scholarly journals A New Predictor of Disease Severity in Patients with COVID-19 in Wuhan, China

2020 ◽  
Author(s):  
Ying Zhou ◽  
Zhen Yang ◽  
Yanan Guo ◽  
Shuang Geng ◽  
Shan Gao ◽  
...  
Keyword(s):  
Risk Factors ◽  
Disease Severity ◽  
Predictive Value ◽  
Demographic Data ◽  
Characteristic Curve ◽  
Severe Pneumonia ◽  
Internal Validation ◽  
Predictive Values ◽  
Radiological Data ◽  
D Dimer

Abstract Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) broke out in Wuhan, Hubei, China. This study sought to elucidate a novel predictor of disease severity in patients with coronavirus disease-19 (COVID-19) cased by SARS-CoV-2.Methods Patients enrolled in this study were all hospitalized with COVID-19 in the Central Hospital of Wuhan, China. Clinical features, chronic comorbidities, demographic data, and laboratory and radiological data were reviewed. The outcomes of patients with severe pneumonia and those with non-severe pneumonia were compared to explore risk factors. The receiver operating characteristic curve was used to screen optimal predictors from the risk factors and the predictive power was verified by internal validation.Results A total of 377 patients diagnosed with COVID-19 were enrolled in this study, including 117 with severe pneumonia and 260 with non-severe pneumonia. The independent risk factors for severe pneumonia were age, N/L, CRP and D-dimer. We identified a product of N/L*CRP*D-dimer as having an important predictive value for the severity of COVID-19. The cutoff value was 5.32. The negative predictive value of less than 5.32 for the N/L*CRP*D-dimer was 93.75%, while the positive predictive value was 46.03% in the test sets. In the training sets, the negative and positive predictive values were 93.80% and 41.32%.Conclusions A product of N/L*CRP*D-dimer may be an important predictor of disease severity in patients with COVID-19.

scholarly journals A New Predictor of Disease Severity in Patients with COVID-19 in Wuhan, China

2020 ◽  
Author(s):  
Ying Zhou ◽  
Zhen Yang ◽  
Yanan Guo ◽  
Shuang Geng ◽  
Shan Gao ◽  
...  
Keyword(s):  
Risk Factors ◽  
Disease Severity ◽  
Predictive Value ◽  
Demographic Data ◽  
Characteristic Curve ◽  
Severe Pneumonia ◽  
Internal Validation ◽  
Predictive Values ◽  
Radiological Data ◽  
D Dimer

AbstractBackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) broke out in Wuhan, Hubei, China. This study sought to elucidate a novel predictor of disease severity in patients with coronavirus disease-19 (COVID-19) cased by SARS-CoV-2.MethodsPatients enrolled in this study were all hospitalized with COVID-19 in the Central Hospital of Wuhan, China. Clinical features, chronic comorbidities, demographic data, and laboratory and radiological data were reviewed. The outcomes of patients with severe pneumonia and those with non-severe pneumonia were compared using the Statistical Package for the Social Sciences (IBM Corp., Armonk, NY, USA) to explore clinical characteristics and risk factors. The receiver operating characteristic curve was used to screen optimal predictors from the risk factors and the predictive power was verified by internal validation.ResultsA total of 377 patients diagnosed with COVID-19 were enrolled in this study, including 117 with severe pneumonia and 260 with non-severe pneumonia. The independent risk factors for severe pneumonia were age [odds ratio (OR): 1.059, 95% confidence interval (CI): 1.036–1.082; p < 0.001], N/L (OR: 1.322, 95% CI: 1.180–1.481; p < 0.001), CRP (OR: 1.231, 95% CI: 1.129–1.341; p = 0.002), and D-dimer (OR: 1.059, 95% CI: 1.013–1.107; p = 0.011). We identified a product of N/L*CRP*D-dimer as having an important predictive value for the severity of COVID-19. The cutoff value was 5.32. The negative predictive value of less than 5.32 for the N/L*CRP*D-dimer was 93.75%, while the positive predictive value was 46.03% in the test sets. The sensitivity and specificity were 89.47% and 67.42%. In the training sets, the negative and positive predictive values were 93.80% and 41.32%, respectively, with a specificity of 70.76% and a sensitivity of 89.87%.ConclusionsA product of N/L*CRP*D-dimer may be an important predictor of disease severity in patients with COVID-19.

scholarly journals Development and validation of a nomogram for predicting the probability of nontraumatic osteonecrosis of the femoral head

2020 ◽  
Author(s):  
Qiang Xu ◽  
Hangjun Chen ◽  
Sihai Chen ◽  
Jing Shan ◽  
Guoming Xia ◽  
...  
Keyword(s):  
Risk Factors ◽  
Femoral Head ◽  
Demographic Data ◽  
Characteristic Curve ◽  
Model Development ◽  
Lasso Regression ◽  
Early Screening ◽  
Good Discrimination ◽  
Internal Validation ◽  
Selection Operator

Abstract Background Although corticosteroids and alcohol are two major risk factors for nontraumatic osteonecrosis of the femoral head (NONFH), the effects of other factors have rarely been studied, thereby making early diagnosis and treatment of NONFH difficult. This study aimed to develop and validate a nomogram to estimate the probability of NONFH using clinical risk factors other than corticosteroids and alcohol consumption. Methods A training cohort of 790 patients (n=434, NONFH; n=356, femoral neck fractures [non-NONFH]) diagnosed in our hospital from January 2011 to December 2016 was used for model development. A least absolute shrinkage and selection operator (lasso) regression model was used for date dimension reduction and optimal predictor selection. A predictive model was developed from univariate and multivariate logistic regression analyses. Performance characterisation of the resulting nomogram included calibration, discriminatory ability, and clinical usefulness. After internal validation, the nomogram was further evaluated in a separate cohort of 300 consecutive patients included between January 2017 and December 2018. Results The simple prediction nomogram included five predictors from univariate and multivariate analyses, including gender, total cholesterol levels, triglyceride levels, white blood cell count, and platelet count. Internal validation showed that the model had good discrimination (area under the receiver operating characteristic curve [AUC]=0.80) and calibration. Good discrimination (AUC=0.81) and calibration were preserved in the validation cohort. Decision curve analysis showed that the predictive nomogram was clinically useful. Conclusions The simple diagnostic nomogram, which combines demographic data and laboratory blood test results, was able to quantify the probability of NONFH in cases of early screening and diagnosis.

scholarly journals Evaluation of Risk Factors for Exacerbations in Children with Adenoviral Pneumonia

2020 ◽  
Vol 2020 ◽  
pp. 1-5
Author(s):  
Na Xu ◽  
Peng Chen ◽  
Ying Wang
Keyword(s):  
Risk Factors ◽  
Disease Severity ◽  
Clinical Manifestations ◽  
Severe Pneumonia ◽  
Clinical Implications ◽  
Laboratory Findings ◽  
Severe Group ◽  
Adenovirus Pneumonia ◽  
The Difference ◽  
D Dimer

Purpose. The aim of this work was to analyze clinical features and laboratory findings of children with adenovirus pneumonia and guide clinical diagnosis, treatment, and assessment of disease severity. Material and Methods. Retrospective analysis of clinical data of 285 children with adenoviral pneumonia who were hospitalized in Wuhan Children’s Hospital from December 2018 to October 2019. According to the assessment criteria for severe pneumonia, it was divided into the severe group (92 cases) and the nonsevere group (193 cases). Collected clinical manifestations, complications, and laboratory test indicators in two groups of children and conducted all statistical analyses. Results. The risk of fever and wheezing was significantly higher in the severe group than in the nonsevere group. The difference was statistically significant (P<0.05). The risk of complications in the severe group was significantly higher than that in the nonsevere group. The difference was statistically significant (P<0.05). The levels of AST, LDH-L, PCT, ferritin, and D-dimer in the severe group were significantly higher than those in the nonsevere group. The difference was statistically significant (P<0.05). Conclusion. Children with severe adenovirus pneumonia have severe clinical manifestations and many complications. AST, LDH-L, PCT, ferritin, and D-dimer levels have important clinical implications for assessing disease severity.

scholarly journals Clinical Characteristics of Patients with Severe Pneumonia Caused by the 2019 Novel Coronavirus in Wuhan, China

2020 ◽  
Author(s):  
Yafei Wang ◽  
Ying Zhou ◽  
Zhen Yang ◽  
Dongping Xia ◽  
Shuang Geng
Keyword(s):  
Risk Factors ◽  
Clinical Characteristics ◽  
Early Stage ◽  
Demographic Data ◽  
Severe Pneumonia ◽  
Advanced Age ◽  
Ct Scans ◽  
Risk Patients ◽  
Novel Coronavirus ◽  
D Dimer

AbstractBackgroundA new virus broke out in Wuhan, Hubei, China, and was later named 2019 novel coronavirus (2019-nCoV). The clinical characteristics of severe pneumonia caused by 2019-nCoV are still not clear.ObjectivesThe aim of this study was to explore the clinical characteristics and risk factors of the severe pneumonia caused by the 2019-nCoV in Wuhan, China.MethodThe study included patients hospitalized at the central hospital of Wuhan who had been diagnosed with a pneumonia caused by the novel coronavirus. Clinical features, chronic co-morbidities, demographic data, laboratory examinations, and chest computed tomography (CT) scans were reviewed through electronic medical records. SPSS was used for data analysis to explore the clinical characteristics and risk factors of the patients with the severe pneumonia.ResultsA total of 110 patients diagnosed with 2019 novel coronavirus pneumonia were included in the study, including 38 with severe pneumonia and 72 with non-severe pneumonia. Statistical analysis showed that advanced age, an increase of D-dimer, and a decrease of lymphocytes were characteristics of the patients with severe pneumonia. Moreover, in the early stage of the disease, chest CT scans of patients with the severe pneumonia showed the illness can progress rapidly.ConclusionsAdvanced age, lymphocyte decline, and D-dimer elevation are important characteristics of patients with severe pneumonia. Clinicians should focus on these characteristics to identify high-risk patients at an early stage.

scholarly journals Clinical Characteristics, Risk Factors and Predictive Value of COVID-19 Pneumonia: A Retrospective Study of 173 Patients in Wuhan, China

2020 ◽  
Author(s):  
Yang Zhang ◽  
Jun Xue ◽  
Mi Yan ◽  
Jing Chen ◽  
Hai Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Predictive Model ◽  
Disease Severity ◽  
Roc Curve ◽  
Predictive Value ◽  
Older Patients ◽  
Clinical Characteristics ◽  
Lactic Dehydrogenase ◽  
Older Age ◽  
Hospital Outcome

Abstract Background: COVID-19 is a globally emerging infectious disease. As the global epidemic continues to spread, the risk of COVID-19 transmission and diffusion in the world will also remain. Currently, several studies describing its clinical characteristics have focused on the initial outbreak, but rarely to the later stage. Here we described clinical characteristics, risk factors for disease severity and in-hospital outcome in patients with COVID-19 pneumonia from Wuhan. Methods: Patients with COVID-19 pneumonia admitted to Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology from February 13 to March 8, 2020, were retrospectively enrolled. Multivariable logistic regression analysis was used to identify risk factors for disease severity and in-hospital outcome and establish predictive models. Receiver operating characteristic (ROC) curve was used to assess the predictive value of above models.Results: 106 (61.3%) of the patients were female. The mean age of study populations was 62.0 years, of whom 73 (42.2%) had underlying comorbidities mainly including hypertension (24.9%). The most common symptoms on admission were fever (67.6%) and cough (60.1%), digestive symptoms (22.0%) was also very common. Older age (OR: 3.420; 95%Cl: 1.415-8.266; P=0.006), diarrhea (OR: 0.143; 95%Cl: 0.033-0.611; P=0.009) and lymphopenia (OR: 4.769; 95%Cl: 2.019-11.266; P=0.000) were associated with severe illness on admission; the area under the ROC curve (AUC) of predictive model were 0.860 (95%CI: 0.802-0.918; P=0.000). Older age (OR: 0.309; 95%Cl: 0.142-0.674; P=0.003), leucopenia (OR: 0.165; 95%Cl: 0.034-0.793; P=0.025), increased lactic dehydrogenase (OR: 0.257; 95%Cl: 0.100-0.659; P=0.005) and interleukins-6 levels (OR: 0.294; 95%Cl: 0.099-0.872; P=0.027) were associated with poor in-hospital outcome; AUC of predictive model were 0.752 (95%CI: 0.681-0.824; P=0.000).Conclusion: Older patients with diarrhea and lymphopenia need early identification and timely intervention to prevent the progression to severe COVID-19 pneumonia. However, older patients with leucopenia, increased lactic dehydrogenase and interleukins-6 levels are at a high risk for poor in-hospital outcome.Trial registration: ChiCTR2000029549

scholarly journals COMPARATIVE ASSESSMENT OF PREDICTIVE VALUE OF TOTAL CARDIOVASCULAR RISK FACTORS FOR PROGNOSIS OF STROKE AND HEART ATTACK IN HYPERTENSION

2009 ◽  
Vol 15 (6) ◽  
pp. 665-670
Author(s):  
A. A. Dzizinskij ◽  
G. M. Sinkova ◽  
V. V. Sprach ◽  
A. V. Sinkov
Keyword(s):  
Risk Factors ◽  
Cardiovascular Risk ◽  
Cardiovascular Risk Factors ◽  
Predictive Value ◽  
Heart Attack ◽  
Comparative Assessment ◽  
Predictive Values ◽  
Hypertensive Patients ◽  
Design And Methods

Objective. To assess predictive value of total cardiovascular risk (CV) factors for prognosis of stroke and heart attack in hypertension. Design and methods. 841 hypertensive patients (197 men, 644 women) 19-95 years old were examined. Results. It was established that total CV risk factors have different predictive values. The majority of factors were more valuable for prognosis of heart attack, but not for stroke.

scholarly journals Validity of ICD-10-CM codes for determination of diabetes type for persons with youth-onset type 1 and type 2 diabetes

2019 ◽  
Vol 7 (1) ◽  
pp. e000547 ◽  
Author(s):  
Gloria C Chi ◽  
Xia Li ◽  
Sara Y Tartof ◽  
Jeff M Slezak ◽  
Corinna Koebnick ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Predictive Value ◽  
Gold Standard ◽  
Characteristic Curve ◽  
Diabetes Type ◽  
Predictive Values ◽  
Icd 10

ObjectiveDiagnosis codes might be used for diabetes surveillance if they accurately distinguish diabetes type. We assessed the validity ofInternational Classification of Disease, 10th Revision, Clinical Modification(ICD-10-CM) codes to discriminate between type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) among health plan members with youth-onset (diagnosis age <20 years) diabetes.Research design and methods. Diabetes case identification and abstraction of diabetes type was done as part of the SEARCH for Diabetes in Youth Study. The gold standard for diabetes type is the physician-assigned diabetes type documented in patients’ medical records. Using all healthcare encounters with ICD-10-CM codes for diabetes, we summarized codes within each encounter and determined diabetes type using percent of encounters classified as T2DM. We chose 50% as the threshold from a receiver operating characteristic curve because this threshold yielded the largest Youden’s index. Persons with ≥50% T2DM-coded encounters were classified as having T2DM. Otherwise, persons were classified as having T1DM. We calculated sensitivity, specificity, positive and negative predictive values, and accuracy overall and by demographic characteristics.ResultsAccording to the gold standard, 1911 persons had T1DM and 652 persons had T2DM (mean age (SD): 19.1 (6.5) years). We obtained 90.6% (95% CI 88.4% to 92.9%) sensitivity, 96.3% (95% CI 95.4% to 97.1%) specificity, 89.3% (95% CI 86.9% to 91.6%) positive predictive value, 96.8% (95% CI 96.0% to 97.6%) negative predictive value, and 94.8% (95% CI 94.0% to 95.7%) accuracy for discriminating T2DM from T1DM.ConclusionsICD-10-CM codes can accurately classify diabetes type for persons with youth-onset diabetes, showing promise for rapid, cost-efficient diabetes surveillance.

scholarly journals Association of Biomarkers Related to Preoperative Inflammatory and Coagulation with Postoperative In-Hospital Deaths in Patients with Type A Acute Aortic Dissection

2021 ◽  
Author(s):  
Ming Li ◽  
Haifeng Sun ◽  
Suochun Xu ◽  
Yang Yan ◽  
Haichen Wang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Predictive Value ◽  
Multivariate Logistic Regression Analysis ◽  
Type A ◽  
Hospital Deaths ◽  
Independent Risk Factors ◽  
D Dimer

Abstract Background: The aim of this study was to analyze the predictive value of biomarkers related to preoperative inflammatory and coagulation in the prognosis of patients with type A acute aortic dissection (AAD). Methods: A total of 206 patients with type A AAD who had received surgical treatment were enrolled. Patients were divided into two groups according to whether they died during hospitalization. Peripheral blood samples were collected before anesthesia induction. Preoperative levels of D-dimer, fibrinogen (FIB), platelet (PLT), white blood cells (WBC) and neutrophil (NEU) between the two groups were compared. Univariate and multivariate logistic regression analysis were utilized to identify the independent risk factors for postoperative in-hospital deaths of patients with type A AAD. Receiver operating characteristic (ROC) curve were used to analyze the predictive value of D-dimer, FIB, PLT, WBC, NEU and CRP in the prognosis of the patients. Results: Univariate logistic regression analysis showed that the P values of the five parameters including D-dimer, FIB, PLT, WBC and NEU were all less than 0.1, which may be risk factors for postoperative in-hospital deaths of patients with type A AAD. Further multivariate logistic regression analysis indicated that higher preoperative D-dimer and WBC levels were independent risk factors for in-hospital deaths of patients with type A AAD. ROC curve analysis indicated that FIB+PLT combination is provided with the highest predictive value for in-hospital deaths.Conclusion: Both preoperative D-dimer and WBC in patients with type A AAD may be used as independent risk factors for the prognosis of such patients. Combined use of FIB and PLT may improve the accuracy and accessibility of clinical prognostic assessment.

scholarly journals Predictive values of neutrophil-to-lymphocyte ratio on disease severity and mortality in COVID-19 patients: a systematic review and meta-analysis

2020 ◽  
Author(s):  
Xiaoming Li ◽  
Chao Liu ◽  
Zhi Mao ◽  
Minglu Xiao ◽  
Li Wang ◽  
...  
Keyword(s):  
Systematic Review ◽  
Disease Severity ◽  
Predictive Value ◽  
Meta Analysis ◽  
Neutrophil To Lymphocyte Ratio ◽  
Global Public Health ◽  
China National Knowledge Infrastructure ◽  
Predictive Values ◽  
Lymphocyte Ratio ◽  
Registration Number

Abstract Background：Coronavirus disease 2019 (COVID-19), a highly infectious disease, has been rapidly spreading all over the world and posted a great threat to global public health. Patients diagnosed with severe or critical cases have a poor prognosis. Hence, it is crucial for us to identify potential severe or critical cases early, and give timely treatments for the targeted patients. In the clinical practice of treating COVID-19 patients, we have observed that the neutrophil-to-lymphocyte ratio (NLR) of severe patients is higher than that in mild patients. We performed this systematic review and meta-analysis to evaluate the predictive values of NLR on disease severity and mortality in COVID-19 patients. Methods: We searched PubMed, EMBASE, China National Knowledge Infrastructure (CNKI) and Wanfang databases to identify eligible studies (up to August 11, 2020). Two authors independently screened studies and extracted data. The methodological quality of the included studies was assessed by Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2).Results: Thirteen studies involving 1579 patients reported the predictive value of NLR on disease severity. The pooled sensitivity (SEN), specificity (SPE) and area under curve (AUC) were 0.78 (95% CI 0.70-0.84), 0.78 (95% CI 0.73-0.83) and 0.85 (95% CI 0.81-0.88), respectively. Ten studies involving 2967 patients reported the predictive value of NLR on mortality. The pooled SEN, SPE and AUC were 0.83 (95% CI 0.75-0.89), 0.83 (95% CI 0.74-0.89) and 0.90 (95% CI 0.87-0.92), respectively. Conclusions: NLR has good predictive values on disease severity and mortality in COVID-19 patients. Evaluating NLR can help clinicians identify potentially severe cases early, conduct early triage and initiate effective management in time, which may reduce the overall mortality of COVID-19. Moreover, we can better allocate scarce medical resources in this unprecedented time. Trial registration: This meta-analysis was prospectively registered on PROSPERO database (Registration number: CRD42020203612).

scholarly journals Maximum chest CT score predicts 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):  
Risk Factors ◽  
Disease Progression ◽  
Clinical Laboratory ◽  
Characteristic Curve ◽  
Severe Illness ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Factors Associated ◽  
Radiological Data ◽  
Symptom Progression

Abstract Background: We investigated the clinical course and imaging findings of hospitalized patients who were initially diagnosed with moderate COVID-19 symptoms to identify risk factors associated with progression to severe/critical symptoms.Methods: This study was a retrospective single-center study at The Central Hospital of Wuhan. 243 patients with confirmed COVID­19 pneumonia were enrolled in the analysis, of which 40 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 symptom types. 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). We found that the combination of chronic obstructive pulmonary disease and high maximum CT scores was associated with disease progression. Maximum CT scores (≥11) had the greatest predictive value for disease progression. The area under the receiver operating characteristic curve (ROC) was 0.861 (95% CI: 0.811-0.902).Conclusions: Maximum CT scores and COPD are associated with patient deterioration. Maximum CT scores (≥11) are associated with severe illness.

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