenida Professor Alfredo Balena, 110 room 107. Santa Efigênia, Belo Horizonte, MG, CEP 30130-100, Brazil.
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8
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Yang DM, Chang TJ, Hung KF, Wang ML, Cheng YF, Chiang SH, Chen MF, Liao YT, Lai WQ, Liang KH. Smart healthcare: A prospective future medical approach for COVID-19. J Chin Med Assoc 2023; 86:138-146. [PMID: 36227021 PMCID: PMC9847685 DOI: 10.1097/jcma.0000000000000824] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
COVID-19 has greatly affected human life for over 3 years. In this review, we focus on smart healthcare solutions that address major requirements for coping with the COVID-19 pandemic, including (1) the continuous monitoring of severe acute respiratory syndrome coronavirus 2, (2) patient stratification with distinct short-term outcomes (eg, mild or severe diseases) and long-term outcomes (eg, long COVID), and (3) adherence to medication and treatments for patients with COVID-19. Smart healthcare often utilizes medical artificial intelligence (AI) and cloud computing and integrates cutting-edge biological and optoelectronic techniques. These are valuable technologies for addressing the unmet needs in the management of COVID. By leveraging deep learning/machine learning capabilities and big data, medical AI can perform precise prognosis predictions and provide reliable suggestions for physicians' decision-making. Through the assistance of the Internet of Medical Things, which encompasses wearable devices, smartphone apps, internet-based drug delivery systems, and telemedicine technologies, the status of mild cases can be continuously monitored and medications provided at home without the need for hospital care. In cases that develop into severe cases, emergency feedback can be provided through the hospital for rapid treatment. Smart healthcare can possibly prevent the development of severe COVID-19 cases and therefore lower the burden on intensive care units.
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Affiliation(s)
- De-Ming Yang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Microscopy Service Laboratory, Basic Research Division, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Institute of Biophotonics, School of Biomedical Science and Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Address correspondence. Dr. De-Ming Yang, Microscopy Service Laboratory, Basic Research Division, Department of Medical Research, Taipei Veterans General Hospital, 201, Section 2, Shi-Pai Road, Taipei 112, Taiwan, ROC. E-mail address: (D.-M. Yang). and Dr. Kung-Hao Liang, Laboratory of Systems Biomedical Science, Department of Medical Research, Taipei Veterans General Hospital, 201, Section 2, Shi-Pai Road, Taipei 112, Taiwan, ROC. E-mail: (K.-H. Liang)
| | - Tai-Jay Chang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Laboratory of Genome Research, Basic Research Division, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- School of Biomedical science and Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| | - Kai-Feng Hung
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Mong-Lien Wang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Yen-Fu Cheng
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Su-Hua Chiang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Mei-Fang Chen
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Yi-Ting Liao
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Laboratory of Systems Biomedical Science, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Institute of Food Safety and Health Risk Assessment, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| | - Wei-Qun Lai
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Microscopy Service Laboratory, Basic Research Division, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Institute of Biophotonics, School of Biomedical Science and Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| | - Kung-Hao Liang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Laboratory of Systems Biomedical Science, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Institute of Food Safety and Health Risk Assessment, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Address correspondence. Dr. De-Ming Yang, Microscopy Service Laboratory, Basic Research Division, Department of Medical Research, Taipei Veterans General Hospital, 201, Section 2, Shi-Pai Road, Taipei 112, Taiwan, ROC. E-mail address: (D.-M. Yang). and Dr. Kung-Hao Liang, Laboratory of Systems Biomedical Science, Department of Medical Research, Taipei Veterans General Hospital, 201, Section 2, Shi-Pai Road, Taipei 112, Taiwan, ROC. E-mail: (K.-H. Liang)
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9
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Pál K, Molnar AA, Huțanu A, Szederjesi J, Branea I, Timár Á, Dobreanu M. Inflammatory Biomarkers Associated with In-Hospital Mortality in Critical COVID-19 Patients. Int J Mol Sci 2022; 23:ijms231810423. [PMID: 36142336 PMCID: PMC9499352 DOI: 10.3390/ijms231810423] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/02/2022] [Accepted: 09/06/2022] [Indexed: 01/08/2023] Open
Abstract
The COVID-19 pandemic poses global healthcare challenges due to its unpredictable clinical course. The aim of this study is to identify inflammatory biomarkers and other routine laboratory parameters associated with in-hospital mortality in critical COVID-19 patients. We performed a retrospective observational study on 117 critical COVID-19 patients. Following descriptive statistical analysis of the survivor and non-survivor groups, optimal cut-off levels for the statistically significant parameters were determined using the ROC method, and the corresponding Kaplan-Meier survival curves were calculated. The inflammatory parameters that present statistically significant differences between survivors and non-survivors are IL-6 (p = 0.0004, cut-off = 27.68 pg/mL), CRP (p = 0.027, cut-off = 68.15 mg/L) and IL-6/Ly ratio (p = 0.0003, cut-off = 50.39). Additionally, other statistically significant markers are creatinine (p = 0.031, cut-off = 0.83 mg/dL), urea (p = 0.0002, cut-off = 55.85 mg/dL), AST (p = 0.0209, cut-off = 44.15 U/L), INR (p = 0.0055, cut-off = 1.075), WBC (p = 0.0223, cut-off = 11.68 × 109/L) and pH (p = 0.0055, cut-off = 7.455). A survival analysis demonstrated significantly higher in-hospital mortality rates of patients with values of IL-6, IL-6/Ly, AST, INR, and pH exceeding previously mentioned thresholds. In our study, IL-6 and IL-6/Ly have a predictive value for the mortality of critically-ill patients diagnosed with COVID-19. The integration of these parameters with AST, INR and pH could contribute to a prognostic score for the risk stratification of critical patients, reducing healthcare costs and facilitating clinical decision-making.
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Affiliation(s)
- Krisztina Pál
- Department of Laboratory Medicine, Emergency Clinical County Hospital, 540136 Targu Mures, Romania
- M2 Department, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540142 Targu Mures, Romania
| | - Anca Alexandra Molnar
- Department of Laboratory Medicine, Emergency Clinical County Hospital, 540136 Targu Mures, Romania
- M2 Department, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540142 Targu Mures, Romania
| | - Adina Huțanu
- Department of Laboratory Medicine, Emergency Clinical County Hospital, 540136 Targu Mures, Romania
- Department of Laboratory Medicine, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540142 Targu Mures, Romania
- Correspondence:
| | - János Szederjesi
- Department of Anesthesiology and Intensive Care, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540142 Targu Mures, Romania
- Department of Anesthesiology and Intensive Care, Emergency Clinical County Hospital, 540136 Targu Mures, Romania
| | - Ionuț Branea
- Department of Anesthesiology and Intensive Care, Emergency Clinical County Hospital, 540136 Targu Mures, Romania
| | - Ágota Timár
- Department of Anesthesiology and Intensive Care, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540142 Targu Mures, Romania
- Department of Anesthesiology and Intensive Care, Emergency Clinical County Hospital, 540136 Targu Mures, Romania
| | - Minodora Dobreanu
- Department of Laboratory Medicine, Emergency Clinical County Hospital, 540136 Targu Mures, Romania
- Department of Laboratory Medicine, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540142 Targu Mures, Romania
- Center for Advanced Medical and Pharmaceutical Research, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540136 Targu Mures, Romania
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