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Abstract: Continuous blood glucose monitoring systems (CGMS) capture interstitial glucose levels at frequent intervals over time, and are used by people with diabetes and their health care professionals to assess glycaemic variability. This information helps to adjust treatment to achieve optimum glycaemic control, as well as potentially providing early warning of imminent and dangerous hypoglycaemia. Although a number of studies has reported the possibilities of predicting hypoglycaemia in insulin dependent type 1 diabetes (T1DM) patients, the prediction paradigm is still unreliable, as glucose fluctuations in people with diabetes are highly volatile and depend on many factors. Studies have proposed the use of linear auto-regressive (AR) and state space time series models to analyse the glucose profiles for predicting upcoming glucose levels. However, these modelling approaches have not adequately addressed the inherent dependencies and volatility aspects in the glucose profiles. We have investigated the utility of generalized autoregressive conditional heteroscedasticity (GARCH) models to explore glucose time-series trends and volatility, and possibility of reliable short-term forecasting of glucose levels. GARCH models were explored using CGMS profiles of young children (4 to <10 years) with T1DM. The prediction performances of GARCH approach were compared with other contemporary modelling approaches such as lower and higher order AR, and the state space models. The GARCH approach appears to be successful in both realizing the volatility in glucose profiles and offering potentially more reliable forecasting of upcoming glucose levels. Keywords: Diabetes, blood glucose prediction, generalized ARCH models, glycaemic management. Download Full Article |
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Abstract: The authors investigate the effects of clinical covariates upon the outcome of Intra-cerebral Hemorrhage (ICH) patients by applying a discriminate model of logistic regression. About 985 patients’s data with ICH have been collected using the International classification of diseases; ninth revision codes are also included. Diagnostic codes (434 for stroke and 431 for ICH) were used to identify patients and confirmed by neuro-imaging of the patients using CT scan and MRI. A univariate analysis of 88 covariates was undertaken and 46 of them reached statistical significance at an acceptable level of p < 0.05. The multivariable analysis exhibited a significant negative relationship between ICH and hypertension. The improvement among ICH patients having hypertension was found to be 0.5 with the p=0.001, ARR=0.5, 95% C.I. 0.3 – 0.8. The development among ICH patients using antihypertensive medicine was 1.3 with p = 0.021, ARR=1.3, 95% C.I. 1.0 – 1.6. Thus present study manifested that ICH has strong relationship with use of antihypertensive medicine. The rate of perfection in the patients physiological conditions using antihypertensive medicine at the time of discharge was 2.9 times acquiring p < 0.001, ARR=2.9, 95% C.I. 2.7 – 3.2 as compared to those who could not use antihypertensive medicine. The change in ARR from 1.3 to 2.9 times depict that the exercise of antihypertensive medicine and ICH outcome are positively associated. The fluctuations in ARR of hypertensive range of systolic blood pressure (SBP) also indicate that the blood pressure range and ICH outcome are negatively correlated. The neurological symptomoatology, indistinct speech and double vision are important factors of proposed models. Moreover, a clear decrease was found in mental status from normal to coma in most suitable model. Surgery is an important part of recovery, and estimated that the improvement among the ICH patients, who were treated under surgical aspects, was 1.4 times with significant p-value in the best models. The complication of pneumonia during treatment of ICH subjects has highly significant showing negative correlation with the given outcome variable. The current model has 89.3% area under the curve with sensitivity (82.6%), specificity (81.3%) and p-value (0.308). This indicates that the constructed model bestows the well performance of the ICH outcome and the model is considered as excellent. Keywords: Intracerebral Hemorrhage, clinical covariates, multivariable analysis, logistic regression, Hosmer-Lemeshow test, discriminate model, sensitivity and specificity.Download Full Article |
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Abstract: Patient reported outcome measures (PROMs) are increasingly being used in research to explore experiences of cancer survivors. Techniques to predict symptoms, with the aim of providing triage care, rely on the ability to analyse trends in symptoms or quality of life and at present are limited. The secondary analysis in this study uses a statistical method involving the application of autoregression (AR) to PROMs in order to predict symptom intensity following radiotherapy, and to explore its feasibility as an analytical tool. The technique is demonstrated using an existing dataset of 94 prostate cancer patients who completed a validated battery of PROMs over time. In addition the relationship between symptoms was investigated and symptom clusters were identified to determine their value in assisting predictive modeling. Three symptom clusters, namely urinary, gastrointestinal and emotional were identified. The study indicates that incorporating symptom clustering into predictive modeling helps to identify the most informative predictor variables. The analysis also showed that the degree of rise of symptom intensity during radiotherapy has the ability to predict later radiotherapy-related symptoms. The method was most successful for the prediction of urinary and gastrointestinal symptoms. Quantitative or qualitative prediction was possible on different symptoms. The application of this technique to predict radiotherapy outcomes could lead to increased use of PROMs within clinical practice. This in turn would contribute to improvements in both patient care after radiotherapy and also strategies to prevent side effects. In order to further evaluate the predictive ability of the approach, the analysis of a larger dataset with a longer follow up was identified as the next step. Keywords: Predictive Modeling, Patient Reported Outcome Measures, Autoregression, Radiotherapy-Related Side Effects, Longitudinal Study.Download Full Article |
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Abstract: Asthma prevalence in children and adolescents in Spain is 10-17%. It is the most common chronic illness during childhood. Prevalence has been increasing over the last 40 years and there is considerable evidence that, among other factors, continued exposure to cigarette smoke results in asthma in children. No statistical or simulation model exist to forecast the evolution of childhood asthma in Europe. Such a model needs to incorporate the main risk factors that can be managed by medical authorities, such as tobacco (OR = 1.44), to establish how they affect the present generation of children. A simulation model using conditional probability and discrete event simulation for childhood asthma was developed and validated by simulating realistic scenario. The parameters used for the model (input data) were those found in the bibliography, especially those related to the incidence of smoking in Spain. We also used data from a panel of experts from the Hospital del Mar (Barcelona) related to actual evolution and asthma phenotypes. The results obtained from the simulation established a threshold of a 15-20% smoking population for a reduction in the prevalence of asthma. This is still far from the current level in Spain, where 24% of people smoke. We conclude that more effort must be made to combat smoking and other childhood asthma risk factors, in order to significantly reduce the number of cases. Once completed, this simulation methodology can realistically be used to forecast the evolution of childhood asthma as a function of variation in different risk factors. Keywords: Children, Asthma, Model, Discrete event, Probability, Tobacco.Download Full Article |
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Abstract: Objective: To identify predictors of blood pressure (BP) in children and explore the predictors` effects on the conditional quantile functions of systolic blood pressure and diastolic blood pressure. Methods: A secondary data analysis was performed using data from the South African National Income Dynamics Study (2014-2015). From this particular secondary data, data for children aged between 10-17 years were extracted for analysis. The variables used in the study were systolic blood pressure (SBP), diastolic blood pressure (DBP), body mass index (BMI), age, smoking, alcohol consumption, exercises, gender and race. Two parameter estimation methods were used, ordinary least squares (OLS) and quantile regression (QR). Results: BMI had positive statistically significant estimated OLS and conditional quantile functions with both the BP measures except the 95th quantile for SBP. Age had also positive statistically significant estimated OLS and QR coefficients except for the 95th percentile, with both DBP and SBP respectively. Gender was found to be inversely related to both DBP and SBP except the 10th quantile for DBP. Race was partially significant to DBP. Smoking, alcohol consumption and exercises did not present any statistically significant relations with both DBP and SBP for all the estimated OLS and QR coefficients. Conclusion: BMI, age, gender and partially race were found to be predictors of BP in South African children using both OLS and QR techniques. Exercises, smoking and alcohol consumption did not present any statistically significant relations with both DBP and SBP probably because few participants exercise regularly, smoke and drink alcohol to bring out a significant change in both BP measurements. Keywords: Body Mass Index (BMI), Diastolic Blood Pressure (DBP), Systolic Blood Pressure (SBP), Ordinary Least Squares Regression (OLS), Quantile Regression (QR). |