The Prediction Model of Risk Factors for COVID-19 Developing into Severe Illness Based on 1046 Patients with COVID-19

This study analyzed the risk factors for patients with COVID-19 developing severe illnesses and explored the value of applying the logistic model combined with ROC curve analysis to predict the risk of severe illnesses at COVID-19 patients’ admissions. The clinical data of 1046 COVID-19 patients admitted to a designated hospital in a certain city from July to September 2020 were retrospectively analyzed, the clinical characteristics of the patients were collected, and a multivariate unconditional logistic regression analysis was used to determine the risk factors for severe illnesses in COVID-19 patients during hospitalization. Based on the analysis results, a prediction model for severe conditions and the ROC curve were constructed, and the predictive value of the model was assessed. Logistic regression analysis showed that age (OR = 3.257, 95% CI 10.466–18.584), complications with chronic obstructive pulmonary disease (OR = 7.337, 95% CI 0.227–87.021), cough (OR = 5517, 95% CI 0.258–65.024), and venous thrombosis (OR = 7322, 95% CI 0.278–95.020) were risk factors for COVID-19 patients developing severe conditions during hospitalization. When complications were not taken into consideration, COVID-19 patients’ ages, number of diseases, and underlying diseases were risk factors influencing the development of severe illnesses. The ROC curve analysis results showed that the AUC that predicted the severity of COVID-19 patients at admission was 0.943, the optimal threshold was −3.24, and the specificity was 0.824, while the sensitivity was 0.827. The changes in the condition of severe COVID-19 patients are related to many factors such as age, clinical symptoms, and underlying diseases. This study has a certain value in predicting COVID-19 patients that develop from mild to severe conditions, and this prediction model is a useful tool in the quick prediction of the changes in patients’ conditions and providing early intervention for those with risk factors.

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The Value of Preoperative Alpha-L-Fucosidase Levels in Evaluation of Malignancy and Differential Diagnosis of Urothelial Neoplasms

Journal of Oncology ◽

10.1155/2020/6723616 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Dongshan Chen ◽

Naidong Xing ◽

Zhanwu Cui ◽

Cong Zhang ◽

Zhao Zhang ◽

...

Keyword(s):

Logistic Regression ◽

Differential Diagnosis ◽

Regression Analysis ◽

Urothelial Carcinoma ◽

Logistic Regression Analysis ◽

Roc Curve ◽

Curve Analysis ◽

Clinicopathological Parameters ◽

Roc Curve Analysis ◽

Urothelial Tumors

Purpose. To evaluate the role of Alpha-L-fucosidase (AFU) in diagnosis and differential diagnosis of pure urothelial carcinoma (UC), urothelial carcinoma with squamous differentiation (UCSD), and squamous cell carcinoma (SqCC). Methods. A retrospective study was performed for 599 patients who were histologically confirmed with urothelial tumor. Preoperative AFU levels were compared across the distinct subgroups with different clinicopathological parameters. ROC curve analysis and logistic regression analysis were performed to further evaluate the clinical application value of serum AFU levels in diagnosis and differential diagnosis of urothelial tumors. Results. There were no statistically significant differences in the AFU levels between different groups with different malignant degrees (UC versus papilloma and papillary urothelial neoplasm of low malignant potential [PUNLMP], high-grade UC versus low-grade UC, invasive versus noninvasive malignant uroepithelial tumor) and different pathological types (UC, UCSD, and SqCC) (all P>0.05). ROC curve analysis and logistic regression analysis showed that there was no statistically significant association between AFU levels and the tumor characteristics (all P>0.05). Conclusions. Preoperative AFU levels cannot serve as a reliable predictor for malignant degree and differential diagnosis, including pure UC, UCSD, and SqCC of urothelial tumors.

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Computed Tomography Based Score of Early Ischemic Changes Predicts Malignant Infarction

Frontiers in Neurology ◽

10.3389/fneur.2021.669828 ◽

2021 ◽

Vol 12 ◽

Author(s):

Matthias Bechstein ◽

Lukas Meyer ◽

Silke Breuel ◽

Tobias D. Faizy ◽

Uta Hanning ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

Roc Curve ◽

Curve Analysis ◽

Multivariable Logistic Regression Analysis ◽

Roc Curve Analysis ◽

Imaging Parameters ◽

Malignant Infarction ◽

Enhanced Ct

Background and Purpose: Identification of ischemic stroke patients at high risk of developing life-threatening malignant infarction at an early stage is critical to consider more rigorous monitoring and further therapeutic measures. We hypothesized that a score consisting of simple measurements of visually evident ischemic changes in non-enhanced CT (NEMMI score) predicts malignant middle cerebral artery (MCA) infarctions (MMI) with similar diagnostic power compared to other baseline clinical and imaging parameters.Methods: One hundred and nine patients with acute proximal MCA occlusion were included. Fifteen (13.8%) patients developed MMI. NEMMI score was defined using the sum of the maximum diameter (anterior-posterior plus medio-lateral) of the hypoattenuated lesion in baseline-CT multiplied by a hypoattenuation factor (3-point visual grading in non-enhanced CT, no/subtle/clear hypoattenuation = 1/2/3). Receiver operating characteristic (ROC) curve analysis and multivariable logistic regression analysis were used to calculate the predictive values of the NEMMI score, baseline clinical and other imaging parameters.Results: The median NEMMI score at baseline was 13.6 (IQR: 11.6–31.1) for MMI patients, and 7.7 (IQR: 3.9–11.2) for patients with non-malignant infarctions (p < 0.0001). Based on ROC curve analysis, a NEMMI score >10.5 identified MMI with good discriminative power (AUC: 0.84, sensitivity/specificity: 93.3/70.7%), which was higher compared to age (AUC: 0.76), NIHSS (AUC: 0.61), or ischemic core volume (AUC: 0.80). In multivariable logistic regression analysis, NEMMI score was significantly and independently associated with MMI (OR: 1.33, 95%CI: 1.13–1.56, p < 0.001), adjusted for recanalization status.Conclusion: The NEMMI score is a quick and simple rating tool of early ischemic changes on CT and could serve as an important surrogate marker for developing malignant edema. Its diagnostic accuracy was similar to CTP and clinical parameters.

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Multivariable Logistic Regression Equation to Predict Prostate Cancer

10.21203/rs.3.rs-923836/v1 ◽

2021 ◽

Author(s):

Lu Ma ◽

Dong Cheng ◽

Qinghua Li ◽

Jingbo Zhu ◽

Yu Wang ◽

...

Keyword(s):

Prostate Cancer ◽

Logistic Regression ◽

Regression Analysis ◽

Prediction Model ◽

Prostate Specific Antigen ◽

Sensitivity And Specificity ◽

Logistic Regression Analysis ◽

Roc Curve ◽

Specific Antigen ◽

Clinical Feature

Abstract Objective: To explore the predictive value of white blood cell (WBC), monocyte (M), neutrophil-to-lymphocyte ratio (NLR), fibrinogen (FIB), free prostate-specific antigen (fPSA) and free prostate-specific antigen/prostate-specific antigen (f/tPSA) in prostate cancer (PCa).Materials and methods: Retrospective analysis of 200 cases of prostate biopsy and collection of patients' systemic inflammation indicators, biochemical indicators, PSA and fPSA. First, the dimensionality of the clinical feature parameters is reduced by the Lass0 algorithm. Then, the logistic regression prediction model was constructed using the reduced parameters. The cut-off value, sensitivity and specificity of PCa are predicted by the ROC curve analysis and calculation model. Finally, based on Logistic regression analysis, a Nomogram for predicting PCa is obtained.Results: The six clinical indicators of WBC, M, NLR, FIB, fPSA, and f/tPSA were obtained after dimensionality reduction by Lass0 algorithm to improve the accuracy of model prediction. According to the regression coefficient value of each influencing factor, a logistic regression prediction model of PCa was established: logit P=-0.018-0.010×WBC+2.759×M-0.095×NLR-0.160×FIB-0.306×fPSA-2.910×f/tPSA. The area under the ROC curve is 0.816. When the logit P intercept value is -0.784, the sensitivity and specificity are 72.5% and 77.8%, respectively.Conclusion: The establishment of a predictive model through Logistic regression analysis can provide more adequate indications for the diagnosis of PCa. When the logit P cut-off value of the model is greater than -0.784, the model will be predicted to be PCa.

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Insights into the Therapeutic of Glucocorticoid for Refractory Mycoplasma Pneumoniae Pneumonia in Children

10.21203/rs.3.rs-56843/v1 ◽

2020 ◽

Author(s):

Zhenli Zhu ◽

Tongqiang Zhang ◽

Wei Guo ◽

Yaoyao Ling ◽

Jiao Tian ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Mycoplasma Pneumoniae ◽

Logistic Regression Analysis ◽

Roc Curve ◽

Severe Illness ◽

Group Iii ◽

Group I ◽

Group Ii ◽

Mycoplasma Pneumoniae Pneumonia

Abstract Objective To observe the efficacy and safety of different doses of glucocorticoid for refractory mycoplasma pneumoniae pneumonia in children, analyze the clinical characteristics in different groups of patients, and explore the factors related to affect illness severity for children with refractory mycoplasma pneumoniae pneumonia and guide the dosage of glucocorticoids.Methods Retrospective analysis was performed on 279 children with refractory mycoplasma pneumoniae pneumonia hospitalized in our hospital between September 2018 and October 2019. 23 children were excluded, the remaining 256 children were divided into three subgroups: Group I was not given methylprednisolone (n=75), group II (n=115) was given methylprednisolone ≤125mg/d, and group III was given methylprednisolone >125mg/d (n=66). The clinical features, laboratory data, radiological manifestations between three subgroups of children were compared, relevant indicators with meaningful were used for ROC curve and multiple logistic regression analysis, and the optimal values of related factors were analyzed.Results The median age and median weight of the group III were greater than the group II（P <0.05）, the median age and median weight of the group I were greater than the group II（P <0.05）, there was no statistical significance in median age and median weight between group III and group I（P>0.05）. The group II is more serious than that of group I, and group III is more serious than that of group II, higher incidence of hypoxemia, longer fever, longer hospital stays, higher incidence of extrapulmonary complications, and more severe of radiological findings (P <0.05). The more severe presentation of disease, hormones dosage was larger, the use rate of gamma globulin was higher, the use rate of bronchoscopy was higher, and higher incidence of plastic bronchitis (P <0.05). Meanwhile, WBC, CRP, LDH, FER, D-D dimer, APTT, PLT, PCT, IL-6, ALT and the percentage of neutrophils in the three groups showed a gradual upward trend (P <0.05). In ROC curve analysis, WBC, neutrophils percentage, CRP, LDH, Fer, PCT and IL-6 can be used to distinguish RMPP with different severity and to guide the dosage of glucocorticoids. Multivariate logistic regression analysis showed that LDH 424.5IU/L, PCT 0.145ng/ml, IL-6 26.69pg/ml and lung consolidation were significant predictors for the severity of RMPP and glucocorticoids dose.Conclusions LDH 424.5IU/L, PCT 0.145 ng/ml, IL-6 26.69pg/ml and pulmonary consolidation as markers of disease severity in patients with RMPP and the dosage of glucocorticoids, which can aid in early recognition of children with severe illness, use appropriate doses of hormones, and reduce sequelae.

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Title: Preoperative albumin-to-fibrinogen ratio predicts postoperative pulmonary complications in patients after minimally invasive esophagectomy

10.21203/rs.3.rs-604731/v1 ◽

2021 ◽

Author(s):

Zhang Peng ◽

Zhao Song

Keyword(s):

Risk Factors ◽

Esophageal Cancer ◽

Logistic Regression ◽

Roc Curve ◽

Training Group ◽

Pulmonary Complications ◽

Curve Analysis ◽

Postoperative Pulmonary Complications ◽

Multivariate Logistic Regression ◽

Roc Curve Analysis

Abstract Background Postoperative pulmonary complications (PPCs) are the most common postoperative complications in patients with esophageal cancer. Prediction of PPCs by establishing a preoperative physiological function parameter model can help patients make adequate preoperative preparation, reduce treatment costs, and improve prognosis and quality of life. The purpose of this study was to investigate the relationship between albumin-to-fibrinogen ratio (AFR), prognostic nutritional index (PNI), albumin-to-globulin ratio (AGR), neutrophils-to-lymphocyte ratio (NLR), platelet-to-lymphocyte (PLR), and monocyte-to -lymphocyte ratio (MLR) and other preoperative laboratory tests and PPCs in patients after esophagectomy. Methods Retrospective analysis was performed on total 712 consecutive patients who underwent esophagectomy in the Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University from July 2018 to December 2020. Patients were divided into training (535 patients) and validation (177) groups for comparison of baseline data, perioperative indicators, and laboratory examination data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the efficacy, sensitivity and specificity of AFR, and Youden’s index was used to calculate the cut-off values of AFR. Univariate and multivariate logistic regression analyses were used to assess the risk factors for PPCs in training group. Results 112 (20.9%) in training group and 36 (20.3%) in validation group developed PPCs. The AUC value predicted by AFR using ROC curve analysis was 0.817, sensitivity 76.2% and specificity 78.7% in training group while AUC 0.803, sensitivity 69.4% and specificity 85.8%. Multivariate logistic regression analysis showed that smoking index, American Society of Anesthesiologists (ASA), AFR, and recurrent laryngeal nerve palsy were independent risk factors for PPCs. Conclusion Preoperative AFR can effectively predict the occurrence of PPCs in patients with esophageal cancer

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Identification of risk factors for daptomycin-associated creatine phosphokinase elevation and development of a risk prediction model for incidence probability

Open Forum Infectious Diseases ◽

10.1093/ofid/ofab568 ◽

2021 ◽

Author(s):

Masaru Samura ◽

Naoki Hirose ◽

Takenori Kurata ◽

Keisuke Takada ◽

Fumio Nagumo ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Regression Analysis ◽

Prediction Model ◽

Risk Prediction ◽

Risk Score ◽

Logistic Regression Analysis ◽

Creatine Phosphokinase ◽

Risk Prediction Model ◽

Risk Scores

Abstract Background In this study, we investigated the risk factors for daptomycin-associated creatine phosphokinase (CPK) elevation and established a risk score for CPK elevation. Methods Patients who received daptomycin at our hospital were classified into the normal or elevated CPK group based on their peak CPK levels during daptomycin therapy. Univariable and multivariable analyses were performed, and a risk score and prediction model for the incidence probability of CPK elevation were calculated based on logistic regression analysis. Results The normal and elevated CPK groups included 181 and 17 patients, respectively. Logistic regression analysis revealed that concomitant statin use (odds ratio [OR] 4.45, 95% confidence interval [CI] 1.40–14.47, risk score 4), concomitant antihistamine use (OR 5.66, 95% CI 1.58–20.75, risk score 4), and trough concentration (Cmin) between 20 and <30 µg/mL (OR 14.48, 95% CI 2.90–87.13, risk score 5) and ≥30.0 µg/mL (OR 24.64, 95% CI 3.21–204.53, risk score 5) were risk factors for daptomycin-associated CPK elevation. The predicted incidence probabilities of CPK elevation were <10% (low risk), 10%–<25% (moderate risk), and ≥25% (high risk) with the total risk scores of ≤4, 5–6, and ≥8, respectively. The risk prediction model exhibited a good fit (area under the receiving-operating characteristic curve 0.85, 95% CI 0.74–0.95). Conclusions These results suggested that concomitant use of statins with antihistamines and Cmin ≥20 µg/mL were risk factors for daptomycin-associated CPK elevation. Our prediction model might aid in reducing the incidence of daptomycin-associated CPK elevation.

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Association Between D-Dimer Level and In-Hospital Death of Pulmonary Embolism Patients

Dose-Response ◽

10.1177/1559325820968430 ◽

2020 ◽

Vol 18 (4) ◽

pp. 155932582096843

Author(s):

Zi-Kai Song ◽

Haidi Wu ◽

Xiaoyan Xu ◽

Hongyan Cao ◽

Qi Wei ◽

...

Keyword(s):

Pulmonary Embolism ◽

Logistic Regression ◽

Regression Analysis ◽

Roc Curve ◽

Curve Analysis ◽

Hospital Death ◽

Roc Curve Analysis ◽

Survival Group ◽

Death Group ◽

D Dimer

To investigate whether D-dimer level could predict pulmonary embolism (PE) severity and in-hospital death, a total of 272 patients with PE were divided into a survival group (n = 249) and a death group (n = 23). Comparisons of patient characteristics between the 2 groups were performed using Mann-Whitney U test. Significant variables in univariate analysis were entered into multivariate logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was performed to determine the predictive value of D-dimer level alone or together with the simplified Pulmonary Embolism Severity Index (sPESI) for in-hospital death. Results showed that patients in the death group were significantly more likely to have hypotension (P = 0.008), tachycardia (P = 0.000), elevated D-dimer level (P = 0.003), and a higher sPESI (P = 0.002) than those in the survival group. Multivariable logistic regression analysis showed that D-dimer level was an independent predictor of in-hospital death (OR = 1.07; 95% CI, 1.003-1.143; P = 0.041). ROC curve analysis showed that when D-dimer level was 3.175 ng/ml, predicted death sensitivity and specificity were 0.913 and 0.357, respectively; and when combined with sPESI, specificity (0.838) and area under the curve (0.740) were increased. Thus, D-dimer level is associated with in-hospital death due to PE; and the combination with sPESI can improve the prediction level.

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Serum Irisin Predicts Mortality Risk in Acute Heart Failure Patients

Cellular Physiology and Biochemistry ◽

10.1159/000477867 ◽

2017 ◽

Vol 42 (2) ◽

pp. 615-622 ◽

Cited By ~ 12

Author(s):

Shutong Shen ◽

Rongrong Gao ◽

Yihua Bei ◽

Jin Li ◽

Haifeng Zhang ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

Multivariate Logistic Regression Analysis ◽

Curve Analysis ◽

Multivariate Logistic Regression ◽

Roc Curve Analysis ◽

Kaplan Meier ◽

All Cause Mortality

Background/Aims: Irisin is a peptide hormone cleaved from a plasma membrane protein fibronectin type III domain containing protein 5 (FNDC5). Emerging studies have indicated association between serum irisin and many major chronic diseases including cardiovascular diseases. However, the role of serum irisin as a predictor for mortality risk in acute heart failure (AHF) patients is not clear. Methods: AHF patients were enrolled and serum was collected at the admission and all patients were followed up for 1 year. Enzyme-linked immunosorbent assay was used to measure serum irisin levels. To explore predictors for AHF mortality, the univariate and multivariate logistic regression analysis, and receiver-operator characteristic (ROC) curve analysis were used. To determine the role of serum irisin levels in predicting survival, Kaplan-Meier survival analysis was used. Results: In this study, 161 AHF patients were enrolled and serum irisin level was found to be significantly higher in patients deceased in 1-year follow-up. The univariate logistic regression analysis identified 18 variables associated with all-cause mortality in AHF patients, while the multivariate logistic regression analysis identified 2 variables namely blood urea nitrogen and serum irisin. ROC curve analysis indicated that blood urea nitrogen and the most commonly used biomarker, NT-pro-BNP, displayed poor prognostic value for AHF (AUCs ≤ 0.700) compared to serum irisin (AUC = 0.753). Kaplan-Meier survival analysis demonstrated that AHF patients with higher serum irisin had significantly higher mortality (P<0.001). Conclusion: Collectively, our study identified serum irisin as a predictive biomarker for 1-year all-cause mortality in AHF patients though large multicenter studies are highly needed.

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Design and validation of a scoring system for rapid identification of severe acute pancreatitis in patients with acute pancreatitis

10.21203/rs.2.21711/v1 ◽

2020 ◽

Author(s):

Ling Wang ◽

Shaohong Wang ◽

Jingguo Zhang

Keyword(s):

Risk Factors ◽

Acute Pancreatitis ◽

Logistic Regression ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

Roc Curve ◽

Scoring System ◽

Rapid Identification ◽

Cutoff Values ◽

History Of

Abstract Purpose: Early identification of SAP and take necessary treatment can reduce the mortality rate of patients with AP. This study aimed to design a scoring system for rapid identification of SAP (RISAP) and evaluate its performance in predicting SAP in patients with AP. Methods: In the first phase, 1024 patients with AP who were admitted to the people's hospital of Qiandongnan Miao and Dong Autonomous Prefecture ,The Second Affiliated Hospital of Guizhou Medical University,the First People's Hospital of Kaili from January 2015 to June 2017 were included. Easily obtained indicators including patients’ gender, age, previous history of pancreatitis, acute diffuse peritonitis (ADP), pleural effusion (PE), heart rate (HR), respiratory rate (RR), and systolic blood pressure (SBP) measured under adequate analgesia, quietness conditions at admission were selected. Logistic regression analysis was performed to identify the risk factors for SAP. After determination of the cutoff values of the identified risk factors using ROC curve analysis, RISAP scoring system was designed. In the second phase, a total of 740 patients with AP who were admitted to our hospital from July 2017 to October 2019 were included and divided into SAP and non-SAP groups. RISAP, RANSON and BISAP scores were measured and compared between groups. The ROC curve was draw to analyze the ability of RISAP score in predicting SAP. Results: The number of patients who had history of pancreatitis,ADP, PE, HR, BR were significantly higher in the SAP group than in the non-SAP group (P <0.05). Logistic regression analysis showed that PE, HR, and RR were independent risk factors for SAP. Then RISAP score was designed based on the cutoff values of the three risk factors (0.5, 95.5, 22.5, respectively). The RISA, RANSON, and BISAP scores were significantly higher in the SAP group than that in the non-SAP group (U = -9.501,-3.701, -8.520 P <0.05). Compared with the RANSON, and BISAP scores, RISAP had the highest AUC values, sensitivity and specificity. Conclusion: The designed RISAP score is simple, convenient, economical, non-invasive, and highly repeatable, which is superior in rapid identification of SAP in patients with AP.

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Preoperative Albumin-to-Fibrinogen Ratio Predicts Postoperative Pneumonia in Patients with Esophageal Squamous Cell Carcinoma: A Retrospective Study

10.21203/rs.3.rs-127793/v1 ◽

2020 ◽

Author(s):

Peng Zhang ◽

Song Zhao

Keyword(s):

Risk Factors ◽

Esophageal Cancer ◽

Logistic Regression ◽

Roc Curve ◽

Laboratory Tests ◽

Curve Analysis ◽

Postoperative Pneumonia ◽

Function Parameter ◽

Multivariate Logistic Regression ◽

Roc Curve Analysis

Abstract Background: Postoperative pneumonia is the most common postoperative complication in patients with esophageal cancer. Prediction of postoperative pneumonia by establishing a preoperative physiological function parameter model can help patients make adequate preoperative preparation, reduce treatment costs, and improve prognosis and quality of life. The purpose of this study was to investigate the relationship between albumin, fibrinogen, albumin-to-fibrinogen ratio(AFR) , and other preoperative laboratory tests and postoperative pneumonia in patients with esophageal cancer after esophagectomy.Methods: Retrospective analysis was performed on 177 consecutive patients who underwent esophagectomy in the Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University from December 2018 to December 2019.Postoperative pneumonia was defined according to the revised Uniform Pneumonia Score(rUPS).Patients were divided into pneumonia and non-pneumonia groups for comparison of baseline data, perioperative indicators, and laboratory examination data.(Receiver operating characteristic)ROC curve analysis was used to evaluate the efficacy, sensitivity and specificity of AFR, and Youden’s index was used to calculate the cut-off values of AFR and other laboratory tests data. Univariate and multivariate logistic regression analyses were used to assess the risk factors for postoperative pneumoniaResults: Of the 177 patients, 32 (18%) developed postoperative pneumonia. The AUC value predicted by AFR using ROC curve analysis was 0.767, 65.6% sensitivity and 83.4% specificity. Multivariate logistic regression analysis showed that albumin (P=0.013), creatinine (P=0.01), and AFR (P=0.016) were independent risk factors for postoperative pneumonia.Conclusion: Preoperative AFR can effectively predict the occurrence of postoperative pneumonia in patients with esophageal cancer

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