Seven Criteria of Severe COVID-19 (SCSC): A New Pre-Hospital Prognostic Scoring Tool Suggested for Screening of Probable/Confirmed COVID-19 Patients with Severe Outcomes

Introduction: COVID-19 pandemic led to various consequences in medical care that had been long provided for the patients referred to the hospitals. Objective: We conducted this study to derive and validate a new scoring system that can accurately differentiate COVID-19 patients who may have a worse outcome from others at the prehospital stage. Methods: This study was performed on probable/confirmed COVID-19 patients, who were transferred to the hospitals by Tehran emergency medical services (EMS). Occurrence of one of the items including: in-hospital death, intensive care unit (ICU) admission, or hospitalization for more than 20 days was considered to indicate a “severe disease”. Univariate and multivariate logistic regression were used for assessment of the relationship between all independent variables and the outcome. In the validity assessment step, area under the receiver operating characteristic (ROC) curve was calculated for a data set independent from the data based on which the model was designed. The sensitivity and specificity were also presented based on the best suggested cutoff point. Results: In this study, the data of 557 cases were analyzed in the derivation step and 356 cases were assessed in the validation step. The univariate logistic regression showed that age, weakness and fatigue, disease history, systolic blood pressure, SpO2, respiratory rate, and Glasgow coma scale (GCS) were statistically significant in severe disease group. The area under the ROC curve (AUC-ROC) of the tool was 0.808 (95% CI: 0.779, 0.834). The best cut-off point for screening was the score of ≥4, in which the sensitivity and specificity of the tool for the best cut-off point were 71.87% and 78.06%, respectively. In the validation step, the AUCROC of the tool was 0.723. Conclusions: Seven criteria of severe COVID-19 (SCSC) tool could properly differentiate probable/confirmed COVID-19 patients with severe outcomes in the pre-hospital stage.

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LDH, CRP and ALB predict nucleic acid turn negative within 14 days in symptomatic patients with COVID-19

Scottish Medical Journal ◽

10.1177/0036933021994243 ◽

2021 ◽

pp. 003693302199424

Author(s):

Gaoli Liu ◽

Bicheng Zhang ◽

Shaowen Zhang ◽

Haifeng Hu ◽

TingTing Liu

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Nucleic Acid ◽

Hospital Stay ◽

Prognostic Marker ◽

Roc Curve ◽

Prognostic Value ◽

Statistical Significance ◽

Biochemical Indicators ◽

The Relationship

Aims To search for biochemical indicators that can identify symptomatic patients with COVID-19 whose nucleic acid could turn negative within 14 days, and assess the prognostic value of these biochemical indicators in patients with COVID-19. Patients and methods We collected the clinical data of patients with COVID-19 admitted to our hospital, by using logistic regression analysis and AUC curves, explored the relationship between biochemical indicators and nucleic acid positive duration, the severity of COVID-19, and hospital stay respectively. Results A total of two hundred and thirty-three patients with COVID-19 were enrolled in the study. We found patients whose nucleic acid turned negative within 14 days had lower LDH, CRP and higher ALB ( P < 0.05). ROC curve results indicated that lower LDH, TP, CRP and higher ALB predicted the nucleic acid of patients turned negative within 14 days with statistical significance( P < 0.05), AST, LDH, CRP and PCT predicted the severe COVID-19 with statistical significance, and CRP predicted hospital stay >31days with statistical significance ( P < 0.05). After verification, the probability of nucleic acid turning negative within 14 days in patients with low LDH (<256 U/L), CRP (<44.5 mg/L) and high ALB (>35.8 g/L) was about 4 times higher than that in patients with high LDH, CRP and low ALB ( P < 0.05). Conclusions LDH, CRP and ALB are useful prognostic marker for predicting nucleic acid turn negative within 14 days in symptomatic patients with COVID-19.

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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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Association Between Triglyceride Glucose Index and Non-Small Cell Lung Cancer Risk in Chinese Population

Frontiers in Oncology ◽

10.3389/fonc.2021.585388 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xin Yan ◽

Yujuan Gao ◽

Jingzhi Tong ◽

Mi Tian ◽

Jinghong Dai ◽

...

Keyword(s):

Lung Cancer ◽

Logistic Regression ◽

Regression Analysis ◽

Cancer Risk ◽

Small Cell Lung Cancer ◽

Roc Curve ◽

Small Cell ◽

Small Cell Lung ◽

Tyg Index ◽

The Relationship

BackgroundNumerous studies showed that insulin resistance (IR) was associated with cancer risk. However, few studies investigated the relationship between IR and non-small cell lung cancer (NSCLC). The aim of this study is to explore the association of triglyceride glucose (TyG) index, a simple surrogate marker of IR, with NSCLC risk.Methods791 histologically confirmed NSCLC cases and 787 controls were enrolled in the present study. Fasting blood glucose and triglyceride were measured. The TyG index was calculated as ln [fasting triglycerides (mg/dl) ×fasting glucose (mg/dl)/2]. Logistic regression analysis was performed to estimate the relationship between NSCLC risk and the TyG index.ResultsThe TyG index was significantly higher in patients with NSCLC than that in controls (8.42 ± 0.55 vs 8.00 ± 0.45, P < 0.01). Logistic regression analysis showed that the TyG index (OR = 3.651, 95%CI 2.461–5.417, P < 0.001) was independently associated with NSCLC risk after adjusting for conventional risk factors. In addition, a continuous rise in the incidence of NSCLC was observed along the tertiles of the TyG index (29.4 vs 53.8 vs 67.2%, P < 0.001). However, there were no differences of the TyG index in different pathological or TNM stages. In receiver operating characteristic (ROC) curve analysis, the optimal cut-off level for the TyG index to predict incident NSCLC was 8.18, and the area under the ROC curve (AUROC) was 0.713(95% CI 0.688–0.738).ConclusionsThe TyG index is significantly correlated with NSCLC risk, and it may be suitable as a predictor for NSCLC.

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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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Topic-marking prepositions in Swedish: A corpus-based analysis of adpositional synonymy

Nordic Journal of Linguistics ◽

10.1017/s0332586514000201 ◽

2014 ◽

Vol 37 (2) ◽

pp. 257-296 ◽

Cited By ~ 2

Author(s):

Anton Granvik ◽

Susanna Taimitarha

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Multinomial Logistic Regression ◽

Initial Position ◽

The Other ◽

Data Set ◽

Multinomial Logistic Regression Analysis ◽

Usage Patterns ◽

Head Element ◽

The Relationship

This study analyses the relationship between four near-synonymous Swedish prepositions, namely angående, beträffande, gällande and rörande, which are used to establish what we call a topic-marking relation, as in statens avtal angående finansieringen ‘the agreement of the state regarding the financing’. By focusing on a single, loosely defined genre consisting of the written texts included in the Swedish PAROLE corpus, we address the question of what differences there are among these four prepositions, which intuitively seem highly similar and mutually interchangeable. In order to find out which contextual and grammatical factors might influence the choice of one preposition over the others, two complementary analyses were performed. First, a so-called collostructional analysis (see Stefanowitsch & Gries 2003, Gries & Stefanowitsch 2004) was performed on 791 cases of these prepositions found in the PAROLE corpus. Secondly, the corpus examples were annotated according to ten syntactic and four semantic criteria and a multinomial logistic regression analysis was performed on the annotated data set. The results show some tendencies pointing to differing usage patterns of the four prepositions. Beträffande stands out as the most frequent of them all and is also preferably used when no explicit head element is present, typically in sentence-initial position. Angående prefers words of communication while rörande is used when another topic-marking preposition is also present. On the other hand, neither of the two analyses leads to a clear distinction among the four prepositions, thus pointing to the fact that these topic-marking prepositions indeed constitute a fairly good case of adpositional synonymy, with few distinguishing factors separating one from the other.

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P1174STUDY ON THE RELATIONSHIP BETWEEN HYPOCHLOREMIA AND ALL-CAUSE DEATH IN PATIENTS RECEIVING CONTINUOUS AMBULATORY PERITONEAL DIALYSIS

Nephrology Dialysis Transplantation ◽

10.1093/ndt/gfaa142.p1174 ◽

2020 ◽

Vol 35 (Supplement_3) ◽

Author(s):

Tao Xu ◽

Niansong Wang ◽

Cheng Qiao

Keyword(s):

Logistic Regression ◽

Risk Factor ◽

Peritoneal Dialysis ◽

Continuous Ambulatory Peritoneal Dialysis ◽

Roc Curve ◽

Independent Risk Factor ◽

Chloride Level ◽

Kaplan Meier ◽

Serum Chloride ◽

The Relationship

Abstract Background and Aims To investigate the relationship between hypochloremia on all-cause death in patients receiving continuous ambulatory peritoneal dialysis (CAPD). Method 300 CAPD patients from January 2013 to December 2019 in the Sixth People's Hospital affiliated to Shanghai Jiaotong University. According to the serum chloride level, the patients were divided into two groups: hypochloremia group (serum chlorine ≤ 96mmol / L, n = 135) and normal chloride group (106mmol / L < serum chlorine > 96mmol / L, n = 165). The endpoint was all-cause death. We used the receiver-operating characteristic (ROC) curve to analysis the diagnostic value and logistic regression to assess the predictive value in relation to serum chloride with all-cause death in CAPD patients. Kaplan Meier curve was used to evaluate the effect of serum chloride on all-cause death survival analysis. All statistics were analyzed by SPSS 20.0 software, P < 0.05, indicating significant difference. Results 114 cases of all-cause death occurred in CAPD patients during follow-up (62.1 ± 11.1 months). The results of correlation analysis showed that serum chloride was positively correlated with serum sodium and potassium (r=0.721,0.199, P=0.001) and the negative correlation between serum chloride and dialysis age and serum phosphorus (r=-0.321, - 0.300, P=0.001). ROC curve analysis showed that serum chloride was statistically significant in predicting all-cause death in CAPD patients (AUC = 0.666, 95% Cl = 0.601-0.730, sensitivity / specificity = 64.6% / 59.8%, best threshold = 95.5mmol/l). Kaplan Meier analysis of all-cause death risk curve shows that the incidence of all-cause death in the low chloride group is higher than that in the normal serum chloride group. Logistic regression analysis showed that low chloride level was an independent risk factor for all-cause death in CAPD patients. Conclusion Hypochloremia is an independent risk factor for all-cause death in CAPD patients.

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Establish a Scoring Model for High-Risk Population of Gastric Cancer and Study on the Pattern of Opportunistic Screening

Gastroenterology Research and Practice ◽

10.1155/2020/5609623 ◽

2020 ◽

Vol 2020 ◽

pp. 1-6

Author(s):

Wei Tao ◽

Hai-Xia Wang ◽

Yu-Feng Guo ◽

Li Yang ◽

Peng Li

Keyword(s):

Risk Factors ◽

Gastric Cancer ◽

Logistic Regression ◽

High Risk ◽

Sensitivity And Specificity ◽

Roc Curve ◽

Opportunistic Screening ◽

Scoring Model ◽

High Risk Factors ◽

Risk Scoring

Objective. To investigate and study the related risk factors of gastric cancer (GC) patients, to establish a high-risk scoring model of GC by multiple logistic regression analysis, and to explore the establishment of a GC screening mode with clinical opportunistic screening as the main method, and by using the pattern of opportunistic screening to establish the screening of high-risk GC patients and the choice of screening methods in the clinical outpatient work. Methods. Collected the epidemiological questionnaire of 99 GC cases and 284 non-GC patients (other chronic gastric diseases and normal) diagnosed by the General Hospital of Ningxia Medical University from October 2017 to March 2019. Serum pepsinogen (PG) levels were measured by enzyme-linked immunosorbent assay (ELISA) and confirmed Helicobacter pylori (Hp) infection in gastric mucosa tissues by Giemsa staining. Determined the high-risk factors and established a scoring model through unconditional logistic regression model analysis, and the ROC curve determined the cut-off value. Then, we followed up 26 patients of nongastric cancer patients constituted a validation group, which validated the model. Results. The high-risk factors of GC included age≥55, male, drinking cellar or well water, family history of GC, Hp infection, PGI≤43.6 μg/L, and PGI/PGII≤2.1. Established the high-risk model: Y=A×age+30×gender+30×drinking water+30×Hp infection+50×family history of GC+B×PG level. The ROC curve determined that the cut-off value for high-risk GC population was ≥155, and the area under the curve (AUC) was 0.875, the sensitivity and specificity were 87.9% and 71.5%. Conclusions. According to the risk factors of GC, using statistical methods can establish a high-risk scoring model of GC, and the score≥155 is divided into the screening cut-off value for high-risk GC population. Using this model for clinical outpatient GC screening is cost-effective and has high sensitivity and specificity.

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Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods

Healthcare ◽

10.3390/healthcare9030317 ◽

2021 ◽

Vol 9 (3) ◽

pp. 317

Author(s):

Manuel A. Soto-Murillo ◽

Jorge I. Galván-Tejada ◽

Carlos E. Galván-Tejada ◽

Jose M. Celaya-Padilla ◽

Huizilopoztli Luna-García ◽

...

Keyword(s):

Support Vector Machine ◽

Logistic Regression ◽

Roc Curve ◽

Heart Sounds ◽

Machine Learning Algorithms ◽

World Health ◽

Support Vector ◽

Computer Assisted ◽

Data Set ◽

The World

The main cause of death in Mexico and the world is heart disease, and it will continue to lead the death rate in the next decade according to data from the World Health Organization (WHO) and the National Institute of Statistics and Geography (INEGI). Therefore, the objective of this work is to implement, compare and evaluate machine learning algorithms that are capable of classifying normal and abnormal heart sounds. Three different sounds were analyzed in this study; normal heart sounds, heart murmur sounds and extra systolic sounds, which were labeled as healthy sounds (normal sounds) and unhealthy sounds (murmur and extra systolic sounds). From these sounds, fifty-two features were calculated to create a numerical dataset; thirty-six statistical features, eight Linear Predictive Coding (LPC) coefficients and eight Cepstral Frequency-Mel Coefficients (MFCC). From this dataset two more were created; one normalized and one standardized. These datasets were analyzed with six classifiers: k-Nearest Neighbors, Naive Bayes, Decision Trees, Logistic Regression, Support Vector Machine and Artificial Neural Networks, all of them were evaluated with six metrics: accuracy, specificity, sensitivity, ROC curve, precision and F1-score, respectively. The performances of all the models were statistically significant, but the models that performed best for this problem were logistic regression for the standardized data set, with a specificity of 0.7500 and a ROC curve of 0.8405, logistic regression for the normalized data set, with a specificity of 0.7083 and a ROC curve of 0.8407, and Support Vector Machine with a lineal kernel for the non-normalized data; with a specificity of 0.6842 and a ROC curve of 0.7703. Both of these metrics are of utmost importance in evaluating the performance of computer-assisted diagnostic systems.

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Sequential changes in Rotterdam CT scores related to outcomes for patients with traumatic brain injury who undergo decompressive craniectomy

Journal of Neurosurgery ◽

10.3171/2015.4.jns142760 ◽

2016 ◽

Vol 124 (6) ◽

pp. 1640-1645 ◽

Cited By ~ 4

Author(s):

Kenji Fujimoto ◽

Masaki Miura ◽

Tadahiro Otsuka ◽

Jun-ichi Kuratsu

Keyword(s):

Traumatic Brain Injury ◽

Logistic Regression ◽

Brain Injury ◽

Decompressive Craniectomy ◽

Roc Curve ◽

Area Under The Curve ◽

Prognostic Indicator ◽

Unfavorable Outcome ◽

Multivariable Logistic Regression Analysis ◽

The Relationship

OBJECT Rotterdam CT scoring is a CT classification system for grouping patients with traumatic brain injury (TBI) based on multiple CT characteristics. This retrospective study aimed to determine the relationship between initial or preoperative Rotterdam CT scores and TBI prognosis after decompressive craniectomy (DC). METHODS The authors retrospectively reviewed the medical records of all consecutive patients who underwent DC for nonpenetrating TBI in 2 hospitals from January 2006 through December 2013. Univariate and multivariate logistic regression and receiver operating characteristic (ROC) curve analyses were used to determine the relationship between initial or preoperative Rotterdam CT scores and mortality at 30 days or Glasgow Outcome Scale (GOS) scores at least 3 months after the time of injury. Unfavorable outcomes were GOS Scores 1–3 and favorable outcomes were GOS Scores 4 and 5. RESULTS A total of 48 cases involving patients who underwent DC for TBI were included in this study. Univariate analyses showed that initial Rotterdam CT scores were significantly associated with mortality and both initial and preoperative Rotterdam CT scores were significantly associated with unfavorable outcomes. Multivariable logistic regression analysis adjusted for established predictors of TBI outcomes showed that initial Rotterdam CT scores were significantly associated with mortality (OR 4.98, 95% CI 1.40–17.78, p = 0.01) and unfavorable outcomes (OR 3.66, 95% CI 1.29–10.39, p = 0.02) and preoperative Rotterdam CT scores were significantly associated with unfavorable outcomes (OR 15.29, 95% CI 2.50–93.53, p = 0.003). ROC curve analyses showed cutoff values for the initial Rotterdam CT score of 5.5 (area under the curve [AUC] 0.74, 95% CI 0.59–0.90, p = 0.009, sensitivity 50.0%, and specificity 88.2%) for mortality and 4.5 (AUC 0.71, 95% CI 0.56–0.86, p = 0.02, sensitivity 62.5%, and specificity 75.0%) for an unfavorable outcome and a cutoff value for the preoperative Rotterdam CT score of 4.5 (AUC 0.81, 95% CI 0.69–0.94, p < 0.001, sensitivity 90.6%, and specificity 56.2%) for an unfavorable outcome. CONCLUSIONS Assessment of changes in Rotterdam CT scores over time may serve as a prognostic indicator in TBI and can help determine which patients require DC.

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The Relationship Between Preoperative Cervical Sagittal Balance and Clinical Outcome of Patients With Hirayama Disease Treated With Anterior Cervical Discectomy and Fusion

Neurospine ◽

10.14245/ns.2142564.282 ◽

2021 ◽

Vol 18 (3) ◽

pp. 618-627

Author(s):

Xiao Lu ◽

Guang-Yu Xu ◽

Cong Nie ◽

Yu Xuan Zhang ◽

Jian Song ◽

...

Keyword(s):

Logistic Regression ◽

Cobb Angle ◽

Clinical Outcomes ◽

Roc Curve ◽

Sagittal Balance ◽

Postoperative Outcome ◽

Anterior Cervical Discectomy ◽

Hirayama Disease ◽

Cervical Sagittal Balance ◽

The Relationship

Objective: Anterior cervical discectomy and fusion (ACDF) is a common surgical method used to treat patients with Hirayama disease. And sagittal balance indexes have been revealed to be predictors of clinical outcomes in patients with cervical diseases, but their relationships with ACDF-treated Hirayama disease outcomes remain unknown. The purpose of this study is to evaluate the relationship of preoperative cervical sagittal balance indexes and clinical outcomes in ACDF-treated Hirayama disease patients.Methods: Eighty patients with Hirayama disease treated by ACDF were reviewed retrospectively. Six cervical sagittal balance parameters were collected including Cobb angle, T1 slope, C1–7 sagittal vertical axis (SVA), C2–7 SVA, center of gravity of the head (CGH)-C7 SVA, range of motion. The recovery outcomes of the patients were divided into 2 groups by Odom score and the differences in recovery between the 2 groups were confirmed by electromyography. The correlation between imaging parameters and postoperative outcome was evaluated with logistic regression. The receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) were used to evaluate the significant result of logistic regression and the optimal diagnostic value.Results: Only 2 parameters, Cobb angle and CGH-C7 SVA, showed statistical correlation with the postoperative outcome assessment by logistic regression. AUC of Cobb angle and CGH-C7 SVA were 0.559 and 0.702 respectively. The optimal predictive threshold was 1.50° and 5.40 mm, respectively.Conclusion: A larger Cobb angle and smaller CGH-C7 SVA seemed to correlate with a better postoperative outcome. These 2 factors could be used to predict the outcome of surgical treatment of Hirayama disease preoperatively.

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