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

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.

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

Scientific Reports ◽

10.1038/s41598-020-75651-z ◽

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.

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Az anti-Jo-1-pozitív antiszintetáz szindróma jellegzetességei gondozott betegeink alapján

Orvosi Hetilap ◽

10.1556/650.2016.30400 ◽

2016 ◽

Vol 157 (15) ◽

pp. 575-583 ◽

Cited By ~ 1

Author(s):

Katalin Szabó ◽

Melinda Nagy-Vincze ◽

Levente Bodoki ◽

Katalin Hodosi ◽

Katalin Dankó ◽

...

Keyword(s):

Medical Records ◽

Clinical Laboratory ◽

Clinical Symptoms ◽

Clinical Phenotype ◽

Demographic Data ◽

Clinical Results ◽

C Reactive Protein ◽

Protein Levels ◽

Reactive Protein ◽

Worse Prognosis

Introduction: In idiopathic inflammatory myopathies, the presence of anti-Jo-1 antibody defines a distinct clinical phenotype (myositis, arthritis, interstitial lung disease, Raynaud’s phenomenon fever, mechanic’s hands), called antisynthetase syndrome. Aim: To determine the demographic data as well as clinical, laboratory and terapeutical features of anti-Jo1 positive patients, followed by the department of the authors. Method: The medical records of 49 consecutive anti-Jo1 patients were reviewed. Results: Demographic and clinical results were very similar to those published by other centers. Significant correlation was found between the anti-Jo-1 titer and the creatine kinase and C-reactive protein levels. Distinct laboratory results measured at the time of diagnosis of the disease (C-reactive protein, antigen A associated with Sjogren’s syndrome, positive rheumatoid factor), and the presence of certain clinical symptoms (fever, vasculitic skin) may indicate a worse prognosis within the antisyntetase positive patient group. Conclusion: In the cases above more agressive immunosuppressive therapy may be required. Orv. Hetil., 2016, 157(15), 575–583.

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First and second SARS-CoV-2 waves in inner London: A comparison of admission characteristics and the effects of the B.1.1.7 variant

10.1101/2021.03.16.21253377 ◽

2021 ◽

Author(s):

Luke B Snell ◽

Wenjuan Wang ◽

Adela Alcolea-Medina ◽

Themoula Charalampous ◽

Gaia Nebbia ◽

...

Keyword(s):

Genome Sequence ◽

Clinical Data ◽

Clinical Laboratory ◽

Sequence Data ◽

Data Extraction ◽

Demographic Data ◽

Severe Disease ◽

Time Integration ◽

Valuable Insight ◽

Genome Sequence Data

Introduction: A second wave of SARS-CoV-2 infection spread across the UK in 2020 linked with emergence of the more transmissible B.1.1.7 variant. The emergence of new variants, particularly during relaxation of social distancing policies and implementation of mass vaccination, highlights the need for real-time integration of detailed patient clinical data alongside pathogen genomic data. We linked clinical data with viral genome sequence data to compare patients admitted during the first and second waves of SARS-CoV-2 infection. Methods: Clinical, laboratory and demographic data from five electronic health record (EHR) systems was collected for all cases with a positive SARS-CoV-2 RNA test between March 13th 2020 and February 17th 2021. SARS-CoV-2 viral sequencing was performed using Oxford Nanopore Technology. Descriptive data are presented comparing cases between waves, and between cases of B.1.1.7 and non-B.1.1.7 variants. Results: There were 5810 SARS-CoV-2 RNA positive cases comprising inpatients (n=2341), healthcare workers (n=1549), outpatients (n=874), emergency department (ED) attenders not subsequently admitted (n=532), inter-hospital transfers (n=281) and nosocomial cases (n=233). There were two dominant waves of admissions starting from around March 13th and October 20th, both with a temporally aligned rise in nosocomial cases (n=96 in wave one, n=137 in wave two). 1470 SARS-CoV-2 isolates were successfully sequenced, including 216/838 (26%) admitted cases from wave one, 472/1503 (31%) admitted cases in wave two and 121/233 (52%) nosocomial cases. 400/472 (85%) of sequenced isolates from admitted cases in wave two were the B.1.1.7 variant. The first B.1.1.7 variant was identified on 15th November 2020 and increased rapidly to comprise almost all sequenced isolates in January 2021 (n=600/617, 97%). Females made up a larger proportion of admitted cases in wave two (47.2% vs 41.8%, p=0.012), and in those infected with the B.1.1.7 variant compared to non-B.1.1.7 variants (48.0% vs 41.8%, p=0.042). A diagnosis of frailty was less common in wave two (11.5% v 22.8%, p<0.001) and in the group infected with B.1.1.7 (14.5% v 22.4%, p=0.001). There was no difference in severity on admission between waves, as measured by hypoxia at admission (wave one: 64.3% vs wave two: 65.6%, p=0.658). However, a higher proportion of cases infected with the B.1.1.7 variant were hypoxic on admission compared to other variants (70.0% vs 62.5%, p=0.029). Conclusions: Automated EHR data extraction linked with SARS-CoV-2 genome sequence data provides valuable insight into the evolving characteristics of cases admitted to hospital with COVID-19. The proportion of cases with hypoxia on admission was greater in those infected with the B.1.1.7 variant, which supports evidence the B.1.1.7 variant is associated with more severe disease. The number of nosocomial cases was similar in both waves despite introduction of many infection control interventions before wave two, an observation requiring further investigation.

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CORRELATION BETWEEN SERUM C-REACTIVE PROTEIN LEVEL WITH SEVERITY OF THE DISEASE IN COVID-19 PATIENTS

International Journal of Medical and Biomedical Studies ◽

10.32553/ijmbs.v5i11.2302 ◽

2021 ◽

Vol 5 (11) ◽

Author(s):

Sangeeta Gahlot ◽

Surendra Yadav ◽

Makkhan Lal Saini

Keyword(s):

Severe Disease ◽

Serum Levels ◽

Cross Sectional Study ◽

P Value ◽

C Reactive Protein ◽

Cross Sectional ◽

Reactive Protein ◽

Group I ◽

Moderate Disease ◽

Severity Of The Disease

Background: To find the levels of serum CRP in confirmed Covid-19 patients and to compare their levels in patients with mild to moderate disease and patients with severe disease who required ICU care for management. Methods: A Cross sectional study was carried out on 100 confirmed cases of Covid-19, in whom Serum levels of Random sugar (RBS), Creatinine, Urea, C- reactive protein (CRP) were measured. Results: The levels of serum Urea, Creatinine were significantly increased in group II when compared to group 1, and the levels of CRP were significantly increased with p value <0.0001 in group IIwhen compared to group I. Conclusion: Findings of our study suggest that determination of biochemical parameters like CRP at the time of hospitalization helps in predicting the severity of disease and need for ICU for better treatment management and prevention of adverse outcome. Keywords: Severe acute respiratory syndrome, Covid-19, C- reactive protein, Intensive care unit.

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Mortality Predictors in Patients Diagnosed with COVID-19 in the Emergency Department: ECG, Laboratory and CT

Medicina ◽

10.3390/medicina57060629 ◽

2021 ◽

Vol 57 (6) ◽

pp. 629

Author(s):

Aslı Türkay Kunt ◽

Nalan Kozaci ◽

Ebru Torun

Keyword(s):

Emergency Department ◽

Mortality Risk ◽

Troponin I ◽

Clinical Laboratory ◽

Peripheral Oxygen Saturation ◽

Reactive Protein ◽

Creatine Kinase Mb ◽

Group 2 ◽

Electrocardiogram Ecg ◽

Mortality Predictors

Background and Objectives: The aim of this study was to investigate parameters that can be used to predict mortality in patients diagnosed with COVID-19 in the emergency department (ED). Materials and Methods: Patients diagnosed with COVID-19 in the ED were included in this prospective study. The patients were divided into two groups. The surviving patients were included in Group 1 (survivors), and the patients who died were included in Group 2 (non-survivors). The electrocardiogram (ECG), laboratory results and chest computerized tomography (CCT) findings of the two groups were compared. The CCT images were classified according to the findings as normal, mild, moderate and severe. Results: Of the 419 patients included in the study, 347 (83%) survived (survivor) and 72 (17%) died (non-survivor). The heart rate and respiratory rate were found to be higher, and the peripheral oxygen saturation (SpO2) and diastolic blood pressure (DBP) were found to be lower in the non-survivor patients. QRS and corrected QT interval (QTc) were measured as longer in the non-survivor patients. In the CCT images, 79.2% of the non-survivor patients had severe findings, while 11.5% of the survivor patients had severe findings. WBC, neutrophil, NLR, lactate, D-dimer, fibrinogen, C- Reactive Protein (CRP), urea, creatinine, creatine kinase-MB (CK-MB) and hs-Troponin I levels were found to be higher and partial pressure of carbon dioxide (PCO2), base excess (BE), bicarbonate (HCO3), lymphocyte eosinophil levels were found to be lower in non-survivor patients. The highest AUC was calculated at the SpO2 level and the eosinophil level. Conclusions: COVID-19 is a fatal disease whose mortality risk can be estimated when the clinical, laboratory and imaging studies of the patients are evaluated together in the ED. SpO2 that is measured before starting oxygen therapy, the eosinophil levels and the CT findings are all important predictors of mortality risk.

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Predictive values of biochemical markers as early indicators for severe COVID-19 cases in admission

Future Virology ◽

10.2217/fvl-2020-0319 ◽

2021 ◽

Author(s):

Emin Gemcioglu ◽

Mehmet Davutoglu ◽

Ramis Catalbas ◽

Berkan Karabuga ◽

Enes Kaptan ◽

...

Keyword(s):

Biochemical Markers ◽

Severe Disease ◽

High Morbidity ◽

C Reactive Protein ◽

Prognostic Information ◽

Protein Ratio ◽

Predictive Values ◽

Albumin Ratio ◽

Reactive Protein ◽

The Relationship

Aim: COVID-19 is a pandemic that causes high morbidity and mortality, especially in severe patients. In this study, we aimed to search and explain the relationship between biochemical markers, which are more common, easily available and applicable to diagnose and to stage the disease. Materials & methods: In this study, 609 patients were evaluated retrospectively. 11 biochemical parameters were included in analysis to explain the relationship with severity of disease. Results: Nearly, all the parameters that have been evaluated in this study were statistically valuable as a predictive parameter for severe disease. Areas under the curve of blood urea nitrogen (BUN)/albumin ratio (BAR), CALL score and lymphocyte/C-reactive protein ratio were 0.795, 0.778 and 0.770. The BUN/BAR and neutrophil/albumin ratios provide important prognostic information for decision-making in severe patients with COVID-19. Conclusion: High BUN/BAR and neutrophil/albumin ratios may be a better predictor of severity COVID-19 than other routinely used parameters in admission.

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Can Fetuin-A Level, CRP, and WBC be a Predictive Value in the Diagnosis of Acute Appendicitis in Children with Abdominal Pain?

10.20944/preprints201908.0055.v1 ◽

2019 ◽

Author(s):

Abuzer Coskun ◽

Cengiz Güney

Keyword(s):

Emergency Department ◽

Regression Analysis ◽

Abdominal Pain ◽

Acute Appendicitis ◽

Acute Abdomen ◽

Perforated Appendicitis ◽

Multivariate Regression Analysis ◽

Predictive Values ◽

Fetuin A ◽

Reactive Protein

Background: Acute appendicitis (AA) is the most common cause of emergency surgery. Perforation is more common than adults. Early diagnosis and new markers are needed. The aim of this study was to investigate the effects of plasma Fetuin-A (FA) levels in patients with the acute abdomen (AB). Material and Method: This prospective study included 107 patients younger than 16 years of age who were admitted to the emergency department for abdominal pain between January 2018 and December 2018. The patients who presented to abdominal pain were divided into two groups as AA and other causes (OC) of AB. T Patients with acute appendicitis; intraperitoneal, retrocolic / retrocecal and appendicitis were divided into three groups. Also, the AA group was divided into two groups as perforated appendicitis and non-perforated appendicitis. Serum FA levels of the patients were evaluated in the emergency department. Results: In the AA group, C-reactive protein (CRP) and white blood cell (WBC) levels were higher, and FA levels were significantly lower than in the AB group. Intraperitoneal localization was 95.2% and perforation was frequent. When significant values in the univariate regression analysis for acute abdomen and perforation were compared in the multivariate regression analysis, CRP, WBC, and FA levels were found to be prognostic. Also, decreased FA levels were associated with AA while too much decreased FA levels were associated with the risk of perforation. Conclusion: While trying to diagnose AA in children, the FA level, CRP and WBC may be predictive values to identify risk factors.

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Clinical features and disease severity in an Iranian population of COVID-19 patients

10.21203/rs.3.rs-51568/v1 ◽

2020 ◽

Author(s):

Shima Nabavi ◽

Zahra Javidarabshahi ◽

Abolghasem Allahyari ◽

Mohammad Ramezani ◽

Mohsen Seddigh-Shamsi ◽

...

Keyword(s):

Disease Severity ◽

Clinical Features ◽

Clinical Laboratory ◽

Severe Disease ◽

Cross Sectional Study ◽

Cross Sectional ◽

Reactive Protein ◽

Radiologic Findings ◽

Severity Of The Disease ◽

Lung Ct

Abstract Objectives: Coronavirus disease 2019 (COVID-19) can present with a variety of symptoms. Severity of the disease may be associated with several factors. Here, we review clinical features of COVID-19 patients with different severities.Methods: This cross-sectional study was performed in Imam Reza hospital, Mashhad, Iran, during February-April 2020. COVID-19 patients with typical computed tomography (CT) patterns and/or positive reverse-transcriptase polymerase chain reaction (RT-PCR) were included. The patients were classified into three groups of moderate, severe, and critical based on disease severity. Demographic, clinical, laboratory, and radiologic findings were collected and compared. P<0.05 was considered statistically significant.Results: Overall, 200 patients with mean age of 69.75±6.39 years, of whom 82 (41%) were female were studied. Disease was severe/critical in the majority of patients (167, 83.5%). Disease severity was significantly associated with age, malignant comorbidities, dyspnea, nausea/vomiting, confusion, respiratory rate, pulse rate, O2 saturation, extent of CT involvement, serum C-reactive protein (CRP), pH, pO2, and aspartate transaminase (P<0.05). Moreover, complications including shock, coagulopathy, acidosis, sepsis, acute respiratory distress syndrome (ARDS), intensive care unit (ICU) admission, and intubation were significantly higher in patients with higher severities. O2 saturation, nausea/vomiting, and extent of lung CT involvement were independent predictors of severe/critical COVID-19 (OR=0.342, 45.93, and 25.48, respectively; P<0.05).Conclusions: Our results indicate O2 saturation, nausea/vomiting, and extent of lung CT involvement as independent predictors of severe COVID-19 conditions. Serum CRP levels and pO2 were also considerably higher patients with higher severity and can be used along with other factors as possible predictors of severe disease in COVID-19 patients.

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Early prediction of mortality risk among patients with severe COVID-19, using machine learning

International Journal of Epidemiology ◽

10.1093/ije/dyaa171 ◽

2020 ◽

Author(s):

Chuanyu Hu ◽

Zhenqiu Liu ◽

Yanfeng Jiang ◽

Oumin Shi ◽

Xin Zhang ◽

...

Keyword(s):

Machine Learning ◽

Predictive Model ◽

Mortality Risk ◽

Protein Level ◽

Lymphocyte Count ◽

External Validation ◽

High Sensitivity ◽

C Reactive Protein ◽

Reactive Protein ◽

D Dimer

Abstract Background Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 infection, has been spreading globally. We aimed to develop a clinical model to predict the outcome of patients with severe COVID-19 infection early. Methods Demographic, clinical and first laboratory findings after admission of 183 patients with severe COVID-19 infection (115 survivors and 68 non-survivors from the Sino-French New City Branch of Tongji Hospital, Wuhan) were used to develop the predictive models. Machine learning approaches were used to select the features and predict the patients’ outcomes. The area under the receiver operating characteristic curve (AUROC) was applied to compare the models’ performance. A total of 64 with severe COVID-19 infection from the Optical Valley Branch of Tongji Hospital, Wuhan, were used to externally validate the final predictive model. Results The baseline characteristics and laboratory tests were significantly different between the survivors and non-survivors. Four variables (age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level) were selected by all five models. Given the similar performance among the models, the logistic regression model was selected as the final predictive model because of its simplicity and interpretability. The AUROCs of the external validation sets were 0.881. The sensitivity and specificity were 0.839 and 0.794 for the validation set, when using a probability of death of 50% as the cutoff. Risk score based on the selected variables can be used to assess the mortality risk. The predictive model is available at [https://phenomics.fudan.edu.cn/risk_scores/]. Conclusions Age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level of COVID-19 patients at admission are informative for the patients’ outcomes.

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External validation of a rapid, non-invasive tool for periodontitis screening in a medical care setting

Clinical Oral Investigations ◽

10.1007/s00784-021-03952-2 ◽

2021 ◽

Author(s):

N. Nijland ◽

F. Overtoom ◽

V. E. A. Gerdes ◽

M. J. L. Verhulst ◽

N. Su ◽

...

Keyword(s):

Medical Care ◽

Care Setting ◽

Demographic Data ◽

Characteristic Curve ◽

External Validation ◽

Medical Setting ◽

Predictive Values ◽

Medical Study ◽

Academic Center ◽

Severe Periodontitis

Abstract Objectives Medical professionals should advise their patients to visit a dentist if necessary. Due to the lack of time and knowledge, screening for periodontitis is often not done. To alleviate this problem, a screening model for total (own teeth/gum health, gum treatment, loose teeth, mouthwash use, and age)/severe periodontitis (gum treatment, loose teeth, tooth appearance, mouthwash use, age, and sex) in a medical care setting was developed in the Academic Center of Dentistry Amsterdam (ACTA) [1]. The purpose of the present study was to externally validate this tool in an outpatient medical setting. Materials and methods Patients were requited in an outpatient medical setting as the validation cohort. The self-reported oral health questionnaire was conducted, demographic data were collected, and periodontal examination was performed. Algorithm discrimination was expressed as the area under the receiver operating characteristic curve (AUROCC). Sensitivity, specificity, and positive and negative predictive values were calculated. Calibration plots were made. Results For predicting total periodontitis, the AUROCC was 0.59 with a sensitivity of 49% and specificity of 68%. The PPV was 57% and the NPV scored 55%. For predicting severe periodontitis, the AUROCC was 0.73 with a sensitivity of 71% and specificity of 63%. The PPV was 39% and the NPV 87%. Conclusions The performance of the algorithm for severe periodontitis is found to be sufficient in the current medical study population. Further external validation of periodontitis algorithms in non-dental school populations is recommended. Clinical relevance Because general physicians are obligated to screen patients for periodontitis, it is our general goal that they can use a prediction model in medical settings without an oral examination.

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