Here are the steps to modify probability from test results:
1) Determine the pre-test probability (prior probability) of disease based on risk factors, symptoms, etc.
2) Determine the likelihood ratios (LRs) for the specific test result:
- Positive LR = Sensitivity / (1 - Specificity)
- Negative LR = (1 - Sensitivity) / Specificity
3) Use the LR to modify the pre-test probability into the post-test probability:
- Post-test probability for positive test = (pre-test odds x positive LR) / (1 + pre-test odds x positive LR)
- Post-test probability for negative test = (pre-test odds x negative LR
This presentation mainly explains about the type of patients that are encountered in day to day practice as well as how each of them should be handled to improve the communication between a doctor and the patient.
DISABILITY COMPETENCIES IN HEALTH PROFESSION EDUCATION.
Competencies are abilities & attributes that are essential to effective health care delivery.
Disability competencies are skills and attributes essential to providing health care to patients with disability.
Or Minimum expected out of Indian medical graduate about the disability to provide compensate care to all.
This presentation mainly explains about the type of patients that are encountered in day to day practice as well as how each of them should be handled to improve the communication between a doctor and the patient.
DISABILITY COMPETENCIES IN HEALTH PROFESSION EDUCATION.
Competencies are abilities & attributes that are essential to effective health care delivery.
Disability competencies are skills and attributes essential to providing health care to patients with disability.
Or Minimum expected out of Indian medical graduate about the disability to provide compensate care to all.
Clinical Reasoning: How Some Doctors Think and the Rest of Us Try ToSHMLive
Case presentations for Dr. Dan Brotman's session at Hospital Medicine 2015. An expert clinician is brought in to try to troubleshoot medical cases on the spot, and attendees try to come to the best conclusion before the expert. "Clinical Reasoning: How Some Doctors Think and the Rest of Us Try To" is a session at the Society of Hospital Medicine's annual meeting, Hospital Medicine 2015 held March 30th to April 1st.
• Definition- pg 46 + 48 in Du Toit
• Concepts within transcultural nursing care- pg 47 in Du Toit
• Leininger’s transcultural nursing theory- pg 47-48 in Du Toit
• Transcultural nursing assessment model of Giger & Davidhizar (transcultural variations)- pg 49-51 in Du Toit
This presentation talks about the Diagnostics & Healthcare industry in India.It includes Marketing Mix,STPD Analysis,SWOT Analysis of some of the top healthcare organizations.
08.18.08: Diagnostic Reasoning I and IIOpen.Michigan
Slideshow is from the University of Michigan Medical School's M1 Patients and Populations: Medical Decision-Making Sequence
View additional course materials on Open.Michigan:
openmi.ch/med-M1PatientsPopulations
Slideshow is from the University of Michigan Medical School's M1 Patients and Populations: Medical Decision-Making Sequence
View additional course materials on Open.Michigan:
openmi.ch/med-M1PatientsPopulations
Clinical Reasoning: How Some Doctors Think and the Rest of Us Try ToSHMLive
Case presentations for Dr. Dan Brotman's session at Hospital Medicine 2015. An expert clinician is brought in to try to troubleshoot medical cases on the spot, and attendees try to come to the best conclusion before the expert. "Clinical Reasoning: How Some Doctors Think and the Rest of Us Try To" is a session at the Society of Hospital Medicine's annual meeting, Hospital Medicine 2015 held March 30th to April 1st.
• Definition- pg 46 + 48 in Du Toit
• Concepts within transcultural nursing care- pg 47 in Du Toit
• Leininger’s transcultural nursing theory- pg 47-48 in Du Toit
• Transcultural nursing assessment model of Giger & Davidhizar (transcultural variations)- pg 49-51 in Du Toit
This presentation talks about the Diagnostics & Healthcare industry in India.It includes Marketing Mix,STPD Analysis,SWOT Analysis of some of the top healthcare organizations.
08.18.08: Diagnostic Reasoning I and IIOpen.Michigan
Slideshow is from the University of Michigan Medical School's M1 Patients and Populations: Medical Decision-Making Sequence
View additional course materials on Open.Michigan:
openmi.ch/med-M1PatientsPopulations
Slideshow is from the University of Michigan Medical School's M1 Patients and Populations: Medical Decision-Making Sequence
View additional course materials on Open.Michigan:
openmi.ch/med-M1PatientsPopulations
Slideshow is from the University of Michigan Medical School's M1 Patients and Populations: Medical Decision-Making Sequence
View additional course materials on Open.Michigan:
openmi.ch/med-M1PatientsPopulations
This is a lecture by Joe Lex, MD from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
A lecture on the information cycle by Dr. Rajesh Mangrulkar, M.D. This lecture was taught as a part of the University of Michigan Medical School's M1 - Patients and Populations Sequence.
View the course materials:
http://open.umich.edu/education/med/m1/patientspop-decisionmaking/2010/materials
Creative Commons Attribution-Non Commercial-Share Alike 3.0 License
http://creativecommons.org/licenses/by-nc-sa/3.0/
GEMC - Trauma Patient Care in the Emergency Department : Pitfalls to AvoidOpen.Michigan
This is a lecture from the Ghana Emergency Medicine Collaborative (GEMC). To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
A lecture on uncertainty by Dr. Rajesh Mangrulkar, M.D. This lecture was taught as a part of the University of Michigan Medical School's M1 - Patients and Populations Sequence.
View the course materials:
http://open.umich.edu/education/med/m1/patientspop-decisionmaking/2010/materials
Creative Commons Attribution-Non Commercial-Share Alike 3.0 License
http://creativecommons.org/licenses/by-nc-sa/3.0/
This presentation focuses on informed decision making in clinical practice making use of evidence based practice. It addresses the use of PICO to formulate clinical question, searching the evidence/literature, critically appraising the evidence, and application of the evidence to improve the quality of clinical practice
GEMC - Musculoskeletal Emergencies - for NursesOpen.Michigan
This is a lecture by Katherine A Perry from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
GEMC: Myasthenia Gravis (Case of the Week): Resident TrainingOpen.Michigan
This is a lecture by Dr. Chris Oppong from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
GEMC - Conquering the Sign-Out Challenge Open.Michigan
This is a lecture by Dr. Pamela Fry and Dr. Alison Haddock from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
GEMC: Conquering the Sign-Out Challenge: Resident Training Open.Michigan
This is a lecture by Dr. Pamela Fry and Dr. Alison Haddock from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
This is a presentation about the importance of Evidence Based Medicine and how it acts as a crucial tool in decision making to empower the quality of medical services for better patient outcomes.
It highlights the steps in EBM process, how to identify the parts of a well built clinical question, resources for literature search, critical appraisal of the evidence, and how to apply the evidence to the patient.
GEMC- Approach to Acute Chest Pain- for ResidentsOpen.Michigan
This is a lecture by Rockefeller Oteng from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
This is a lecture by Dr. Joseph House from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
This is a lecture by Dr. Joseph House from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
This is a lecture by Jim Holliman, MD from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
This is a lecture by Joe Lex, MD from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
GEMC- Alterations in Body Temperature: The Adult Patient with a Fever- Reside...Open.Michigan
This is a lecture by Joe Lex, MD from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
GEMC- Rapid Sequence Intubation & Emergency Airway Support in the Pediatric E...Open.Michigan
This is a lecture by Michele Nypaver, MD from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
This is a lecture by Joe Lex, MD from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
GEMC- Disorders of the Pleura, Mediastinum, and Chest Wall- Resident TrainingOpen.Michigan
This is a lecture by Andrew Barnosky, DO from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
GEMC- Dental Emergencies and Common Dental Blocks- Resident TrainingOpen.Michigan
This is a lecture by Joe Lex, MD from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
This is a lecture by Joe Lex, MD from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
GEMC- Arthritis and Arthrocentesis- Resident TrainingOpen.Michigan
This is a lecture by Joe Lex, MD from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
GEMC- Bursitis, Tendonitis, Fibromyalgia, and RSD- Resident TrainingOpen.Michigan
This is a lecture by Joe Lex, MD from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
GEMC- Right Upper Quadrant Ultrasound- Resident TrainingOpen.Michigan
This is a lecture by Jeff Holmes from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
This is a lecture by Joe Lex, MD from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
This is a lecture by Joe Lex, MD from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
GEMC: Nursing Process and Linkage between Theory and PracticeOpen.Michigan
This is a lecture by Jeremy Lapham from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
2014 gemc-nursing-lapham-general survey and patient care managementOpen.Michigan
This is a lecture by Dr. Jeremy Lapham from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
This is a lecture by Dr. Jessica Holly from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
GEMC: The Role of Radiography in the Initial Evaluation of C-Spine TraumaOpen.Michigan
This is a lecture by Dr. Stephen Hartsell from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
This is a lecture by Dr. Jim Holliman from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
GEMC- Sickle Cell Disease: Special Considerations in Pediatrics- Resident Tra...Open.Michigan
This is a lecture by Hannah Smith, MD from the Ghana Emergency Medicine Collaborative. To download the editable version (in PPT), to access additional learning modules, or to learn more about the project, see http://openmi.ch/em-gemc. Unless otherwise noted, this material is made available under the terms of the Creative Commons Attribution Share Alike-3.0 License: http://creativecommons.org/licenses/by-sa/3.0/.
Basavarajeeyam is an important text for ayurvedic physician belonging to andhra pradehs. It is a popular compendium in various parts of our country as well as in andhra pradesh. The content of the text was presented in sanskrit and telugu language (Bilingual). One of the most famous book in ayurvedic pharmaceutics and therapeutics. This book contains 25 chapters called as prakaranas. Many rasaoushadis were explained, pioneer of dhatu druti, nadi pareeksha, mutra pareeksha etc. Belongs to the period of 15-16 century. New diseases like upadamsha, phiranga rogas are explained.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
Adv. biopharm. APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMSAkankshaAshtankar
MIP 201T & MPH 202T
ADVANCED BIOPHARMACEUTICS & PHARMACOKINETICS : UNIT 5
APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMS By - AKANKSHA ASHTANKAR
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
ABDOMINAL TRAUMA in pediatrics part one.drhasanrajab
Abdominal trauma in pediatrics refers to injuries or damage to the abdominal organs in children. It can occur due to various causes such as falls, motor vehicle accidents, sports-related injuries, and physical abuse. Children are more vulnerable to abdominal trauma due to their unique anatomical and physiological characteristics. Signs and symptoms include abdominal pain, tenderness, distension, vomiting, and signs of shock. Diagnosis involves physical examination, imaging studies, and laboratory tests. Management depends on the severity and may involve conservative treatment or surgical intervention. Prevention is crucial in reducing the incidence of abdominal trauma in children.
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
Maxilla, Mandible & Hyoid Bone & Clinical Correlations by Dr. RIG.pptx
Diagnostic Reasoning I and II
1. Author(s): Rajesh Mangrulkar, M.D., 2011
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3. Patients and Populations
Medical Decision-Making: Diagnostic
Reasoning I and II
Rajesh S. Mangrulkar, M.D.
University of Michigan
Department of Internal Medicine
Division of General Medicine
Fall 2011
4. Industry Relationship
Disclosures
Industry Supported Research and
Outside Relationships
• None
6. PICO: A Tool to Structure the
Foreground Question
Therapy Diagnosis
P Patient Pop Disease
I Intervention Test
C Comparison Gold Standard
O Outcome Accuracy
7. Foreground Questions - Case
Using the PICO model, jot down 1 foreground
question with your partner that will help you care for
this patient:
A 42 year old woman comes to her primary care
practitioner’s office for follow up of her diabetes. She
is currently on glyburide 10 mg twice daily. However,
her blood sugars still stay elevated. After you see
this patient, your attending asks whether you think
she should add metformin to her regimen.
Patient - Intervention - Comparison - Outcome
8. Foreground Questions - Therapy
• In type II diabetics, is metformin and
glyburide better than glyburide alone at
lowering blood sugar?
• Among women with type II diabetes, are there
more instances of low blood sugar events in
patients on both metformin and glyburide,
compared to glyburide alone?
9. Sources for Foreground
Questions
• MEDLINE
• Practice Guidelines
• Evidence Based-Databases
– Cochrane Library
– ACP Journal Club
11. Initial Diagnostic Reasoning
The Odyssey Reloaded
The Mechanic The Clinician
• Failure to entertain • Entertain all important
all possibilities possibilities
• Failure to pay • Elicit and pay attention
attention to all to description of all
symptoms symptoms
• Failure to inform • Inform and involve
customer patients
• Failure to perform • Perform effective
diagnostic tests diagnostic tests
12. The Odyssey: Conclusion
50 Prime, flickr
Initial Possibilities
#1: Trunk latch defect (recall
pending)
#2: Ajar sensing defect on
side door
#3: Side door not closing
properly
13. The Odyssey: Conclusion
50 Prime, flickr
Initial Possibilities The Answer
#1: Trunk latch defect (recall
pending)
#2: Ajar sensing defect on #2: Ajar sensing defect on
side door side door
#3: Side door not closing
properly
14. Learning Objectives
By the end of this lecture, you will…
•demonstrate diagnostic question formulation
•define and calculate sensitivity, specificity, and
predictive values for diagnostic tests
•explain how risk factors drive prior
probabilities, and how this concept relates to
prevalence
•modify probabilities from test results through
2x2 table calculations, Bayesian reasoning, and
Likelihood Ratios
15. Case: Diagnostic Reasoning
• The case: A 60 year old man without heart
disease presents with sudden onset of
shortness of breath.
• Description of the problem: Yesterday, after
flying in from California the day before, the
patient awoke at 3AM with sudden
shortness of breath. His breathing is not
worsened while lying down.
16. Diagnostic Reasoning: Your Intake
• Q: “What other symptoms were you
feeling at the time?”
• A: He has had no chest pain, no leg
pain, no swelling. He just returned
yesterday from a long plane ride. He
has no history of this problem before.
He takes an aspirin every day. He
smokes a pack of cigarettes a day.
17. Diagnostic Reasoning: First Steps
The differential diagnosis
Basic Tasks:
• Assign likelihoods to each possibility
– E.g. P(X) = probability that “X” is the cause of the
patient’s symptoms
• Place the possibilities in descending order of
likelihood
• State why (rationale)
18. My list
My differential diagnosis
– Pulmonary embolism
– Congestive heart failure
– Emphysema exacerbation
– Asthma exacerbation
19. Probabilities
(1) PE P(PE) = 40%
(2) CHF P(CHF) = 30%
(3) Emphysema P(emphysema) = 20%
(4) Asthma P(asthma) = 10%
• What is the probability that the shortness of
breath is due to either PE or CHF? 70%*
*provided that both do not happen simultaneously (i.e., they are “mutually exclusive”).
If there is a 10% chance that 2 events may happen, then this number is 60%
(make sure you understand why).
20. Prior Probabilities
• Based on many factors:
– Clinician experience
– Patient demographics
– Characteristics of the patient presentations
(history and physical exam)
– Previous testing
– Basic science knowledge
• Quite variable but can be standardized
– Clinical Prediction Rules
– http://medcalc3000.com/PulmonaryEmbRiskPisa.htm
21. More information
• Family history: he has had a DVT in the past (age 40)
• Physical Exam:
– His blood oxygen saturation is normal on room air
– His respiratory rate is 16, but his pulse rate is 105
beats per minute
– Examination of his lungs reveals some crackles and
wheezes, but no pleural rub or evidence of
consolidation.
– Swollen right leg, with firm vein below the knee
• CXR: normal
• EKG: sinus tachycardia
http://medcalc3000.com/PulmonaryEmbRiskPisa.htm
22. Diagnostic Reasoning: Testing
• If a Test existed that could “rule in” PE
as the diagnosis with 100% certainty:
then P(PE | Test+) = 100%
• Two questions:
– What is this test called? Gold Standard
– Does P(CHF | Test+) = 0%? No
23. Diagnostic Testing
• Facilitates the modification of probabilities.
• Can include any/all of the following:
– Further history taking
– Physical Examination maneuver
– Simple testing (laboratory analysis,
radiographs)
– Complex technology (stress testing, $$$
angiography, CT/MRI, nuclear scans)
24. PICO: The Anatomy of a Diagnostic
Foreground Question
D
P • Patient: define the clinical condition or disease
clearly.
I
T • Intervention: define the diagnostic test clearly
G
C • Comparison group: define the accepted gold
standard diagnostic test to compare the results
against.
O
P • Outcomes of interest: the outcomes of interest
are the properties of the test itself (e.g.,
performance and others we’ll discuss).
25. Practice PICO
Case: A 60 year old man without heart disease
presents with sudden onset of shortness of
breath. Considering PE.
Diagnostic Test to consider:
Ventilation / Perfusion
Scanning
Gold standard: Pulmonary
angiography
Need: Diagnostic performance
26. Practice PICO
Case: A 60 year old man without heart disease
presents with sudden onset of shortness of
P
breath. Considering PE.
Diagnostic Test to consider:
Ventilation / Perfusion
Scanning
Gold standard: Pulmonary
angiography
Need: Diagnostic performance
27. Practice PICO
Case: A 60 year old man without heart disease
presents with sudden onset of shortness of
P
breath. Considering PE.
Diagnostic Test to consider:
Ventilation / Perfusion
Scanning I
Gold standard: Pulmonary
angiography
Need: Diagnostic performance
28. Practice PICO
Case: A 60 year old man without heart disease
presents with sudden onset of shortness of
P
breath. Considering PE.
Diagnostic Test to consider:
Ventilation / Perfusion
Scanning I
C
Gold standard: Pulmonary
angiography
Need: Diagnostic performance
29. Practice PICO
Case: A 60 year old man without heart disease
presents with sudden onset of shortness of
P
breath. Considering PE.
Diagnostic Test to consider:
Ventilation / Perfusion
Scanning I
C
Gold standard: Pulmonary
angiography
O
Need: Diagnostic performance
30. Can the test be used?
Step 1 - Accuracy and Precision
• Accuracy - The result of the test
corresponds consistently with the true
result.
– The test yields the correct value
• Precision - The measure of the test’s
reproducibility when repeated on the
same sample.
– The test yields the same value
35. Can the test be used?
Step 2 - Diagnostic Performance
1. A well-defined group of people being
evaluated for a condition undergo:
- an experimental test, and
- the gold standard test.
2. Comparison is made between the
result of the new test and that of the
gold standard.
36. Diagnostic Performance: Statistical
Significance
• Statistical significance: strength of the association
between…
– Diagnostic study results (for the diagnosis of a
particular disease)
– Gold standard results (for the diagnosis of the same
disease, in the same population)
• Strength = degree of correlation
37. Diagnostic Performance: Clinical
Significance
• Clinical significance: how likely is the diagnostic test
going to affect patient care?
– Magnitude of the association between test results and
the accepted gold standard
– Other literature (including those of the gold standard)
– Cost of the test, reproducibility of test
– Disease characteristics (will the test result affect
management of the disease?)
38. What are the results - Diagnosis
Diagnostic performance is an association
between test result and diagnosis of a
condition (as assessed by the gold
standard) Disease + Disease -
BONUS Test + A B
What type of
variable is TP FP
FN TN
disease
state? Test - C D
39. Which test characteristics?
• There are prevalence-dependent and
prevalence-independent measures in
diagnostic tests.
• Prevalence-independent: sensitivity and
specificity.
• Prevalence-dependent: positive and
negative predictive values.
40. Test Characteristics: SeNsitivity
Sensitivity:
• The probability that the test will be positive
when the disease is present.
P (Test + | Disease +)
• Of all the people WITH the disease, the
percentage that will test positive.
• A seNsitive test is one that will detect most
of the patients who have the disease (low
false-Negative rate).
41. Test Characteristics: SPecificity
Specificity:
• The probability that the test will be
negative when the disease is absent.
P (Test - | Disease -)
• Of all the people WITHOUT the disease,
the percentage that will test negative.
• A sPecific test is one that will rarely be
positive in patients who don’t have the
disease (low false-Positive rate).
42. Test Characteristics: Predictive Values
• Positive predictive value: the probability
that a patient has a disease, given a
positive result on a test.
P (Disease + | Test +)
• Negative predictive value: the probability
that a patient does not have a disease,
given a negative result on a test.
P (Disease - | Test -)
44. To reflect upon...
Why?
Sensitivity and Specificity
Prevalence-Independent characteristics
Positive and Negative Predictive Values
Prevalence-Dependent characteristics
45. Let’s try it out
Case: To determine the diagnostic V/Q scan
performance of V/Q scans for
detecting pulmonary embolism, a
study was conducted where 300
patients underwent both a V/Q
and pulmonary angiogram. 150
patients were found to have a PE
by PA gram. Of those, 75 Pulmonary Angiogram
patients had a high probability
VQ scan. Of the 150 patients
without a PE, 125 had a non-
high probability VQ scan.
46. Let’s try it out
Case: To determine the
diagnostic performance of V/Q PE+ PE-
scans for detecting pulmonary
embolism, a study was
conducted where 300 patients VQ hi 75 25
underwent both a V/Q and
pulmonary angiogram. 150
patients were found to have a VQ
PE by PA gram. Of those, 75
patients had a high probability other 75 125
VQ scan. Of the 150 patients
without a PE, 125 did not
have a high probability VQ
scan (VQ other). 150 150
47. Let’s try it out
PE+ PE- • Sens = 75/(75+75)
= 50%
VQ hi 75 25 • Spec = 125/(125+25)
= 83%
VQ • PPV = 75/(75+25)
75 125 = 75%
other
• NPV = 125/(125+75)
150 150 = 63%
48. Modification of Probability
Pretest Test result
Probability Test changes the
P (Disease) Result probability of
disease
P (Disease|Test Result)
49. Test Characteristics and Prevalence
• Sens = A/(A+C) Dx+ Dx-
• Spec = D/(B+D) T+ A B
• PPV = A/(A+B)
T- C D
• NPV = D/(C+D)
Disease
A+C B+D
Prevalence
51. Populations and Patients
Population view Patient view
• Prevalence reflects • Same concept
the number of implies how likely an
people with the individual patient
disease at a given has the disease
moment • P (Disease)
52. Modification of Probability
Pretest Test result
Probability Test changes the
P (Disease) Result probability of
disease
P (Disease|Test Result)
Disease
Prevalence
53. An Important Question and
Assumption
Question: Are certain test characteristics fixed?
Answer: Generally, yes.
Sensitivity and specificity are constants,
regardless of the prevalence of the
disease in the studied population
(prevalence-INdependent)*
*Exceptions and caveats to this assumption are real, but are beyond the
scope of this course
54. Modification of Probability
Pretest Test result
Probability Test changes the
P (Disease) Result probability of
disease
P (Disease|Test Result)
Disease sensitivity
Prevalence specificity
55. Importance of Pre-Test Probability
• Hi-prob V/Q: Sens = 50%, Spec = 83%
Post-TP
PV
D+ D-
Pre-TP/Prev PPV NPV T+ 75 25
50% 75% 63% T- 75 125
How do our predictive values relate to our
probability after the test result is obtained (our
post-test probabilities)?
56. Importance of Pre-Test Probability
• Hi-prob V/Q: Sens = 50%, Spec = 83%
Post-TP
PV
D+ D-
Pre-TP/Prev PPV NPV T+ 75 25
50% 75% 63% T- 75 125
• If our Pre-test Probability was 50%, and we
obtain a hi-prob V/Q scan on this patient,
what is our Post-test probability? 75%
57. Importance of Pre-Test Probability
• Hi-prob V/Q: Sens = 50%, Spec = 83%
Post-TP
PV
D+ D-
Pre-TP/Prev PPV NPV T+ 75 25
50% 75% 63% T- 75 125
• If our Pre-test Probability was 50%, and we
obtain a V/Q-other scan on this patient, what
is our Post-test probability? 37% (tricky: 1-63%)
58. What did we just do?
100
75% = P(PE|T+)
hi
VQ
50%
VQ
o ther
37% = P(PE|T-)
0
P (PE) P (PE | Test)
59. Modification of Probability
Pretest Test result
Probability Test changes the
P (Disease) Result probability of
disease
P (Disease|Test Result)
Disease sensitivity
Prevalence specificity Predictive Values
(Positive and Negative)
60. Fundamental Assumptions
Sensitivity and specificity are constants,
regardless of the prevalence of the
disease in the studied population
(prevalence-INdependent)*
Positive and Negative Predictive Values are
dependent on the prevalence of the
disease in the studied population
(prevalence-DEpendent)
*with exceptions
61. Now, what do we do?
*clickers
75% = P(PE|T+)
Q1: Choices:
a)Treat as if patient has PE
b)Decide to get another test
c)Decide that patient does not have a PE
What factors do you consider when making the next decision?
62. Now, what do we do?
*clickers
Q2: Choices:
a)Treat as if patient has PE
b)Decide to get another test
c)Decide that patient does not have a PE
37% = P(PE|T-)
What factors do you consider when making the next decision?
63. Now, what do we do?
Choices:
•Treat as if patient has PE
75% = P(PE|T+) •Decide to get another test
•Decide that patient does not have a PE
Choices:
37% = P(PE|T-) •Treat as if patient has PE
•Decide to get another test
•Decide that patient does not have a PE
What factors do you consider when making the next decision?
64. What if we change our pretest
probability?
• In essence, we are simultaneously
changing the prevalence:
– Original pre-TP = P(PE) = 50% HIGH RISK
– New pre-TP = P(PE) = 25% MED RISK
• Assuming that sensitivity and specificity
are fixed…then we must recalculate our
predictive values to determine our new
post-test probabilities.
65. Importance of Pre-Test Probability
• Hi-prob V/Q: Sens = 50%, Spec = 83%
D+ D-
Post-TP
T+ 75 25
Pre-TP/Prev PPV NPV
T- 75 125
hi risk 50% 75% 63%
D+ D-
med risk 25% 50% 83%
38/(38+38) 187/(187+37) T+ 38 38
Our Pre-test Probability was 25%, we obtain a V/Q-other scan
T- 37 187
on this patient, our Post-test probability is now…17%
66. Decision time
*clickers
Q3: Choices:
a)Treat as if patient has PE
b)Decide to get another test
c)Decide that patient does not have a PE
50% = P(PE|T+)
67. Decision time
*clickers
Q4: Choices:
a)Treat as if patient has PE
b)Decide to get another test
c)Decide that patient does not have a PE
17% = P(PE|T-)
68. Decision time
Choices:
•Treat as if patient has PE
•Decide to get another test
•Decide that patient does not have a PE
50% = P(PE|T+)
Choices:
•Treat as if patient has PE
•Decide to get another test
17% = P(PE|T-)
•Decide that patient does not have a PE
69. Let’s change it again…
• Again, we are changing the prevalence:
– Young woman, no risk factors, some
dyspnea, no history, normal exam
– If we consult our clinical prediction rule:
• New pre-TP = P(PE) = 5%: LOW RISK
70. Importance of Pre-Test Probability
• Hi-prob V/Q: Sens = 50%, Spec = 83%
D+ D-
Pred Val
T+ 75 25
Pre-TP/Prev PPV NPV
T- 75 125
hi risk 50% 75% 63%
D+ D-
lo risk 5% 15% 97%
8/(8+47) 238/(238+7) T+ 8 47
T- 7 238
71. What did we just do?
Observation
As prevalence (pre-test probability) decreases,
positive tests are more likely to be false-positives
75% = P(PE|T+)
hi
VQ
50%
VQ
o ther
37% = P(PE|T-)
VQ h i 15% = P(PE|T+)
5%
0 VQ other 3% = P(PE|T-)
P (PE) P (PE | Test)
72. Fundamentally...
Question: If you get a high probability V/Q scan
for the diagnosis of pulmonary embolism, is it
more likely to represent a false positive test if
the patient presented with…
(a) many clinical features of PE (shortness of
breath, chest pain, long plane ride), or
(b) no clinical features of PE (no shortness of
breath, no chest pain, no leg swelling, no long
plane ride)?
75. Combining Rates - Method 1
Likelihood Ratios (LR)
• Concept - LRs depict the relationship
between true and false rates
– TPR/FPR = LR for a positive test result
– FNR/TNR = LR for a negative test result
TPR sens FNR 1-sens
LR = --------- = ------------- LR = --------- = -------------
FPR 1-spec TNR spec
Typically >1, excellent >10 Typically <1, excellent <0.1
76. Application
Likelihood Ratios (LR)
Key Concept: LRs can be Remember our scenario:
combined with pre-test High risk pt - 50% (PreTP)
odds to get post-test 0.50
odds
LR (VQ hi) = --------- = 2.94
1-0.83
Pre * Pre x LR = Post * Post
TP TO TO TP
50% 1.0 2.94 2.94 75%
*converting odds to probability and vice and versa - many references online
77. Combining Rates - Method 2
ROC Curves
Visual depiction of LR
• Tests with continuous
values only
• Sensitivity-specificity
tradeoff at different
cutoffs
• TPR plotted against
FPR
78. Application
ROC Curves
ROC Curves
• Area under the curve
determines overall utility
of the test
• Inflection point reflects
optimal threshold
• More in Small Group
Exercise
– Assignment 3
79. Take Home Points
• Research studies of diagnostic tests give you test
characteristics, not predictive values.
• Relationships between sensitivity and specificity can
be captured in ROC curves (for tests with thresholds)
and Likelihood Ratios (LRs)
• Appropriate use of tests stem from large differences
between pre-test and post-test probabilities, resulting
from LRs that strongly deviate from 1.
• If your pre-test probability is very low (<10%) or very
high (>90%), it is rare that a single test can help.
80. Diagnostic Reasoning
The Odyssey Returns
The Mechanic The Clinician
• Failure to entertain • Entertain all important
all possibilities possibilities
• Failure to pay • Elicit and pay attention
attention to all to description of all
symptoms symptoms
• Failure to inform • Inform and involve
customer patients
• Failure to perform • Perform effective
diagnostic tests diagnostic tests