Correlation between shunt series and scoliosis radiographs in children with myelomeningoceles

2012 ◽  
Vol 17 (5) ◽  
pp. 410-414 ◽  
Author(s):  
Jonathan G. Thomas ◽  
Steven W. Hwang ◽  
Todd J. Blumberg ◽  
William E. Whitehead ◽  
Daniel J. Curry ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Medical Records ◽  
Clinical Suspicion ◽  
Scoliotic Curve ◽  
Radiographic Evidence ◽  
Curve Magnitude ◽  
The Mean ◽  
The Impact ◽  
Shunt Series

Object Over 85% of patients with myelomeningoceles require placement of a ventriculoperitoneal shunt for hydrocephalus, and between 25% and 85% of these patients develop scoliosis. Although most patients undergo repeated shunt series radiography to evaluate for device malfunction, scoliosis radiographs are less consistently obtained. The authors sought to determine if a correlation exists between these 2 radiographic techniques for a given patient, as shunt series are obtained with the patient supine, whereas scoliosis radiographs are acquired with the patient standing upright. The authors also endeavored to study if shunt series radiographs can reliably detect significant scoliosis. Methods The authors retrospectively reviewed a single institution's series of 593 patients with myelomeningoceles and identified all patients in whom a shunt series and scoliosis radiographs were obtained within a 6-month period. They reviewed the medical records and radiographs of these patients for demographic and radiographic parameters. They then applied a linear regression model and determined shunt series curve cutoffs to detect scoliotic curves greater than 20° and 50°. Results Of the 593 patients identified, 116 did not have radiographs available for interpretation. Of the remaining 477 patients, 201 had radiographic evidence of scoliosis (42%), and 66 had both a shunt series and a scoliosis radiographs acquired within a 6-month interval. In 4 patients, both end vertebrae of the scoliotic curve could not be visualized on a single radiograph. The mean age of the remaining cohort was 10.6 ± 5.2 years and the mean curve magnitude was 58° ± 37°. Using identical end vertebrae, the mean shunt series curve magnitude was 49° ± 35°. The mean interval between both radiographs was 2.3 ± 3.3 months. The regression model showed a strong linear association between shunt series and scoliosis series curves. A curve greater than 19° on shunt series radiographs would detect significant curves of greater than 20° on scoliosis series with 91% sensitivity and 78% specificity. A shunt series curve greater than 37° had 100% sensitivity and 93% specificity in identifying significant scoliotic curves greater than 50°. Conclusions Although shunt series radiographs may not precisely depict scoliotic curve magnitude because the impact of gravity is negated, they may be useful in helping to confirm clinical suspicion of scoliosis. The authors' results suggest a strong correlation between both types of radiographs.

Cognition predicts days-alive-out-of-hospital after LVAD implantation

2021 ◽  
pp. 039139882110184
Author(s):  
Marykay A Pavol ◽  
Amelia K Boehme ◽  
Melana Yuzefpolskaya ◽  
Mathew S Maurer ◽  
Jesus Casida ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Left Ventricular ◽  
Motor Speed ◽  
Ventricular Assist ◽  
Hospitalization Rates ◽  
Left Ventricular Assist ◽  
Patient Groups ◽  
The Impact

Objective: Cognition influences hospitalization rates for a variety of patient groups but this association has not been examined in heart failure (HF) patients undergoing left ventricular assist device (LVAD) implantation. We used cognition to predict days-alive-out-of-hospital (DAOH) in patients after LVAD surgery. Methods: We retrospectively identified 59 HF patients with cognitive assessment prior to LVAD. Cognitive tests of attention, memory, language, and visual motor speed were averaged into one score. DAOH was converted to a percentage based on total days from LVAD surgery to either heart transplant or 900 days post-LVAD. Variables significantly associated with DAOH in univariate analyses were included in a linear regression model to predict DAOH. Results: A linear regression model including LVAD type (continuous or pulsatile flow) and cognition significantly predicted DAOH (F(2,54) = 6.44, p = 0.003, R2 = .19). Inspection of each variable revealed that cognition was a significant predictor in the model (β = .11, SE = .04, p = 0.007) but LVAD type was not ( p = 0.08). Conclusions: Cognitive performance assessed prior to LVAD implantation predicted how much time patients spent out of the hospital following surgery. Further studies are warranted to identify the impact of pre-LVAD cognition on post-LVAD hospitalization.

scholarly journals A Linear Regression Model for THD Sensitivity in PV Based Microgrid with Varying Insolation Levels

2022 ◽  
pp. 179-186
Author(s):  
Yuvraj Praveen Soni ◽  
Eugene Fernandez
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Solar Irradiance ◽  
Harmonic Distortion ◽  
System Control ◽  
Solar Pv ◽  
Pv System ◽  
Simulink Model ◽  
The Impact

Solar PV systems can be used for powering small microgrids in rural area of developing countries. Generally, a solar power microgrid consists of a PV array, an MPPT, a dc-dc converter and an inverter, particularly as the general loads are A.C in nature. In a PV system, reactive current, unbalancing in currents, and harmonics are generated due to the power electronics-based converters as well as nonlinear loads (computers induction motors etc). Thus, estimation of the harmonics levels measured by the Total Harmonic Distortion (THD) is an essential aspect of performance assessment of a solar powered microgrid. A major issue that needs to be examined is the impact of PV system control parameters on the THD. In this paper, we take up this assessment for a small PV based rural microgrid with varying levels of solar irradiance. A Simulink model has been developed for the study from which the THD at equilibrium conditions is estimated. This data is in turn used to design a generalized Linear Regression Model, which can be used to observe the sensitivity of three control variables on the magnitude of the THD. These variables are: Solar Irradiance levels, Power Factor (PF) of connected load magnitude of the connected load (in kVA) The results obtained show that the greatest sensitivity is obtained for load kVA variation.

scholarly journals End-Of-Life Discussions Reduce The Utilization of Life-Sustaining Treatments During The Last Three Months of Life in Cancer Patients : A Nationwide Population-Based Cohort Study

2021 ◽  
Author(s):  
Shang-Yih Chan ◽  
Yun-Ju Lai ◽  
Yu-Yen Hsin Chen ◽  
Shuo-Ju Chiang ◽  
Yi-Fan Tsai ◽  
...  
Keyword(s):  
Cohort Study ◽  
Cardiopulmonary Resuscitation ◽  
End Of Life ◽  
Cancer Patients ◽  
Medical Records ◽  
Population Based ◽  
Lower Likelihood ◽  
The Mean ◽  
The Impact ◽  
To Receive

Abstract Purpose Studies to examine the impact of end-of-life (EOL) discussions on the utilization of life-sustaining treatments near death were limited and had inconsistent findings. This nationwide population-based cohort study determined the impact of EOL discussions on the utilization of life-sustaining treatments in the last three months of life in Taiwanese cancer patients. Methods This cohort study included adult cancer patients from 2012–2018, which were confirmed by pathohistological reports. Life-sustaining treatments during the last three months of life included cardiopulmonary resuscitation, intubation, and defibrillation. EOL discussions in cancer patients were confirmed by their medical records. Association of EOL discussions with utilization of life-sustaining treatments were assessed using multiple logistic regression. Results Of 381,207 patients, the mean age was 70.5 years and 19.4% of the subjects utilized life-sustaining treatments during the last three months of life. After adjusting for other covariates, those who underwent EOL discussions were less likely to receive life-sustaining treatments during the last three months of life compared to those who did not (Adjusted odds ratio [AOR]: 0.82; 95% confidence interval [CI]: 0.80–0.84). Considering the type of treatments, EOL discussions correlated with a lower likelihood of receiving cardiopulmonary resuscitation (AOR = 0.43, 95% CI: 0.41–0.45), endotracheal intubation (AOR = 0.87, 95%CI: 0.85–0.89), and defibrillation (AOR = 0.52, 95%CI: 0.48–0.57). Conclusion EOL discussions correlated with a lower utilization of life-sustaining treatments during the last three months of life among cancer patients. Our study supports the importance of providing these discussions to cancer patients to better align care with preferences during the EOL treatment.

High Correlation between Ethanol Concentrations in Postmortem Femoral Blood and in Alternative Biological Specimens, but Large Uncertainty When the Linear Regression Model Was Used for Prediction in Individual Cases

2020 ◽  
Vol 44 (5) ◽  
pp. 415-421 ◽  
Author(s):  
G Thelander ◽  
F C Kugelberg ◽  
A W Jones
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Pleural Cavity ◽  
Medical Examiner ◽  
Outcome Variable ◽  
Large Uncertainty ◽  
Lung Fluid ◽  
Femoral Blood ◽  
The Mean ◽  
Alternative Specimen

Abstract In connection with medicolegal autopsies peripheral blood (e.g. from a femoral vein) is the specimen of choice for toxicological analysis, although alternative specimens are also sometimes submitted, such as bile, cerebrospinal fluid (CSF), vitreous humor (VH), bladder urine, pleural effusions and/or lung fluid. Ethanol concentrations were determined in duplicate in femoral blood and in various alternative biological specimens by headspace gas chromatography. The analysis was carried out on two different fused silica capillary columns furnishing different retention times for ethanol and both n-propanol and t-butanol were used as internal standards. The results were evaluated by linear regression using blood alcohol concentration (BAC) as dependent or outcome variable and the concentrations in an alternative specimen as independent or predictor variable. The Pearson correlation coefficients were all statistically highly significant (P < 0.001); r = 0.94 (bile), r = 0.98 (CSF), r = 0.97 (VH), r = 0.92 (urine), r = 0.94 (lung fluid) and r = 0.96 (pleural cavity effusions). When the regression model was used to predict femoral BAC from the mean concentration in an alternative specimen the mean and 95% prediction intervals were 1.12 ± 0.824 g/L (bile), 1.41 ± 0.546 g/L (CSF), 1.15 ± 0.42 g/L (VH), 1.29 ± 0.780 g/L (urine), 1.25 ± 0.772 g/L (lung fluid) and 0.68 ± 0.564 g/L (pleural cavity effusions). This large uncertainty for a single new observation needs to be considered when alcohol-related deaths are evaluated and interpreted. However, the analysis of alternative specimens is recommended in medical examiner cases to provide supporting evidence with regard to the origin of ethanol, whether this reflects antemortem (AM) ingestion or postmortem (PM) synthesis.

scholarly journals A Multi Linear Regression Model to Derive Dust PM10 in the Sahel Using AERONET Aerosol Optical Depth and CALIOP Aerosol Layer Products

2020 ◽  
Vol 12 (18) ◽  
pp. 3099
Author(s):  
Jean-François Léon ◽  
Nadège Martiny ◽  
Sébastien Merlet
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Linear Regression Model ◽  
Population Exposure ◽  
Aerosol Layer ◽  
Multi Linear Regression ◽  
Sun Photometer ◽  
The Impact

Due to a limited number of monitoring stations in Western Africa, the impact of mineral dust on PM10 surface concentrations is still poorly known. We propose a new method to retrieve PM10 dust surface concentrations from sun photometer aerosol optical depth (AOD) and CALIPSO/CALIOP Level 2 aerosol layer products. The method is based on a multi linear regression model that is trained using co-located PM10, AERONET and CALIOP observations at 3 different locations in the Sahel. In addition to the sun photometer AOD, the regression model uses the CALIOP-derived base and top altitude of the lowermost dust layer, its AOD, the columnar total and columnar dust AOD. Due to the low revisit period of the CALIPSO satellite, the monthly mean annual cycles of the parameters are used as predictor variables rather than instantaneous observations. The regression model improves the correlation coefficient between monthly mean PM10 and AOD from 0.15 (AERONET AOD only) to 0.75 (AERONET AOD and CALIOP parameters). The respective high and low PM10 concentration during the winter dry season and summer season are well produced. Days with surface PM10 above 100 μg/m3 are better identified when using the CALIOP parameters in the multi linear regression model. The number of true positives (actual and predicted concentrations above the threshold) is increased and leads to an improvement in the classification sensitivity (recall) by a factor 1.8. Our methodology can be extrapolated to the whole Sahel area provided that satellite derived AOD maps are used in order to create a new dataset on population exposure to dust events in this area.

scholarly journals Influence of the key account manager in the provisioning management: Evidence from staple companies during the events of COVID-19

Accounting ◽  
2022 ◽  
Vol 8 (2) ◽  
pp. 161-170 ◽  
Author(s):  
Luis-Ricardo Flores-Vilcapoma ◽  
Cynthia-Paola A lbengrin-Mendoza ◽  
Gabriela-Briggite Gomez-Rojas ◽  
Yuri Sánchez-Solis ◽  
Wagner Vicente-Ramos
Keyword(s):  
Experimental Design ◽  
Linear Regression ◽  
Correlation Analysis ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Cross Sectional ◽  
Key Account ◽  
The Impact

The purpose of this research was to evaluate the degree of influence exercised by the Key Account Manager in the provisioning management in the main companies called Staple in Peru, during the events of COVID-19. The research was of type quantitative, cross-sectional and temporal, with a non-experimental design, using a multiple linear regression model and correlation analysis to determine the impact that exists between the variables. The data belongs to the Industrias San Miguel company, distributed in a weekly period from June 2019 to March 2021, which gives 88 observations. The results allow us to conclude that the Key Account Manager is an important manager of the supply of goods during the crisis caused by COVID-19 in staple companies.

scholarly journals Extreme return, extreme volatility and investor sentiment

Filomat ◽  
2016 ◽  
Vol 30 (15) ◽  
pp. 3949-3961 ◽  
Author(s):  
Xu Gong ◽  
Fenghua Wen ◽  
Zhifang He ◽  
Jia Yang ◽  
Xiaoguang Yang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Quantile Regression ◽  
Regression Model ◽  
Least Squares ◽  
Linear Regression Model ◽  
Investor Sentiment ◽  
Generalized Least Squares ◽  
Regression Methods ◽  
Leading Role ◽  
The Impact

The extreme return and extreme volatility have great influences on the investor sentiment in stock market. However, few researchers have taken the phenomenon into consideration. In this paper, we first distinguish the extreme situations from non-extreme situations. Then we use the ordinary generalized least squares and quantile regression methods to estimate a linear regression model by applying the standardized AAII, the return and volatility of SP 500. The results indicate that, except for extremely negative return, other return sequences can cause great changes in investor sentiment, and non-extreme return plays a leading role in affecting the overall American investor sentiment. Extremely positive (negative) return can rapidly improve (further reduce) the level of investor sentiment when investors encounter extremely pessimistic situations. The impact gradually decreases with improvement of the sentiment until the situation turns optimistic. In addition, we find that extreme and non-extreme volatility cannot a_ect the overall investor sentiment.

scholarly journals Distribusi Belanja Desa dan Capaian Status Pembangunan Desa di Provinsi Riau

2021 ◽  
Vol 5 (2) ◽  
pp. 407
Author(s):  
Bakti Kharisma ◽  
Werry Darta Taifur ◽  
Fajri Muharja
Keyword(s):  
Linear Regression ◽  
Rural Development ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Development Process ◽  
Multiple Linear Regression Model ◽  
Urban Bias ◽  
The Impact ◽  
The Village

The Village Law has become one of the berakhthroughs in overcoming the impact of development that tends to be urban bias. Village is no longer only an object of development but the main actor in rural development process. The source of the budget for the implamentation of rural development has increased significantly with the village fund policy. This study aims to analyze the impact of village budgets and village typology on the achievement of village status in Riau Province. Multiple linear regression model was used to analyze the impact of village budget and village typology has a significant impact on the increase in the developing village index in Riau Province.

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