scholarly journals Quantifying Uncertainty and Robustness in a Biomathematical Model–Based Patient-Specific Response Metric for Glioblastoma

2019 ◽  
pp. 1-8 ◽  
Author(s):  
Andrea Hawkins-Daarud ◽  
Sandra K. Johnston ◽  
Kristin R. Swanson
Keyword(s):  
Synthetic Data ◽  
Magnetic Resonance Images ◽  
Patient Specific ◽  
Specific Response ◽  
Time To Progression ◽  
Interobserver Error ◽  
Standard Response ◽  
Clinical Variants ◽  
Tumor Kinetics ◽  
The Impact

Purpose Glioblastomas, lethal primary brain tumors, are known for their heterogeneity and invasiveness. A growing body of literature has been developed demonstrating the clinical relevance of a biomathematical model, the proliferation-invasion model, of glioblastoma growth. Of interest here is the development of a treatment response metric, days gained (DG). This metric is based on individual tumor kinetics estimated through segmented volumes of hyperintense regions on T1-weighted gadolinium-enhanced and T2-weighted magnetic resonance images. This metric was shown to be prognostic of time to progression. Furthermore, it was shown to be more prognostic of outcome than standard response metrics. Although promising, the original article did not account for uncertainty in the calculation of the DG metric, leaving the robustness of this cutoff in question. Methods We harnessed the Bayesian framework to consider the impact of two sources of uncertainty: (1) image acquisition and (2) interobserver error in image segmentation. We first used synthetic data to characterize what nonerror variants are influencing the final uncertainty in the DG metric. We then considered the original patient cohort to investigate clinical patterns of uncertainty and to determine how robust this metric is for predicting time to progression and overall survival. Results Our results indicate that the key clinical variants are the time between pretreatment images and the underlying tumor growth kinetics, matching our observations in the clinical cohort. Finally, we demonstrated that for this cohort, there was a continuous range of cutoffs between 94 and 105 for which the prediction of the time to progression was over 80% reliable. Conclusion Although additional validation must be performed, this work represents a key step in ascertaining the clinical utility of this metric.

scholarly journals Quantifying Uncertainty and Robustness in a Biomathematical Model Based Patient-Specific Response Metric for Glioblastoma

2018 ◽  
Author(s):  
Andrea Hawkins-Daarud ◽  
Sandra K. Johnston ◽  
Kristin R. Swanson
Keyword(s):  
Synthetic Data ◽  
Magnetic Resonance Images ◽  
Patient Specific ◽  
Specific Response ◽  
Time To Progression ◽  
Interobserver Error ◽  
Standard Response ◽  
Pre Treatment ◽  
Tumor Kinetics ◽  
The Impact

AbstractGlioblastomas, lethal primary brain tumors, are known for their heterogeneity and invasiveness. A growing literature has been developed demonstrating the clinical relevance of a biomathematical model, the Proliferation-Invasion (PI) model, of glioblastoma growth. Of interest here is the development of a treatment response metric, Days Gained (DG). This metric is based on individual tumor kinetics estimated through segmented volumes of hyperintense regions on T1-weighted gadolinium enhanced (T1Gd) and T2-weighted magnetic resonance images (MRIs). This metric was shown to be prognostic of time to progression. Further, it was shown to be more prognostic of outcome than standard response metrics. While promising, the original paper did not account for uncertainty in the calculation of the DG metric leaving the robustness of this cutoff in question. We harness the Bayesian framework to consider the impact of two sources of uncertainty: 1) image acquisition and 2) interobserver error in image segmentation. We first utilize synthetic data to characterize what non-error variants are influencing the final uncertainty in the DG metric. We then consider the original patient cohort to investigate clinical patterns of uncertainty and to determine how robust this metric is for predicting time to progression and overall survival. Our results indicate that the key clinical variants are the time between pre-treatment images and the underlying tumor growth kinetics, matching our observations in the clinical cohort. Finally, we demonstrated that for this cohort there was a continuous range of cutoffs between 94 and 105 for which the prediction of the time to progression and was over 80% reliable. While further validation must be done, this work represents a key step in ascertaining the clinical utility of this metric.

scholarly journals Pharmacological and Patient-Specific Response Determinants in Patients with Hospital-Acquired Pneumonia Treated with Tigecycline

2011 ◽  
Vol 56 (2) ◽  
pp. 1065-1072 ◽  
Author(s):  
Sujata M. Bhavnani ◽  
Christopher M. Rubino ◽  
Jeffrey P. Hammel ◽  
Alan Forrest ◽  
Nathalie Dartois ◽  
...  
Keyword(s):  
Clinical Success ◽  
Statistical Significance ◽  
Patient Specific ◽  
Specific Response ◽  
Multivariable Logistic Regression Model ◽  
Time Curve ◽  
Response Data ◽  
Hospital Acquired Pneumonia ◽  
Hospital Acquired ◽  
The Impact

ABSTRACTPharmacokinetic and clinical data from tigecycline-treated patients with hospital-acquired pneumonia (HAP) who were enrolled in a phase 3 clinical trial were integrated in order to evaluate pharmacokinetic-pharmacodynamic (PK-PD) relationships for efficacy. Univariable and multivariable analyses were conducted to identify factors associated with clinical and microbiological responses, based on data from 61 evaluable HAP patients who received tigecycline intravenously as a 100-mg loading dose followed by 50 mg every 12 h for a minimum of 7 days and for whom there were adequate clinical, pharmacokinetic, and response data. The final multivariable logistic regression model for clinical response contained albumin and the ratio of the free-drug area under the concentration-time curve from 0 to 24 h (fAUC0–24) to the MIC (fAUC0–24:MIC ratio). The odds of clinical success were 13.0 times higher for every 1-g/dl increase in albumin (P< 0.001) and 8.42 times higher for patients withfAUC0–24:MIC ratios of ≥0.9 compared to patients withfAUC0–24:MIC ratios of <0.9 (P= 0.008). Average model-estimated probabilities of clinical success for the albumin/fAUC0–24:MIC ratio combinations of <2.6/<0.9, <2.6/≥0.9, ≥2.6/<0.9, and ≥2.6/≥0.9 were 0.21, 0.57, 0.64, and 0.93, respectively. For microbiological response, the final model contained albumin and ventilator-associated pneumonia (VAP) status. The odds of microbiological success were 21.0 times higher for every 1-g/dl increase in albumin (P< 0.001) and 8.59 times higher for patients without VAP compared to those with VAP (P= 0.003). Among the remaining variables evaluated, the MIC had the greatest statistical significance, an observation which was not surprising given the differences in MIC distributions between VAP and non-VAP patients (MIC50and MIC90values of 0.5 and 0.25 mg/liter versus 16 and 1 mg/liter for VAP versus non-VAP patients, respectively;P= 0.006). These findings demonstrated the impact of pharmacological and patient-specific factors on the clinical and microbiological responses.

John Keats and Indian Medicine

Romanticism ◽  
2016 ◽  
Vol 22 (2) ◽  
pp. 157-166
Author(s):  
Nikki Hessell
Keyword(s):  
Medical Knowledge ◽  
John Keats ◽  
Specific Response ◽  
Medical Studies ◽  
Sources Of Information ◽  
Medical Practices ◽  
Traditional Medicines ◽  
Medical Imagery ◽  
Indian Medicine ◽  
The Impact

John Keats's medical studies at Guy's Hospital coincided with a boom in interest in both the traditional medicines of the sub-continent and the experiences of British doctors and patients in India. Despite extensive scholarship on the impact of Keats's medical knowledge on his poetry, little consideration has been given to Keats's exposure to Indian medicine. The poetry that followed his time at Guy's contains numerous references to the contemporary state of knowledge about India and its medical practices, both past and present. This essay focuses on Isabella and considers the major sources of information about Indian medicine in the Regency. It proposes that some of Keats's medical imagery might be read as a specific response to the debates about medicine in the sub-continent.

Uncovering the Diversification of Tissue Engineering on the Emergent Areas of Stem Cells, Nanotechnology and Biomaterials

2020 ◽  
Vol 15 (3) ◽  
pp. 187-201 ◽  
Author(s):  
Sunil K. Dubey ◽  
Amit Alexander ◽  
Munnangi Sivaram ◽  
Mukta Agrawal ◽  
Gautam Singhvi ◽  
...  
Keyword(s):  
Tissue Engineering ◽  
Stem Cells ◽  
Immune System ◽  
Clinical Application ◽  
Cell Types ◽  
Patient Specific ◽  
Potential Strategy ◽  
Induced Pluripotent ◽  
Treat All ◽  
The Impact

Damaged or disabled tissue is life-threatening due to the lack of proper treatment. Many conventional transplantation methods like autograft, iso-graft and allograft are in existence for ages, but they are not sufficient to treat all types of tissue or organ damages. Stem cells, with their unique capabilities like self-renewal and differentiate into various cell types, can be a potential strategy for tissue regeneration. However, the challenges like reproducibility, uncontrolled propagation and differentiation, isolation of specific kinds of cell and tumorigenic nature made these stem cells away from clinical application. Today, various types of stem cells like embryonic, fetal or gestational tissue, mesenchymal and induced-pluripotent stem cells are under investigation for their clinical application. Tissue engineering helps in configuring the stem cells to develop into a desired viable tissue, to use them clinically as a substitute for the conventional method. The use of stem cell-derived Extracellular Vesicles (EVs) is being studied to replace the stem cells, which decreases the immunological complications associated with the direct administration of stem cells. Tissue engineering also investigates various biomaterials to use clinically, either to replace the bones or as a scaffold to support the growth of stemcells/ tissue. Depending upon the need, there are various biomaterials like bio-ceramics, natural and synthetic biodegradable polymers to support replacement or regeneration of tissue. Like the other fields of science, tissue engineering is also incorporating the nanotechnology to develop nano-scaffolds to provide and support the growth of stem cells with an environment mimicking the Extracellular matrix (ECM) of the desired tissue. Tissue engineering is also used in the modulation of the immune system by using patient-specific Mesenchymal Stem Cells (MSCs) and by modifying the physical features of scaffolds that may provoke the immune system. This review describes the use of various stem cells, biomaterials and the impact of nanotechnology in regenerative medicine.

scholarly journals Impact of a patient-specific national programme aimed at increasing the use of emollient moisturisers to reduce the risk of skin tears: a longitudinal cohort study

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e039579
Author(s):  
Anna K Moffat ◽  
Kerrie P Westaway ◽  
Jemisha Apajee ◽  
Oliver Frank ◽  
Russell Shute ◽  
...  
Keyword(s):  
Interrupted Time Series ◽  
Self Report ◽  
Additional Patient ◽  
Patient Specific ◽  
Department Of Veterans Affairs ◽  
National Programme ◽  
Sustained Effect ◽  
Skin Tears ◽  
Comparison Groups ◽  
The Impact

ObjectivesTo evaluate the impact of a patient-specific national programme targeting older Australians and health professionals that aimed to increase use of emollient moisturisers to reduce to the risk of skin tears.DesignA prospective cohort intervention.ParticipantsThe intervention targeted 52 778 Australian Government’s Department of Veterans’ Affairs patients aged over 64 years who had risk factors for wound development, and their general practitioners (GPs) (n=14 178).Outcome measuresAn interrupted time series model compared the rate of dispensing of emollients in the targeted cohort before and up to 23 months after the intervention. Commitment questions were included in self-report forms.ResultsIn the first month after the intervention, the rate of claims increased 6.3-fold (95% CI: 5.2 to 7.6, p<0.001) to 10 emollient dispensings per 1000 patients in the first month after the intervention. Overall, the intervention resulted in 10 905 additional patient-months of treatment. The increased rate of dispensing among patients who committed to talking to their GP about using an emollient was six times higher (rate ratio: 6.2, 95% CI: 4.4 to 8.7) than comparison groups.ConclusionsThe intervention had a sustained effect over 23 months. Veterans who responded positively to commitment questions had higher uptake of emollients than those who did not.

scholarly journals Shooting from the ‘Scrip: Scope of Practice Laws and Access to Immunizations in the Pharmacy Setting

Vaccines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 444
Author(s):  
Charles Stoecker
Keyword(s):  
Positive Impact ◽  
National Level ◽  
State Level ◽  
The United States ◽  
Scope Of Practice ◽  
Patient Specific ◽  
Behavioral Risk ◽  
Influenza Immunization ◽  
Immunization Rates ◽  
The Impact

In the past two decades, most states in the United States have added authorization for pharmacists to administer some vaccinations. Expansions of this authority have also come with prescription requirements or other regulatory burdens. The objective of this study was to evaluate the impact of these expansions on influenza immunization rates in adults age 65 and over. A panel data, differences-in-differences regression framework to control for state-level unobserved confounders and shocks at the national level was used on a combination of a dataset of state-level statute and regulatory changes and influenza immunization data from the Behavioral Risk Factor Surveillance System. Giving pharmacists permission to vaccinate had a positive impact on adult influenza immunization rates of 1.4 percentage points for adults age 65 and over. This effect was diminished by the presence of laws requiring pharmacists to obtain patient-specific prescriptions. There was no evidence that allowing pharmacists to administer vaccinations led patients to have fewer annual check-ups with physicians or not have a usual source of health care. Expanding pharmacists’ scope of practice laws to include administering the influenza vaccine had a positive impact on influenza shot uptake. This may have implications for relaxing restrictions on other forms of care that could be provided by pharmacists.

scholarly journals Adversarial Data Augmentation on Breast MRI Segmentation

2021 ◽  
Vol 11 (10) ◽  
pp. 4554
Author(s):  
João F. Teixeira ◽  
Mariana Dias ◽  
Eva Batista ◽  
Joana Costa ◽  
Luís F. Teixeira ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Medical Image Analysis ◽  
Synthetic Data ◽  
Breast Mri ◽  
Semantic Segmentation ◽  
Adversarial Networks ◽  
Augmentation Strategy ◽  
Magnetic Resonance Imaging Mri ◽  
Segmentation Task ◽  
The Impact

The scarcity of balanced and annotated datasets has been a recurring problem in medical image analysis. Several researchers have tried to fill this gap employing dataset synthesis with adversarial networks (GANs). Breast magnetic resonance imaging (MRI) provides complex, texture-rich medical images, with the same annotation shortage issues, for which, to the best of our knowledge, no previous work tried synthesizing data. Within this context, our work addresses the problem of synthesizing breast MRI images from corresponding annotations and evaluate the impact of this data augmentation strategy on a semantic segmentation task. We explored variations of image-to-image translation using conditional GANs, namely fitting the generator’s architecture with residual blocks and experimenting with cycle consistency approaches. We studied the impact of these changes on visual verisimilarity and how an U-Net segmentation model is affected by the usage of synthetic data. We achieved sufficiently realistic-looking breast MRI images and maintained a stable segmentation score even when completely replacing the dataset with the synthetic set. Our results were promising, especially when concerning to Pix2PixHD and Residual CycleGAN architectures.

scholarly journals Demographics associated with US healthcare disparities are exacerbated by the telemedicine surge during the COVID-19 pandemic

2021 ◽  
pp. 1357633X2110259
Author(s):  
Kristin N Gmunder ◽  
Jose W Ruiz ◽  
Dido Franceschi ◽  
Maritza M Suarez
Keyword(s):  
Income Tax ◽  
Healthcare Disparities ◽  
Descriptive Statistics ◽  
Internal Revenue Service ◽  
Patient Specific ◽  
Postal Code ◽  
The Us ◽  
Hispanic Patients ◽  
The Impact ◽  
Racial Ethnic

Introduction As coronavirus disease 2019 (COVID-19) hit the US, there was widespread and urgent implementation of telemedicine programs nationwide without much focus on the impact on patient populations with known existing healthcare disparities. To better understand which populations cannot access telemedicine during the coronavirus disease 2019 pandemic, this study aims to demographically describe and identify the most important demographic predictors of telemedicine visit completion in an urban health system. Methods Patient de-identified demographics and telemedicine visit data ( N = 362,764) between March 1, 2020 and October 31, 2020 were combined with Internal Revenue Service 2018 individual income tax data by postal code. Descriptive statistics and mixed effects logistic regression were used to determine impactful patient predictors of telemedicine completion, while adjusting for clustering at the clinical site level. Results Many patient-specific demographics were found to be significant. Descriptive statistics showed older patients had lower rates of completion ( p < 0.001). Also, Hispanic patients had statistically significant lower rates ( p < 0.001). Overall, minorities (racial, ethnic, and language) had decreased odds ratios of successful telemedicine completion compared to the reference. Discussion While telemedicine use continues to be critical during the coronavirus disease 2019 pandemic, entire populations struggle with access—possibly widening existing disparities. These results contribute large datasets with significant findings to the limited research on telemedicine access and can help guide us in improving telemedicine disparities across our health systems and on a wider scale.

scholarly journals A flexible framework for sequential estimation of model parameters in computational hemodynamics

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Christopher J. Arthurs ◽  
Nan Xiao ◽  
Philippe Moireau ◽  
Tobias Schaeffter ◽  
C. Alberto Figueroa
Keyword(s):  
Unscented Kalman Filter ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Sequential Estimation ◽  
Wall Material ◽  
Patient Specific ◽  
Model Parameters ◽  
Computational Framework ◽  
Lumped Parameter ◽  
Estimation Of Model Parameters

AbstractA major challenge in constructing three dimensional patient specific hemodynamic models is the calibration of model parameters to match patient data on flow, pressure, wall motion, etc. acquired in the clinic. Current workflows are manual and time-consuming. This work presents a flexible computational framework for model parameter estimation in cardiovascular flows that relies on the following fundamental contributions. (i) A Reduced-Order Unscented Kalman Filter (ROUKF) model for data assimilation for wall material and simple lumped parameter network (LPN) boundary condition model parameters. (ii) A constrained least squares augmentation (ROUKF-CLS) for more complex LPNs. (iii) A “Netlist” implementation, supporting easy filtering of parameters in such complex LPNs. The ROUKF algorithm is demonstrated using non-invasive patient-specific data on anatomy, flow and pressure from a healthy volunteer. The ROUKF-CLS algorithm is demonstrated using synthetic data on a coronary LPN. The methods described in this paper have been implemented as part of the CRIMSON hemodynamics software package.

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