A Systematic Analysis on COVID-19 Patients in Inner Mongolia Based on Dynamic Monitoring

COVID-19 has spread globally with over 90,000,000 incidences and 1,930,000 deaths by Jan 11, 2021, which poses a big threat to public health. It is urgent to distinguish COVID-19 from common pneumonia. In this study, we reported multiple clinical feature analyses on COVID-19 in Inner Mongolia for the first time. We dynamically monitored multiple clinical features of all 75 confirmed COVID-19 patients, 219 pneumonia patients, and 68 matched healthy people in Inner Mongolia. Then, we studied the association between COVID-19 and clinical characteristics, based on which to construct a novel logistic regression model for predicting COVID-19. As a result, among the tested clinical characteristics, WBC, hemoglobin, C-reactive protein (CRP), ALT, and Cr were significantly different between COVID-19 patients and patients in other groups. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was 0.869 for the logistic regression model using multiple factors associated with COVID-19. Furthermore, the CRP reaction showed five different time-series patterns with one-peak and double-peak modes. In conclusion, our study identified a few clinical characteristics significantly different between COVID-19 patients and others in Inner Mongolia. The features can be used to establish a reliable logistic regression model for predicting COVID-19.

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Screening the Influence of Biomarkers for Metabolic Syndrome in Occupational Population Based on the Lasso Algorithm

Frontiers in Public Health ◽

10.3389/fpubh.2021.743731 ◽

2021 ◽

Vol 9 ◽

Author(s):

Qiao-Ying Xie ◽

Ming-Wei Wang ◽

Zu-Ying Hu ◽

Cheng-Jian Cao ◽

Cong Wang ◽

...

Keyword(s):

Risk Assessment ◽

Metabolic Syndrome ◽

Logistic Regression ◽

Prediction Model ◽

Regression Model ◽

Logistic Regression Model ◽

Area Under The Curve ◽

Assessment Model ◽

Risk Assessment Model ◽

Lasso Regression

Aim: Metabolic syndrome (MS) screening is essential for the early detection of the occupational population. This study aimed to screen out biomarkers related to MS and establish a risk assessment and prediction model for the routine physical examination of an occupational population.Methods: The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to MS. Then, the accuracy of the logistic regression model was further verified based on the Lasso regression algorithm. The areas under the receiving operating characteristic curves were used to evaluate the selection accuracy of biomarkers in identifying MS subjects with risk. The screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. A nomogram risk prediction model was established based on the selected biomarkers, and the consistency index (C-index) and calibration curve were derived.Results: A total of 2,844 occupational workers were included, and 10 biomarkers related to MS were screened. The number of non-MS cases was 2,189 and that of MS was 655. The area under the curve (AUC) value for non-Lasso and Lasso logistic regression was 0.652 and 0.907, respectively. The established risk assessment model revealed that the main risk biomarkers were absolute basophil count (OR: 3.38, CI:1.05–6.85), platelet packed volume (OR: 2.63, CI:2.31–3.79), leukocyte count (OR: 2.01, CI:1.79–2.19), red blood cell count (OR: 1.99, CI:1.80–2.71), and alanine aminotransferase level (OR: 1.53, CI:1.12–1.98). Furthermore, favorable results with C-indexes (0.840) and calibration curves closer to ideal curves indicated the accurate predictive ability of this nomogram.Conclusions: The risk assessment model based on the Lasso logistic regression algorithm helped identify MS with high accuracy in physically examining an occupational population.

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A Logistic Regression Model for Noninvasive Prediction of AFP-Negative Hepatocellular Carcinoma

Technology in Cancer Research & Treatment ◽

10.1177/1533033819846632 ◽

2019 ◽

Vol 18 ◽

pp. 153303381984663 ◽

Cited By ~ 3

Author(s):

Chang-Liang Luo ◽

Yuan Rong ◽

Hao Chen ◽

Wu-Wen Zhang ◽

Long Wu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Logistic Regression ◽

Regression Model ◽

Logistic Regression Model ◽

Hematological Parameters ◽

Area Under The Curve ◽

Diagnostic Efficiency ◽

Healthy Individuals ◽

Platelet Volume ◽

Model Combining

α-Fetoprotein is commonly used in the diagnosis of hepatocellular carcinoma. However, the diagnostic significance of α-fetoprotein has been questioned because a number of patients with hepatocellular carcinoma are α-fetoprotein negative. It is therefore necessary to develop novel noninvasive techniques for the early diagnosis of hepatocellular carcinoma, particularly when α-fetoprotein level is low or negative. The current study aimed to evaluate the diagnostic efficiency of hematological parameters to determine which can act as surrogate markers in α-fetoprotein–negative hepatocellular carcinoma. Therefore, a retrospective study was conducted on a training set recruited from Zhongnan Hospital of Wuhan University—including 171 α-fetoprotein–negative patients with hepatocellular carcinoma and 102 healthy individuals. The results show that mean values of mean platelet volume, red blood cell distribution width, mean platelet volume–PC ratio, neutrophils–lymphocytes ratio, and platelet count–lymphocytes ratio were significantly higher in patients with hepatocellular carcinoma in comparison to the healthy individuals. Most of these parameters showed moderate area under the curve in α-fetoprotein–negative patients with hepatocellular carcinoma, but their sensitivities or specificities were not satisfactory enough. So, we built a logistic regression model combining multiple hematological parameters. This model presented better diagnostic efficiency with area under the curve of 0.922, sensitivity of 83.0%, and specificity of 93.1%. In addition, the 4 validation sets from different hospitals were used to validate the model. They all showed good area under the curve with satisfactory sensitivities or specificities. These data indicate that the logistic regression model combining multiple hematological parameters has better diagnostic efficiency, and they might be helpful for the early diagnosis for α-fetoprotein–negative hepatocellular carcinoma.

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The Relationship Between Tactical Positioning and the Race Outcome in 800-M Running at the 2016 Olympic Games and 2017 IAAF World Championship

Journal of Human Kinetics ◽

10.2478/hukin-2019-0090 ◽

2020 ◽

Vol 71 (1) ◽

pp. 299-305

Author(s):

Fernando González-Mohíno ◽

Jesús Santos del Cerro ◽

Andrew Renfree ◽

Inmaculada Yustres ◽

José Mª González-Ravé

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Logistic Regression Model ◽

Olympic Games ◽

Area Under The Curve ◽

Predictive Ability ◽

Significance Test ◽

Final Position ◽

The Difference ◽

The Relationship

AbstractThe purpose of this analysis was to quantify the probability of achieving a top-3 finishing position during 800-m races at a global championship, based on dispersion of the runners during the first and second laps and the difference in split times between laps. Overall race times, intermediate and finishing positions and 400 m split times were obtained for 43 races over 800 m (21 men’s and 22 women’s) comprising 334 individual performances, 128 of which resulted in higher positions (top-3) and 206 the remaining positions. Intermediate and final positions along with times, the dispersion of the runners during the intermediate and final splits (SS1 and SS2), as well as differences between the two split times (Dsplits) were calculated. A logistic regression model was created to determine the influence of these factors in achieving a top-3 position. The final position was most strongly associated with SS2, but also with SS1 and Dsplits. The Global Significance Test showed that the model was significant (p < 0.001) with a predictive ability of 91.08% and an area under the curve coefficient of 0.9598. The values of sensitivity and specificity were 96.8% and 82.5%, respectively. The model demonstrated that SS1, SS2 and Dplits explained the finishing position in the 800-m event in global championships.

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Clinical Characteristics and Prognostic Factors of Posterior Segment Intraocular Foreign Body: Canadian Experience from a Tertiary University Hospital in Quebec

Journal of Ophthalmology ◽

10.1155/2021/9990290 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Jean-Philippe Rozon ◽

Guillaume Lavertu ◽

Mélanie Hébert ◽

Eunice You ◽

Serge Bourgault ◽

...

Keyword(s):

Logistic Regression ◽

Foreign Body ◽

Regression Model ◽

Predictive Factors ◽

Clinical Characteristics ◽

Logistic Regression Model ◽

Visual Outcome ◽

Posterior Segment ◽

Multiple Logistic Regression ◽

Multiple Logistic Regression Model

Purpose. To identify predictive factors for visual outcomes of patients presenting with a posterior segment intraocular foreign body (IOFB). Methods. A retrospective chart review was performed for all consecutive patients operated for posterior segment IOFB removal between January 2009 and December 2018. Data were collected for patient demographics, clinical characteristics at presentation, IOFB characteristics, surgical procedures, and postoperative outcomes. A multiple logistic regression model was built for poor final visual acuity (VA) as an outcome (defined as final VA 50 letters or worse [Snellen equivalent: 20/100]). Results. Fifty-four patients were included in our study. Ninety-three percent of patients were men, with a mean age of 40.4 ± 12.6 years. Metallic IOFB comprised 88% of cases with a mean ± standard deviation (SD) size of 5.31 ± 4.62 mm. VA improved in 70% of patients after IOFB removal. Predictive factors for poor VA outcome included poor baseline VA, larger IOFB size, high number of additional diagnoses, an anterior chamber extraction, a second intervention, the use of C3F8 or silicone tamponade, and the presence of vitreous hemorrhage, hyphema, and iris damage. Predictive factors for a better visual outcome included first intention intraocular lens (IOL) implantation and the use of air tamponade. In the multiple logistic regression model, both baseline VA ( p = 0.009) and number of additional complications ( p = 0.01) were independent risk factors for a poor final VA. Conclusions. A high number of concomitant complications and poor baseline VA following posterior segment IOFB were significant predictive factors of poor visual outcome.

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Establishment of Metabolic Syndrome Prediction Model for Occupational Population based on the Lasso Regression Algorithm

10.21203/rs.3.rs-136449/v1 ◽

2020 ◽

Author(s):

Qiao-Ying Xie ◽

Ming-Wei Wang ◽

Zu-Ying Hu ◽

Yan-Ming Chu ◽

Cheng-Jian Cao ◽

...

Keyword(s):

Risk Assessment ◽

Metabolic Syndrome ◽

Logistic Regression ◽

Prediction Model ◽

Regression Model ◽

Logistic Regression Model ◽

Area Under The Curve ◽

Assessment Model ◽

Risk Assessment Model ◽

Lasso Regression

Abstract Background: Metabolic syndrome (MS) screening is important for the early detection of occupational population. This study aimed to screen out biomarkers related to MS and establish a risk assessment and prediction model for the routine physical examination of an occupational population.Methods: The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to MS. Then, the accuracy of the logistic regression model was further verified based on the Lasso regression algorithm. Finally, the screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. Results: A total of 2844 occupational workers were included, and 10 biomarkers related to MS were screened. The area under the curve (AUC) value for non-Lasso and Lasso regression was 0.652 and 0.907, respectively. The established risk assessment model revealed that the main risk factors were basophil absolute count (OR: 3.38), platelet packed volume (OR: 2.63), leukocyte count (OR: 2.01), red blood cell count (OR: 1.99), and alanine aminotransferase level (OR: 1.53). Conclusion: The risk assessment model based on the Lasso regression algorithm helped identify Metabolic syndrome with high accuracy in physically examining an occupational population.

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A-140 Single-Item Screening for Anxiety and Depression using the Neurobehavioral Symptom Inventory

Archives of Clinical Neuropsychology ◽

10.1093/arclin/acaa068.140 ◽

2020 ◽

Vol 35 (6) ◽

pp. 933-933

Author(s):

Rolin S ◽

Kitchen Andren K ◽

Mullen C ◽

Kurniadi N ◽

Davis J

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Regression Models ◽

Logistic Regression Model ◽

Area Under The Curve ◽

Anxiety And Depression ◽

Average Score ◽

Characteristic Analysis ◽

Logistic Regression Models ◽

Symptom Inventory

Abstract Objective Previous research in a Veterans Affairs sample proposed using single items on the Neurobehavioral Symptom Inventory (NSI) to screen for anxiety (item 19) and depression (item 20). This study examined the approach in an outpatient physical medicine and rehabilitation sample. Method Participants (N = 84) underwent outpatient neuropsychological evaluation using the NSI, BDI-II, GAD-7, MMPI-2-RF, and Memory Complaints Inventory (MCI) among other measures. Anxiety and depression were psychometrically determined via cutoffs on the GAD-7 (>4) and MMPI-2-RF ANX (>64 T), and BDI-II (>13) and MMPI-2-RF RC2 (>64 T), respectively. Analyses included receiver operating characteristic analysis (ROC) and logistic regression. Logistic regression models used dichotomous anxiety and depression as outcomes and relevant NSI items and MCI average score as predictors. Results ROC analysis using NSI items to classify cases showed area under the curve (AUC) values of .77 for anxiety and .85 for depression. The logistic regression model predicting anxiety correctly classified 80% of cases with AUC of .86. The logistic regression model predicting depression correctly classified 79% of cases with AUC of .88. Conclusion Findings support the utility of NSI anxiety and depression items as screening measures in a rehabilitation population. Consideration of symptom validity via the MCI improved classification accuracy of the regression models. The approach may be useful in other clinical settings for quick assessment of psychological issues warranting further evaluation.

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Risk assessment of severe tricyclic antidepressant overdose

Human & Experimental Toxicology ◽

10.1177/0960327109106970 ◽

2009 ◽

Vol 28 (8) ◽

pp. 511-519 ◽

Cited By ~ 11

Author(s):

Florian Eyer ◽

Jochen Stenzel ◽

Tibor Schuster ◽

Norbert Felgenhauer ◽

Rudi Pfab ◽

...

Keyword(s):

Risk Assessment ◽

Logistic Regression ◽

Regression Model ◽

Clinical Characteristics ◽

Logistic Regression Model ◽

Early Stage ◽

Statistical Significance ◽

Time Lag ◽

Tricyclic Antidepressant ◽

Multivariable Logistic Regression Model

Prognostic factors for severe complications in tricyclic antidepressant (TCA) overdose remain unclear. We therefore evaluated the value of clinical characteristics and electrocardiograph (ECG) parameters to predict serious events (seizures, arrhythmia, death) in severe TCA overdose of 100 patients using logistic regression models for risk assessment. The overall fatality rate was 6%, arrhythmia occurred in 21% and 31% of the patients developed seizures. Using an univariable logistic regression model, the maximal QRS interval (OR 1.22; 95% CI 1.06-1.41; p = .005), the time lag between ingestion and occurrence of first symptoms of overdose (OR 1.13; 95% CI 0.99-1.29; p = .072) and the age (OR 0.73; 95% CI 0.55-0.98; p = .038) were determined as the solely predictive parameters. In the multivariable logistic regression model, the QRS interval could not be established as independent predictor, however, the terminal 40-ms frontal plane QRS vector (T40) reached statistical significance regarding prediction of serious events (odds ration [OR] 1.70; 95% confidence interval [CI] 1.02-2.84; p = .041), along with age and time lag between ingestion and onset of symptoms of overdose with a sensitivity and specificity of 71% and 70%, respectively. Evaluation of both clinical characteristics and ECG-parameters in the early stage of TCA overdose may help to identify those patients who urgently need further aggressive medical observation and management.

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Do gliosarcomas have distinct imaging features on routine MRI?

The Neuroradiology Journal ◽

10.1177/19714009211012345 ◽

2021 ◽

pp. 197140092110123

Author(s):

Christoph J Maurer ◽

Irina Mader ◽

Felix Joachimski ◽

Ori Staszewski ◽

Bruno Märkl ◽

...

Keyword(s):

Logistic Regression ◽

Multivariate Analysis ◽

Regression Model ◽

Logistic Regression Model ◽

External Validation ◽

Area Under The Curve ◽

Training Dataset ◽

Validation Dataset ◽

Imaging Features ◽

Age And Sex

Purpose The aim of this study was the development and external validation of a logistic regression model to differentiate gliosarcoma (GSC) and glioblastoma multiforme (GBM) on standard MR imaging. Methods A univariate and multivariate analysis was carried out of a logistic regression model to discriminate patients histologically diagnosed with primary GSC and an age and sex-matched group of patients with primary GBM on presurgical MRI with external validation. Results In total, 56 patients with GSC and 56 patients with GBM were included. Evidence of haemorrhage suggested the diagnosis of GSC, whereas cystic components and pial as well as ependymal invasion were more commonly observed in GBM patients. The logistic regression model yielded a mean area under the curve (AUC) of 0.919 on the training dataset and of 0.746 on the validation dataset. The accuracy in the validation dataset was 0.67 with a sensitivity of 0.85 and a specificity of 0.5. Conclusions Although some imaging criteria suggest the diagnosis of GSC or GBM, differentiation between these two tumour entities on standard MRI alone is not feasible.

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Lack of Association Between Patients Characteristics and the Carriage of Extended-spectrum β-Lactamase Producing Enterobacteriaceae in Community Settings in Blantyre, Malawi

10.21203/rs.3.rs-322264/v1 ◽

2021 ◽

Author(s):

Onduru Gervas Onduru ◽

Susan Fred Rumisha ◽

Rajhab Sawasawa Mkakosya ◽

Gabriel Kambale Bunduki ◽

Said Aboud

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Clinical Characteristics ◽

Logistic Regression Model ◽

Community Settings ◽

Factors Associated ◽

Extended Spectrum ◽

The Mean ◽

Degree Of Association ◽

Community Patients

Abstract Objective: This study examined factors associated with the carriage of extended-spectrum β-lactamase (ESBL) producing Enterobacteriaceae in community patients in Blantyre, Malawi. ResultsA total of 50 community patients with ESBL producing Enterobacteriaceae (ESBL-E) carriage were identified from 300 adults recruited in the study, which gave a prevalence of 16.67% (50/300, 95% CI=12.43-20.91%). The mean age ±SD was 32.41±12.07 years; range, 18-75 years and 54.33% (163/300) were women. The results of unadjusted logistic regression model fitted to identify factors associated with ESBL-E carriage in community patients showed that there was no any degree of association between carriage of extended-spectrum β-lactamase producing Enterobacteriaceae in community patients with either their demographic or clinical characteristics.

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Development and Validation of an Intraoperative Predictive Model for Unplanned Postoperative Intensive Care

Anesthesiology ◽

10.1097/aln.0b013e31829ce8fd ◽

2013 ◽

Vol 119 (3) ◽

pp. 516-524 ◽

Cited By ~ 25

Author(s):

Jonathan P. Wanderer ◽

John Anderson-Dam ◽

Wilton Levine ◽

Edward A. Bittner

Keyword(s):

Logistic Regression ◽

Intensive Care ◽

Regression Model ◽

Predictive Model ◽

Real Time ◽

Apgar Score ◽

Logistic Regression Model ◽

Area Under The Curve ◽

Surgical Apgar Score ◽

Bootstrap Validation

Abstract Background: The allocation of intensive care unit (ICU) beds for postoperative patients is a challenging daily task that could be assisted by the real-time detection of ICU needs. The goal of this study was to develop and validate an intraoperative predictive model for unplanned postoperative ICU use. Methods: With the use of anesthesia information management system, postanesthesia care unit, and scheduling data, a data set was derived from adult in-patient noncardiac surgeries. Unplanned ICU admissions were identified (4,847 of 71,996; 6.7%), and a logistic regression model was developed for predicting unplanned ICU admission. The model performance was tested using bootstrap validation and compared with the Surgical Apgar Score using area under the curve for the receiver operating characteristic. Results: The logistic regression model included 16 variables: age, American Society of Anesthesiologists physical status, emergency case, surgical service, and 12 intraoperative variables. The area under the curve was 0.905 (95% CI, 0.900–0.909). The bootstrap validation model area under the curves were 0.513 at booking, 0.688 at 3 h before case end, 0.738 at 2 h, 0.791 at 1 h, and 0.809 at case end. The Surgical Apgar Score area under the curve was 0.692. Unplanned ICU admissions had more ICU-free days than planned ICU admissions (5 vs. 4; P < 0.001) and similar mortality (5.6 vs. 6.0%; P = 0.248). Conclusions: The authors have developed and internally validated an intraoperative predictive model for unplanned postoperative ICU use. Incorporation of this model into a real-time data sniffer may improve the process of allocating ICU beds for postoperative patients.

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