T68. RELATIONSHIP BETWEEN GLOBAL AND COGNITIVE FUNCTIONING IN PATIENTS WITH SCHIZOPHRENIA IN A GENERAL HOSPITAL IN PERU

Abstract Background Schizophrenia is a chronic mental disorder, which produces high costs and has a substantial impact on health care budgets globally. This is mainly due to poor global and cognitive functioning in these patients. The objective of this study was to relate global functioning and cognitive functioning in patients with schizophrenia who attend the outpatient clinic of a general hospital in Peru during the years 2018–2019. Methods Non-experimental quantitative study of descriptive cross-sectional correlational type. The sample was for convenience, and consisted of 53 patients with schizophrenia from the “Hospital Nacional de la PNP”. Functioning Assessment Short Test (FAST) was used to assess global functioning, the Screen for Cognitive Impairment (SCIP), cognitive functioning, and a data collection sheets with the history of the disease. The relationship between FAST and SCIP with the qualitative variables was evaluated by the Mann-Whitney U test or Student’s t test depending on whether they met the normality assumptions. The linear correlation between the FAST, the SCIP and the other quantitative variables was evaluated using Spearman’s Rho. A multiple linear regression model was constructed in which all variables other than the total FAST result are considered using the forward method. Results It was found that 34 (62.2%) were male; 52 (98.1%), single; 39, (73.6%) without a current job. We found worse overall functioning in patients with lower educational level (p = 0.005) and without a current job (p = 0.004). The total FAST was correlated with the time of the disease (ρ = 0.334, p <0.05) and with the number of previous psychotic episodes (ρ = 0.354, p <0.01). We found worse cognitive functioning in patients with lower educational level (p = 0.000) and without a current job (p = 0.017). The SCIP total was correlated to the FAST (ρ = 0.542, p <0.01). The multiple linear regression analysis with the total FAST score as the explained variable evidenced the existence of a relationship between variables that is explained by the equation: Y = 57.032 + (-0.521) X1 + (1.896) X2 Where Y is the total FAST score, X1 the total SCIP and X2 the number of previous psychotic episodes. The coefficient of determination was 0.392 and the mean square error of 161.46. The Durbin-Watson statistic was 1,529. Discussion This is the first exploratory pilot analysis of the factors associated with global functioning, with special emphasis on cognitive functioning and the history of the disease, in Peruvian patients with schizophrenia. Patients with higher educational level and those who have a current job showed a better global and cognitive functioning. It was found an indirect and significant relationship of moderate intensity between cognitive functioning and its subtest with global functioning, that is, a lower level of cognitive functioning is related to greater difficulties in the daily functioning of patients with schizophrenia. Both the bivariate analysis and the linear regression model found a relationship between global and cognitive functioning. In the multiple linear regression model, it was found that the total SCIP was the one that had the most influence on global functioning (Beta = -0.528), that is, lower levels of cognitive functioning are related to high levels of poor global functioning. Taking these results into account, we recommend implementing functional and cognitive evaluation programs in patients with schizophrenia at the “Hospital Nacional de la PNP”. Future longitudinal studies should be performed on samples from larger patients, especially with a first psychotic episode, with the purpose of seeking a cause-effect relationship between global and cognitive functioning in patients with schizophrenia.

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An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327910666191218124350 ◽

2020 ◽

Vol 10 (4) ◽

pp. 473-483

Author(s):

Pundra Chandra Shaker Reddy ◽

Alladi Sureshbabu

Keyword(s):

Linear Regression ◽

Regression Model ◽

Multiple Linear Regression ◽

Linear Regression Model ◽

Weather Prediction ◽

Big Data Analytics ◽

Multiple Linear Regression Model ◽

Seasonal Rainfall ◽

Climate Data ◽

Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

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A Multiple Linear Regression Model for Maintainability Analysis Considering Maintainability Design Characteristics

Proceedings of the 4th International Conference on Computer Science and Application Engineering ◽

10.1145/3424978.3424988 ◽

2020 ◽

Author(s):

Zhenya Wu ◽

Jianping Hao ◽

Peng Wang ◽

Chenguang Liu ◽

Zhen Li

Keyword(s):

Linear Regression ◽

Regression Model ◽

Multiple Linear Regression ◽

Linear Regression Model ◽

Multiple Linear Regression Model ◽

Design Characteristics ◽

Maintainability Analysis

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Magnetization Transfer Ratio of Peripheral Nerve and Skeletal Muscle

Clinical Neuroradiology ◽

10.1007/s00062-021-01067-5 ◽

2021 ◽

Author(s):

Olivia Fösleitner ◽

Véronique Schwehr ◽

Tim Godel ◽

Fabian Preisner ◽

Philipp Bäumer ◽

...

Keyword(s):

Skeletal Muscle ◽

Body Weight ◽

Sciatic Nerve ◽

Linear Regression ◽

Peripheral Nerve ◽

Regression Model ◽

Multiple Linear Regression ◽

Linear Regression Model ◽

Magnetization Transfer ◽

Multiple Linear Regression Model

Abstract Purpose To assess the correlation of peripheral nerve and skeletal muscle magnetization transfer ratio (MTR) with demographic variables. Methods In this study 59 healthy adults evenly distributed across 6 decades (mean age 50.5 years ±17.1, 29 women) underwent magnetization transfer imaging and high-resolution T2-weighted imaging of the sciatic nerve at 3 T. Mean sciatic nerve MTR as well as MTR of biceps femoris and vastus lateralis muscles were calculated based on manual segmentation on six representative slices. Correlations of MTR with age, body height, body weight, and body mass index (BMI) were expressed by Pearson coefficients. Best predictors for nerve and muscle MTR were determined using a multiple linear regression model with forward variable selection and fivefold cross-validation. Results Sciatic nerve MTR showed significant negative correlations with age (r = −0.47, p < 0.001), BMI (r = −0.44, p < 0.001), and body weight (r = −0.36, p = 0.006) but not with body height (p = 0.55). The multiple linear regression model determined age and BMI as best predictors for nerve MTR (R2 = 0.40). The MTR values were different between nerve and muscle tissue (p < 0.0001), but similar between muscles. Muscle MTR was associated with BMI (r = −0.46, p < 0.001 and r = −0.40, p = 0.002) and body weight (r = −0.36, p = 0.005 and r = −0.28, p = 0.035). The BMI was selected as best predictor for mean muscle MTR in the multiple linear regression model (R2 = 0.26). Conclusion Peripheral nerve MTR decreases with higher age and BMI. Studies that assess peripheral nerve MTR should consider age and BMI effects. Skeletal muscle MTR is primarily associated with BMI but overall less dependent on demographic variables.

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