718 research outputs found
An investigation into the effects of commencing haemodialysis in the critically ill
<b>Introduction:</b>
We have aimed to describe haemodynamic changes when haemodialysis is instituted in the critically ill. 3
hypotheses are tested: 1)The initial session is associated with cardiovascular instability, 2)The initial session is
associated with more cardiovascular instability compared to subsequent sessions, and 3)Looking at unstable
sessions alone, there will be a greater proportion of potentially harmful changes in the initial sessions compared
to subsequent ones.
<b>Methods:</b>
Data was collected for 209 patients, identifying 1605 dialysis sessions. Analysis was performed on hourly
records, classifying sessions as stable/unstable by a cutoff of >+/-20% change in baseline physiology
(HR/MAP). Data from 3 hours prior, and 4 hours after dialysis was included, and average and minimum values
derived. 3 time comparisons were made (pre-HD:during, during HD:post, pre-HD:post). Initial sessions were
analysed separately from subsequent sessions to derive 2 groups. If a session was identified as being unstable,
then the nature of instability was examined by recording whether changes crossed defined physiological ranges.
The changes seen in unstable sessions could be described as to their effects: being harmful/potentially harmful,
or beneficial/potentially beneficial.
<b>Results:</b>
Discarding incomplete data, 181 initial and 1382 subsequent sessions were analysed. A session was deemed to
be stable if there was no significant change (>+/-20%) in the time-averaged or minimum MAP/HR across time
comparisons. By this definition 85/181 initial sessions were unstable (47%, 95% CI SEM 39.8-54.2). Therefore
Hypothesis 1 is accepted. This compares to 44% of subsequent sessions (95% CI 41.1-46.3). Comparing these
proportions and their respective CI gives a 95% CI for the standard error of the difference of -4% to 10%.
Therefore Hypothesis 2 is rejected. In initial sessions there were 92/1020 harmful changes. This gives a
proportion of 9.0% (95% CI SEM 7.4-10.9). In the subsequent sessions there were 712/7248 harmful changes.
This gives a proportion of 9.8% (95% CI SEM 9.1-10.5). Comparing the two unpaired proportions gives a
difference of -0.08% with a 95% CI of the SE of the difference of -2.5 to +1.2. Hypothesis 3 is rejected. Fisherâs
exact test gives a result of p=0.68, reinforcing the lack of significant variance.
<b>Conclusions:</b>
Our results reject the claims that using haemodialysis is an inherently unstable choice of therapy. Although
proportionally more of the initial sessions are classed as unstable, the majority of MAP and HR changes are
beneficial in nature
Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinsonâs Disease Affected by COVIDâ19: A Narrative Review
Background and Motivation: Parkinsonâs disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVIDâ19 causes the ML systems to be-come severely nonâlinear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no wellâexplained ML paradigms. Deep neural networks are powerful learning machines that generalize nonâlinear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVIDâ19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVIDâ19 framework. We study the hypothesis that PD in the presence of COVIDâ19 can cause more harm to the heart and brain than in nonâ COVIDâ19 conditions. COVIDâ19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVIDâ19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVIDâ19 lesions, office and laboratory arterial atherosclerotic imageâbased biomarkers, and medicine usage for the PD patients for the design of DL pointâbased models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVIDâ 19 environment and this was also verified. DL architectures like long shortâterm memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVIDâ19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVIDâ19. © 2022 by the authors. Licensee MDPI, Basel, Switzerland
Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinsonâs Disease Affected by COVIDâ19: A Narrative Review
Background and Motivation: Parkinsonâs disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVIDâ19 causes the ML systems to be-come severely nonâlinear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no wellâexplained ML paradigms. Deep neural networks are powerful learning machines that generalize nonâlinear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVIDâ19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVIDâ19 framework. We study the hypothesis that PD in the presence of COVIDâ19 can cause more harm to the heart and brain than in nonâ COVIDâ19 conditions. COVIDâ19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVIDâ19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVIDâ19 lesions, office and laboratory arterial atherosclerotic imageâbased biomarkers, and medicine usage for the PD patients for the design of DL pointâbased models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVIDâ 19 environment and this was also verified. DL architectures like long shortâterm memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVIDâ19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVIDâ19
DYNAMIC PREDICTION OF SURVIVAL DATA USING SINGLE OR MULTIPLE LONGITUDINAL MARKERS
Recurrent events and time-to-event data occur frequently in longitudinal studies. In large clinical trials with survival endpoints, researchers collect a multitude of longitudinal markers. There is a growing need to utilize these rich longitudinal information to build prediction models and assess their prognostic performance. In this dissertation research, I propose a novel approach of integrating longitudinal markers in modeling the recurrent event or terminal event data, and conduct dynamic prediction of event risks. Under joint a model framework, I jointly model a longitudinal outcome and a recurrent event process with the two process correlated via shared latent function. The probability of having a new occurrence of recurrent event in a given time interval is predicted based on subject-speciïŹc longitudinal proïŹle and disease history. When multivariate longitudinal outcomes are considered, traditional joint model method has limitation on specifying ap propriate longitudinal structures and computation problem occur when using Bayesian approach. To avoid these potential issues, I employ multivariate functional principal component analysis approach which is more ïŹexible, robust and time eïŹcient. For terminal event data, I specify a prognostic model incorporating multivariate longitudinal information, the prediction can be updated with accumulated data over time. I also propose a recurrent event model integrating multiple longitudinal markers and conduct personalized dynamic prediction of new recurrent event risk, which helps physicians to identify patients at risk and give personalized health care
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