This case study describes a patient undergoing coronary artery bypass grafting (CABG) surgery who is now in the surgical intensive care unit (SICU) post-operatively on mechanical ventilation. Effective clinical decision support could help clinicians:
1. Monitor the patient's vital signs and other parameters to determine when spontaneous breathing trials should begin to assess viability for extubation.
2. Utilize guidelines and best practices for extubation readiness based on the patient's status and progress to safely discontinue mechanical ventilation as quickly as possible to reduce health risks and costs.
3. Provide alerts and recommendations during the weaning process based on continuous monitoring to prevent complications and optimize outcomes.
This document discusses knowledge engineering in oncology and developing decision support systems from patient data. It notes that current medical decisions are limited by the large volume of data and evidence. Rapid learning from patient data can help guide individualized treatment decisions. The document outlines MAASTRO's approach to knowledge engineering, which involves collecting data from multiple centers while keeping the data within each institution. Ontologies and semantic interoperability are used to integrate the data and develop prediction models using machine learning. The models are validated on independent data to evaluate their ability to classify outcomes and estimate survival probabilities. The goal is to develop validated models that can provide clinical decision support and help personalize cancer treatment.
The document discusses shared decision making (SDM) in clinical encounters at Mayo Clinic. It describes the work of the Knowledge and Evaluation Research (KER) Unit, which designs and evaluates decision aids to facilitate SDM between clinicians and patients. Decision aids provide unbiased information on healthcare options and help patients consider what matters most to them. Studies show decision aids improve patient knowledge and involvement without increasing consultation time. The KER Unit has created over 20 decision aids covering various medical topics. Their goal is to create meaningful conversations centered around patient needs and values to improve healthcare outcomes and experience.
Theera-Ampornpunt N. Information and technology: emergency medical informatics. Presented at: The International Conference in Emergency Medicine: Challenges in Emergency Medicine: It’s Time for Change!; 2012 Feb 1; Bangkok, Thailand. Invited speaker.
Fundamentals of Medical Device ConnectivityNuvon, Inc.
I this three part series, John R. Zaleski, PhD, CPHIMS, Vice President of Clinical Applications & CTO of Nuvon, Inc. discusses 3 areas of Medical Device Connectivity beginning with the Fundamentals, to Clinical Decision Support, and next generation Mobile Device Connectivity.
This document summarizes key concepts around using clinical data and informatics tools to improve healthcare services. It discusses how data from multiple sources can be linked and analyzed to provide intelligence to decision-makers. However, issues around data protection, privacy and ensuring data is used appropriately must be addressed. Effective presentation of data is important so clinicians view it as valid and are motivated to change practices. Feedback of performance data can improve quality when done constructively and by considering the local context.
This document summarizes key concepts around using clinical data and informatics tools to improve healthcare services. It discusses how data from multiple sources can be linked and analyzed to provide intelligence to decision-makers. However, issues around data protection, privacy and ensuring data is used appropriately must be addressed. Effective presentation of data is important so clinicians view it as valid and are motivated to change practices. Feedback of performance data can improve quality when done constructively and by considering the local context.
How to extract quantitative data for systematic review and meta analysis - Pu...Pubrica
Process involved in the data extraction for Meta-Analysis and Systematic Review:
How to extract the data for the management of Superior Mesenteric Artery Syndrome (SMAS)?
Risk of bias (quality) assessment)
Example of a PRISMA diagram used for data extraction
Continue Reading: https://bit.ly/3nLlKjD
For our services: https://pubrica.com/services/research-services/meta-analysis/
Why Pubrica:
When you order our services, We promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Biostatistical experts | High-quality Subject Matter Experts.
Contact us:
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44- 74248 10299
Theera-Ampornpunt N. The intersection of ICT and health informatics research. Presented at: Faculty of ICT, Mahidol University; 2012 Feb 24; Bangkok, Thailand.
This document discusses knowledge engineering in oncology and developing decision support systems from patient data. It notes that current medical decisions are limited by the large volume of data and evidence. Rapid learning from patient data can help guide individualized treatment decisions. The document outlines MAASTRO's approach to knowledge engineering, which involves collecting data from multiple centers while keeping the data within each institution. Ontologies and semantic interoperability are used to integrate the data and develop prediction models using machine learning. The models are validated on independent data to evaluate their ability to classify outcomes and estimate survival probabilities. The goal is to develop validated models that can provide clinical decision support and help personalize cancer treatment.
The document discusses shared decision making (SDM) in clinical encounters at Mayo Clinic. It describes the work of the Knowledge and Evaluation Research (KER) Unit, which designs and evaluates decision aids to facilitate SDM between clinicians and patients. Decision aids provide unbiased information on healthcare options and help patients consider what matters most to them. Studies show decision aids improve patient knowledge and involvement without increasing consultation time. The KER Unit has created over 20 decision aids covering various medical topics. Their goal is to create meaningful conversations centered around patient needs and values to improve healthcare outcomes and experience.
Theera-Ampornpunt N. Information and technology: emergency medical informatics. Presented at: The International Conference in Emergency Medicine: Challenges in Emergency Medicine: It’s Time for Change!; 2012 Feb 1; Bangkok, Thailand. Invited speaker.
Fundamentals of Medical Device ConnectivityNuvon, Inc.
I this three part series, John R. Zaleski, PhD, CPHIMS, Vice President of Clinical Applications & CTO of Nuvon, Inc. discusses 3 areas of Medical Device Connectivity beginning with the Fundamentals, to Clinical Decision Support, and next generation Mobile Device Connectivity.
This document summarizes key concepts around using clinical data and informatics tools to improve healthcare services. It discusses how data from multiple sources can be linked and analyzed to provide intelligence to decision-makers. However, issues around data protection, privacy and ensuring data is used appropriately must be addressed. Effective presentation of data is important so clinicians view it as valid and are motivated to change practices. Feedback of performance data can improve quality when done constructively and by considering the local context.
This document summarizes key concepts around using clinical data and informatics tools to improve healthcare services. It discusses how data from multiple sources can be linked and analyzed to provide intelligence to decision-makers. However, issues around data protection, privacy and ensuring data is used appropriately must be addressed. Effective presentation of data is important so clinicians view it as valid and are motivated to change practices. Feedback of performance data can improve quality when done constructively and by considering the local context.
How to extract quantitative data for systematic review and meta analysis - Pu...Pubrica
Process involved in the data extraction for Meta-Analysis and Systematic Review:
How to extract the data for the management of Superior Mesenteric Artery Syndrome (SMAS)?
Risk of bias (quality) assessment)
Example of a PRISMA diagram used for data extraction
Continue Reading: https://bit.ly/3nLlKjD
For our services: https://pubrica.com/services/research-services/meta-analysis/
Why Pubrica:
When you order our services, We promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Biostatistical experts | High-quality Subject Matter Experts.
Contact us:
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44- 74248 10299
Theera-Ampornpunt N. The intersection of ICT and health informatics research. Presented at: Faculty of ICT, Mahidol University; 2012 Feb 24; Bangkok, Thailand.
The document discusses integrating genomics data and evidence-based medicine into electronic health records (EHRs) for precision healthcare. It notes the gap between what is known and what is done in healthcare. Integrating genomics could help do the right thing for each patient through pharmacogenomics. However, challenges include representing huge volumes of molecular data in a usable way in EHRs. A three step approach is proposed: 1) get genomic data into EHRs in a structured format, 2) use that data for clinical decision support, 3) evaluate outcomes and continually improve the system.
Just-in-time Decision-Support for Improving and Optimising Professional Practices. Geissbühler A. eHealth week 2010 (Barcelona: CCIB Convention Centre; 2010)
Michael P. O'Brien has over 12 years of experience as a biostatistician and SAS programmer in the pharmaceutical industry. He holds an MS in Statistics from Rutgers University and is SAS certified. The document outlines his skills, experience analyzing clinical trials and observational data, education and training in biostatistics and SAS programming, and honors received.
BioVariance provides data analysis and visualization services for drug development using its scientific context platform. The platform combines data analyses with high-quality visualization capabilities, which form the two pillars of BioVariance's offering. BioVariance turns complex data into usable insights for clients to efficiently discuss key results within their project teams.
The document provides guidance on conducting searches for evidence-based medicine. It outlines the 5 steps as asking questions, acquiring evidence by searching databases, appraising the evidence, applying the evidence, and assessing performance. It discusses selecting appropriate databases, using keywords and indexing terms, applying search hedges like Boolean operators, and using filters to refine results. The goal is to efficiently search for the current best evidence to answer clinical questions.
This document discusses a new vision for open access innovation in personalized medicine through community-based approaches. The key points made are:
1. Transitioning to this new vision will be challenging but inevitable.
2. Without engaging citizens, this vision will be too expensive to achieve.
3. Sharing of data and models between researchers, particularly between universities, will need to fundamentally change.
The Uneven Future of Evidence-Based MedicineIda Sim
An Apple ResearchKit study enrolled 22,000 people in five days. A
study claims that Twitter can be used to identify depressed patients. A computer program crunches genomic data, the published literature, and electronic health record data to guide cancer treatment. The pace, the data sources, and the methods for generating medical evidence are changing radically. What will — what should — evidence-based medicine look like in a faster, personalized, data-dense tomorrow?
- Presented as the 3rd Annual Cochrane Lecture, October 2015 in Vienna, Austria.
Dr. Chute will overview progress around data normalization and high throughput clinical phenotyping (recognizing groups of patients for quality, practice, or research use-cases from electronic medical records (EMRs). These techniques were demonstrated to generate comparable and consistent information from multiple academic medical centers with heterogeneous EMR systems and record structures in the NHGRI funded eMERGE consortium (gwas.net). Tools and techniques for data normalization and phenotyping have been generalized and partially commoditized as open-source archetypes software in the ongoing SHARPn (SHARPn.org).
Presentation given at Health Informatics and Knowledge Management conference
(http://publichealth.curtin.edu.au/HIKM/), as part of Australasian Computer Science Week 2012.
http://www.cs.rmit.edu.au/acsw2012/
Research Frontier: Cognitive Performance GenomicsMelanie Swan
Research Frontier: Cognitive Performance Genomics
New category in personal genomics research
Working with the brain: virtually all cognitive performance and mental health issues are a question of awareness of state or behavior
Characterizing clinical questions of occupational therapists, physical therap...Lorie Kloda
This study explored the clinical questions of occupational therapists, physical therapists, and speech-language pathologists. 129 clinical questions were collected from 15 rehabilitation therapists. The questions focused on treatment selection (33%), clinical manifestations of disease (17%), and prognosis (13%). Questions had on average 1-2 structural elements including problem (69%), intervention (41%), and population (39%). The findings suggest evidence-based practice frameworks are inadequate for rehabilitation as questions had 12 possible foci and 8 possible structural elements. More research is needed to understand how to best support information needs in rehabilitation contexts.
Do you have responses to open-ended questions or want to use qualitative data to evaluate CE/QI interventions? Qualitative Analysis Boot Camp at the ACEHP 2013 meeting in San Francisco on 1 February has tools to get you started.
This document provides an overview of a qualitative analysis boot camp session covering topics such as qualitative research introduction, data collection, coding and analysis, reporting, and resources. The session includes a coding practice exercise and time for questions. Presenters will discuss qualitative vs quantitative research, applications in health education and promotion, sample methodologies like interviews and focus groups, online data collection methods, grounded theory, coding with software assistance, visualizing data, and reporting trends and themes from qualitative analysis.
Clinical questions asked and pursued by rehabilitation therapists: An explora...Lorie Kloda
This document summarizes the key points from an oral defense of a PhD candidate's dissertation exploring the clinical questions asked by rehabilitation therapists. The summary includes:
1. The study aimed to explore the types and formulation of clinical questions rehabilitation therapists ask in their practice, and how they choose which questions to pursue.
2. Key findings were that therapists' questions focused on 12 areas, most commonly treatment selection and clinical manifestations of disease. Questions were commonly formulated with elements like problem, intervention, and population.
3. Therapists chose to pursue questions due to factors like memory, belief an answer existed, intended use of the answer, feeling responsible, effort required, self-efficacy, and perceived organizational support.
Nexus of Biology and Computing - a look at how biologically-inspired models are supplementing traditional linear computational methodologies
Audio: http://feeds.feedburner.com/BroaderPerspectivePodcast
The Learning Health System: Thinking and Acting Across ScalesPhilip Payne
A Learning Health System (LHS) can be defined as an environment in which knowledge generation processes are embedded into daily clinical practice in order to continually improve the quality, safety, and outcomes of healthcare delivery. While still largely an aspirational goal, the promise of the LHS is a future in which every patient encounter is an opportunity to learn and improve that patient’s care, as well as the care their family and broader community receives. The foundation for building such an LHS can and should be the Electronic Health Record (EHR), which provides the basis for the comprehensive instrumentation and measurement of clinical phenotypes, as well as a means of delivering new evidence at the patient- and population levels. In this presentation, we will explore the ways in which such EHR-derived phenotypes can be combined with complementary data across a spectrum from biomolecules to population level trends, to both generate insights and deliver such knowledge in the right time, place, and format, ultimately improving clinical outcomes and value.
Evidence based medicine involves integrating clinical expertise with the best available research evidence and patient values. It aims to apply the most appropriate interventions for individual patients based on scientific evidence. The key steps involve formulating an answerable clinical question using the PICO framework, searching for and critically appraising the relevant evidence, and applying the findings to clinical practice. While evidence based medicine improves clinical decision making, it also faces criticisms such as being time-consuming and potentially reducing clinical reasoning.
John Ioannidis slides IOM workshop on Sharing Clinical Research Data, October...Marilyn Mann
This document discusses experiences and techniques for clinical research data sharing. It outlines some uses of shared individual participant data, including meta-analyses, exploratory analyses, predictive modeling, and reproducibility checks. Some basic principles discussed are that it is more difficult to access data after the fact, and it is better to arrange for full individual-level data sharing upfront. Additional problems that can arise include politics clashes and selective reporting biases if not all individual-level data is shared. The document advocates that agreeing to share individual-level data before it is generated can eliminate many of these issues.
This abstract discusses a risk-based approach to clinical data management. It identifies key risks like poor study design, lack of priority identification, and lack of understanding study goals. It proposes assessing these risks through factors like likelihood and impact. Risks can then be mitigated through proportional monitoring and documentation. The abstract recommends identifying high risks and implementing actions to address them, as well as utilizing powerful clinical data management systems to help manage risks.
- Video recording of this lecture in English language: https://youtu.be/Pt1nA32sdHQ
- Video recording of this lecture in Arabic language: https://youtu.be/uFdc9F0rlP0
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Pictorial and detailed description of patellar instability with sign and symptoms and how to diagnose , what investigations you should go with and how to approach with treatment options . I have presented this slide in my 2nd year junior residency in orthopedics at LLRM medical college Meerut and got good reviews for it
After getting it read you will definitely understand the topic.
The document discusses integrating genomics data and evidence-based medicine into electronic health records (EHRs) for precision healthcare. It notes the gap between what is known and what is done in healthcare. Integrating genomics could help do the right thing for each patient through pharmacogenomics. However, challenges include representing huge volumes of molecular data in a usable way in EHRs. A three step approach is proposed: 1) get genomic data into EHRs in a structured format, 2) use that data for clinical decision support, 3) evaluate outcomes and continually improve the system.
Just-in-time Decision-Support for Improving and Optimising Professional Practices. Geissbühler A. eHealth week 2010 (Barcelona: CCIB Convention Centre; 2010)
Michael P. O'Brien has over 12 years of experience as a biostatistician and SAS programmer in the pharmaceutical industry. He holds an MS in Statistics from Rutgers University and is SAS certified. The document outlines his skills, experience analyzing clinical trials and observational data, education and training in biostatistics and SAS programming, and honors received.
BioVariance provides data analysis and visualization services for drug development using its scientific context platform. The platform combines data analyses with high-quality visualization capabilities, which form the two pillars of BioVariance's offering. BioVariance turns complex data into usable insights for clients to efficiently discuss key results within their project teams.
The document provides guidance on conducting searches for evidence-based medicine. It outlines the 5 steps as asking questions, acquiring evidence by searching databases, appraising the evidence, applying the evidence, and assessing performance. It discusses selecting appropriate databases, using keywords and indexing terms, applying search hedges like Boolean operators, and using filters to refine results. The goal is to efficiently search for the current best evidence to answer clinical questions.
This document discusses a new vision for open access innovation in personalized medicine through community-based approaches. The key points made are:
1. Transitioning to this new vision will be challenging but inevitable.
2. Without engaging citizens, this vision will be too expensive to achieve.
3. Sharing of data and models between researchers, particularly between universities, will need to fundamentally change.
The Uneven Future of Evidence-Based MedicineIda Sim
An Apple ResearchKit study enrolled 22,000 people in five days. A
study claims that Twitter can be used to identify depressed patients. A computer program crunches genomic data, the published literature, and electronic health record data to guide cancer treatment. The pace, the data sources, and the methods for generating medical evidence are changing radically. What will — what should — evidence-based medicine look like in a faster, personalized, data-dense tomorrow?
- Presented as the 3rd Annual Cochrane Lecture, October 2015 in Vienna, Austria.
Dr. Chute will overview progress around data normalization and high throughput clinical phenotyping (recognizing groups of patients for quality, practice, or research use-cases from electronic medical records (EMRs). These techniques were demonstrated to generate comparable and consistent information from multiple academic medical centers with heterogeneous EMR systems and record structures in the NHGRI funded eMERGE consortium (gwas.net). Tools and techniques for data normalization and phenotyping have been generalized and partially commoditized as open-source archetypes software in the ongoing SHARPn (SHARPn.org).
Presentation given at Health Informatics and Knowledge Management conference
(http://publichealth.curtin.edu.au/HIKM/), as part of Australasian Computer Science Week 2012.
http://www.cs.rmit.edu.au/acsw2012/
Research Frontier: Cognitive Performance GenomicsMelanie Swan
Research Frontier: Cognitive Performance Genomics
New category in personal genomics research
Working with the brain: virtually all cognitive performance and mental health issues are a question of awareness of state or behavior
Characterizing clinical questions of occupational therapists, physical therap...Lorie Kloda
This study explored the clinical questions of occupational therapists, physical therapists, and speech-language pathologists. 129 clinical questions were collected from 15 rehabilitation therapists. The questions focused on treatment selection (33%), clinical manifestations of disease (17%), and prognosis (13%). Questions had on average 1-2 structural elements including problem (69%), intervention (41%), and population (39%). The findings suggest evidence-based practice frameworks are inadequate for rehabilitation as questions had 12 possible foci and 8 possible structural elements. More research is needed to understand how to best support information needs in rehabilitation contexts.
Do you have responses to open-ended questions or want to use qualitative data to evaluate CE/QI interventions? Qualitative Analysis Boot Camp at the ACEHP 2013 meeting in San Francisco on 1 February has tools to get you started.
This document provides an overview of a qualitative analysis boot camp session covering topics such as qualitative research introduction, data collection, coding and analysis, reporting, and resources. The session includes a coding practice exercise and time for questions. Presenters will discuss qualitative vs quantitative research, applications in health education and promotion, sample methodologies like interviews and focus groups, online data collection methods, grounded theory, coding with software assistance, visualizing data, and reporting trends and themes from qualitative analysis.
Clinical questions asked and pursued by rehabilitation therapists: An explora...Lorie Kloda
This document summarizes the key points from an oral defense of a PhD candidate's dissertation exploring the clinical questions asked by rehabilitation therapists. The summary includes:
1. The study aimed to explore the types and formulation of clinical questions rehabilitation therapists ask in their practice, and how they choose which questions to pursue.
2. Key findings were that therapists' questions focused on 12 areas, most commonly treatment selection and clinical manifestations of disease. Questions were commonly formulated with elements like problem, intervention, and population.
3. Therapists chose to pursue questions due to factors like memory, belief an answer existed, intended use of the answer, feeling responsible, effort required, self-efficacy, and perceived organizational support.
Nexus of Biology and Computing - a look at how biologically-inspired models are supplementing traditional linear computational methodologies
Audio: http://feeds.feedburner.com/BroaderPerspectivePodcast
The Learning Health System: Thinking and Acting Across ScalesPhilip Payne
A Learning Health System (LHS) can be defined as an environment in which knowledge generation processes are embedded into daily clinical practice in order to continually improve the quality, safety, and outcomes of healthcare delivery. While still largely an aspirational goal, the promise of the LHS is a future in which every patient encounter is an opportunity to learn and improve that patient’s care, as well as the care their family and broader community receives. The foundation for building such an LHS can and should be the Electronic Health Record (EHR), which provides the basis for the comprehensive instrumentation and measurement of clinical phenotypes, as well as a means of delivering new evidence at the patient- and population levels. In this presentation, we will explore the ways in which such EHR-derived phenotypes can be combined with complementary data across a spectrum from biomolecules to population level trends, to both generate insights and deliver such knowledge in the right time, place, and format, ultimately improving clinical outcomes and value.
Evidence based medicine involves integrating clinical expertise with the best available research evidence and patient values. It aims to apply the most appropriate interventions for individual patients based on scientific evidence. The key steps involve formulating an answerable clinical question using the PICO framework, searching for and critically appraising the relevant evidence, and applying the findings to clinical practice. While evidence based medicine improves clinical decision making, it also faces criticisms such as being time-consuming and potentially reducing clinical reasoning.
John Ioannidis slides IOM workshop on Sharing Clinical Research Data, October...Marilyn Mann
This document discusses experiences and techniques for clinical research data sharing. It outlines some uses of shared individual participant data, including meta-analyses, exploratory analyses, predictive modeling, and reproducibility checks. Some basic principles discussed are that it is more difficult to access data after the fact, and it is better to arrange for full individual-level data sharing upfront. Additional problems that can arise include politics clashes and selective reporting biases if not all individual-level data is shared. The document advocates that agreeing to share individual-level data before it is generated can eliminate many of these issues.
This abstract discusses a risk-based approach to clinical data management. It identifies key risks like poor study design, lack of priority identification, and lack of understanding study goals. It proposes assessing these risks through factors like likelihood and impact. Risks can then be mitigated through proportional monitoring and documentation. The abstract recommends identifying high risks and implementing actions to address them, as well as utilizing powerful clinical data management systems to help manage risks.
- Video recording of this lecture in English language: https://youtu.be/Pt1nA32sdHQ
- Video recording of this lecture in Arabic language: https://youtu.be/uFdc9F0rlP0
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Pictorial and detailed description of patellar instability with sign and symptoms and how to diagnose , what investigations you should go with and how to approach with treatment options . I have presented this slide in my 2nd year junior residency in orthopedics at LLRM medical college Meerut and got good reviews for it
After getting it read you will definitely understand the topic.
“Psychiatry and the Humanities”: An Innovative Course at the University of Mo...Université de Montréal
“Psychiatry and the Humanities”: An Innovative Course at the University of Montreal Expanding the medical model to embrace the humanities. Link: https://www.psychiatrictimes.com/view/-psychiatry-and-the-humanities-an-innovative-course-at-the-university-of-montreal
Giloy in Ayurveda - Classical Categorization and SynonymsPlanet Ayurveda
Giloy, also known as Guduchi or Amrita in classical Ayurvedic texts, is a revered herb renowned for its myriad health benefits. It is categorized as a Rasayana, meaning it has rejuvenating properties that enhance vitality and longevity. Giloy is celebrated for its ability to boost the immune system, detoxify the body, and promote overall wellness. Its anti-inflammatory, antipyretic, and antioxidant properties make it a staple in managing conditions like fever, diabetes, and stress. The versatility and efficacy of Giloy in supporting health naturally highlight its importance in Ayurveda. At Planet Ayurveda, we provide a comprehensive range of health services and 100% herbal supplements that harness the power of natural ingredients like Giloy. Our products are globally available and affordable, ensuring that everyone can benefit from the ancient wisdom of Ayurveda. If you or your loved ones are dealing with health issues, contact Planet Ayurveda at 01725214040 to book an online video consultation with our professional doctors. Let us help you achieve optimal health and wellness naturally.
PGx Analysis in VarSeq: A User’s PerspectiveGolden Helix
Since our release of the PGx capabilities in VarSeq, we’ve had a few months to gather some insights from various use cases. Some users approach PGx workflows by means of array genotyping or what seems to be a growing trend of adding the star allele calling to the existing NGS pipeline for whole genome data. Luckily, both approaches are supported with the VarSeq software platform. The genotyping method being used will also dictate what the scope of the tertiary analysis will be. For example, are your PGx reports a standalone pipeline or would your lab’s goal be to handle a dual-purpose workflow and report on PGx + Diagnostic findings.
The purpose of this webcast is to:
Discuss and demonstrate the approaches with array and NGS genotyping methods for star allele calling to prep for downstream analysis.
Following genotyping, explore alternative tertiary workflow concepts in VarSeq to handle PGx reporting.
Moreover, we will include insights users will need to consider when validating their PGx workflow for all possible star alleles and options you have for automating your PGx analysis for large number of samples. Please join us for a session dedicated to the application of star allele genotyping and subsequent PGx workflows in our VarSeq software.
Breast cancer: Post menopausal endocrine therapyDr. Sumit KUMAR
Breast cancer in postmenopausal women with hormone receptor-positive (HR+) status is a common and complex condition that necessitates a multifaceted approach to management. HR+ breast cancer means that the cancer cells grow in response to hormones such as estrogen and progesterone. This subtype is prevalent among postmenopausal women and typically exhibits a more indolent course compared to other forms of breast cancer, which allows for a variety of treatment options.
Diagnosis and Staging
The diagnosis of HR+ breast cancer begins with clinical evaluation, imaging, and biopsy. Imaging modalities such as mammography, ultrasound, and MRI help in assessing the extent of the disease. Histopathological examination and immunohistochemical staining of the biopsy sample confirm the diagnosis and hormone receptor status by identifying the presence of estrogen receptors (ER) and progesterone receptors (PR) on the tumor cells.
Staging involves determining the size of the tumor (T), the involvement of regional lymph nodes (N), and the presence of distant metastasis (M). The American Joint Committee on Cancer (AJCC) staging system is commonly used. Accurate staging is critical as it guides treatment decisions.
Treatment Options
Endocrine Therapy
Endocrine therapy is the cornerstone of treatment for HR+ breast cancer in postmenopausal women. The primary goal is to reduce the levels of estrogen or block its effects on cancer cells. Commonly used agents include:
Selective Estrogen Receptor Modulators (SERMs): Tamoxifen is a SERM that binds to estrogen receptors, blocking estrogen from stimulating breast cancer cells. It is effective but may have side effects such as increased risk of endometrial cancer and thromboembolic events.
Aromatase Inhibitors (AIs): These drugs, including anastrozole, letrozole, and exemestane, lower estrogen levels by inhibiting the aromatase enzyme, which converts androgens to estrogen in peripheral tissues. AIs are generally preferred in postmenopausal women due to their efficacy and safety profile compared to tamoxifen.
Selective Estrogen Receptor Downregulators (SERDs): Fulvestrant is a SERD that degrades estrogen receptors and is used in cases where resistance to other endocrine therapies develops.
Combination Therapies
Combining endocrine therapy with other treatments enhances efficacy. Examples include:
Endocrine Therapy with CDK4/6 Inhibitors: Palbociclib, ribociclib, and abemaciclib are CDK4/6 inhibitors that, when combined with endocrine therapy, significantly improve progression-free survival in advanced HR+ breast cancer.
Endocrine Therapy with mTOR Inhibitors: Everolimus, an mTOR inhibitor, can be added to endocrine therapy for patients who have developed resistance to aromatase inhibitors.
Chemotherapy
Chemotherapy is generally reserved for patients with high-risk features, such as large tumor size, high-grade histology, or extensive lymph node involvement. Regimens often include anthracyclines and taxanes.
Osvaldo Bernardo Muchanga-GASTROINTESTINAL INFECTIONS AND GASTRITIS-2024.pdfOsvaldo Bernardo Muchanga
GASTROINTESTINAL INFECTIONS AND GASTRITIS
Osvaldo Bernardo Muchanga
Gastrointestinal Infections
GASTROINTESTINAL INFECTIONS result from the ingestion of pathogens that cause infections at the level of this tract, generally being transmitted by food, water and hands contaminated by microorganisms such as E. coli, Salmonella, Shigella, Vibrio cholerae, Campylobacter, Staphylococcus, Rotavirus among others that are generally contained in feces, thus configuring a FECAL-ORAL type of transmission.
Among the factors that lead to the occurrence of gastrointestinal infections are the hygienic and sanitary deficiencies that characterize our markets and other places where raw or cooked food is sold, poor environmental sanitation in communities, deficiencies in water treatment (or in the process of its plumbing), risky hygienic-sanitary habits (not washing hands after major and/or minor needs), among others.
These are generally consequences (signs and symptoms) resulting from gastrointestinal infections: diarrhea, vomiting, fever and malaise, among others.
The treatment consists of replacing lost liquids and electrolytes (drinking drinking water and other recommended liquids, including consumption of juicy fruits such as papayas, apples, pears, among others that contain water in their composition).
To prevent this, it is necessary to promote health education, improve the hygienic-sanitary conditions of markets and communities in general as a way of promoting, preserving and prolonging PUBLIC HEALTH.
Gastritis and Gastric Health
Gastric Health is one of the most relevant concerns in human health, with gastrointestinal infections being among the main illnesses that affect humans.
Among gastric problems, we have GASTRITIS AND GASTRIC ULCERS as the main public health problems. Gastritis and gastric ulcers normally result from inflammation and corrosion of the walls of the stomach (gastric mucosa) and are generally associated (caused) by the bacterium Helicobacter pylor, which, according to the literature, this bacterium settles on these walls (of the stomach) and starts to release urease that ends up altering the normal pH of the stomach (acid), which leads to inflammation and corrosion of the mucous membranes and consequent gastritis or ulcers, respectively.
In addition to bacterial infections, gastritis and gastric ulcers are associated with several factors, with emphasis on prolonged fasting, chemical substances including drugs, alcohol, foods with strong seasonings including chilli, which ends up causing inflammation of the stomach walls and/or corrosion. of the same, resulting in the appearance of wounds and consequent gastritis or ulcers, respectively.
Among patients with gastritis and/or ulcers, one of the dilemmas is associated with the foods to consume in order to minimize the sensation of pain and discomfort.
Are you looking for a long-lasting solution to your missing tooth?
Dental implants are the most common type of method for replacing the missing tooth. Unlike dentures or bridges, implants are surgically placed in the jawbone. In layman’s terms, a dental implant is similar to the natural root of the tooth. It offers a stable foundation for the artificial tooth giving it the look, feel, and function similar to the natural tooth.
Discover the benefits of homeopathic medicine for irregular periods with our guide on 5 common remedies. Learn how these natural treatments can help regulate menstrual cycles and improve overall menstrual health.
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The skin is the largest organ and its health plays a vital role among the other sense organs. The skin concerns like acne breakout, psoriasis, or anything similar along the lines, finding a qualified and experienced dermatologist becomes paramount.
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Clinical Decision Support
1. Part 2: Clinical Decision Support Systems
JOHN R. ZALESKI, PHD, CPHIMS
VICE PRESIDENT OF CLINICAL APPLICATIONS & CTO
JZALESKI@NUVON.COM
C: +1 484 319 7345
O: +1 215 966 6142
10. NEEDS IN THE CLINICAL WORKSPACES
10
Sunday, May 29, 2011
11. Key Workspaces with Unmet Needs
• OR, ICU, Med-Surg
– Staffing & Resource shortages top list of unmet needs
associated with high-acuity environments
– Others:
• Faster/More accurate diagnoses
• Faster/unimpeded access to patient information
• Improved care protocols
• Better alerting and notification of patient status
• Treatment maps and pathways
• Risk-scoring and acuity prioritization support
Clinical Decision Support (CDS):
(1) Enables early prediction and identification of ICU patients at risk,.
(2) Allows ICU clinicians to focus their attention on critical cases, preventing
complications, reducing length of stay, and improving outcomes.
11
Sunday, May 29, 2011
12. State of Acute Care
American College of Physicians estimates 500,000 deaths
annually in ICUs (U.S.)
Key Drivers
Patient safety
Longitudinal EMR deployment
Increase efficiency
Staffing shortages
Increasing numbers of CC beds
Larger amounts of hemodynamic, respiratory, I&O
information will be automated
Motivates enterprise integration
Reduces charting workload
Improves completeness, accuracy
12
Sunday, May 29, 2011
13. Surgical Intensive Care
Anesthesia
Intra-
Aortic
Balloon
Monitors
Mechanical Pumps
Ventilation
Highly Technologically-Dependent Patients
Bed
Infusion
13
Sunday, May 29, 2011
14. Types of Data Most Used in ICU
Clinical Decision Making
Data Type Value
Monitors and monitoring 13%
Observations 21%
Laboratory 33%
Drugs, I&O, IV 22%
Blood gas 9%
Other 2%
14
Sunday, May 29, 2011 Source: E.H. Shortliffe and J.J. Cimino, Biomedical Informatics Computer Applications in Health Care and Biomedicine, page 605.
15. Types of Data Most Used in ICU
Clinical Decision Making
Data Type Value
Monitors and monitoring 13%
Observations 21%
Laboratory 33%
Drugs, I&O, IV 22%
Blood gas 9%
Other 2%
15
Sunday, May 29, 2011 Source: E.H. Shortliffe and J.J. Cimino, Biomedical Informatics Computer Applications in Health Care and Biomedicine, page 605.
16. Types of Data Most Used in ICU
Clinical Decision Making
Data Type Value
Monitors and monitoring 13%
Observations 21%
Laboratory 33%
Drugs, I&O, IV 22%
Blood gas 9%
Other 2%
16
Sunday, May 29, 2011 Source: E.H. Shortliffe and J.J. Cimino, Biomedical Informatics Computer Applications in Health Care and Biomedicine, page 605.
17. Types of Data Most Used in ICU
Clinical Decision Making
Data Type Value
Monitors and monitoring 13%
Observations 21%
Laboratory 33%
Drugs, I&O, IV 22%
Blood gas 9%
Other 2%
17
Sunday, May 29, 2011 Source: E.H. Shortliffe and J.J. Cimino, Biomedical Informatics Computer Applications in Health Care and Biomedicine, page 605.
18. Types of Data Most Used in ICU
Clinical Decision Making
Data Type Value
Monitors and monitoring 13%
Observations 21%
Laboratory 33%
Drugs, I&O, IV 22%
Blood gas 9%
Other 2%
18
Sunday, May 29, 2011 Source: E.H. Shortliffe and J.J. Cimino, Biomedical Informatics Computer Applications in Health Care and Biomedicine, page 605.
19. CASE STUDY 1
Needs in the Clinical Workspaces
Case Study 2
Mobile Device Connectivity Benefits
Supporting Technologies
Summary
19
Sunday, May 29, 2011
20. CDSS Sample Case:
When to discontinue post-operative mechanical ventilation
• Discontinuation from mechanical ventilation a key activity in
surgical intensive care unit (SICU), yet, no guarantees as to
outcomes:
– When to begin spontaneous breathing trials?
– When is patient viable to be extubated?
• Discontinue as quickly as possible
– Longer time on ventilator higher likelihood of adverse events
• Ventilator acquired pneumonia
• Respiratory distress
– Can exacerbate co-morbidities
– Cost
• Candidate patients: Coronary artery bypass grafting (CABG)
– Fairly common procedure
– Technologically-dependent patients
20
Sunday, May 29, 2011
21. Source: J. Zaleski
Case Study: CABG Patient
Restart Determine
Patient
On Heart / Transfer Monitoring & Viability
Arrives in Induction Extubate
Bypass Off to SICU Management for
OR
Bypass Weaning
21
Sunday, May 29, 2011
22. Source: J. Zaleski
Case Study: CABG Patient
Restart Determine
Patient
On Heart / Transfer Monitoring & Viability
Arrives in Induction Extubate
Bypass Off to SICU Management for
OR
Bypass Weaning
Time In: 7:15 Induction: Isoflurane Pt Ht: 157 cm
CABG x 3 40 CCs fentanyl (15 g/kg) BSA: 1.7 m^2
15 mg
Pancuronium
pancuronium
Time HR (bpm) ABP (s/d) O2Sat CO (L/m) T Core T blad ETCO2 RR Vt fentanyl g lopressor Notes
mg
7:15 76 121/64 98 7 0.5
7:30
7:40
83
57
117/66
93/52
99
100
4.3
Meds & Drips
7:45 66 100/55 100 300 7
8:00 61 95/57 100 Swan in place
8:05 62 101/60 100 34.3
8:10
8:25
64
86
Continuous
97/58
132/78
100
100
34.4
34.3
34.9
34.7 29
8:30 116 Monitoring
116/76 99 34.3 35.2 27
8:35 98 116/75 99 34.2 35 29
8:40 92 112/74 100 34.1 34.9 29
8:45 100 113/70 99 34.1 34.8 29
8:50 96 112/71 99 34 34.7 29
9:00 91 97/62 99 34 34.7 31
9:05 97 109/70 100 33.9 34.5 30
9:20 93 114/68 100 33.8 34.4 31
22
Sunday, May 29, 2011
9:30 103 95/61 100 33.7 34.2 32
23. Source: J. Zaleski
Case Study: CABG Patient
Restart Determine
Patient
On Heart / Transfer Monitoring & Viability
Arrives in Induction Extubate
Bypass Off to SICU Management for
OR
Bypass Weaning
pancuronium
Time HR (bpm) ABP (s/d) O2Sat CO (L/m) T Core T blad ETCO2 RR Vt fentanyl g lopressor Notes
mg
Canula placed-
9:35 94 93/60 100 33.6 34.2 30 rt. atria;
bypassing heart
9:40 94 103/65 100 33.6 34.1 36
Core temperature reduction
9:45 94 112/67 100 33.6 34.1 36 3 mg (up)
9:50 94 113/68 100 33.6 34 33
9:55 95 103/69 100 33.6 33.9 29
Fibrillation.
10:00 99 101/68 100 33.6 33.9 28 12 0.48
Cross-Clamp
K injection
10:07
Heart stoppage 20.8
commenced
10:08 16
10:09 12
K injection
10:11 10
complete
10:15 33 32.5
10:20 32.8 32.7
Myocard temp:
10:30 32.9 33
14
10:35 33.1 33
10:45 33 33
23
Sunday, May 29, 2011
10:50 33.3 33.4 Begin re-warm
26. 10
15
20
25
30
35
40
45
0
5
OR
12:44:18
Patient
Arrives in
12:57:33
13:35:52
13:47:42
13:59:32
Sunday, May 29, 2011
14:11:23
RRsp
(/min)
14:23:13
Induction
14:35:03
RRm (/min)
14:46:53
14:58:43
15:10:34
15:22:24
15:34:15
On
15:46:05
Bypass
15:57:55
16:09:45
• pH = 7.44
16:21:35
16:33:25
16:45:16 • Time: 12:45
16:57:07
Off
17:08:57
Bypass
Heart /
Restart
• PO2 = 100 mmHg
• PCO2 = 31 mmHg
17:20:47
17:32:37
17:44:27
17:56:17
18:08:07
18:19:57
18:31:48
to SICU
18:43:38
Transfer
18:55:28
19:07:18
19:19:08
19:30:59
19:42:49
19:54:39
20:06:29
• Initial blood gas obtained upon patient arrival
20:18:20
20:30:11
Monitoring &
Management
20:42:01
20:53:51
21:05:41
21:17:31
21:29:22
21:41:12
for
21:53:02
Viability
Weaning
Determine
Case Study: CABG Patient
Extubate
26
Source: J. Zaleski
27. Source: J. Zaleski
Case Study: CABG Patient
Restart Determine
Patient
On Heart / Transfer Monitoring & Viability
Arrives in Induction Extubate
Bypass Off to SICU Management for
OR
Bypass Weaning
• Patient initially supported by
45 mechanical ventilator on synchronous
40
RRm (/min)
intermittent mandatory ventilation
35 RRsp
(/min)
(SIMV) mode of 12 breaths per
30 minute, tidal volume of 0.85
25 liters, PEEP of 5 cmH2O
20
• Patient spontaneous breathing is absent upon
15
arrival due to the anesthesia and paralytic drugs
10 administered during surgery
5
0
12:44:18
12:57:33
13:35:52
13:47:42
13:59:32
14:11:23
14:23:13
14:35:03
14:46:53
14:58:43
15:10:34
15:22:24
15:34:15
15:46:05
15:57:55
16:09:45
16:21:35
16:33:25
16:45:16
16:57:07
17:08:57
17:20:47
17:32:37
17:44:27
17:56:17
18:08:07
18:19:57
18:31:48
18:43:38
18:55:28
19:07:18
19:19:08
19:30:59
19:42:49
19:54:39
20:06:29
20:18:20
20:30:11
20:42:01
20:53:51
21:05:41
21:17:31
21:29:22
21:41:12
21:53:02
27
Sunday, May 29, 2011
28. Source: J. Zaleski
Case Study: CABG Patient
Restart Determine
Patient
On Heart / Transfer Monitoring & Viability
Arrives in Induction Extubate
Bypass Off to SICU Management for
OR
Bypass Weaning
45
RRm (/min)
• Second blood gas obtained
40
RRsp
• Time: 14:00
35
(/min) • pH = 7.41
30
• PCO2 = 29 mmHg
25 • PO2 = 202 mmHg
20
15
• Decision made to reduce ventilatory support
10
5
0
12:44:18
12:57:33
13:35:52
13:47:42
13:59:32
14:11:23
14:23:13
14:35:03
14:46:53
14:58:43
15:10:34
15:22:24
15:34:15
15:46:05
15:57:55
16:09:45
16:21:35
16:33:25
16:45:16
16:57:07
17:08:57
17:20:47
17:32:37
17:44:27
17:56:17
18:08:07
18:19:57
18:31:48
18:43:38
18:55:28
19:07:18
19:19:08
19:30:59
19:42:49
19:54:39
20:06:29
20:18:20
20:30:11
20:42:01
20:53:51
21:05:41
21:17:31
21:29:22
21:41:12
21:53:02
28
Sunday, May 29, 2011
29. Source: J. Zaleski
Case Study: CABG Patient
Restart Determine
Patient
On Heart / Transfer Monitoring & Viability
Arrives in Induction Extubate
Bypass Off to SICU Management for
OR
Bypass Weaning
45
• Support reduced to 8 br/min
RRm (/min)
40
35 RRsp • Some spontaneous breathing.
(/min)
30
Clinicians choose to evaluate and
25
await re-warming and third blood gas
20
before attempting spontaneous
15
breathing trial
10
5
0
12:44:18
12:57:33
13:35:52
13:47:42
13:59:32
14:11:23
14:23:13
14:35:03
14:46:53
14:58:43
15:10:34
15:22:24
15:34:15
15:46:05
15:57:55
16:09:45
16:21:35
16:33:25
16:45:16
16:57:07
17:08:57
17:20:47
17:32:37
17:44:27
17:56:17
18:08:07
18:19:57
18:31:48
18:43:38
18:55:28
19:07:18
19:19:08
19:30:59
19:42:49
19:54:39
20:06:29
20:18:20
20:30:11
20:42:01
20:53:51
21:05:41
21:17:31
21:29:22
21:41:12
21:53:02
29
Sunday, May 29, 2011
30. Source: J. Zaleski
Case Study: CABG Patient
Restart Determine
Patient
On Heart / Transfer Monitoring & Viability
Arrives in Induction Extubate
Bypass Off to SICU Management for
OR
Bypass Weaning
45 • Third blood gas obtained
40
RRm (/min)
• Time: 16:35
35 RRsp • pH = 7.40
(/min)
30 • PCO2 = 37 mmHg
25
• PO2 = 183 mmHg
20
• Re-warming complete
15
• Decision made to reduce to CPAP in
10
preparation for spontaneous breathing
5
trials
0
12:44:18
12:57:33
13:35:52
13:47:42
13:59:32
14:11:23
14:23:13
14:35:03
14:46:53
14:58:43
15:10:34
15:22:24
15:34:15
15:46:05
15:57:55
16:09:45
16:21:35
16:33:25
16:45:16
16:57:07
17:08:57
17:20:47
17:32:37
17:44:27
17:56:17
18:08:07
18:19:57
18:31:48
18:43:38
18:55:28
19:07:18
19:19:08
19:30:59
19:42:49
19:54:39
20:06:29
20:18:20
20:30:11
20:42:01
20:53:51
21:05:41
21:17:31
21:29:22
21:41:12
21:53:02
30
Sunday, May 29, 2011
31. 10
15
20
25
30
35
40
45
0
5
OR
12:44:18
Patient
Arrives in
12:57:33
13:35:52
13:47:42
13:59:32
Sunday, May 29, 2011
14:11:23
RRsp
(/min)
14:23:13
Induction
14:35:03
RRm (/min)
14:46:53
14:58:43
15:10:34
15:22:24
15:34:15
On
15:46:05
Bypass
15:57:55
16:09:45
16:21:35
16:33:25
16:45:16
• Respirations, RSBI normal
16:57:07
Off
17:08:57
Bypass
Heart /
Restart
17:20:47
17:32:37
17:44:27
17:56:17
18:08:07
18:19:57
18:31:48
to SICU
18:43:38
Transfer
18:55:28
19:07:18
19:19:08
19:30:59
19:42:49
19:54:39
20:06:29
20:18:20
20:30:11
Monitoring &
Management
20:42:01
20:53:51
21:05:41
21:17:31
21:29:22
21:41:12
for
21:53:02
Viability
Weaning
Determine
Case Study: CABG Patient
Extubate
31
Source: J. Zaleski
32. Key Parameters Used to Determine
Viability for Extubation
Parameter Threshold Value/Range Our Patient
Vital Capacity, Vc > 10mL/kg
Positive End-Expiratory 5 cm H2O
Pressure, PEEP
Negative Inspiratory Force, NIF -20 cm H2O
Inspired Oxygen Fraction, FiO2 < 0.6
Spontaneous Tidal Volume, Vt > 5 mL/kg
Parameters,
Spontaneous Respirations Value Rresp < 30
8 < Thresholds, Patient Values,
P i Vpth Vpti
Blood Alkalinity/Acidity 7.32 < pH <i 7.48
Partial Pressure of Oxygen, PO2 > 80 mmHg
Partial Pressure of Carbon Dioxide, 30 mmHg < PCO2 < 50 mmHg
PCO2
Normal Body Temperature, Tcore ~37 C
Ventilation Mode CPAP
32
Sunday, May 29, 2011
33. Key Parameters Used to Determine
Viability for Extubation
Parameter Threshold Value/Range Our Patient
Vital Capacity, Vc > 10mL/kg
Positive End-Expiratory 5 cm H2O
Pressure, PEEP
Negative Inspiratory Force, NIF -20 cm H2O
Inspired Oxygen Fraction, FiO2 < 0.6
Spontaneous Tidal Volume, Vt > 5 mL/kg
Spontaneous Respirations 8 < Rresp < 30
Blood Alkalinity/Acidity 7.32 < pH < 7.48
Partial Pressure of Oxygen, PO2 > 80 mmHg
Partial Pressure of Carbon Dioxide, 30 mmHg < PCO2 < 50 mmHg
PCO2
Normal Body Temperature, Tcore ~37 C
Ventilation Mode CPAP
33
Sunday, May 29, 2011
34. Key Parameters Used to Determine
Viability for Extubation
Parameter Threshold Value/Range Our Patient
Vital Capacity, Vc > 10mL/kg
Positive End-Expiratory 5 cm H2O
Pressure, PEEP
Negative P1
Inspiratory Force, NIF -20 cm H2O
Inspired Oxygen Fraction, FiO2 < 0.6
P2
Spontaneous Tidal Volume, Vt Parameters Used to Determine
Key > 5 mL/kg Extubation Viability
P3
Spontaneous Respirations 8 < Rresp < 30
Blood…
Alkalinity/Acidity 7.32 < pH < 7.48 CDSS
Partial Pressure of Oxygen, PO2 > 80 mmHg
Partial Pressure of Carbon Dioxide, <
Vpt1 30 mmHg < PCO2 < 50 mmHg
Vpt2 < Vpti <
PCO2 … Action
Vpth1 Vpth2 Vpthi
Normal Body Temperature, Tcore ~37 C
Ventilation Mode CPAP 34
Sunday, May 29, 2011
35. 10
15
20
25
30
35
40
45
0
5
OR
12:44:18
Patient
Arrives in
12:57:33
13:35:52
13:47:42
13:59:32
Sunday, May 29, 2011
14:11:23
RRsp
(/min)
14:23:13
Induction
14:35:03
RRm (/min)
14:46:53
14:58:43
15:10:34
15:22:24
15:34:15
On
15:46:05
Bypass
15:57:55
16:09:45
16:21:35
16:33:25
16:45:16
• Respirations, RSBI normal
16:57:07
Off
17:08:57
Bypass
Heart /
Restart
17:20:47
17:32:37
17:44:27
17:56:17
18:08:07
18:19:57
18:31:48
to SICU
18:43:38
Transfer
18:55:28
19:07:18
19:19:08
19:30:59
19:42:49
19:54:39
20:06:29
20:18:20
20:30:11
Monitoring &
Management
20:42:01
20:53:51
21:05:41
• Vc = 1.2 liters
21:17:31
21:29:22
and in normal range
21:41:12
• NIF = -24 cmH2O
for
21:53:02
Viability
Weaning
Determine
Case Study: CABG Patient
• Vital capacity & NIF test performed
Extubate
35
Source: J. Zaleski
36. 10
15
20
25
30
35
40
45
0
5
OR
12:44:18
Patient
Arrives in
12:57:33
13:35:52
13:47:42
13:59:32
Sunday, May 29, 2011
14:11:23
RRsp
(/min)
14:23:13
Induction
14:35:03
RRm (/min)
14:46:53
14:58:43
15:10:34
15:22:24
15:34:15
On
15:46:05
Bypass
15:57:55
16:09:45
16:21:35
16:33:25
16:45:16
16:57:07
Off
17:08:57
Bypass
Heart /
Restart
17:20:47
17:32:37
17:44:27
17:56:17
18:08:07
18:19:57
18:31:48
to SICU
18:43:38
Transfer
18:55:28
19:07:18
19:19:08
19:30:59
19:42:49
19:54:39
20:06:29
20:18:20
20:30:11
Monitoring &
Management
20:42:01
20:53:51
21:05:41
21:17:31
could have led to earlier extubation
21:29:22
21:41:12
for
21:53:02
Updated real-time knowledge of patient data
Viability
Weaning
Determine
Case Study: CABG Patient
Extubate
36
Source: J. Zaleski
37. Key Parameters Used to Determine
Viability for Extubation
Data suggest attempts at Threshold Value/Range trials could begin much
Parameter spontaneous breathing Our Patient
Vital Capacity, Vc sooner than 10mL/kg occurred
> actually 1.2L (70 kg)
Positive End-Expiratory 5 cm H2O 5 cm H2O
Pressure, PEEP
Negative Inspiratory Force, NIF -20 cm H2O -24 cm H2O
Inspired Oxygen Fraction, FiO2 < 0.6 0.35
Spontaneous Tidal Volume, Vt > 5 mL/kg 0.55L (70 kg)
Spontaneous Respirations 8 < Rresp < 30 ~20
Blood Alkalinity/Acidity 7.32 < pH < 7.48 7.4
Partial Pressure of Oxygen, PO2 > 80 mmHg 183 mmHg
Partial Pressure of Carbon Dioxide, 30 mmHg < PCO2 < 50 mmHg 37 mmHg
PCO2
Normal Body Temperature, Tcore ~37 C ~37 C
Ventilation Mode CPAP CPAP
37
Sunday, May 29, 2011
38. Workflow Considerations
• Data show patient meets extubation criteria many hours
before actual extubation
– Indicates clear benefit of utilizing these data for patient care
– Simple reminders to staff can achieve great benefits for patient
• Notification of readiness to wean important for clinical
workflow, patient care management
– Is patient viable or is it too early?
– Any co-morbidities that can influence the outcome?
– All necessary staff so informed and aligned on plans?
• Notification as to life-threatening events requires up-to-
date and accurate information
– Hemodynamic instabilities/Shock
– Respiratory distress
38
Sunday, May 29, 2011
39. CASE STUDY 2
Case Study 1
Mobile Device Connectivity Benefits
Supporting Technologies
Summary
39
Sunday, May 29, 2011
40. HEART RATE VARIABILITY MONITORING
& SEPSIS ONSET
• SEPSIS W/ ACUTE ORGAN
DYSFUNCTION
– IS LEADING CAUSE OF DEATH IN
NON-CORONARY ICU;
– ACCOUNTS FOR MORE THAN
750,000 DIAGNOSED CASES IN
US ANNUALLY1,2,3
• CLINICAL STUDIES: CHANGES IN
HRV HERALD ONSET OF SEPSIS http://biology.about.com/library/organs/heart/blsinoatrialnode.htm
BLOOD BORNE INFECTIONS IN
ADULTS4
1 MedScape Today; 2http://www.procalcitonin.com/default.aspx?tree=_2_0&key=intro1 ;
3http://www.survivingsepsis.org/Pages/default.aspx
4Saif
Ahmad et al., “Continuous Multi-Parameter Heart Rate Variability Analysis Heralds
Onset of Sepsis in Adults.” PLoS ONE, August 2009 | Volume 4 | Issue 8
40
Sunday, May 29, 2011
41. SIGNIFICANCE: ONSET OF SEPSIS CORRELATED TO HRV
IN CONTINUOUSLY MONITORED ADULTS (AHMAD ET AL)
• 24 HOUR HOLTER MONITOR OF PATIENTS UNDERGOING BONE MARROW
TRANSPLANTS (BMT):
– HIGH-RISK GROUP OF PATIENTS, OWING TO HIGH RISK OF INFECTION (80%) &
MORTALITY (5%)
– START MONITORING 1 DAY PRIOR TO BMT, CONTINUING THROUGH RECOVERY OR
WITHDRAWAL (HOLTER MONITORING: ZYMED DIGITRACK-PLUS)
• MONITORED RR INTERVALS OF NORMAL SINUS RHYTHM (NSR) BEATS:
RR Interval
Saif Ahmad et al., “Continuous Multi-Parameter Heart Rate Variability Analysis Heralds
Onset of Sepsis in Adults.” PLoS ONE, August 2009 | Volume 4 | Issue 8
41
Sunday, May 29, 2011
42. KEY STUDY FINDINGS
• ONSET OF SEPSIS DETERMINATION:
– SYSTEMIC INFLAMMATORY RESPONSE SYNDROME (SIRS) W/
CLINICALLY SUSPECTED INFECTION REQUIRING TREATMENT
• 17 PATIENTS OF 21 COMPLETED STUDY:
– 14 PATIENTS DEVELOPED SEPSIS, REQUIRING ANTIBIOTIC THERAPY
– 12 OF 14 INFECTED PATIENTS (86%) SHOWED 25% DROP IN HRV 35
HOURS (AVE) PRIOR TO SEPSIS ONSET
– NO SIGNIFICANT DROP REPRESENTED IN NON-INFECTED POPULATION
• PROMISING: ONSET CORRELATION DETERMINED USING SIMPLE
MEASUREMENTS TYPICALLY AVAILABLE W/O EXPENSIVE LAB TESTS
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Sunday, May 29, 2011