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De Rop L, Bos DA, Stegeman I, Holtman G, Ochodo EA, Spijker R, Otieno JA, Alkhlaileh F, Deeks JJ, Dinnes J, Van den Bruel A, McInnes MD, Leeflang MM, Verbakel JY. Accuracy of routine laboratory tests to predict mortality and deterioration to severe or critical COVID-19 in people with SARS-CoV-2. Cochrane Database Syst Rev 2024; 8:CD015050. [PMID: 39105481 PMCID: PMC11301994 DOI: 10.1002/14651858.cd015050.pub2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/07/2024]
Abstract
BACKGROUND Identifying patients with COVID-19 disease who will deteriorate can be useful to assess whether they should receive intensive care, or whether they can be treated in a less intensive way or through outpatient care. In clinical care, routine laboratory markers, such as C-reactive protein, are used to assess a person's health status. OBJECTIVES To assess the accuracy of routine blood-based laboratory tests to predict mortality and deterioration to severe or critical (from mild or moderate) COVID-19 in people with SARS-CoV-2. SEARCH METHODS On 25 August 2022, we searched the Cochrane COVID-19 Study Register, encompassing searches of various databases such as MEDLINE via PubMed, CENTRAL, Embase, medRxiv, and ClinicalTrials.gov. We did not apply any language restrictions. SELECTION CRITERIA We included studies of all designs that produced estimates of prognostic accuracy in participants who presented to outpatient services, or were admitted to general hospital wards with confirmed SARS-CoV-2 infection, and studies that were based on serum banks of samples from people. All routine blood-based laboratory tests performed during the first encounter were included. We included any reference standard used to define deterioration to severe or critical disease that was provided by the authors. DATA COLLECTION AND ANALYSIS Two review authors independently extracted data from each included study, and independently assessed the methodological quality using the Quality Assessment of Prognostic Accuracy Studies tool. As studies reported different thresholds for the same test, we used the Hierarchical Summary Receiver Operator Curve model for meta-analyses to estimate summary curves in SAS 9.4. We estimated the sensitivity at points on the SROC curves that corresponded to the median and interquartile range boundaries of specificities in the included studies. Direct and indirect comparisons were exclusively conducted for biomarkers with an estimated sensitivity and 95% CI of ≥ 50% at a specificity of ≥ 50%. The relative diagnostic odds ratio was calculated as a summary of the relative accuracy of these biomarkers. MAIN RESULTS We identified a total of 64 studies, including 71,170 participants, of which 8169 participants died, and 4031 participants deteriorated to severe/critical condition. The studies assessed 53 different laboratory tests. For some tests, both increases and decreases relative to the normal range were included. There was important heterogeneity between tests and their cut-off values. None of the included studies had a low risk of bias or low concern for applicability for all domains. None of the tests included in this review demonstrated high sensitivity or specificity, or both. The five tests with summary sensitivity and specificity above 50% were: C-reactive protein increase, neutrophil-to-lymphocyte ratio increase, lymphocyte count decrease, d-dimer increase, and lactate dehydrogenase increase. Inflammation For mortality, summary sensitivity of a C-reactive protein increase was 76% (95% CI 73% to 79%) at median specificity, 59% (low-certainty evidence). For deterioration, summary sensitivity was 78% (95% CI 67% to 86%) at median specificity, 72% (very low-certainty evidence). For the combined outcome of mortality or deterioration, or both, summary sensitivity was 70% (95% CI 49% to 85%) at median specificity, 60% (very low-certainty evidence). For mortality, summary sensitivity of an increase in neutrophil-to-lymphocyte ratio was 69% (95% CI 66% to 72%) at median specificity, 63% (very low-certainty evidence). For deterioration, summary sensitivity was 75% (95% CI 59% to 87%) at median specificity, 71% (very low-certainty evidence). For mortality, summary sensitivity of a decrease in lymphocyte count was 67% (95% CI 56% to 77%) at median specificity, 61% (very low-certainty evidence). For deterioration, summary sensitivity of a decrease in lymphocyte count was 69% (95% CI 60% to 76%) at median specificity, 67% (very low-certainty evidence). For the combined outcome, summary sensitivity was 83% (95% CI 67% to 92%) at median specificity, 29% (very low-certainty evidence). For mortality, summary sensitivity of a lactate dehydrogenase increase was 82% (95% CI 66% to 91%) at median specificity, 60% (very low-certainty evidence). For deterioration, summary sensitivity of a lactate dehydrogenase increase was 79% (95% CI 76% to 82%) at median specificity, 66% (low-certainty evidence). For the combined outcome, summary sensitivity was 69% (95% CI 51% to 82%) at median specificity, 62% (very low-certainty evidence). Hypercoagulability For mortality, summary sensitivity of a d-dimer increase was 70% (95% CI 64% to 76%) at median specificity of 56% (very low-certainty evidence). For deterioration, summary sensitivity was 65% (95% CI 56% to 74%) at median specificity of 63% (very low-certainty evidence). For the combined outcome, summary sensitivity was 65% (95% CI 52% to 76%) at median specificity of 54% (very low-certainty evidence). To predict mortality, neutrophil-to-lymphocyte ratio increase had higher accuracy compared to d-dimer increase (RDOR (diagnostic Odds Ratio) 2.05, 95% CI 1.30 to 3.24), C-reactive protein increase (RDOR 2.64, 95% CI 2.09 to 3.33), and lymphocyte count decrease (RDOR 2.63, 95% CI 1.55 to 4.46). D-dimer increase had higher accuracy compared to lymphocyte count decrease (RDOR 1.49, 95% CI 1.23 to 1.80), C-reactive protein increase (RDOR 1.31, 95% CI 1.03 to 1.65), and lactate dehydrogenase increase (RDOR 1.42, 95% CI 1.05 to 1.90). Additionally, lactate dehydrogenase increase had higher accuracy compared to lymphocyte count decrease (RDOR 1.30, 95% CI 1.13 to 1.49). To predict deterioration to severe disease, C-reactive protein increase had higher accuracy compared to d-dimer increase (RDOR 1.76, 95% CI 1.25 to 2.50). The neutrophil-to-lymphocyte ratio increase had higher accuracy compared to d-dimer increase (RDOR 2.77, 95% CI 1.58 to 4.84). Lastly, lymphocyte count decrease had higher accuracy compared to d-dimer increase (RDOR 2.10, 95% CI 1.44 to 3.07) and lactate dehydrogenase increase (RDOR 2.22, 95% CI 1.52 to 3.26). AUTHORS' CONCLUSIONS Laboratory tests, associated with hypercoagulability and hyperinflammatory response, were better at predicting severe disease and mortality in patients with SARS-CoV-2 compared to other laboratory tests. However, to safely rule out severe disease, tests should have high sensitivity (> 90%), and none of the identified laboratory tests met this criterion. In clinical practice, a more comprehensive assessment of a patient's health status is usually required by, for example, incorporating these laboratory tests into clinical prediction rules together with clinical symptoms, radiological findings, and patient's characteristics.
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Affiliation(s)
- Liselore De Rop
- Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
| | - David Ag Bos
- Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
| | - Inge Stegeman
- Department of Otorhinolaryngology and Head & Neck Surgery, University Medical Center Utrecht, Utrecht, Netherlands
- Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Gea Holtman
- Department of Primary- and Long-term Care, University of Groningen, University Medical Centre Groningen, Groningen, Netherlands
| | - Eleanor A Ochodo
- Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya
- Centre for Evidence-based Health Care, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - René Spijker
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Medical Library, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health, Amsterdam, Netherlands
| | - Jenifer A Otieno
- Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya
| | - Fade Alkhlaileh
- Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
| | - Jonathan J Deeks
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Jacqueline Dinnes
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Ann Van den Bruel
- Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
| | - Matthew Df McInnes
- Department of Radiology, The Ottawa Hospital Research Institute, University of Ottawa, Ottawa, Canada
| | - Mariska Mg Leeflang
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Jan Y Verbakel
- Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
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Hai CN, Duc TB, Minh TN, Quang LN, Tung SLC, Duc LT, Duong-Quy S. Predicting mortality risk in hospitalized COVID-19 patients: an early model utilizing clinical symptoms. BMC Pulm Med 2024; 24:24. [PMID: 38200490 PMCID: PMC10777603 DOI: 10.1186/s12890-023-02838-1] [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: 07/23/2023] [Accepted: 12/29/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Despite global efforts to control the COVID-19 pandemic, the emergence of new viral strains continues to pose a significant threat. Accurate patient stratification, optimized resource allocation, and appropriate treatment are crucial in managing COVID-19 cases. To address this, a simple and accurate prognostic tool capable of rapidly identifying individuals at high risk of mortality is urgently needed. Early prognosis facilitates predicting treatment outcomes and enables effective patient management. The aim of this study was to develop an early predictive model for assessing mortality risk in hospitalized COVID-19 patients, utilizing baseline clinical factors. METHODS We conducted a descriptive cross-sectional study involving a cohort of 375 COVID-19 patients admitted and treated at the COVID-19 Patient Treatment Center in Military Hospital 175 from October 2021 to December 2022. RESULTS Among the 375 patients, 246 and 129 patients were categorized into the survival and mortality groups, respectively. Our findings revealed six clinical factors that demonstrated independent predictive value for mortality in COVID-19 patients. These factors included age greater than 50 years, presence of multiple underlying diseases, dyspnea, acute confusion, saturation of peripheral oxygen below 94%, and oxygen demand exceeding 5 L per minute. We integrated these factors to develop the Military Hospital 175 scale (MH175), a prognostic scale demonstrating significant discriminatory ability with an area under the curve (AUC) of 0.87. The optimal cutoff value for predicting mortality risk using the MH175 score was determined to be ≥ 3 points, resulting in a sensitivity of 96.1%, specificity of 63.4%, positive predictive value of 58%, and negative predictive value of 96.9%. CONCLUSIONS The MH175 scale demonstrated a robust predictive capacity for assessing mortality risk in patients with COVID-19. Implementation of the MH175 scale in clinical settings can aid in patient stratification and facilitate the application of appropriate treatment strategies, ultimately reducing the risk of death. Therefore, the utilization of the MH175 scale holds significant potential to improve clinical outcomes in COVID-19 patients. TRIAL REGISTRATION An independent ethics committee approved the study (Research Ethics Committee of Military Hospital 175 (No. 3598GCN-HDDD; date: October 8, 2021), which was performed in accordance with the Declaration of Helsinki, Guidelines for Good Clinical Practice.
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Affiliation(s)
- Cong Nguyen Hai
- Department of Tuberculosis and Respiratory Pathology, Military Hospital 175, Ho Chi Minh City, Vietnam.
| | | | - The Nguyen Minh
- Department of Tuberculosis and Respiratory Pathology, Military Hospital 175, Ho Chi Minh City, Vietnam
| | - Lich Ngo Quang
- Department of Tuberculosis and Respiratory Pathology, Military Hospital 175, Ho Chi Minh City, Vietnam
| | - Son Luong Cao Tung
- Department of Tuberculosis and Respiratory Pathology, Military Hospital 175, Ho Chi Minh City, Vietnam
| | - Loi Trinh Duc
- Department of Tuberculosis and Respiratory Pathology, Military Hospital 175, Ho Chi Minh City, Vietnam
| | - Sy Duong-Quy
- Clinical Research Unit, Lam Dong Medical College and Bio-Medical Research Centre, Dalat City, Vietnam
- Immuno-Allergology Division, Hershey Medical Center, Penn State College of Medicine, Hershey, Pennsylvania, USA
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3
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Riela PM. Artificial intelligence for COVID-19 mortality prediction: improvement of risk stratification and clinical decision-making. Intern Emerg Med 2023; 18:1617-1618. [PMID: 37452261 DOI: 10.1007/s11739-023-03358-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 06/25/2023] [Indexed: 07/18/2023]
Affiliation(s)
- Paolo Marco Riela
- Department of Mathematics and Computer Science, University of Catania, Catania, Italy.
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Casas-Rojo JM, Ventura PS, Antón Santos JM, de Latierro AO, Arévalo-Lorido JC, Mauri M, Rubio-Rivas M, González-Vega R, Giner-Galvañ V, Otero Perpiñá B, Fonseca-Aizpuru E, Muiño A, Del Corral-Beamonte E, Gómez-Huelgas R, Arnalich-Fernández F, Llorente Barrio M, Sancha-Lloret A, Rábago Lorite I, Loureiro-Amigo J, Pintos-Martínez S, García-Sardón E, Montaño-Martínez A, Rojano-Rivero MG, Ramos-Rincón JM, López-Escobar A. Improving prediction of COVID-19 mortality using machine learning in the Spanish SEMI-COVID-19 registry. Intern Emerg Med 2023; 18:1711-1722. [PMID: 37349618 DOI: 10.1007/s11739-023-03338-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 06/01/2023] [Indexed: 06/24/2023]
Abstract
COVID-19 is responsible for high mortality, but robust machine learning-based predictors of mortality are lacking. To generate a model for predicting mortality in patients hospitalized with COVID-19 using Gradient Boosting Decision Trees (GBDT). The Spanish SEMI-COVID-19 registry includes 24,514 pseudo-anonymized cases of patients hospitalized with COVID-19 from 1 February 2020 to 5 December 2021. This registry was used as a GBDT machine learning model, employing the CatBoost and BorutaShap classifier to select the most relevant indicators and generate a mortality prediction model by risk level, ranging from 0 to 1. The model was validated by separating patients according to admission date, using the period 1 February to 31 December 2020 (first and second waves, pre-vaccination period) for training, and 1 January to 30 November 2021 (vaccination period) for the test group. An ensemble of ten models with different random seeds was constructed, separating 80% of the patients for training and 20% from the end of the training period for cross-validation. The area under the receiver operating characteristics curve (AUC) was used as a performance metric. Clinical and laboratory data from 23,983 patients were analyzed. CatBoost mortality prediction models achieved an AUC performance of 84.76 (standard deviation 0.45) for patients in the test group (potentially vaccinated patients not included in model training) using 16 features. The performance of the 16-parameter GBDT model for predicting COVID-19 hospital mortality, although requiring a relatively large number of predictors, shows a high predictive capacity.
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Affiliation(s)
- José-Manuel Casas-Rojo
- Internal Medicine Department, Infanta Cristina University Hospital, Parla, 28981, Madrid, Spain
| | - Paula Sol Ventura
- Department of Pediatric Endocrinology, Hospital HM Nens, HM Hospitales, 08009, Barcelona, Spain
| | | | | | | | - Marc Mauri
- Data Scientist, Kaizen AI, Barcelona, Spain
| | - Manuel Rubio-Rivas
- Internal Medicine Department, Bellvitge University Hospital, Hospitalet de Llobregat, Barcelona, Spain
| | - Rocío González-Vega
- Internal Medicine Department, Hospital Costa del Sol, Marbella, Málaga, Spain
| | - Vicente Giner-Galvañ
- Internal Medicine Department, Hospital Universitario San Juan. San Juan de Alicante, Alicante, Spain
| | | | - Eva Fonseca-Aizpuru
- Internal Medicine Department, Hospital Universitario de Cabueñes, Gijón, Asturias, Spain
| | - Antonio Muiño
- Internal Medicine Department, Hospital Universitario Gregorio Marañón, Madrid, Spain
| | | | - Ricardo Gómez-Huelgas
- Internal Medicine Department, Regional University Hospital of Málaga, Biomedical Research Institute of Málaga (IBIMA), University of Málaga (UMA), Málaga, Spain
| | | | | | | | - Isabel Rábago Lorite
- Internal Medicine Department, Hospital Universitario Infanta Sofía. San Sebastián de los Reyes, Madrid, Spain
| | - José Loureiro-Amigo
- Internal Medicine Department, Hospital Moisès Broggi, Sant Joan Despí, Barcelona, Spain
| | - Santiago Pintos-Martínez
- Internal Medicine Department, Hospital Universitario de Sagunto, Puerto de Sagunto, Valencia, Spain
| | - Eva García-Sardón
- Internal Medicine Department, Hospital Universitario de Cáceres, Cáceres, Spain
| | | | | | | | - Alejandro López-Escobar
- Pediatrics Department, Clinical Research Unit, Hospital Universitario Vithas Madrid La Milagrosa, Fundación Vithas, Madrid, Spain.
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Popescu IM, Margan MM, Anghel M, Mocanu A, Laitin SMD, Margan R, Capraru ID, Tene AA, Gal-Nadasan EG, Cirnatu D, Chicin GN, Oancea C, Anghel A. Developing Prediction Models for COVID-19 Outcomes: A Valuable Tool for Resource-Limited Hospitals. Int J Gen Med 2023; 16:3053-3065. [PMID: 37489130 PMCID: PMC10363379 DOI: 10.2147/ijgm.s419206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 07/08/2023] [Indexed: 07/26/2023] Open
Abstract
Purpose Coronavirus disease is a global pandemic with millions of confirmed cases and hundreds of thousands of deaths worldwide that continues to create a significant burden on the healthcare systems. The aim of this study was to determine the patient clinical and paraclinical profiles that associate with COVID-19 unfavourable outcome and generate a prediction model that could separate between high-risk and low-risk groups. Patients and Methods The present study is a multivariate observational retrospective study. A total of 483 patients, residents of the municipality of Timișoara, the biggest city in the Western Region of Romania, were included in the study group that was further divided into 3 sub-groups in accordance with the disease severity form. Results Increased age (cOR=1.09, 95% CI: 1.06-1.11, p<0.001), cardiovascular diseases (cOR=3.37, 95% CI: 1.96-6.08, p<0.001), renal disease (cOR=4.26, 95% CI: 2.13-8.52, p<0.001), and neurological disorder (cOR=5.46, 95% CI: 2.71-11.01, p<0.001) were all independently significantly correlated with an unfavourable outcome in the study group. The severe form increases the risk of an unfavourable outcome 19.59 times (95% CI: 11.57-34.10, p<0.001), while older age remains an independent risk factor even when disease severity is included in the statistical model. An unfavourable outcome was positively associated with increased values for the following paraclinical parameters: white blood count (WBC; cOR=1.10, 95% CI: 1.05-1.15, p<0.001), absolute neutrophil count (ANC; cOR=1.15, 95% CI: 1.09-1.21, p<0.001) and C-reactive protein (CRP; cOR=1.007, 95% CI: 1.004-1.009, p<0.001). The best prediction model including age, ANC and CRP achieved a receiver operating characteristic (ROC) curve with the area under the curve (AUC) = 0.845 (95% CI: 0.813-0.877, p<0.001); cut-off value = 0.12; sensitivity = 72.3%; specificity = 83.9%. Conclusion This model and risk profiling may contribute to a more precise allocation of limited healthcare resources in a clinical setup and can guide the development of strategies for disease management.
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Affiliation(s)
- Irina-Maria Popescu
- Department of Infectious Diseases, Discipline of Epidemiology, “Victor Babes” University of Medicine and Pharmacy, Timisoara, Romania
| | - Madalin-Marius Margan
- Department of Functional Sciences, Discipline of Public Health, “Victor Babes” University of Medicine and Pharmacy, Timisoara, Romania
| | - Mariana Anghel
- Department of Infectious Diseases, Discipline of Epidemiology, “Victor Babes” University of Medicine and Pharmacy, Timisoara, Romania
| | - Alexandra Mocanu
- Department of Infectious Diseases, Discipline of Infectious Diseases, “Victor Babes” University of Medicine and Pharmacy, Timisoara, Romania
| | - Sorina Maria Denisa Laitin
- Department of Infectious Diseases, Discipline of Epidemiology, “Victor Babes” University of Medicine and Pharmacy, Timisoara, Romania
| | - Roxana Margan
- Department of Functional Sciences, Discipline of Physiology, “Victor Babes” University of Medicine and Pharmacy, Timisoara, Romania
| | - Ionut Dragos Capraru
- Department of Infectious Diseases, Discipline of Epidemiology, “Victor Babes” University of Medicine and Pharmacy, Timisoara, Romania
| | | | - Emanuela-Georgiana Gal-Nadasan
- Department of Balneology, Medical Rehabilitation and Rheumatology, Discipline of Medical Rehabilitation, “Victor Babes” University of Medicine and Pharmacy, Timisoara, Romania
| | - Daniela Cirnatu
- Regional Center of Public Health Timisoara, Timisoara, Romania
- Department of Medicine, “Vasile Goldis” Western University, Faculty of Medicine, Arad, Romania
| | - Gratiana Nicoleta Chicin
- Regional Center of Public Health Timisoara, Timisoara, Romania
- Department of Epidemiology, Infectious Diseases and Preventive Medicine, “Vasile Goldis” Western University, Faculty of Medicine, Arad, Romania
| | - Cristian Oancea
- Center for Research and Innovation in Precision Medicine of Respiratory Disease, “Victor Babes” University of Medicine and Pharmacy, Timisoara, Romania
| | - Andrei Anghel
- Department of Biochemistry and Pharmacology, Discipline of Biochemistry, “Victor Babes” University of Medicine and Pharmacy, Timisoara, Romania
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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: 6] [Impact Index Per Article: 6.0] [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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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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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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Segalo S, Kiseljakovic E, Papic E, Joguncic A, Pasic A, Sahinagic M, Lepara O, Sporisevic L. The Role of Hemogram-derived Ratios in COVID-19 Severity Stratification in a Primary Healthcare Facility. Acta Inform Med 2023; 31:41-47. [PMID: 37038490 PMCID: PMC10082658 DOI: 10.5455/aim.2023.31.41-47] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 03/15/2023] [Indexed: 04/12/2023] Open
Abstract
Background Coronavirus disease 2019 (COVID-19) can cause a wide clinical spectrum, ranging from asymptomatic to severe disease with a high mortality rate. In view of the current pandemic and the increasing influx of patients into healthcare facilities, there is a need to identify simple and reliable tools for stratifying patients. Objective Study aimed to analyze whether hemogram-derived ratios (HDRs) can be used to identify patients with a risk of developing a severe clinical form and admission to hospital. Methods This cross-sectional and observational study included 500 patients with a confirmed diagnosis of COVID-19. Data on clinical features and laboratory parameters were collected from medical records and 13 HDRs were calculated and analyzed. Descriptive and inferential statistics were included in the analysis. Results Of the 500 patients, 43.8% had a severe form of the disease. Lymphocytopenia, monocytopenia, higher C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR) were found in severe patients (p < 0.05). Significantly higher neutrophil-to-lymphocyte ratio (NLR), derived NLR (dNLR), neutrophil-to-platelet ratio (NPR), neutrophil-to-lymphocyte-to-platelet ratio (NLPR) and CRP-to-lymphocyte ratio (CRP/Ly) values were found in severe patients (p < 0.001). In addition, they have statistically significant prognostic potential (p < 0.001). The area under the curve (AUC) for CRP/Ly, dNLR, NLPR, NLR, and NPR were 0.693, 0.619, 0.619, 0.616, and 0.603, respectively. The sensitivity and specificity were 65.7% and 65.6% for CRP/Ly, 51.6% and 70.8 for dNLR, 61.6% and 57.3% for NLPR, 40.6% and 80.4% for NLR, and 48.8% and 69.1% for NPR. Conclusion The results of the study suggest that NLR, dNLR, CRP/Ly, NPR, and NLPR can be considered as potentially useful markers for stratifying patients with a severe form of the disease. HDRs derived from routine blood tests results should be included in common laboratory practice since they are readily available, easy to calculate, and inexpensive.
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Affiliation(s)
- Sabina Segalo
- University of Sarajevo, Faculty of Health Studies, Sarajevo, Bosnia and Herzegovina
| | - Emina Kiseljakovic
- University of Sarajevo, Faculty of Medicine, Sarajevo, Bosnia and Herzegovina
| | - Emsel Papic
- University of Sarajevo, Faculty of Health Studies, Sarajevo, Bosnia and Herzegovina
| | - Anes Joguncic
- University of Sarajevo, Faculty of Health Studies, Sarajevo, Bosnia and Herzegovina
- Public Health Institute of Canton Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Aleksandra Pasic
- University of Sarajevo, Faculty of Health Studies, Sarajevo, Bosnia and Herzegovina
- Clinical Center University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Mubera Sahinagic
- Public Institution Medical Center of Sarajevo Canton, Sarajevo, Bosnia and Herzegovina
| | - Orhan Lepara
- University of Sarajevo, Faculty of Medicine, Sarajevo, Bosnia and Herzegovina
| | - Lutvo Sporisevic
- Public Institution Medical Center of Sarajevo Canton, Sarajevo, Bosnia and Herzegovina
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10
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Barough SS, Safavi-Naini SAA, Siavoshi F, Tamimi A, Ilkhani S, Akbari S, Ezzati S, Hatamabadi H, Pourhoseingholi MA. Generalizable machine learning approach for COVID-19 mortality risk prediction using on-admission clinical and laboratory features. Sci Rep 2023; 13:2399. [PMID: 36765157 PMCID: PMC9911952 DOI: 10.1038/s41598-023-28943-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: 10/11/2022] [Accepted: 01/27/2023] [Indexed: 02/12/2023] Open
Abstract
We aimed to propose a mortality risk prediction model using on-admission clinical and laboratory predictors. We used a dataset of confirmed COVID-19 patients admitted to three general hospitals in Tehran. Clinical and laboratory values were gathered on admission. Six different machine learning models and two feature selection methods were used to assess the risk of in-hospital mortality. The proposed model was selected using the area under the receiver operator curve (AUC). Furthermore, a dataset from an additional hospital was used for external validation. 5320 hospitalized COVID-19 patients were enrolled in the study, with a mortality rate of 17.24% (N = 917). Among 82 features, ten laboratories and 27 clinical features were selected by LASSO. All methods showed acceptable performance (AUC > 80%), except for K-nearest neighbor. Our proposed deep neural network on features selected by LASSO showed AUC scores of 83.4% and 82.8% in internal and external validation, respectively. Furthermore, our imputer worked efficiently when two out of ten laboratory parameters were missing (AUC = 81.8%). We worked intimately with healthcare professionals to provide a tool that can solve real-world needs. Our model confirmed the potential of machine learning methods for use in clinical practice as a decision-support system.
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Affiliation(s)
- Siavash Shirzadeh Barough
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyed Amir Ahmad Safavi-Naini
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Fatemeh Siavoshi
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Atena Tamimi
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Saba Ilkhani
- Department of Surgery, Center for Surgery and Public Health, Brigham and Women's Hospital, Harvard Medical School and Harvard T.H Chan School of Public Health, Boston, MA, USA
| | - Setareh Akbari
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sadaf Ezzati
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamidreza Hatamabadi
- Department of Emergency Medicine, School of Medicine, Safety Promotion and Injury Prevention Research Center, Imam Hossein Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohamad Amin Pourhoseingholi
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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11
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Ramos-Rincón JM, Ventura PS, Casas-Rojo JM, Mauri M, Bermejo CL, de Latierro AO, Rubio-Rivas M, Mérida-Rodrigo L, Pérez-Casado L, Barrientos-Guerrero M, Giner-Galvañ V, Gallego-Lezaun C, Milián AH, Manzano L, Blázquez-Encinar JC, Solís-Marquínez MN, García MG, Lobo-García J, Valente VAR, Roig-Martí C, León-Téllez M, Tellería-Gómez P, González-Juárez MJ, Gómez-Huelgas R, López-Escobar A, Bermejo CL, Núñez-Cortés JM, Santos JMA, Huelgas RG, Corbella X, Pérez FF, Homs N, Montero A, Mora-Luján JM, Rubio-Rivas M, Bandera VA, Alegría JG, Jiménez-García N, del Pino JL, Escalante MDM, Romero FN, Rodriguez VN, Sierra JO, de Blas PA, Cañas CA, Ayuso B, Morejón JB, Escudero SC, Frías MC, Tejido SC, de Miguel Campo B, Pedroche CD, Simon RD, Reyne AG, Veganzones LI, Huerta LJ, Blanco AL, Gonzalo JL, Lora-Tamayo J, Bermejo CL, de la Calle GM, Godoy RM, Perpiña BO, Ruiz DP, Fernández MS, Montes JT, Suárez AMÁ, Vergés CD, Martínez RFM, Aizpuru EMF, Carrasco AG, Amezua CH, Caleya JFL, Martínez DL, del Mar Martínez López M, Zapico AM, Iscar CO, Casado LP, Martínez MLT, Chamorro LMT, Casas LA, de Oña ÁA, Beato RA, Gonzalo LA, Muñoz JA, Oblitas CMA, García CA, Cebrián MB, Corral JB, Guerrero MB, Estrada ADB, Moreno MC, Fernández PC, Carrillo R, Pérez SC, Muñoz EC, Moreno ADC, Carvajal MCC, de Santos S, Gómez AE, Carracedo EF, Jenaro MMFM, Valle FG, Garcia A, Fernandez-Bravo IG, Leoni MEG, Antúnez MG, Narciso CGS, Gurjian AA, Ibáñez LJ, Olleros CL, Mendo CL, García SL, Jimeno VM, Nohales CM, Núñez-Cortés JM, Ledesma SM, Míguez AM, Delgado CM, Ortega LO, Sánchez SP, Virto AP, Sanz MTP, Llorente BP, Ruiz SP, Fernández-Llamazares GS, Macías MT, Samaniego NT, do Rego AT, Garcia MVV, Villarreal G, Etayo MZ, Lara RA, Fernandez IC, García JCC, García García GM, Granados JG, Sánchez BG, Periáñez FJM, Perez MJP, Pérez JLB, Méndez MLS, Rivera NA, Vieitez AC, del Corral Beamonte E, Manglano JD, Mera IF, del Mar Garcia Andreu M, Aseguinolaza MG, Lezaun CG, Laorden CJ, Murgui RM, Sanz MTM, Ayala-Gutiérrez MM, López RB, Fonseca JB, Buonaiuto VA, Martínez LFC, Palacios LC, Muriel CC, de Windt F, Christophel ATFT, Ocaña PG, Huelgas RG, García JG, Oliver JAH, Jansen-Chaparro S, López-Carmona MD, Quirantes PL, Sampalo AL, Lorenzo-Hernández E, Sevilla JJM, Carmona JM, Pérez-Belmonte LM, de Pedro IP, Pineda-Cantero A, Gómez CR, Ricci M, Cánovas JS, Troncoso JÁ, Fernández FA, Quintana FB, Arenzana CB, Molina SC, Candalija AC, Bengoa GD, de Gea Grela A, de Lorenzo Hernández A, Vidal AD, Capitán CF, Iglesias MFG, Muñoz BG, Gil CRH, Martínez JMH, Hontañón V, Hernández MJJ, Lahoz C, Calvo CM, Gutiérrez JCM, Prieto MM, Robles EM, Saldaña AM, Fernández AM, Prieto JMM, Mozo AN, López CMO, Peláez EP, Pampyn MP, Simón MAQ, Ramos Ramos JC, Ruperto LR, Purificación AS, Bueso TS, Torre RS, Abanedes CIS, Tabares YU, Mayoral MV, Manau JV, del Carmen Beceiro Abad M, Romero MAF, Castro SM, Guillan EMP, Nuñez MP, Fontan PMP, de Larriva APA, Espinal PC, Lista JD, Fuentes-Jiménez F, del Carmen Guerrero Martínez M, Vázquez MJG, Torres JJ, Pérez LL, López-Miranda J, Piedra LM, Orge MM, Vinagre JP, Pérez-Martinez P, Vílchez MER, Martínez AR, Cabrera JLR, Torres-Peña JD, Tomás MA, Balaz D, Tur DB, Navarro RC, Pérez PC, Redondo JC, White ED, Espínola ME, Del Barrio LE, Atiénzar PJE, Cervera CG, Núñez DFG, Navarro FG, Galvañ VG, Uranga AG, Martínez JG, Isasi IH, Villar LL, Sempere VM, Cruz JMN, Fernández SP, García JJP, Pleguezuelos RP, Pérez AR, Ripoll JMS, Mira AS, Wikman-Jorgensen P, Ayllón JAA, Artero A, del Mar Carmona Martín M, Valls MJF, de Mar Fernández Garcés M, Belda ABG, Cruz IL, López MM, Sanchis EM, Gandia JM, Roger LP, Belmonte AMP, García AV, Eisenhofer AA, Milla AA, Pérez IB, Gutiérrez LB, Garay JB, Parra JC, Díaz AC, Da Silva EC, Hernández MC, Díaz RC, Sánchez MJC, Gozalo CC, Martínez VCM, Doblado LD, de la Fuente Moral S, de Santiago AD, Yagüe ID, Velasco ID, Duca AM, del Campo PD, López GE, Palomo EE, Cruz AF, Gómez AG, Prieto SG, Revilla BG, Viejo MÁG, Irusta JG, Merino PG, Abreu EVG, Martín IG, Rojas ÁG, Villanueva AG, Jiménez JH, Estéllez FI, del Estal PL, Sáiz MCM, de Mendoza Fernández C, Urbistondo MM, Vera FM, Seirul-lo MM, Pita SM, Sánchez PAM, Hernández EM, Vargas AM, Concha VMT, De La Torre IM, Rubio EM, de Benito RM, Serrano AM, Palomo PN, Pascual IP, Martín-Vegue AJR, Martínez AR, Olleros CR, Montaud AR, Pizarro YR, García SR, de Domingo DR, Ortiz DS, Chica ES, Almena IS, Martin ES, Chen YT, de Ureta PT, Alijo ÁV, Comendador JMV, Núñez JAV, Yeguas IA, Gómez JA, Cuchillo JB, López IB, Clotet NC, Elías AEC, Manuel EC, de Luque CMC, Benbunan CC, Vilan LD, Hernández CD, Peralta EED, Pérez VE, Fernandez-Castelao S, Saavedra MOF, Klepzig JLG, del Rosario Iguarán Bermúdez M, Ferrer EJ, Rodríguez AM, de Pedro AM, Sánchez RÁM, Bailón MM, Álvarez SM, Orantos MJN, Mata CO, García EO, Mata DO, González CO, Perez-Somarriba J, Mateos PP, Muñoz MER, Regaira XR, Gallardo LMR, Fornie IS, Botrán AS, Robles MS, Urbano ME, González AMV, Martínez MV, Monge Monge D, Pasos EMF, García AV, Comet LS, Giménez LL, Samper UA, Repiso GA, Bruñén JMG, Barrio ML, Martínez MAC, Igual JJG, Fenoll RG, García MA, Monge EA, Rodríguez JÁ, Varela CA, Gòdia MB, Molina MB, Vega MB, Curbelo J, de las Heras Moreno A, Godoy ID, Alvarez ACE, Martín-Caro IF, López-Mosteiro AF, Marquez GG, Blanco MJG, del Álamo Hernández YG, Encina CGR, González NG, Rodríguez CG, Martín NLS, Báez MM, Delgado CM, Caballero PP, Serrano JP, Rodríguez LR, Cortés PR, Franco CR, Roy-Vallejo E, Vega MR, Lloret AS, Moreno BS, Alba MS, Ballesteros JS, Somovilla A, Fernández CS, Tirado MV, Marti AV, Pareja JFP, Fraile IP, Blanco AM, del Castillo Cantero R, López JLV, Lorite IR, Martínez RF, García IS, Rangel LS, Álvarez AA, Juarros OA, López AA, Castiñeira CC, Calviño AC, Sánchez MC, Varela RF, Castro SJF, Trigo AP, Jarel RP, Varea FR, Freán IR, Alonso LR, Pensado FJS, Porto DV, Saavedra CC, Gómez JF, López BG, Garrido MSH, Amorós AIL, Gil SL, de los Reyes Pascual Pérez M, Perea NR, García AT, Lobo JA, Casanovas LF, Amigo JL, Fernández MM, Bermúdez IO, Fernández MP, Rhyman N, Piqueras NV, Pedrajas JNA, García AM, Vargas I, Jiménez IA, González MC, Cobos-Siles M, Corral-Gudino L, Cubero-Morais P, Fernández MG, González JPM, Dehesa MP, Espinosa PS, Blanco SC, Gamboa JOM, Mosteiro CS, Asiain AS, Santos JMA, Barrera ABB, Vela BB, Muiño CB, Fernández CB, Hernáiz RC, López IC, Rojo JMC, Troncoso AC, Romano PC, Deodati F, Santiago AE, Sánchez GGC, Guijarro EG, Sánchez FJG, de la Torre PG, de Guzmán García-Monge M, Luordo D, González MM, Bermejo JAM, Valverde CP, Quero JLP, Rojas FR, García LR, Gonzalo ES, Muñoz FJT, de la Sota JV, Martínez JV, Gómez MG, Sánchez PR, Gonzalez GA, Iraurgi AL, Arostegui AA, Martínez PA, Fernández IMP, Becerro EM, Jiménez AI, Núñez CV, López MA, López EG, Losada MSA, Estévez BR, Muñoz AMA, Fernández MB, Cano V, Moreno RC, Garcia-Tenorio FC, Nájera BDT, González RE, Butenegro MPG, Díez AG, Caverzaschi VG, Pedraza PMG, Moraleja JG, Carvajal RH, Aranda PJ, González RL, Caparachini ÁL, Castañeyra PL, Ancin AL, Garcia JDM, Romero CM, Saiz MJM, Moríñigo HM, Nicolás GM, Platon EM, Oliveri F, Ortiz Ortiz E, Rafael RP, Galán PR, Berrocal MAS, de Ávila VSR, Sierra PT, Aranda YU, Clemente JV, Bergua CY, de la Peña Fernández A, Milián AH, Manrique MA, Erdozain AC, Ruiz ALI, Luque FJB, Carrasco-Sánchez FJ, de-Sousa-Baena M, Leal JD, Rubio AE, Huertas MF, Bravo JAG, Macías AG, Jiménez EG, Jiménez AH, Quintero CL, Reguera CM, Marcos FJM, Beamud FM, Pérez-Aguilar M, Jiménez AP, Castaño VR, dedel AlcazarRío AS, Ruiz LT, González DA, de Zabalza IAP, Hernández SA, Sáenz JC, Dendariena B, del Mazo MG, de Narvajas Urra IM, Hernández SM, Fernández EM, Somovilla JLP, Pejenaute ER, Rodríguez-Solís JB, Osorio LC, del Pilar Fidalgo Montero M, Soriano MIF, Rincón EEL, Hermida AM, Carrilero JM, Santiago JÁP, Robledo MS, Rojas PS, Yebes NJT, Vento V, Vaca LFA, Arnanz AA, García OA, González MB, Sanz PB, Llisto AC, de Pedro Baena S, Del Hoyo Cuenda B, Fabregate-Fuente M, Osorio MAG, Sánchez IG, García AG, Cisneros OAL, Manzano L, Martínez-Lacalzada M, Ortiz BM, Rey-García J, González ER, Díaz CS, Fajardo GS, Carantoña CS, Viteri-Noël A, Zhilina Zhilina S, Claudio GMA, Rodríguez VB, Muñoz CC, Pérez AC, Orbes MVC, Sánchez DE, Revuelta SI, Martín MM, González JIM, Oterino JÁM, Alonso LM, Balbuena SP, García MLP, Prados AR, Rodríguez-Alonso B, Alegría ÁR, Ledesma MS, Pérez RJT, Encinar JCB, Cilleros CM, Martínez IJ, Delange TG, González RF, Noya AG, Ceron CH, Avanzini II, Diez AL, Mato PL, Vizcaya AML, Benítez DP, Zemsch MMP, Expósito LP, Bar MP, González LR, Lara LR, Cabañero D, Ballester MC, Fernández PC, Sánchez RG, Escrig MJ, Amela CM, Gómez LP, Navarro CP, Parra JAT, de Almeida CT, Villarejo MEF, Calvo VP, Otero SP, López BG, Frías CA, Romero VM, Pérez LA, Velado EM, González RA, Boixeda R, Fernández Fernández J, Mármol CL, Navarro MP, Guzmán AR, Fustier AS, Castro JL, Reboiro MLL, González CS, Sala ER, Izuel JMP, Zamrani ZK, Diaz HA, Lopez TD, Pego EM, Pérez CM, Ferro AP, Trigo SS, Sambade DS, Ferrin MT, del Carmen Vázquez Friol M, Maneiro LV, Rodríguez BC, Espartero MEG, Rivas LM, de la Sierra Navas Alcántara M, Tirado-Miranda R, Marquínez MNS, García VA, Suárez DB, Arenas NG, García PM, Copa DC, García AÁ, Álvarez JC, Calderón MJM, Noriega RG, Rubia MC, García JL, Martínez LT, Celeiro JF, Aguilar DEO, Riesco IM, Bécares JV, Mateos AB, García AAT, Casamayor JD, Silvera DG, Díaz AA, Carballo CH, Tejera A, Prieto MJM, Muñoz MBM, Del Arco Delgado JM, Díaz DR, Feria MB, Herrera Herrera FJ, de la Luz Padilla Salazar M, Luis RH, Ledezma EMC, del Mar López Gámez M, Hernández LT, Pérez SC, García SGA, Gainett GC, Hidalgo AG, Daza JM, Peraza MH, Santos RA, Bernabeu-Wittel M, Suárez SR, Nieto M, Miranda LG, Mancera RMG, Torre FE, Quiles CH, Guzmán CC, de la Cuesta JD, Vega JET, del Carmen López Ríos M, Jiménez PD, Franco BB, de Juan CJ, Rivero SG, Tenllado JL, Lara VA, Estrada AG, Ena J, Segado JEG, Ferrer RG, Lorenzo VG, Arroyo RM, García MG, Hernández FJV, González ÁLM, Montes BV, Die RMG, Molinero AM, Regidor MM, Díez RR, Sierra BH, García LFD, Acedo IEA, Cano CMS, García VH, Bernal BR, Jiménez JC, Bazán EC, Reniu AC, Grabalosa JR, Solà JF, De Boulle IC, Xancó CG, Núñez OR, Ripper CJ, Gutiérrez AG, Trallero LER, Novo MFA, Lecumberri JJN, Ruiz NP, Riancho J, García IS, Baena PC, Sevilla JE, Padilla LG, Ronquillo PG, Bustos PG, Botías MN, Taboada JR, Rodríguez MR, Alvarez VA, Suárez NM, Suárez SR, Díaz SS, Pérez LS, Gómez MF, Castaño CM, Rodríguez LM, Vázquez C, Estévanez IC, Gutiérrez CY, Sela MM, Cosío SF, Álvaro CMG, García JL, Piñeiro AP, Viera YC, Rodríguez LC, de Juan Alvarez C, Benitez GF, Escudero LG, Torres JM, Escriche PM, Canteli SP, Pérez MCR, Soler JA, Remolar MB, Álvarez AC, Carlotti DD, Gimeno MJE, Juana SF, López PG, Soler MTG, de la Sota DP, Castellanos GP, Catalán IP, Martí CR, Monzó PR, Padilla JR, Gaya NT, Blasco JU, Pascual MAM, Vidal LJ, Conesa AA, Rivas MCA, Alsina MH, Romero JM, Diez-Canseco AMU, Martínez FA, Vásquez EA, Stablé JCE, Belmonte AH, Peiró AM, Goñi RM, Castellanos MCP, Belda BS, Navarro DV, Lombraña AS, Ugartondo JC, Plaza ABM, Asensio AN, Alves BP, López NV, Téllez ML, Epelde F, Torrente I, Vasco PG, Santacruz AR, Muñoz AV, Giner MJE, Calvo-Sotelo AE, Sardón EG, González JG, Salazar LG, Garcia AA, Días IM, Gomez AS, Matos MC, Gaspar SN, Nieto AG, Méndez RG, Álvarez AR, Hernández OP, Ramírez AP, González MCM, Lorite MNN, Navarrete LG, Negrin JCA, González JFA, Jiménez I, Toledo PO, Ponce EM, Torres XTE, González SG, Fernández CN, Gómez PT, Gisbert OA, Llistosella MB, Casanova PC, Flores AG, Hinojo AG, Martínez AIM, del Carmen Nogales Nieves M, Austrui AR, Cervantes AZ, Castro VA, Lomba AMB, Aparicio RB, Morales MF, Villar JMF, Monteagudo MTL, García CP, Ferreira LR, Llovo DS, Feijoo MBV, Romero JAM, de Albornoz JLSC, Pérez MJS, Martín ES, Astrua TC, Giraldo PTG, Juárez MJG, Fernandez VM, Echevarry AVR, Arche JFV, Rivero MGR, Martínez AM, Bernad RV, Limia C, Fernández CA, Fernández AT, Fajardo LP, de Vega Santos T, Ruiz AL, Míguez HM. Validation of the RIM Score-COVID in the Spanish SEMI-COVID-19 Registry. Intern Emerg Med 2023; 18:907-915. [PMID: 36680737 PMCID: PMC9862219 DOI: 10.1007/s11739-023-03200-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 01/09/2023] [Indexed: 01/22/2023]
Abstract
The significant impact of COVID-19 worldwide has made it necessary to develop tools to identify patients at high risk of severe disease and death. This work aims to validate the RIM Score-COVID in the SEMI-COVID-19 Registry. The RIM Score-COVID is a simple nomogram with high predictive capacity for in-hospital death due to COVID-19 designed using clinical and analytical parameters of patients diagnosed in the first wave of the pandemic. The nomogram uses five variables measured on arrival to the emergency department (ED): age, sex, oxygen saturation, C-reactive protein level, and neutrophil-to-platelet ratio. Validation was performed in the Spanish SEMI-COVID-19 Registry, which included consecutive patients hospitalized with confirmed COVID-19 in Spain. The cohort was divided into three time periods: T1 from February 1 to June 10, 2020 (first wave), T2 from June 11 to December 31, 2020 (second wave, pre-vaccination period), and T3 from January 1 to December 5, 2021 (vaccination period). The model's accuracy in predicting in-hospital COVID-19 mortality was assessed using the area under the receiver operating characteristics curve (AUROC). Clinical and laboratory data from 22,566 patients were analyzed: 15,976 (70.7%) from T1, 4,233 (18.7%) from T2, and 2,357 from T3 (10.4%). AUROC of the RIM Score-COVID in the entire SEMI-COVID-19 Registry was 0.823 (95%CI 0.819-0.827) and was 0.834 (95%CI 0.830-0.839) in T1, 0.792 (95%CI 0.781-0.803) in T2, and 0.799 (95%CI 0.785-0.813) in T3. The RIM Score-COVID is a simple, easy-to-use method for predicting in-hospital COVID-19 mortality that uses parameters measured in most EDs. This tool showed good predictive ability in successive disease waves.
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Affiliation(s)
| | - Paula Sol Ventura
- Fundacio Institut d’Investigacio en Ciències de La Salut Germans Trias I Pujol (IGTP), 08916 Badalona, Spain
| | - José-Manuel Casas-Rojo
- Internal Medicine Department, Infanta Cristina University Hospital, Parla, 28981 Madrid, Spain
| | - Marc Mauri
- Data Scientist, Kaizen AI, Barcelona, Spain
| | | | | | - Manuel Rubio-Rivas
- Department of Internal Medicine, Bellvitge University Hospital, Hospitalet de Llobregat, Barcelona, Spain
| | | | | | | | - Vicente Giner-Galvañ
- Internal Medicine Department. Hospital, Clínico Universitario de Sant Joan d’Alacant, Alicante, Spain
| | | | | | - Luis Manzano
- Internal Medicine Department, Ramón y Cajal University Hospital, Madrid, Spain
| | | | | | | | | | | | | | | | | | | | - Ricardo Gómez-Huelgas
- Internal Medicine Department, Regional University Hospital of Málaga, Biomedical Research Institute of Málaga (IBIMA), University of Málaga (UMA), Málaga, Spain
| | - Alejandro López-Escobar
- Pediatrics Department, Clinical Research Unit, Hospital Universitario Vithas Madrid La Milagrosa, Fundación Vithas. Madrid, Madrid, Spain
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Avelino-Silva VI, Avelino-Silva TJ, Aliberti MJR, Ferreira JC, Cobello Junior V, Silva KR, Pompeu JE, Antonangelo L, Magri MM, Filho TEPB, Souza HP, Kallás EG. Prediction of intensive care admission and hospital mortality in COVID-19 patients using demographics and baseline laboratory data. Clinics (Sao Paulo) 2023; 78:100183. [PMID: 36989546 PMCID: PMC9998300 DOI: 10.1016/j.clinsp.2023.100183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 02/09/2023] [Accepted: 02/22/2023] [Indexed: 03/12/2023] Open
Abstract
INTRODUCTION Optimized allocation of medical resources to patients with COVID-19 has been a critical concern since the onset of the pandemic. METHODS In this retrospective cohort study, the authors used data from a Brazilian tertiary university hospital to explore predictors of Intensive Care Unit (ICU) admission and hospital mortality in patients admitted for COVID-19. Our primary aim was to create and validate prediction scores for use in hospitals and emergency departments to aid clinical decisions and resource allocation. RESULTS The study cohort included 3,022 participants, of whom 2,485 were admitted to the ICU; 1968 survived, and 1054 died in the hospital. From the complete cohort, 1,496 patients were randomly assigned to the derivation sample and 1,526 to the validation sample. The final scores included age, comorbidities, and baseline laboratory data. The areas under the receiver operating characteristic curves were very similar for the derivation and validation samples. Scores for ICU admission had a 75% accuracy in the validation sample, whereas scores for death had a 77% accuracy in the validation sample. The authors found that including baseline flu-like symptoms in the scores added no significant benefit to their accuracy. Furthermore, our scores were more accurate than the previously published NEWS-2 and 4C Mortality Scores. DISCUSSION AND CONCLUSIONS The authors developed and validated prognostic scores that use readily available clinical and laboratory information to predict ICU admission and mortality in COVID-19. These scores can become valuable tools to support clinical decisions and improve the allocation of limited health resources.
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Affiliation(s)
- Vivian I Avelino-Silva
- Department of Infectious and Parasitic Diseases, Faculdade de Medicina da Universidade de São Paulo, SP, Brazil.
| | - Thiago J Avelino-Silva
- Laboratório de Investigação Médica em Envelhecimento (LIM-66), Serviço de Geriatria, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, SP, Brazil
| | - Marlon J R Aliberti
- Laboratório de Investigação Médica em Envelhecimento (LIM-66), Serviço de Geriatria, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, SP, Brazil
| | - Juliana C Ferreira
- Divisão de Pneumologia, Instituto do Coração, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, SP, Brazil
| | - Vilson Cobello Junior
- Núcleo Especializado em Tecnologia da Informação, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, SP, Brazil
| | - Katia R Silva
- Instituto do Coração, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, SP, Brazil
| | - Jose E Pompeu
- Departamento de Fisioterapia, Fonoaudiologia e Terapia Ocupacional, Faculdade de Medicina, Universidade de São Paulo, SP, Brazil
| | - Leila Antonangelo
- Laboratório Central, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, SP, Brazil
| | - Marcello M Magri
- Department of Infectious and Parasitic Diseases, Faculdade de Medicina da Universidade de São Paulo, SP, Brazil
| | - Tarcisio E P Barros Filho
- Instituto de Ortopedia e Traumatologia, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, SP, Brazil
| | - Heraldo P Souza
- Emergency Department, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, SP, Brazil
| | - Esper G Kallás
- Department of Infectious and Parasitic Diseases, Faculdade de Medicina da Universidade de São Paulo, SP, Brazil
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Sarkar S, Khanna P, Singh AK. The Impact of Neutrophil-Lymphocyte Count Ratio in COVID-19: A Systematic Review and Meta-Analysis. J Intensive Care Med 2022; 37:857-869. [PMID: 34672824 PMCID: PMC9160638 DOI: 10.1177/08850666211045626] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 08/20/2021] [Accepted: 08/25/2021] [Indexed: 12/29/2022]
Abstract
Background: The neutrophil-lymphocyte count ratio (NLR) has emerged as a potential prognostic tool for different diseases. In the current coronavirus disease (COVID-19) pandemic, the NLR may be a useful tool for risk scarification and the optimal utilization of limited healthcare resources. However, there is no consensus regarding the optimal value of NLR, and the association with disease severity and mortality. Thus, this study aims to systematically analyze the current evidence of the utility of baseline NLR as a predictive tool for mortality, disease severity in COVID-19 patients. Methods: A compendious screening of electronic databases up to June 15, 2021, was done after enlisting the protocol in PROSPERO (CRD42020202659). Studies evaluating the utility of baseline NLR in COVID-19 are included for this review as per the PRISMA statement. Results: We retrieved a total of 13112 and 12986 COVID-19 patients for survivability and severity over 90 studies. The expired and critically sick patients had elevated baseline NLR on admission, in comparison to survivors and noncritical patients. (SMD = 3.82; 95% CI: 2.79-4.85; I2 = 100% and SMD = 1.42; 95% CI: 1.22-1.63; I2 = 95%, respectively). The summary receiver operating curve analysis for mortality (AUC = 0.87; 95% CI: 0.86-0.87; I2 = 94.7%), and severity (AUC = 0.82; 95% CI: 0.80-0.84; I2 = 79.7%) were also suggestive of its significant predictive value. Conclusions: The elevated NLR on admission in COVID-19 patients is associated with poor outcomes.
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Asaduzzaman MD, Romel Bhuia M, Nazmul Alam ZHM, Zabed Jillul Bari M, Ferdousi T. Significance of hemogram-derived ratios for predicting in-hospital mortality in COVID-19: A multicenter study. Health Sci Rep 2022; 5:e663. [PMID: 35686199 PMCID: PMC9172589 DOI: 10.1002/hsr2.663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 03/27/2022] [Accepted: 05/15/2022] [Indexed: 11/10/2022] Open
Abstract
Background To address the problem of resource limitation, biomarkers having a potential for mortality prediction are urgently required. This study was designed to evaluate whether hemogram-derived ratios could predict in-hospital deaths in COVID-19 patients. Materials and Methods This multicenter retrospective study included hospitalized COVID-19 patients from four COVID-19 dedicated hospitals in Sylhet, Bangladesh. Data on clinical characteristics, laboratory parameters, and survival outcomes were analyzed. Logistic regression models were fitted to identify the predictors of in-hospital death. Results Out of 442 patients, 55 (12.44%) suffered in-hospital death. The proportion of male was higher in nonsurvivor group (61.8%). The mean age was higher in nonsurvivors (69 ± 13 vs. 59 ± 14 years, p < 0.001). Compared to survivors, nonsurvivors exhibited higher frequency of comorbidities, such as chronic kidney disease (34.5% vs. 15.2%, p ≤ 0.001), chronic obstructive pulmonary disease (23.6% vs. 10.6%, p = 0.011), ischemic heart disease (41.8% vs. 19.4%, p < 0.001), and diabetes mellitus (76.4% vs. 61.8%, p = 0.05). Leukocytosis and lymphocytopenia were more prevalent in nonsurvivors (p < 0.05). Neutrophil-to-lymphocyte ratio (NLR), derived NLR (d-NLR), and neutrophil-to-platelet ratio (NPR) were significantly higher in nonsurvivors (p < 0.05). After adjusting for potential covariates, NLR (odds ratio [OR] 1.05; 95% confidence interval [CI] 1.009-1.08), d-NLR (OR 1.08; 95% CI 1.006-1.14), and NPR (OR 1.20; 95% CI 1.09-1.32) have been found to be significant predictors of mortality in hospitalized COVID-19 patients. The optimal cut-off points for NLR, d-NLR, and NPR for prediction of in-hospital mortality for COVID-19 patients were 7.57, 5.52 and 3.87, respectively. Conclusion Initial assessment of NLR, d-NLR, and NPR values at hospital admission is of good prognostic value for predicting mortality of patients with COVID-19.
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Affiliation(s)
- MD Asaduzzaman
- Department of MedicineSylhet MAG Osmani Medical College HospitalSylhetBangladesh
| | - Mohammad Romel Bhuia
- Department of StatisticsShahjalal University of Science and TechnologySylhetBangladesh
| | - ZHM Nazmul Alam
- Department of MedicineSylhet MAG Osmani Medical College HospitalSylhetBangladesh
| | | | - Tasnim Ferdousi
- Department of OphthalmologyBangabandhu Sheikh Mujib Medical UniversityDhakaBangladesh
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ASADUZZAMAN MD, BHUIA MOHAMMADROMEL, ALAM ZHMNAZMUL, BARI MOHAMMADZABEDJILLUL, FERDOUSI TASNIM. Role of hemogram-derived ratios in predicting intensive care unit admission in COVID-19 patients: a multicenter study. IJID REGIONS 2022; 3:234-241. [PMID: 35720134 PMCID: PMC9050181 DOI: 10.1016/j.ijregi.2022.04.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 03/16/2022] [Accepted: 04/25/2022] [Indexed: 02/07/2023]
Abstract
Purpose As hyperinflammation is recognized as a driver of severe COVID-19 disease, checking markers of inflammation is gaining more attention. Our study aimed to evaluate the utility of cost-effective hemogram-derived ratios in predicting intensive care unit (ICU) admission in COVID-19 patients. Methods This multicenter retrospective study included hospitalized COVID-19 patients from four dedicated COVID-19 hospitals in Sylhet, Bangladesh. Data on demographics, clinical characteristics, laboratory parameters and survival outcomes were analyzed. Logistic regression analysis was used to identify the significance of each hemogram-derived ratio in predicting ICU admission. Results Of 442 included patients, 98 (22.17%) required ICU admission. At the time of admission, patients requiring ICU had a higher neutrophil count and lower lymphocyte and platelet counts than patients not requiring ICU. Peripheral capillary oxygen saturation at admission was significantly lower in those who subsequently required ICU admission. Neutrophil-to-lymphocyte ratio, derived neutrophil-to-lymphocyte ratio, neutrophil-to-platelet ratio, and systemic immune-inflammation index were significant predictors of ICU admission. Conclusion Hemogram-derived ratios can be an effective tool in facilitating the early categorization of at-risk patients, enabling timely measures to be taken early in the disease course.
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Affiliation(s)
- MD ASADUZZAMAN
- Department of Medicine, Sylhet MAG Osmani Medical College Hospital, Sylhet-3100, Bangladesh
| | - MOHAMMAD ROMEL BHUIA
- Department of Statistics, Shahjalal University of Science and Technology, Sylhet-3114, Bangladesh
| | - ZHM NAZMUL ALAM
- Department of Medicine, Sylhet MAG Osmani Medical College Hospital, Sylhet-3100, Bangladesh
| | | | - TASNIM FERDOUSI
- Department of Ophthalmology, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh
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Speech as a Biomarker for COVID-19 Detection Using Machine Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6093613. [PMID: 35444694 PMCID: PMC9014833 DOI: 10.1155/2022/6093613] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 03/07/2022] [Accepted: 03/21/2022] [Indexed: 11/30/2022]
Abstract
The use of speech as a biomedical signal for diagnosing COVID-19 is investigated using statistical analysis of speech spectral features and classification algorithms based on machine learning. It is established that spectral features of speech, obtained by computing the short-time Fourier Transform (STFT), get altered in a statistical sense as a result of physiological changes. These spectral features are then used as input features to machine learning-based classification algorithms to classify them as coming from a COVID-19 positive individual or not. Speech samples from healthy as well as “asymptomatic” COVID-19 positive individuals have been used in this study. It is shown that the RMS error of statistical distribution fitting is higher in the case of speech samples of COVID-19 positive speech samples as compared to the speech samples of healthy individuals. Five state-of-the-art machine learning classification algorithms have also been analyzed, and the performance evaluation metrics of these algorithms are also presented. The tuning of machine learning model parameters is done so as to minimize the misclassification of COVID-19 positive individuals as being COVID-19 negative since the cost associated with this misclassification is higher than the opposite misclassification. The best performance in terms of the “recall” metric is observed for the Decision Forest algorithm which gives a recall value of 0.7892.
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Karimpour-razkenari E, Naderi-Behdani F, Salahshoor A, Heydari F, Alipour A, Baradari AG. Melatonin as adjunctive therapy in patients admitted to the Covid-19. Ann Med Surg (Lond) 2022; 76:103492. [PMID: 35287296 PMCID: PMC8908573 DOI: 10.1016/j.amsu.2022.103492] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/03/2022] [Accepted: 03/06/2022] [Indexed: 11/24/2022] Open
Abstract
Objective Coronavirus has disrupted the natural order of the world since September 2019 with no specific medication. The beneficial effects of melatonin on sepsis and viral influenza were demonstrated previously, but its effects on covid-19, especially COVID -19 ICU, is unclear. Therefore, our aim was to determine the effects of melatonin in COVID-19 ICU patients. Methods This is a retrospective cohort study in which the records of patients admitted to COVID -19 ICU of (XXX) during March to June 2020 were reviewed. According to inclusion criteria, patients who received 15 mg of melatonin daily were called MRG and the rest were called NMRG. Results Thirty-one patients were included and analyzed, of which twelve patients were in MRG. Demographic and clinical characteristics, and laboratory data were similar between two groups at ICU admission. Melatonin had no significant effect on ICU duration, CRP and ESR, also the trend of changes was in favor of melatonin. Nevertheless, melatonin significantly reduced the NLR (OR = −9.81, p = 0.003), and also declined mortality marginally (p = 0.09). Melatonin was well tolerated with no major adverse effects, moreover the thrombocytopenia occurrence was significantly lower in MRG (p = 0.005). In MRG, survival increased and mortality risk decreased, although the difference between groups wasn't significant (p = 0.37), which might be related to the small sample-size. Conclusion Our study showed that melatonin is unlikely to reduce mortality among COVID19 patients and with no significant effect on disease-specific biochemical parameters. Coronavirus has disrupted the natural order of the world since September 2019 with no specific medication. The beneficial effects of melatonin on sepsis and viral influenza were demonstrated previously. Our survey showed melatonin had a beneficial effect on survival and mortality risk. As well as platelets and lymphocytes without life-threatening complications. Melatonin was an essential adjuvant therapy in patients admitted to covid-19 ICU.
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Kurzeder L, Jörres RA, Unterweger T, Essmann J, Alter P, Kahnert K, Bauer A, Engelhardt S, Budweiser S. A simple risk score for mortality including the PCR Ct value upon admission in patients hospitalized due to COVID-19. Infection 2022; 50:1155-1163. [PMID: 35218511 PMCID: PMC8881702 DOI: 10.1007/s15010-022-01783-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 02/10/2022] [Indexed: 12/12/2022]
Abstract
Purpose To develop a simple score for the outcomes from COVID-19 that integrates information obtained at the time of admission including the Ct value (cycle threshold) for SARS-CoV-2. Methods Patients with COVID-19 hospitalized from February 1st to May 31st 2021 in RoMed hospitals, Germany, were included. Clinical and laboratory parameters upon admission were recorded and patients followed until discharge or death. Logistic regression analysis was used to determine predictors of outcomes. Regression coefficients were used to develop a risk score for death. Results Of 289 patients (46% female, median age 66 years), 29% underwent high-flow nasal oxygen (HFNO) therapy, 28% were admitted to the Intensive Care Unit (ICU, 51% put on invasive ventilation, IV), and 15% died. Age > 70 years, oxygen saturation ≤ 90%, oxygen supply upon admission, eGFR ≤ 60 ml/min and Ct value ≤ 26 were significant (p < 0.05 each) predictors for death, to which 2, 2, 1, 1 and 2 score points, respectively, could be attributed. Sum scores of ≥ 4 or ≥ 5 points were associated with a sensitivity of 95.0% or 82.5%, and a specificity of 72.5% or 81.7% regarding death. The high predictive value of the score was confirmed using data obtained between December 15th 2020 and January 31st 2021 (n = 215). Conclusion In COVID-19 patients, a simple scoring system based on data available shortly after hospital admission including the Ct value had a high predictive value for death. The score may also be useful to estimate the likelihood for required interventions at an early stage. Supplementary Information The online version contains supplementary material available at 10.1007/s15010-022-01783-1.
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Affiliation(s)
- Luis Kurzeder
- Department of Internal Medicine III, Division of Pulmonary and Respiratory Medicine, RoMed Hospital Rosenheim, Pettenkoferstrasse 10, 83022, Rosenheim, Germany
| | - Rudolf A Jörres
- Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, Member of the German Center for Lung Research (DZL), University Hospital, LMU Munich, Comprehensive Pneumology Center Munich (CPC-M), Munich, Germany
| | - Thomas Unterweger
- Department of Internal Medicine III, Division of Pulmonary and Respiratory Medicine, RoMed Hospital Rosenheim, Pettenkoferstrasse 10, 83022, Rosenheim, Germany
| | - Julian Essmann
- Department of Internal Medicine III, Division of Pulmonary and Respiratory Medicine, RoMed Hospital Rosenheim, Pettenkoferstrasse 10, 83022, Rosenheim, Germany
| | - Peter Alter
- Department of Medicine, Pulmonary and Critical Care Medicine, Member of the German Center for Lung Research (DZL), University of Marburg (UMR), Marburg, Germany
| | - Kathrin Kahnert
- Department of Medicine V, Member of the German Center for Lung Research (DZL), University Hospital, LMU Munich, Comprehensive Pneumology Center Munich (CPC-M), Munich, Germany
| | - Andreas Bauer
- Institute for Anesthesiology and Surgical Intensive Care Medicine, RoMed Hospital Rosenheim, Rosenheim, Germany
| | - Sebastian Engelhardt
- Department of Internal Medicine III, Division of Pulmonary and Respiratory Medicine, RoMed Hospital Rosenheim, Pettenkoferstrasse 10, 83022, Rosenheim, Germany
| | - Stephan Budweiser
- Department of Internal Medicine III, Division of Pulmonary and Respiratory Medicine, RoMed Hospital Rosenheim, Pettenkoferstrasse 10, 83022, Rosenheim, Germany.
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Miller JL, Tada M, Goto M, Chen H, Dang E, Mohr NM, Lee S. Prediction models for severe manifestations and mortality due to COVID-19: A systematic review. Acad Emerg Med 2022; 29:206-216. [PMID: 35064988 DOI: 10.1111/acem.14447] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 12/21/2021] [Accepted: 12/29/2021] [Indexed: 12/19/2022]
Abstract
BACKGROUND Throughout 2020, the coronavirus disease 2019 (COVID-19) has become a threat to public health on national and global level. There has been an immediate need for research to understand the clinical signs and symptoms of COVID-19 that can help predict deterioration including mechanical ventilation, organ support, and death. Studies thus far have addressed the epidemiology of the disease, common presentations, and susceptibility to acquisition and transmission of the virus; however, an accurate prognostic model for severe manifestations of COVID-19 is still needed because of the limited healthcare resources available. OBJECTIVE This systematic review aims to evaluate published reports of prediction models for severe illnesses caused COVID-19. METHODS Searches were developed by the primary author and a medical librarian using an iterative process of gathering and evaluating terms. Comprehensive strategies, including both index and keyword methods, were devised for PubMed and EMBASE. The data of confirmed COVID-19 patients from randomized control studies, cohort studies, and case-control studies published between January 2020 and May 2021 were retrieved. Studies were independently assessed for risk of bias and applicability using the Prediction Model Risk Of Bias Assessment Tool (PROBAST). We collected study type, setting, sample size, type of validation, and outcome including intubation, ventilation, any other type of organ support, or death. The combination of the prediction model, scoring system, performance of predictive models, and geographic locations were summarized. RESULTS A primary review found 445 articles relevant based on title and abstract. After further review, 366 were excluded based on the defined inclusion and exclusion criteria. Seventy-nine articles were included in the qualitative analysis. Inter observer agreement on inclusion 0.84 (95%CI 0.78-0.89). When the PROBAST tool was applied, 70 of the 79 articles were identified to have high or unclear risk of bias, or high or unclear concern for applicability. Nine studies reported prediction models that were rated as low risk of bias and low concerns for applicability. CONCLUSION Several prognostic models for COVID-19 were identified, with varying clinical score performance. Nine studies that had a low risk of bias and low concern for applicability, one from a general public population and hospital setting. The most promising and well-validated scores include Clift et al.,15 and Knight et al.,18 which seem to have accurate prediction models that clinicians can use in the public health and emergency department setting.
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Affiliation(s)
- Jamie L. Miller
- University of Iowa Carver College of Medicine Iowa City Iowa USA
| | - Masafumi Tada
- Department of Health Promotion and Human Behavior School of Public Health, Kyoto University Graduate School of Medicine Kyoto Japan
| | - Michihiko Goto
- Division of Infectious Diseases, Department of Internal Medicine University of Iowa Carver College of Medicine Iowa City Iowa USA
| | - Hao Chen
- University of Iowa Iowa City Iowa USA
| | | | - Nicholas M. Mohr
- Department of Emergency Medicine, Department of Anesthesia, Department of Epidemiology University of Iowa Carver College of Medicine Iowa City Iowa USA
| | - Sangil Lee
- Department of Emergency Medicine The University of Iowa Carver College of Medicine Iowa City Iowa USA
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20
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Fathi Karkan S, Maleki Baladi R, Shahgolzari M, Gholizadeh M, Shayegh F, Arashkia A. The evolving direct and indirect platforms for the detection of SARS-CoV-2. J Virol Methods 2022; 300:114381. [PMID: 34843826 PMCID: PMC8626143 DOI: 10.1016/j.jviromet.2021.114381] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 06/26/2021] [Accepted: 11/25/2021] [Indexed: 01/08/2023]
Abstract
Diagnosis of SARS-CoV-2 by standard screening measures can reduce the chance of COVID-19 spread before the symptoms become severe. Detecting viral RNA and antigens, anti-viral antibodies, and CT-scan are the most routine diagnostic methods. Accordingly, several diagnostic platforms including thermal and isothermal amplifications, CRISPR/Cas‑based approaches, digital PCR, ELISA, NGS, and point-of-care testing methods with variable sensitivities, have been developed that may facilitate managing and preventing the further spread of the infection. Here, we summarized the currently available direct and indirect testing platforms in research and clinical settings, including recent progress in the methods to detect viral RNA, antigens, and specific antibodies. This summary may help in selecting the effective method for a special application sucha as routine laboratory diagnosis, point-of-care tests or tracing the the virus spread and mutations.
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Affiliation(s)
- Sonia Fathi Karkan
- Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran,Department of Medical Nanotechnology, Tabriz Medical University, Tabriz, Iran
| | - Reza Maleki Baladi
- Young Researchers and Elite Club, Tabriz Branch, Islamic Azad University, Tabriz, Iran
| | - Mehdi Shahgolzari
- Department of Medical Nanotechnology, Tabriz Medical University, Tabriz, Iran
| | - Monireh Gholizadeh
- Department of Medical Biotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran,Department of Immunology, Pasteur Institute of Iran, Tehran, Iran
| | - Fahimeh Shayegh
- Department of Medical Biotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Arash Arashkia
- Deaprtment of Molecular Virology, Pasteur Institute of Iran, Tehran, Iran.
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21
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Xia W, Tan Y, Hu S, Li C, Jiang T. Predictive Value of Systemic Immune-Inflammation index and Neutrophil-to-Lymphocyte Ratio in Patients with Severe COVID-19. Clin Appl Thromb Hemost 2022; 28:10760296221111391. [PMID: 35765218 PMCID: PMC9247370 DOI: 10.1177/10760296221111391] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Objective: It was initially reported that a novel coronavirus (COVID-19) had been identified in Wuhan, China, in December 2019.To date, COVID-19 is still threatening all humanity and has affected the public healthcare system and the world economic situation. Neutrophil-to-lymphocyte ratio (NLR) has also been demonstrated that associated with severity of COVID-19, but little is known about systemic immune-inflammation index (SII) relation with COVID-19. Methods: One hundred and twenty-five patients with diagnosed COVID-19 including non-severe cases (n = 77) and severe cases (n = 48) were enrolled in this study. Each patient of clinical characteristic information, blood routine parameters, and the haemogram-derived ratios were collected, calculated, and retrospectively analyzed. Receiver operating characteristics (ROC) was performed to investigate whether these parameters could be used to the predictive value of patients with severe COVID-19. Results: White blood cell count (WBC), neutrophil count (NEU), red cell volume distribution width (RDW), NLR, Platelet to lymphocyte ratio (PLR), neutrophil-to-platelet ratio (NPR), and SII were significantly higher in the severe groups than in the non-severe group (p < 0.01).Conversely, the severe group had a markedly decreased lymphocyte count, basophil (Baso#) count, red blood cell count (RBC), Hemoglobin (HGB), hematocrit (HCT), and lymphocyte-to-monocyte ratio (LMR) (P < 0.01).ROC curve analysis showed the AUC, optimal cut-off value, sensitivity, specificity of NLR and SII to early predict severe-patients with COVID-19 were 0.867, 7.25, 70.83%, 92.21% and 0.860, 887.20, 81.25%, 81.82%, respectively. Conclusion The results suggest that the SII and NLR is a potential new diagnosed biomarker in severe-patients with COVID-19.
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Affiliation(s)
- Wei Xia
- Department of Laboratory Medicine, Jingzhou Central Hospital; Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, Hubei, China
| | - Yafeng Tan
- Department of Laboratory Medicine, Jingzhou Central Hospital; Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, Hubei, China
| | - Shengmei Hu
- Department of Medicine, Xiangyang Vocational and Technical Collage, Xiangyang, Hubei, China
| | - Chengbin Li
- Department of Laboratory Medicine, Jingzhou Central Hospital; Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, Hubei, China
| | - Tao Jiang
- Department of Laboratory Medicine, Jingzhou Central Hospital; Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, Hubei, China
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22
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Prognostic Value of C-Reactive Protein to Lymphocyte Ratio (CLR) in Emergency Department Patients with SARS-CoV-2 Infection. J Pers Med 2021; 11:jpm11121274. [PMID: 34945746 PMCID: PMC8706788 DOI: 10.3390/jpm11121274] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 11/26/2021] [Accepted: 11/29/2021] [Indexed: 01/08/2023] Open
Abstract
(1) Introduction: According to recent studies, the ratio of C-reactive-protein to lymphocyte is more sensitive and specific than other biomarkers associated to systemic inflammatory processes. This study aimed to determine the prognostic value of CLR on COVID-19 severity and mortality at emergency department (ED) admission. (2) Methods: Between 1 March and 30 April 2020, we carried out a multicenter and retrospective study in six major hospitals of northeast France. The cohort was composed of patients hospitalized for a confirmed diagnosis of moderate to severe COVID-19. (3) Results: A total of 1,035 patients were included in this study. Factors associated with infection severity were the CLR (OR: 1.001, CI 95%: (1.000-1.002), p = 0.012), and the lymphocyte level (OR: 1.951, CI 95%: (1.024-3.717), p = 0.042). In multivariate analysis, the only biochemical factor significantly associated with mortality was lymphocyte rate (OR: 2.308, CI 95%: (1.286-4.141), p = 0.005). The best threshold of CLR to predict the severity of infection was 78.3 (sensitivity 79%; specificity 47%), and to predict mortality, was 159.5 (sensitivity 48%; specificity 70%). (4) Conclusion: The CLR at admission to the ED could be a helpful prognostic biomarker in the early screening and prediction of the severity and mortality associated with SARS-CoV-2 infection.
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23
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Bagnato G, La Rosa D, Ioppolo C, De Gaetano A, Chiappalone M, Zirilli N, Viapiana V, Tringali MC, Tomeo S, Aragona CO, Napoli F, Lillo S, Irrera N, Roberts WN, Imbalzano E, Micari A, Ventura Spagnolo E, Squadrito G, Gangemi S, Versace AG. The COVID-19 Assessment for Survival at Admission (CASA) Index: A 12 Months Observational Study. Front Med (Lausanne) 2021; 8:719976. [PMID: 34660631 PMCID: PMC8514624 DOI: 10.3389/fmed.2021.719976] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 09/02/2021] [Indexed: 01/08/2023] Open
Abstract
Objective: Coronavirus disease 2019 (COVID-19) is a disease with a high rate of progression to critical illness. However, the stratification of patients at risk of mortality is not well defined. In this study, we aimed to define a mortality risk index to allocate patients to the appropriate intensity of care. Methods: This is a 12 months observational longitudinal study designed to develop and validate a pragmatic mortality risk score to stratify COVID-19 patients aged ≥18 years and admitted to hospital between March 2020 and March 2021. Main outcome was in-hospital mortality. Results: 244 patients were included in the study (mortality rate 29.9%). The Covid-19 Assessment for Survival at Admission (CASA) index included seven variables readily available at admission: respiratory rate, troponin, albumin, CKD-EPI, white blood cell count, D-dimer, Pa02/Fi02. The CASA index showed high discrimination for mortality with an AUC of 0.91 (sensitivity 98.6%; specificity 69%) and a better performance compared to SOFA (AUC = 0.76), age (AUC = 0.76) and 4C mortality (AUC = 0.82). The cut-off identified (11.994) for CASA index showed a negative predictive value of 99.16% and a positive predictive value of 57.58%. Conclusions: A quick and readily available index has been identified to help clinicians stratify COVID-19 patients according to the appropriate intensity of care and minimize hospital admission to patients at high risk of mortality.
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Affiliation(s)
- Gianluca Bagnato
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | - Daniela La Rosa
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | - Carmelo Ioppolo
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | - Alberta De Gaetano
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | - Marianna Chiappalone
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | - Natalia Zirilli
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | - Valeria Viapiana
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | | | - Simona Tomeo
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | | | - Francesca Napoli
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | - Sara Lillo
- BIOMORF Department, University of Messina, Messina, Italy
| | - Natasha Irrera
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | | | - Egidio Imbalzano
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | - Antonio Micari
- BIOMORF Department, University of Messina, Messina, Italy
| | - Elvira Ventura Spagnolo
- Department for Health Promotion and Mother-Child Care, University of Palermo, Palermo, Italy
| | - Giovanni Squadrito
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | - Sebastiano Gangemi
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
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Hemogram-derived ratios as prognostic markers of ICU admission in COVID-19. BMC Emerg Med 2021; 21:89. [PMID: 34315437 PMCID: PMC8314257 DOI: 10.1186/s12873-021-00480-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 06/04/2021] [Indexed: 01/08/2023] Open
Abstract
Background The vast impact of COVID-19 call for the identification of clinical parameter that can help predict a torpid evolution. Among these, endothelial injury has been proposed as one of the main pathophysiological mechanisms underlying the disease, promoting a hyperinflammatory and prothrombotic state leading to worse clinical outcomes. Leukocytes and platelets play a key role in inflammation and thrombogenesis, hence the objective of the current study was to study whether neutrophil-to-lymphocyte ratio (NLR), platelets-to-lymphocyte ratio (PLR), the systemic immune-inflammation index (SII) as well as the new parameter neutrophil-to-platelet ratio (NPR), could help identify patients who at risk of admission at Intensive Care Units. Methods A retrospective observational study was performed at HM Hospitales including electronic health records from 2245 patients admitted due to COVID-19 from March 1 to June 10, 2020. Patients were divided into two groups, admitted at ICU or not. Results Patients who were admitted at the ICU had significantly higher values in all hemogram-derived ratios at the moment of hospital admission compared to those who did not need ICU admission. Specifically, we found significant differences in NLR (6.9 [4–11.7] vs 4.1 [2.6–7.6], p < 0.0001), PLR (2 [1.4–3.3] vs 1.9 [1.3–2.9], p = 0.023), NPR (3 [2.1–4.2] vs 2.3 [1.6–3.2], p < 0.0001) and SII (13 [6.5–25.7] vs 9 [4.9–17.5], p < 0.0001) compared to those who did not require ICU admission. After multivariable logistic regression models, NPR was the hemogram-derived ratio with the highest predictive value of ICU admission, (OR 1.11 (95% CI: 0.98–1.22, p = 0.055). Conclusions Simple, hemogram-derived ratios obtained from early hemogram at hospital admission, especially the novelty NPR, have shown to be useful predictors of risk of ICU admission in patients hospitalized due to COVID-19. Supplementary Information The online version contains supplementary material available at 10.1186/s12873-021-00480-w.
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25
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Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18126429. [PMID: 34198547 PMCID: PMC8296243 DOI: 10.3390/ijerph18126429] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 06/07/2021] [Accepted: 06/08/2021] [Indexed: 12/12/2022]
Abstract
The COVID-19 outbreak is currently one of the biggest challenges facing countries around the world. Millions of people have lost their lives due to COVID-19. Therefore, the accurate early detection and identification of severe COVID-19 cases can reduce the mortality rate and the likelihood of further complications. Machine Learning (ML) and Deep Learning (DL) models have been shown to be effective in the detection and diagnosis of several diseases, including COVID-19. This study used ML algorithms, such as Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) and DL model (containing six layers with ReLU and output layer with sigmoid activation), to predict the mortality rate in COVID-19 cases. Models were trained using confirmed COVID-19 patients from 146 countries. Comparative analysis was performed among ML and DL models using a reduced feature set. The best results were achieved using the proposed DL model, with an accuracy of 0.97. Experimental results reveal the significance of the proposed model over the baseline study in the literature with the reduced feature set.
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26
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López-Escobar A, Madurga R, Castellano JM, Ruiz de Aguiar S, Velázquez S, Bucar M, Jimeno S, Ventura PS. Hemogram as marker of in-hospital mortality in COVID-19. J Investig Med 2021; 69:962-969. [PMID: 33849952 PMCID: PMC8050870 DOI: 10.1136/jim-2021-001810] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/23/2021] [Indexed: 01/08/2023]
Abstract
The clinical impact of COVID-19 disease calls for the identification of routine variables to identify patients at increased risk of death. Current understanding of moderate-to-severe COVID-19 pathophysiology points toward an underlying cytokine release driving a hyperinflammatory and procoagulant state. In this scenario, white blood cells and platelets play a direct role as effectors of such inflammation and thrombotic response. We investigate whether hemogram-derived ratios such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio and the systemic immune-inflammation index may help to identify patients at risk of fatal outcomes. Activated platelets and neutrophils may be playing a decisive role during the thromboinflammatory phase of COVID-19 so, in addition, we introduce and validate a novel marker, the neutrophil-to-platelet ratio (NPR).Two thousand and eighty-eight hospitalized patients with COVID-19 admitted at any of the hospitals of HM Hospitales group in Spain, from March 1 to June 10, 2020, were categorized according to the primary outcome of in-hospital death.Baseline values, as well as the rate of increase of the four ratios analyzed were significantly higher at hospital admission in patients who died than in those who were discharged (p<0.0001). In multivariable logistic regression models, NLR (OR 1.05; 95% CI 1.02 to 1.08, p=0.00035) and NPR (OR 1.23; 95% CI 1.12 to 1.36, p<0.0001) were significantly and independently associated with in-hospital mortality.According to our results, hemogram-derived ratios obtained at hospital admission, as well as the rate of change during hospitalization, may easily detect, primarily using NLR and the novel NPR, patients with COVID-19 at high risk of in-hospital mortality.
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Affiliation(s)
- Alejandro López-Escobar
- Pediatrics Department, HM Hospitales, Madrid, Spain
- Faculty of Medicine, Universidad San Pablo CEU, Madrid, Spain
- Fundación de Investigación, HM Hospitales, Madrid, Spain
| | - Rodrigo Madurga
- Fundación de Investigación, HM Hospitales, Madrid, Spain
- Faculty of Experimental Sciences, Universidad Francisco de Vitoria, Pozuelo de Alarcon, Comunidad de Madrid, Spain
| | - José María Castellano
- Faculty of Medicine, Universidad San Pablo CEU, Madrid, Spain
- Fundación de Investigación, HM Hospitales, Madrid, Spain
- Cardiology Department, Hospital Universitario HM Montepríncipe, HM Hospitales, Madrid, Spain
| | - Santiago Ruiz de Aguiar
- Fundación de Investigación, HM Hospitales, Madrid, Spain
- Medical Management, HM Hospitales, Madrid, Spain
| | - Sara Velázquez
- Fundación de Investigación, HM Hospitales, Madrid, Spain
- Anaesthesia Department, HM Hospitales, Madrid, Spain
- Anaesthesia Department, Hospital Universitario Santa Cristina, Madrid, Spain
| | - Marina Bucar
- Fundación de Investigación, HM Hospitales, Madrid, Spain
- Internal Medicine Department, HM Hospitales, Madrid, Spain
| | - Sara Jimeno
- Pediatrics Department, HM Hospitales, Madrid, Spain
- Faculty of Medicine, Universidad San Pablo CEU, Madrid, Spain
- Fundación de Investigación, HM Hospitales, Madrid, Spain
| | - Paula Sol Ventura
- Fundación de Investigación, HM Hospitales, Madrid, Spain
- Pediatrics Department Hospital Universitario HM Nens, HM Hospitales, Barcelona, Madrid, Spain
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