Dataset Information

Post-Trial Enhanced Deployment and Technical Performance with the MISTIE Procedure per Lessons Learned.

ABSTRACT:

Objective

We hypothesize that procedure deployment rates and technical performance with minimally invasive surgery and thrombolysis for intracerebral hemorrhage (ICH) evacuation (MISTIE) can be enhanced in post-trial clinical practice, per Phase III trial results and lessons learned.

Materials and methods

We identified ICH patients and those who underwent MISTIE procedure between 2017-2021 at a single site, after completed enrollments in the Phase III trial. Deployment rates, complications and technical outcomes were compared to those observed in the trial. Initial and final hematoma volume were compared between site measurements using ABC/2, MISTIE trial reading center utilizing manual segmentation, and a novel Artificial Intelligence (AI) based volume assessment.

Results

Nineteen of 286 patients were eligible for MISTIE. All 19 received the procedure (6.6% enrollment to screening rate 6.6% compared to 1.6% at our center in the trial; p=0.0018). Sixteen patients (84%) achieved evaculation target < 15 mL residual ICH or > 70% removal, compared to 59.7% in the trial surgical cohort (p=0.034). No poor catheter placement occurred and no surgical protocol deviations. Limitations of ICH volume assessments using the ABC/2 method were shown, while AI based methodology of ICH volume assessments had excellent correlation with manual segmentation by experienced reading centers.

Conclusions

Greater procedure deployment and higher technical success rates can be achieved in post-trial clinical practice than in the MISTIE III trial. AI based measurements can be deployed to enhance clinician estimated ICH volume. Clinical outcome implications of this enhanced technical performance cannot be surmised, and will need assessment in future trials.

SUBMITTER: Mansour A

PROVIDER: S-EPMC8384714 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Post-Trial Enhanced Deployment and Technical Performance with the MISTIE Procedure per Lessons Learned.

Mansour Ali A Loggini Andrea A El Ammar Faten F Alvarado-Dyer Ronald R Polster Sean S Stadnik Agnieszka A Das Paramita P Warnke Peter C PC Yamini Bakhtiar B Lazaridis Christos C Kramer Christopher C Mould W Andrew WA Hildreth Meghan M Sharrock Matthew M Hanley Daniel F DF Goldenberg Fernando D FD Awad Issam A IA

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association 20210722 9

<h4>Objective</h4>We hypothesize that procedure deployment rates and technical performance with minimally invasive surgery and thrombolysis for intracerebral hemorrhage (ICH) evacuation (MISTIE) can be enhanced in post-trial clinical practice, per Phase III trial results and lessons learned.<h4>Materials and methods</h4>We identified ICH patients and those who underwent MISTIE procedure between 2017-2021 at a single site, after completed enrollments in the Phase III trial. Deployment rates, comp ...[more]

PMID: 34303090

Similar Datasets

Project description:BackgroundDuring the COVID-19 pandemic corruption risks were amplified in health systems globally, increasing health inequities within and between countries. During the pandemic, the deployment of COVID-19 vaccines, particularly concerning their procurement and distribution, had corruption risks given the large amounts of public and private funding allocated to them, the need for speed, the involvement of a high number of stakeholders, and often insufficient oversight. To explore this issue further, we conducted a descriptive, qualitative study of corruption risks in the COVID-19 vaccine deployment process.MethodsWe conducted a descriptive, qualitative study triangulating two data sources between May and August 2022: (1) published academic and grey literature and (2) key informant interviews with representatives from organizations involved with the COVAX Facility, representatives from COVAX donor and recipient countries, and individuals with expert knowledge of the COVID-19 vaccine deployment process (e.g., consultants for international organizations involved in COVID-19 vaccine deployment, members of non-governmental organizations, etc.).ResultsWe identified 44 academic articles and policy documents and triangulated. Documentary data with 16 key informant interviews. A review of the literature identified several corruption risks in the international COVID-19 vaccine procurement and distribution process such as a lack of transparency in the vaccine procurement process; a lack of transparency in the operation of the COVAX Facility; a risk of bribery; and a risk of vaccine theft or the introduction of substandard and falsified vaccines at the point of distribution. Key informants further articulated concerns about a lack of transparency in vaccine pricing and contracts and the exclusion of civil society organizations from the vaccine deployment process. Reported anti-corruption, transparency, and accountability (ACTA) mechanisms implemented across the many levels of the vaccine procurement and distribution deployment included institutional oversight processes, blockchain-based supply-chain solutions, and civil society engagements.ConclusionPublic health emergencies require nimble and quick actions on the part of governments, international organizations and other actors Our study on the COVID-19 vaccine deployment process highlights the pressing need for more robust ACTA mechanisms to reduce corruption risks and ensure fair and equitable access to lifesaving vaccines for populations.

Project description:BackgroundBurnout is a significant public health concern affecting more than half of the healthcare workforce; however, passive screening tools to detect burnout are lacking. We investigated the ability of machine learning (ML) techniques to identify burnout using passively collected electronic health record (EHR)-based audit log data.MethodPhysician trainees participated in a longitudinal study where they completed monthly burnout surveys and provided access to their EHR-based audit logs. Using the monthly burnout scores as the target outcome, we trained ML models using combinations of features derived from audit log data-aggregate measures of clinical workload, time series-based temporal measures of EHR use, and the baseline burnout score. Five ML models were constructed to predict burnout as a continuous score: penalized linear regression, support vector machine, neural network, random forest, and gradient boosting machine.Results88 trainee physicians participated and completed 416 surveys; greater than10 million audit log actions were collected (Mean [Standard Deviation] = 25,691 [14,331] actions per month, per physician). The workload feature set predicted burnout score with a mean absolute error (MAE) of 0.602 (95% Confidence Interval (CI), 0.412-0.826), and was able to predict burnout status with an average AUROC of 0.595 (95% CI 0.355-0.808) and average accuracy 0.567 (95% CI 0.393-0.742). The temporal feature set had a similar performance, with MAE 0.596 (95% CI 0.391-0.826), and AUROC 0.581 (95% CI 0.343-0.790). The addition of the baseline burnout score to the workload features improved the model performance to a mean AUROC of 0.829 (95% CI 0.607-0.996) and mean accuracy of 0.781 (95% CI 0.587-0.936); however, this performance was not meaningfully different than using the baseline burnout score alone.ConclusionsCurrent findings illustrate the complexities of predicting burnout exclusively based on clinical work activities as captured in the EHR, highlighting its multi-factorial and individualized nature. Future prediction studies of burnout should account for individual factors (e.g., resilience, physiological measurements such as sleep) and associated system-level factors (e.g., leadership).

Project description:AimIn view of the spread of the contagious coronavirus disease (COVID-19) globally, the present review focuses on the details of past pandemic diseases, along with comparisons and lessons learned. A general awareness of COVID-19 infection is addressed, and it is compared with the Spanish flu pandemic. Based on the successes, failures and lessons learned in the past, governmental efforts must be undertaken to empower citizens by providing accurate information and implementing post-COVID-19 precautions that need to be taken now to stop the spread and recurrence of the virus locally, and to restore health and economic normalcy.MethodsA detailed literature survey of past pandemics is undertaken in order to extract the successes, failures and lessons learned from previous breakouts. The comparison of past pandemics will enable us to determine post-COVID-19 precautions that should be followed. Separate tables are prepared to highlight the lessons learned and measures to be taken. Both general precautions and preventive measures for pregnant women are compiled.ResultsThe literature shows a continuous struggle of humans with disease outbreaks, with the most adverse impact of the Spanish flu killing 20-50 million people. Precautions need to be taken including social distancing, compulsory mask-wearing, avoiding public gatherings and washing hands regularly. The lessons from earlier pandemics show that they were equally devastating, and vaccines were not available at the time of outbreaks. Vaccines developed for polio, H1N1, measles, and other viral diseases have proven to save countless lives. Living with COVID-19 and evolving the work culture of protecting oneself and protecting others also has to be adopted.ConclusionsCOVID-19 has become an everyday topic of discussion throughout the world, indicating the increasing number of COVID-19 cases, deaths and recoveries. The lessons learned from past pandemics such as social distancing, wearing masks, avoiding public gatherings and adherence to guidelines, along with personal hygiene, are the key measures that must be taken in order to live with COVID-19. Precautions for the elderly and pregnant women advised by medical authorities are to be strictly adhered to. These will help in reducing COVID-19 cases and in turn will reduce the pressure on hospitals to serve those in need. India has learned lessons from the past and the present pandemic and will move towards growth through its self-reliance.

Dataset Information

Post-Trial Enhanced Deployment and Technical Performance with the MISTIE Procedure per Lessons Learned.

Objective

Materials and methods

Results

Conclusions

Publications

Post-Trial Enhanced Deployment and Technical Performance with the MISTIE Procedure per Lessons Learned.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets