scholarly journals Development and validation of a clinical score to estimate progression to severe or critical state in COVID-19 pneumonia hospitalized patients

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Francisco Gude ◽  
Vanessa Riveiro ◽  
Nuria Rodríguez-Núñez ◽  
Jorge Ricoy ◽  
Óscar Lado-Baleato ◽  
...  
Keyword(s):  
Decision Making ◽  
Predictive Model ◽  
Disease Progression ◽  
Clinical Laboratory ◽  
Severe Disease ◽  
Invasive Mechanical Ventilation ◽  
Risk Scores ◽  
Brier Score ◽  
Operating Characteristics ◽  
Early Decision

AbstractThe prognosis of a patient with COVID-19 pneumonia is uncertain. Our objective was to establish a predictive model of disease progression to facilitate early decision-making. A retrospective study was performed of patients admitted with COVID-19 pneumonia, classified as severe (admission to the intensive care unit, mechanic invasive ventilation, or death) or non-severe. A predictive model based on clinical, laboratory, and radiological parameters was built. The probability of progression to severe disease was estimated by logistic regression analysis. Calibration and discrimination (receiver operating characteristics curves and AUC) were assessed to determine model performance. During the study period 1152 patients presented with SARS-CoV-2 infection, of whom 229 (19.9%) were admitted for pneumonia. During hospitalization, 51 (22.3%) progressed to severe disease, of whom 26 required ICU care (11.4); 17 (7.4%) underwent invasive mechanical ventilation, and 32 (14%) died of any cause. Five predictors determined within 24 h of admission were identified: Diabetes, Age, Lymphocyte count, SaO2, and pH (DALSH score). The prediction model showed a good clinical performance, including discrimination (AUC 0.87 CI 0.81, 0.92) and calibration (Brier score = 0.11). In total, 0%, 12%, and 50% of patients with severity risk scores ≤ 5%, 6–25%, and > 25% exhibited disease progression, respectively. A risk score based on five factors predicts disease progression and facilitates early decision-making according to prognosis.

scholarly journals Development and validation of a clinical score to estimate progression to severe or critical state in Covid-19 pneumonia hospitalized patients

2020 ◽  
Author(s):  
Francisco Gude ◽  
Luis Valdés ◽  
Lucía Ferreiro
Keyword(s):  
Predictive Model ◽  
Disease Progression ◽  
Clinical Performance ◽  
Severe Disease ◽  
Invasive Mechanical Ventilation ◽  
Clinical Score ◽  
Risk Scores ◽  
Brier Score ◽  
Operating Characteristics ◽  
Early Decision

Abstract The prognosis of a patient with Covid-19 pneumonia is uncertain. Our objective was to establish a predictive model of disease progression to facilitate early decision-making.A retrospective study was performed of patients admitted with Covid-19 pneumonia, classified as severe (admission to the intensive care unit, mechanic invasive ventilation, or death) or non-severe. A predictive model based on clinical, analytical, and radiological parameters was built. The probability of progression to severe disease was estimated by logistic regression analysis. Calibration and discrimination (receiver operating characteristics curves and AUC) were assessed to determine model performance.During the study period 1,152 patients presented with Covid-19 infection, of whom 229 (19.9%) were admitted for pneumonia. During hospitalization, 51 (22.3%) progressed to severe disease, of whom 26 required ICU care (11.4); 17 (7.4%) underwent invasive mechanical ventilation, and 32 (14%) died of any cause. Five predictors determined within 24 hours of admission were identified: Diabetes, Age, Lymphocyte count, SaO2, and pH (DALSH score). The prediction model showed a good clinical performance, including discrimination (AUC 0.87 CI 0.81, 0.92) and calibration (Brier score = 0.11). In total, 0%, 12%, and 50% of patients with severity risk scores ≤5%, 6-25%, and >25% exhibited disease progression, respectively.A simple risk score based on five factors predicts disease progression and facilitates early decision-making according to prognosis.

scholarly journals Epidemiological impact of SARS-CoV-2 vaccination: mathematical modeling analyses

2020 ◽  
Author(s):  
Monia Makhoul ◽  
Houssein H. Ayoub ◽  
Hiam Chemaitelly ◽  
Shaheen Seedat ◽  
Ghina R Mumtaz ◽  
...  
Keyword(s):  
Decision Making ◽  
Disease Progression ◽  
Vaccine Development ◽  
Severe Disease ◽  
Herd Immunity ◽  
Social Contact ◽  
Population Level ◽  
Contact Rate ◽  
Disease Case ◽  
Vaccine Impact

AbstractBackgroundSeveral SARS-CoV-2 vaccine candidates are currently in the pipeline. This study aims to inform SARS-CoV-2 vaccine development, licensure, decision-making, and implementation by determining key preferred vaccine product characteristics and associated population-level impact.MethodsVaccination impact was assessed at various efficacies using an age-structured mathematical model describing SARS-CoV-2 transmission and disease progression, with application for China.ResultsA prophylactic vaccine with efficacy against acquisition (VES) of ≥70% is needed to eliminate this infection. A vaccine with VES <70% will still have a major impact, and may control the infection if it reduces infectiousness or infection duration among those vaccinated who acquire the infection, or alternatively if supplemented with a moderate social-distancing intervention (<20% reduction in contact rate), or complemented with herd immunity. Vaccination is cost-effective. For a vaccine with VES of 50%, number of vaccinations needed to avert one infection is only 2.4, one severe disease case is 25.5, one critical disease case is 33.2, and one death is 65.1. Gains in effectiveness are achieved by initially prioritizing those ≥60 years. Probability of a major outbreak is virtually zero with a vaccine with VES ≥70%, regardless of number of virus introductions. Yet, an increase in social contact rate among those vaccinated (behavior compensation) can undermine vaccine impact.ConclusionsEven a partially-efficacious vaccine can offer a fundamental solution to control SARS-CoV-2 infection and at high cost-effectiveness. In addition to the primary endpoint on infection acquisition, developers should assess natural history and disease progression outcomes and/or proxy biomarkers, since such secondary endpoints may prove critical in licensure, decision-making, and vaccine impact.

scholarly journals Key to successful treatment of COVID-19: accurate identification of severe risks and early intervention of disease progression

2020 ◽  
Author(s):  
Meizhu Chen ◽  
Changli Tu ◽  
Cuiyan Tan ◽  
Xiaobin Zheng ◽  
Xiaohua Wang ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Early Intervention ◽  
Disease Progression ◽  
Case Fatality Rate ◽  
Severe Disease ◽  
Invasive Mechanical Ventilation ◽  
Case Fatality ◽  
List Type ◽  
Fatality Rate ◽  
Medical Burden

AbstractBackgroundCOVID-19 is a new and highly contagious respiratory disease that has caused global spread, high case fatality rate in severe patients, and a huge medical burden due to invasive mechanical ventilation. The current diagnosis and treatment guidelines are still need to be improved, and more excellent clinical experience is needed to provide reference.MethodsWe analyzed and summarized clinical data of 97 confirmed COVID-19 adult patients (including 26 severe cases) admitted to the Fifth Affiliated Hospital of Sun Yat-sen University from January 17, 2020 to March 10, 2020, included laboratory examination results, imaging findings, treatment effect, prognosis, etc, in order to put forward prediction index of severe COVID-19 patients, principles of early intervention and methylprednisolone usages in COVID-19 patients.ResultsHypoxemia, hyperlactic acid, hypoproteinemia, and hypokalemia were prevalent in COVID-19 patients. The significant low lymphocyte count, hypoproteinemia, hypokalemia, the persistent or worsen high CRP, high D-dimer, and high BNP, and the occurrence of hemoptysis and novel coronavirus (SARS-CoV-2) viremia were important indicators for early diagnosis and prediction of severe disease progression.Characteristic images of lung CT had a clear change in COVID-19, Ground-glass opacity (GGO) and high-density linear combinations may indicate different pathological changes. Rapid lobular progression of GGO suggests the possibility of severe disease.Basic principles of early intervention treatment of COVID-19: on the premise of no effective antiviral drugs, treatment is based on supportive and symptomatic therapy (albumin supplementation, supplement of potassium, supplement blood plasma, etc.) in order to maintain the stability of the intracellular environment and adequately reactivate body immunity to clean up SARS-CoV-2.According to severity, oxygenation index, body weight, age, underlying diseases, appropriate amount methylprednisolone application on severe/critical COVID-19 patients on demand, improved blood oxygen and reduced the utilization rate of invasive mechanical ventilation, case fatality rate and medical burden significantly. The most common indications for invasive mechanical ventilation should be strictly control in critical COVID-19 patients.ConclusionsAccurate and timely identification of clinical features in severe risks, and early and appropriate intervention can block disease progression. 2. Appropriate dose of methylprednisolone can effectively avoid invasive mechanical ventilation and reduce case fatality rate in critical COVID-19 patients.

scholarly journals Maximum chest CT score is associated with progression to severe illness in patients with COVID-19: a retrospective study from Wuhan, China

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Jianwei Xiao ◽  
Xiang Li ◽  
Yuanliang Xie ◽  
Zengfa Huang ◽  
Yi Ding ◽  
...  
Keyword(s):  
Disease Progression ◽  
Clinical Laboratory ◽  
Characteristic Curve ◽  
Severe Disease ◽  
Radiological Feature ◽  
Severe Illness ◽  
Chronic Obstructive ◽  
Limited Information ◽  
Obstructive Pulmonary Disease ◽  
Health Crisis

Abstract Background The Coronavirus Disease 2019 (COVID-19) pandemic is a world-wide health crisis. Limited information is available regarding which patients will experience more severe disease symptoms. We evaluated hospitalized patients who were initially diagnosed with moderate COVID-19 for clinical parameters and radiological feature that showed an association with progression to severe/critical symptoms. Methods This study, a retrospective single-center study at the Central Hospital of Wuhan, enrolled 243 patients with confirmed COVID­19 pneumonia. Forty of these patients progressed from moderate to severe/critical symptoms during follow up. Demographic, clinical, laboratory, and radiological data were extracted from electronic medical records and compared between moderate- and severe/critical-type symptoms. Univariable and multivariable logistic regressions were used to identify the risk factors associated with symptom progression. Results Patients with severe/critical symptoms were older (p < 0.001) and more often male (p = 0.046). A combination of chronic obstructive pulmonary disease (COPD) and high maximum chest computed tomography (CT) score was associated with disease progression. Maximum CT score (> 11) had the greatest predictive value for disease progression. The area under the receiver operating characteristic curve was 0.861 (95% confidence interval: 0.811–0.902). Conclusions Maximum CT score and COPD were associated with patient deterioration. Maximum CT score (> 11) was associated with severe illness.

scholarly journals A descriptive and validation study of a predictive model of severity of SARS-COV-2 infection

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Yolanda Villena-Ortiz ◽  
Marina Giralt ◽  
Laura Castellote-Bellés ◽  
Rosa M. Lopez-Martínez ◽  
Luisa Martinez-Sanchez ◽  
...  
Keyword(s):  
Emergency Department ◽  
Predictive Model ◽  
Clinical Laboratory ◽  
Demographic Data ◽  
Severe Disease ◽  
External Validation ◽  
Predictive Values ◽  
Reactive Protein ◽  
Predictive Variables ◽  
Moderate Disease

Abstract Objectives The strain the SARS-COV-2 pandemic is putting on hospitals requires that predictive values are identified for a rapid triage and management of patients at a higher risk of developing severe COVID-19. We developed and validated a prognostic model of COVID-19 severity. Methods A descriptive, comparative study of patients with positive vs. negative PCR-RT for SARS-COV-2 and of patients who developed moderate vs. severe COVID-19 was conducted. The model was built based on analytical and demographic data and comorbidities of patients seen in an Emergency Department with symptoms consistent with COVID-19. A logistic regression model was designed from data of the COVID-19-positive cohort. Results The sample was composed of 410 COVID-positive patients (303 with moderate disease and 107 with severe disease) and 81 COVID-negative patients. The predictive variables identified included lactate dehydrogenase, C-reactive protein, total proteins, urea, and platelets. Internal calibration showed an area under the ROC curve (AUC) of 0.88 (CI 95%: 0.85–0.92), with a rate of correct classifications of 85.2% for a cut-off value of 0.5. External validation (100 patients) yielded an AUC of 0.79 (95% CI: 0.71–0.89), with a rate of correct classifications of 73%. Conclusions The predictive model identifies patients at a higher risk of developing severe COVID-19 at Emergency Department, with a first blood test and common parameters used in a clinical laboratory. This model may be a valuable tool for clinical planning and decision-making.

scholarly journals Early warning scores to assess the probability of critical illness in patients with COVID-19

2021 ◽  
pp. emermed-2020-211054
Author(s):  
Lars Veldhuis ◽  
Milan L Ridderikhof ◽  
Michiel Schinkel ◽  
Joop van den Bergh ◽  
Martijn Beudel ◽  
...  
Keyword(s):  
Early Warning ◽  
Severe Disease ◽  
Early Warning Score ◽  
Risk Scores ◽  
Operating Characteristics ◽  
Icu Patients ◽  
Icu Admission ◽  
Clinical Risk ◽  
High Flow Oxygen ◽  
Early Warning Scores

ObjectiveValidated clinical risk scores are needed to identify patients with COVID-19 at risk of severe disease and to guide triage decision-making during the COVID-19 pandemic. The objective of the current study was to evaluate the performance of early warning scores (EWS) in the ED when identifying patients with COVID-19 who will require intensive care unit (ICU) admission for high-flow-oxygen usage or mechanical ventilation.MethodsPatients with a proven SARS-CoV-2 infection with complete resuscitate orders treated in nine hospitals between 27 February and 30 July 2020 needing hospital admission were included. Primary outcome was the performance of EWS in identifying patients needing ICU admission within 24 hours after ED presentation.ResultsIn total, 1501 patients were included. Median age was 71 (range 19–99) years and 60.3% were male. Of all patients, 86.9% were admitted to the general ward and 13.1% to the ICU within 24 hours after ED admission. ICU patients had lower peripheral oxygen saturation (86.7% vs 93.7, p≤0.001) and had a higher body mass index (29.2 vs 27.9 p=0.043) compared with non-ICU patients. National Early Warning Score 2 (NEWS2) ≥ 6 and q-COVID Score were superior to all other studied clinical risk scores in predicting ICU admission with a fair area under the receiver operating characteristics curve of 0.740 (95% CI 0.696 to 0.783) and 0.760 (95% CI 0.712 to 0.800), respectively. NEWS2 ≥6 and q-COVID Score ≥3 discriminated patients admitted to the ICU with a sensitivity of 78.1% and 75.9%, and specificity of 56.3% and 61.8%, respectively.ConclusionIn this multicentre study, the best performing models to predict ICU admittance were the NEWS2 and the Quick COVID-19 Severity Index Score, with fair diagnostic performance. However, due to the moderate performance, these models cannot be clinically used to adequately predict the need for ICU admission within 24 hours in patients with SARS-CoV-2 infection presenting at the ED.

scholarly journals Maximum chest CT score is associated with progression to severe illness in patients with COVID-19: A retrospective study from Wuhan, China

2020 ◽  
Author(s):  
Jianwei Xiao ◽  
Xiang Li ◽  
Yuanliang Xie ◽  
Zengfa Huang ◽  
Yi Ding ◽  
...  
Keyword(s):  
Disease Progression ◽  
Clinical Laboratory ◽  
Characteristic Curve ◽  
Severe Disease ◽  
Radiological Feature ◽  
Severe Illness ◽  
Chronic Obstructive ◽  
Limited Information ◽  
Obstructive Pulmonary Disease ◽  
Health Crisis

Abstract Background: The Coronavirus Disease 2019 (COVID-19) pandemic is a world-wide health crisis. Limited information is available regarding which patients will experience more severe disease symptoms. We evaluated hospitalized patients who were initially diagnosed with moderate COVID-19 for clinical parameters and radiological feature that showed an association with progression to severe/critical symptoms. Methods: This study, a retrospective single-center study at the Central Hospital of Wuhan, enrolled 243 patients with confirmed COVID­19 pneumonia. Forty of these patients progressed from moderate to severe/critical symptoms during follow up. Demographic, clinical, laboratory, and radiological data were extracted from electronic medical records and compared between moderate- and severe/critical-type symptoms. Univariable and multivariable logistic regressions were used to identify the risk factors associated with symptom progression.Results: Patients with severe/critical symptoms were older (p<0.001) and more often male (p=0.046). A combination of chronic obstructive pulmonary disease (COPD) and high maximum chest computed tomography (CT) score was associated with disease progression. Maximum CT score (>11) had the greatest predictive value for disease progression. The area under the receiver operating characteristic curve was 0.861 (95% confidence interval: 0.811-0.902).Conclusions: Maximum CT score and COPD were associated with patient deterioration. Maximum CT score (>11) was associated with severe illness.

scholarly journals Maximum chest CT score is associated with progression to severe illness in patients with COVID-19: A retrospective study from Wuhan, China

2020 ◽  
Author(s):  
Jianwei Xiao ◽  
Xiang Li ◽  
Yuanliang Xie ◽  
Zengfa Huang ◽  
Yi Ding ◽  
...  
Keyword(s):  
Disease Progression ◽  
Clinical Laboratory ◽  
Characteristic Curve ◽  
Severe Disease ◽  
Radiological Feature ◽  
Severe Illness ◽  
Chronic Obstructive ◽  
Limited Information ◽  
Obstructive Pulmonary Disease ◽  
Health Crisis

Abstract Background: The Coronavirus Disease 2019 (COVID-19) pandemic is a world-wide health crisis. Limited information is available regarding which patients will experience more severe disease symptoms. We evaluated hospitalized patients who were initially diagnosed with moderate COVID-19 for clinical parameters and radiological feature that showed an association with progression to severe/critical symptoms.Methods: This study, a retrospective single-center study at the Central Hospital of Wuhan, enrolled 243 patients with confirmed COVID­19 pneumonia. Forty of these patients progressed from moderate to severe/critical symptoms during follow up. Demographic, clinical, laboratory, and radiological data were extracted from electronic medical records and compared between moderate- and severe/critical-type symptoms. Univariable and multivariable logistic regressions were used to identify the risk factors associated with symptom progression.Results: Patients with severe/critical symptoms were older (p<0.001) and more often male (p=0.046). A combination of chronic obstructive pulmonary disease (COPD) and high maximum chest computed tomography (CT) score was associated with disease progression. Maximum CT score (>11) had the greatest predictive value for disease progression. The area under the receiver operating characteristic curve was 0.861 (95% confidence interval: 0.811-0.902).Conclusions: Maximum CT score and COPD were associated with patient deterioration. Maximum CT score (>11) was associated with severe illness.

scholarly journals The hemocyte counts as a potential biomarker for predicting disease progression in COVID-19: a retrospective study

2020 ◽  
Vol 58 (7) ◽  
pp. 1106-1115 ◽  
Author(s):  
Yufen Zheng ◽  
Ying Zhang ◽  
Hongbo Chi ◽  
Shiyong Chen ◽  
Minfei Peng ◽  
...  
Keyword(s):  
Risk Factors ◽  
Platelet Count ◽  
Disease Progression ◽  
Neutrophil Count ◽  
Calibration Curve ◽  
Lymphocyte Count ◽  
Severe Disease ◽  
Clinical Impact ◽  
Predictive Tool ◽  
Potential Biomarker

AbstractObjectivesIn December 2019, there was an outbreak of coronavirus disease 2019 (COVID-19) in Wuhan, China, and since then, the disease has been increasingly spread throughout the world. Unfortunately, the information about early prediction factors for disease progression is relatively limited. Therefore, there is an urgent need to investigate the risk factors of developing severe disease. The objective of the study was to reveal the risk factors of developing severe disease by comparing the differences in the hemocyte count and dynamic profiles in patients with severe and non-severe COVID-19.MethodsIn this retrospectively analyzed cohort, 141 confirmed COVID-19 patients were enrolled in Taizhou Public Health Medical Center, Taizhou Hospital, Zhejiang Province, China, from January 17, 2020 to February 26, 2020. Clinical characteristics and hemocyte counts of severe and non-severe COVID patients were collected. The differences in the hemocyte counts and dynamic profiles in patients with severe and non-severe COVID-19 were compared. Multivariate Cox regression analysis was performed to identify potential biomarkers for predicting disease progression. A concordance index (C-index), calibration curve, decision curve and the clinical impact curve were calculated to assess the predictive accuracy.ResultsThe data showed that the white blood cell count, neutrophil count and platelet count were normal on the day of hospital admission in most COVID-19 patients (87.9%, 85.1% and 88.7%, respectively). A total of 82.8% of severe patients had lymphopenia after the onset of symptoms, and as the disease progressed, there was marked lymphopenia. Multivariate Cox analysis showed that the neutrophil count (hazard ratio [HR] = 4.441, 95% CI = 1.954–10.090, p = 0.000), lymphocyte count (HR = 0.255, 95% CI = 0.097–0.669, p = 0.006) and platelet count (HR = 0.244, 95% CI = 0.111–0.537, p = 0.000) were independent risk factors for disease progression. The C-index (0.821 [95% CI, 0.746–0.896]), calibration curve, decision curve and the clinical impact curve showed that the nomogram can be used to predict the disease progression in COVID-19 patients accurately. In addition, the data involving the neutrophil count, lymphocyte count and platelet count (NLP score) have something to do with improving risk stratification and management of COVID-19 patients.ConclusionsWe designed a clinically predictive tool which is easy to use for assessing the progression risk of COVID-19, and the NLP score could be used to facilitate patient stratification management.

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