scholarly journals Predictive Model to Differentiate Dengue Fever from Other Febrile Illnesses in Children—Application of Logistic Regression Analysis

2021 ◽  
Vol 3 (1) ◽  
pp. 9-14
Author(s):  
Sangeeta P Sawant ◽  
Surekha Rudraraju ◽  
Alpa S Amin
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Predictive Model ◽  
Dengue Fever ◽  
Logistic Regression Analysis

scholarly journals Impact of prematurity and the CTG repeat length on outcomes in congenital myotonic dystrophy

2020 ◽  
Author(s):  
Yu Saito ◽  
Kenta Matsumura ◽  
Misao Kageyama ◽  
Yuichi Kato ◽  
Eiji Ohta ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Predictive Model ◽  
Myotonic Dystrophy ◽  
Logistic Regression Analysis ◽  
Gestational Age ◽  
Multivariate Logistic Regression Analysis ◽  
Repeat Length ◽  
Congenital Myotonic Dystrophy ◽  
Ctg Repeat

Abstract Objective: Patients with congenital myotonic dystrophy (CDM) tend to be born preterm. Although the CDM severity generally depends on the CTG repeat length, prematurity may also affect the prognosis in patients with CDM. Given that preterm birth is expected to increase the risk of CDM in newborns, we investigated the outcomes of newborns with CDM according to gestational age to assess prematurity and the CTG repeat length for predicting prognosis. Results: We assessed the outcomes of 54 infants with CDM using data collected from our hospitals and previously published studies. The patients were divided into mild and severe groups based on clinical outcomes. Multivariate logistic regression analysis was performed to estimate odds ratios (ORs) for CDM prognosis according to gestational age and the CTG repeat length and to construct a predictive model. Logistic regression analysis showed both the CTG repeat and gestational age were significantly associated with severe outcomes in patients with CDM (OR:150.24, 95% CI: 5.97–3778.14; p = .0023 and OR: 0.73, 95% CI: 0.58–0.93; p = .0094, respectively). This predictive model for CDM prognosis exhibited good sensitivity (63%) and specificity (86%). Both prematurity and the CTG repeat length were significantly associated with the CDM severity.

scholarly journals Impact of prematurity and the CTG repeat length on outcomes in congenital myotonic dystrophy

2020 ◽  
Author(s):  
Yu Saito ◽  
Kenta Matsumura ◽  
Misao Kageyama ◽  
Yuichi Kato ◽  
Eiji Ohta ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Predictive Model ◽  
Myotonic Dystrophy ◽  
Logistic Regression Analysis ◽  
Gestational Age ◽  
Repeat Length ◽  
Congenital Myotonic Dystrophy ◽  
Using Data ◽  
Ctg Repeat

Abstract Objective: Patients with congenital myotonic dystrophy (CDM) tend to be born preterm. Although the CDM severity generally depends on the CTG repeat length, prematurity may also affect the prognosis in patients with CDM. Given that preterm birth is expected to increase the risk of CDM in newborns, we investigated the outcomes of newborns with CDM according to gestational age to assess prematurity and the CTG repeat length for predicting prognosis. Results: We assessed the outcomes of 54 infants with CDM using data collected from our hospitals and previously published studies. The patients were divided into mild and severe groups based on clinical outcomes. Logistic regression analysis was performed to estimate odds ratios (ORs) for CDM prognosis according to gestational age and the CTG repeat length and to construct a predictive model. Logistic regression analysis showed both the CTG repeat and gestational age were significantly associated with severe outcomes in patients with CDM (OR: 32.27, 95% CI: 3.45–300.7; p = .002 and OR: 0.73, 95% CI: 0.58–0.93; p = .0094, respectively). This predictive model for CDM prognosis exhibited good sensitivity (63%) and specificity (86%). Both prematurity and the CTG repeat length were significantly associated with the CDM severity.

Investing Behaviour of the IT Millennials with Respect to Retirement Planning

2019 ◽  
Vol 9 (8) ◽  
pp. 188-201
Author(s):  
Rashmy Moray ◽  
Vanishree Pabalkar ◽  
Nema Buch
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Predictive Model ◽  
Logistic Regression Analysis ◽  
Financial Planning ◽  
Representative Sample ◽  
Retirement Planning ◽  
Demographic Variables ◽  
It Industry ◽  
The Relationship

The objective of this study is to identify the demographic factors and behavioural biases affecting the financial planning of the Millennials. For this purpose the investing behaviour of the Millennials in the IT Industry as a representative sample with respect to retirement planning has been studied using the Retirement Wellness Score. Logistic regression analysis was performed to determine the likelihood of whether the Millennials are ready for retirement with the main aim of showing the relationship between the study variables. An attempt has been made to develop a predictive model that would help in determining the Millennials’ readiness for retirement given their demographic variables and dominant bias presence. This research contributes to developing an understanding of Millennials’ financial planning for retirement.

scholarly journals Prediction of the Length of Intensive Care After Cardiac Surgery Under Cardiopulmonary Bypass: Logistic Regression Analysis Based on Troponin I And EuroSCORE II

2021 ◽  
Vol 24 (4) ◽  
pp. E751-E757
Author(s):  
Tai Lai Ti Tai Wan Gu ◽  
Jianjiang Wu ◽  
Haiting Zhan ◽  
Yidan Huang ◽  
Jiang Wang
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Cardiac Surgery ◽  
Intensive Care ◽  
Cardiopulmonary Bypass ◽  
Predictive Model ◽  
Logistic Regression Analysis ◽  
Troponin I ◽  
Predictive Performance ◽  
Binary Logistic Regression Analysis

Aim: This study is to establish a model for patients undergoing cardiac surgery under cardiopulmonary bypass (CPB) to predict the length of intensive care. Methods: This is a single center retrospective study. A total of 265 patients admitted to the ICU after CPB from 2016 to 2017 were enrolled in the study. Preoperative indicators, intraoperative parameters, and postoperative data were collected. Each patient was scored for EuroSCORE II before surgery. According to the length of intensive care, all patients were divided into two groups: short stay (< 72 h) and long stay (≥ 72 h). A binary logistic regression analysis was performed to establish a regression model to evaluate the predictive performance of the indicators and the EuroSCORE II scoring system on the length of the intensive care. Results: Both troponin I and EuroSCORE II could predict the length of intensive care of patients undergoing cardiac surgery under CPB. After combing the two factors, the prediction efficiency was higher. Comparing the prediction results with the actual data, it showed that the method had high overall accuracy. Conclusions: The predictive model based on cTnI and EuroSCORE II can accurately predict the length of intensive care of patients undergoing cardiac surgery under CPB. This predictive model may help to improve ICU resource management.

Corroboration of polymorphisms in the aromatase (CYP19A1) gene with response to neoadjuvant therapy with letrozole in postmenopausal women with stages II-II ER/PgR-positive breast cancer

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 602-602 ◽  
Author(s):  
J. A. Lopez-Guerrero ◽  
Z. Garcia-Casado ◽  
A. L. Guerrero-Zotano ◽  
A. Calatrava ◽  
A. Ruiz-Sauri ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Neoadjuvant Therapy ◽  
Predictive Model ◽  
Postmenopausal Women ◽  
Logistic Regression Analysis ◽  
Direct Sequencing ◽  
Response To Therapy ◽  
Polymorphism Analysis

602 Background: Aromatase (CYP19A1) is the main enzyme implicated in estrogen biosynthesis. Polymorphisms in CYP19A1 can affect both aromatase expression and activity leading to changes in the estrogen levels. These changes have been previously associated with pathogenesis of estrogen-dependent diseases, including breast cancer. The purpose of this study is to evaluate whether the genotype of CYP19A1 can have an effect on the response to endocrine therapy with the aromatase inhibitor letrozole (L). Methods: We retrospectively analyzed a series of 58 postmenopausal women with stage II-III ER/PgR[+] breast cancer treated with L as neoadjuvant therapy. Response was evaluated by radiology (mammogram or ultrasound) at four months L (OMS) and data was available in 56 cases. A total of ten single nucleotide polymorphisms (SNPs) spanning the gene, a TCT insertion/deletion and a (TTTA)n repeat were evaluated. The (TTTA)n repeat was analyzed by using GeneScan to detect polymorphism length, TCT Ins/Del by direct sequencing and SNPs by allelic discrimination using TaqMan SNP Genotyping Assays (Applied Biosystems). Logistic regression analysis was used to estimate the most accurate predictive model for response to therapy. Results: Median age of our series was 77 years (range: 68 to 90). Twenty women (36%) responded to 4 months L, while 38 (67%) did not (2 PD and 36 EE). Allelic frequencies were determined for each polymorphism and the global genotype data was evaluated in a logistic regression analysis. In a first step, the analysis with the genetic information for the 12 polymorphisms resulted in a model that predicted radiological response to L with an accuracy of 80% (85% specifity, 69% sensibility). However, a reduction to 4 informative polymorphisms generated a simpler predictive model with an accuracy of 76% (76.5% specifity, 75% sensibility). Conclusions: Polymorphism analysis of CYP19A1 could predict for response to neoadjuvant therapy with L. A validation of this predictive model in an independent series is ongoing. No significant financial relationships to disclose.

Rural Special Education Teachers' Plans to Continue or Leave Their Teaching Positions

1996 ◽  
Vol 62 (4) ◽  
pp. 319-335 ◽  
Author(s):  
David L. Westling ◽  
Thomas M. Whitten
Keyword(s):  
Special Education ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Predictive Model ◽  
Logistic Regression Analysis ◽  
Special Education Teachers ◽  
Administrative Support ◽  
Job Requirements ◽  
Written Comments ◽  
Teaching Positions

A survey of 158 rural special education teachers was conducted to determine the teachers' plans for remaining in or leaving their current teaching positions. Only 57% indicated that it was likely that they would return in 5 years. Data were analyzed to determine variables that differed significantly between those likely to stay in their positions. Select variables were entered into a logistic regression analysis to build a predictive model. The results of these analyses, along with teachers' written comments, suggested that administrative support and job requirements played important roles in teachers' 5-year plans.

scholarly journals Impact of prematurity and the CTG repeat length on outcomes in congenital myotonic dystrophy

2020 ◽  
Author(s):  
Yu Saito ◽  
Kenta Matsumura ◽  
Misao Kageyama ◽  
Yuichi Kato ◽  
Eiji Ohta ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Predictive Model ◽  
Myotonic Dystrophy ◽  
Logistic Regression Analysis ◽  
Gestational Age ◽  
Multivariate Logistic Regression Analysis ◽  
Repeat Length ◽  
Congenital Myotonic Dystrophy ◽  
Ctg Repeat

Abstract Objective: Approximately half of patients with congenital myotonic dystrophy (CDM) are born preterm. Although the CDM severity depends on the CTG repeat length, prematurity may also affect the prognosis in patients with CDM. Given that preterm birth is expected to increase the risk of CDM in newborns, we investigated the outcomes of newborns with CDM according to gestational age to assess prematurity and the CTG repeat length for predicting prognosis. Results: We assessed the outcomes of 54 infants with CDM using data collected from our hospitals and previously published studies. The patients were divided into mild and severe groups based on clinical outcomes. Multivariate logistic regression analysis was performed to estimate odds ratios (ORs) for CDM prognosis according to gestational age and the CTG repeat length and to construct a predictive model. Logistic regression analysis showed both the CTG repeat and gestational age were significantly associated with severe outcomes in patients with CDM (OR: 45.17, 95% CI: 3.22–633.24; p = .0047 and OR: 0.73, 95% CI: 0.59–0.91; p = .0043, respectively). This predictive model for CDM prognosis exhibited good sensitivity (67%) and specificity (86%). Both prematurity and the CTG repeat length were significantly associated with the CDM severity.

scholarly journals Pretreatment Absolute Neutrophil-to-Lymphocyte Ratio (NLR) Predict the Risk for Febrile Neutropenia in the First Cycle Adjuvant Chemotherapy for Breast Cancer

2020 ◽  
Vol 5 (3) ◽  
pp. 81-87 ◽  
Author(s):  
Chaichana Chantharakhit ◽  
Nantapa Sujaritvanichpong
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Adjuvant Chemotherapy ◽  
Febrile Neutropenia ◽  
Predictive Model ◽  
Logistic Regression Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
Predictive Biomarker ◽  
Multivariate Logistic Regression

Background: Chemotherapy-induced febrile neutropenia (FN) is a condition affecting mortality and morbidity. The records show that absolute neutrophil-to-lymphocyte ratio (NLR) is associated with the cancer prognosis and reflects the immune response system on the infection. It can be used as an independent prognostic biomarker and predictive marker in patients with chronic inflammatory diseases, cardiovascular diseases, or malignancies. Therefore, we have been conducted on using absolute NLR to predict FN in a patient with breast cancer who has adjuvant chemotherapy. Materials and Methods: The authors retrospectively evaluated the pretreatment absolute NLR of patients with early stage breast cancer who had adjuvant chemotherapy. Then, the relationship to FN was analyzed by using multivariate logistic regression analysis. Results: We conducted a retrospective analysis of 339 patients where 21 patients had developed FN (6.19%). The multivariate logistic regression analysis results indicated that the pretreatment absolute NLR cut-off point equal to or greater than 2.4 was a significant independent predictive biomarker of the chemotherapy-induced FN (odds ratio = 2.810, 95%,; CI 1.061 - 7.442; p = 0.038). The predictive performance of the high level of absolute NLR was an acceptable discrimination [AUC= 0.7626 (95% and CI 0.650 - 0.875)]. Furthermore, a calibration curve and the Hosmer-Lemeshow test to assess the accuracy of the predictive model showed a goodness of fit for a logistic predictive model (Hosmer-Lemeshow chi2 = 2.50; p = 0.645). Conclusions: Pretreatment absolute NLR would be a useful predictive biomarker for febrile neutropenia after the first cycle of adjuvant chemotherapy for breast cancer that would be simple and easy to integrate in daily practice without extra costs.

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