Decision-making in Clinical Medicine Oscar Ty Cabahug, M.D. Clinical Reasoning Most important clinical action rather than recommendation for tests or prescription of medicine Aspects of clinical reasoning: o use of cognitive shortcuts – when the patient sees the patient, he will use cognitive shortcuts or heuristics – he is already formulating in his mind the diagnostic possibilities o generation of diagnostic hypothesis Use of Cognitive Shortcuts Also known as rule of thumb or heuristics Basic types; 1. Representativeness heuristic – match the clinical features with that of the diagnosis – determine if the clinical features fit the pattern or conform to the ‘picture’must take into consideration (Recording: this is the most commonly used; they would see the patient and compare that patient to another patient who was known to have the particular disease; story about medical student with acute Hepatitis A – tea colored urine before having jaundice; prodromal symptoms before jaundice and tea colored urine before jaundice; medical students tends to be in the upper economic class and they are more hygienic; if the patient is from lower economic class, then the patient will get it at a younger age; a medical student would get it later since earlier in their life they have not been exposed; if a 25-year old female from squatters area developed jaundice, then it is not Hep A since more probably they have been exposed already before; another story about acute appendicitis – you compare the patient to patients with acute appendicitis and as soon as it fits the picture then that’s it; they match the clinical feature with the diagnosis and determine if the clinical feature will fit the picture – example: the picture of acute appendicitis and Hepatitis A); you also have to take into consideration other things such as: pre-test probabilities of the disease – pre-test probabitlity of a certain disease in the US is very different from the pretest probability of the said disease in the Philippines; example: 30 yr old patient with pain less jaundice and tea colored urine (nothing else) – the doctor in the Philippines would think that it can’t be biliary carcinoma since the px is too young and it is not hepatitis since there is no prodrome – since the patient is in the Philipines, the doctor will take into consideration biliary tuberculosis; the clinician from the states will never think about it first since the pre-test probability of tuberculosis in the US is very low; this pre-test probability is taken into consideration unconsciously by the clinician in making the diagnosis (Example: biliary tuberculosis and amoebic liver abscess are very rare in the US; in the Philippines, it is so common) number of prior observations from which pattern or ‘picture’ was based on – this is where the disadvantage is: when you have not seen enough cases; if you have seen enough cases of amoebic liver abscess, then it would be very easy for you to diagnose – the px comes in with upper quadrant pain and low grade fever which had been there for 1-2 weeks; you poke the right side of the thorax in between the ribs and it is painful for the patient; hence this is amoebic liver abscess; you only do the ultrasound to confirm; it would be easy if you have seen enough; if you haven’t seen a patient like that, you would not think of amoebic liver abscess right away; you may have only seen one or two cases of amoebic liver abscess but it does not form the picture yet; it only forms part of the picture; if you have seen more than enough cases then you may form the pictute because you will notice the consistency between those 50 patients – some patient may not have fever while some have fever but they will always have the point tenderness) 2. Availability heuristic – based on how easy prior cases can be recalled or accessed consider recall bias – often used but not very accurate; based on the ability to recall (example: premonition – you saw black butterfly just before your grandfather died; if you saw a black butterfly but nothing significant happened then you will not remember seeing that butterfly; when somebody dies, you will remember seeing the black butterfly – this is what we call recall bias; you can remember particular things because something significant happened – this is what we call recall bias; you recall things that were significant in your life; another example: pregnant women have keen recall when their baby would look like a monster; a study was done to determine whether having fever while you are pregnant have something to do with congenital anomalies; they interview mothers who gave birth to infants with birth defect; when you ask them if they have fever when they are pregnant, they will exactly know the exact date, what exactly happened and what are the other symptoms; if you ask mothers
Transcript
Decision-making in Clinical MedicineOscar Ty Cabahug, M.D.
Clinical Reasoning Most important clinical action rather than recommendation for tests or prescription of medicine Aspects of clinical reasoning:
o use of cognitive shortcuts – when the patient sees the patient, he will use cognitive shortcuts or heuristics – he is already formulating in his mind the diagnostic possibilities
o generation of diagnostic hypothesis
Use of Cognitive Shortcuts Also known as rule of thumb or heuristics Basic types;
1. Representativeness heuristic – match the clinical features with that of the diagnosis – determine if the clinical features fit the pattern or conform to the ‘picture’must take into consideration (Recording: this is the most commonly used; they would see the patient and compare that patient to another patient who was known to have the particular disease; story about medical student with acute Hepatitis A – tea colored urine before having jaundice; prodromal symptoms before jaundice and tea colored urine before jaundice; medical students tends to be in the upper economic class and they are more hygienic; if the patient is from lower economic class, then the patient will get it at a younger age; a medical student would get it later since earlier in their life they have not been exposed; if a 25-year old female from squatters area developed jaundice, then it is not Hep A since more probably they have been exposed already before; another story about acute appendicitis – you compare the patient to patients with acute appendicitis and as soon as it fits the picture then that’s it; they match the clinical feature with the diagnosis and determine if the clinical feature will fit the picture – example: the picture of acute appendicitis and Hepatitis A); you also have to take into consideration other things such as:
pre-test probabilities of the disease – pre-test probabitlity of a certain disease in the US is very different from the pretest probability of the said disease in the Philippines; example: 30 yr old patient with pain less jaundice and tea colored urine (nothing else) – the doctor in the Philippines would think that it can’t be biliary carcinoma since the px is too young and it is not hepatitis since there is no prodrome – since the patient is in the Philipines, the doctor will take into consideration biliary tuberculosis; the clinician from the states will never think about it first since the pre-test probability of tuberculosis in the US is very low; this pre-test probability is taken into consideration unconsciously by the clinician in making the diagnosis (Example: biliary tuberculosis and amoebic liver abscess are very rare in the US; in the Philippines, it is so common)
number of prior observations from which pattern or ‘picture’ was based on – this is where the disadvantage is: when you have not seen enough cases; if you have seen enough cases of amoebic liver abscess, then it would be very easy for you to diagnose – the px comes in with upper quadrant pain and low grade fever which had been there for 1-2 weeks; you poke the right side of the thorax in between the ribs and it is painful for the patient; hence this is amoebic liver abscess; you only do the ultrasound to confirm; it would be easy if you have seen enough; if you haven’t seen a patient like that, you would not think of amoebic liver abscess right away; you may have only seen one or two cases of amoebic liver abscess but it does not form the picture yet; it only forms part of the picture; if you have seen more than enough cases then you may form the pictute because you will notice the consistency between those 50 patients – some patient may not have fever while some have fever but they will always have the point tenderness)
2. Availability heuristic – based on how easy prior cases can be recalled or accessed consider recall bias – often used but not very accurate; based on the ability to recall (example: premonition – you saw black butterfly just before your grandfather died; if you saw a black butterfly but nothing significant happened then you will not remember seeing that butterfly; when somebody dies, you will remember seeing the black butterfly – this is what we call recall bias; you can remember particular things because something significant happened – this is what we call recall bias; you recall things that were significant in your life; another example: pregnant women have keen recall when their baby would look like a monster; a study was done to determine whether having fever while you are pregnant have something to do with congenital anomalies; they interview mothers who gave birth to infants with birth defect; when you ask them if they have fever when they are pregnant, they will exactly know the exact date, what exactly happened and what are the other symptoms; if you ask mothers with bouncing baby boys, since they do not care that much, they would tend to say that they did not have fever even if they actually did; they would not remember – this is again recall bias; this will play a very big role in availability heuristics hence this is something that is very unreliable
catastrophic events more likely to be remembered – a patient with acute pancreatitis went to the ER and the doctor missed and since he missed it, he was reprimanded by the consultant and the senior resident for missing it (why didn’t you do a serum amylase test?); so what happended after this incident? She would order serum amylase for all patient who came to the emergency room with epigastic patin since it was catastrophic to him; another example: you see a 50 yr old patient who has pain on the left arm; you brushed it aside saying that it is just muscular pain; it turns out that the patient is having a heart attack; he came back to the ER and died; after this incident, you do ECG to all patients who come with left upper arm pain – this is typical of availability heuristic; this may be good but you tend to recall more those which had been catastrophic to you
the more recent the case, the more likely to be remembered – this is human nature; this is how the brain works3. Anchoring heuristic – estimating a probability by starting from a familiar point or anchor – when they see a patient, they think of
an anchor in the past and they tried to fit it there; this is not like representativeness heuristics wherein you form a picture; in this one, there is only one feature; example: amoebic liver abcess – you’re anchor is point tenderness; forgetting that pyogenic liver
abscess may also have point tenderness; if you use this anchor, then you will be wrong since it is not a whole picture, it is just part of the picture that you are using and you used this as an anchor; you may turn out to be inaccurate especially if the pre-test probability is not considered; you go to the states and you see a Caucasian guy with point tenderness and you diagnose him with amoebic liver abscess forgetting the pre-test probability of that diagnosis is very small in the USl hence you may be wrong
might turn out to be inaccurate if pre-test probability is not considered Note: All these heuristics are developed in the mind of the clinician without them knowing it; they become good at something without them realizing that they had become good (four levels of intelligence: you do not know what you do not know; you know what you do not know; you know what you know; you do not know what you know)
Generation of Diagnostic Hypothesis (After you use the heuristics to form the picture, the next is you develop a diagnostic hypothesis – this is not the primary impression; after you develop the primary impression, you order the test needed; in the US, they do shotgun testing wherein they order for everything; when you go to an academic center, they will not order everything, they will only order what is needed; if you are thinking of amoebic liver abscess, then just order ultrasound of the liver and nothing else; if you are thinking about acute hepatitis A, then test first for SGPT then test for anti-HBs IgM)
The diagnostic hypothesis determines the steps to be followed and provides testable predictions Expert clinicians do not follow fixed pattern in patient examination since they are already generating and discarding diagnostic
possibilities during the process Influenced by the acuteness or severity of a diagnostic possibility
Note: Do not believe in algorithms too much because algorithms are meant only as a guide; a clinician may order an ultrasound for one patient
but he may order CT scan for another patient though they have the same clinical features; in the mind of the physician, the diagnosis for the two patients are different; hence if he is thinking of amoebic liver abscess, he will order for ultrasound but if he was thinking about hepatocellular carcinoma in a patient’s whose liver is not enlarged, then he will no longer order for ultrasound instead he will go straight to CT Scan;
Expert clinicians do not follow fixed pattern; “Intern: sir, why did you order for ECG on that patient? Doctor: I don’t know, I just ordered it” – then the doctor was right, it was in fact and atypical presentation of acute myocardial infarction; while the clinician is listening to you and they look so bored, they are already actually generating a hypothesis, refining it, and discarding some of it; when they go to the patient themselves, they only need confirmation – this is exactly what they do when the patient comes; even before the patient sits down, they are already thinking of a diagnosis even without talking to the patient yet; doctors may already have a diagnosis even before they examine the patient but remember, even if you already know, you have to let the patient lie down, touch the patient and pretend that you are examining them because otherwise they will feel bad paying your professional fee
Sometimes, it is also influenced by the acuteness or severity of a diagnostic possibility; example: when the patient comes in into the emergency room clutching their chest, the first thing you have to think of is acute MI but what is the other thing that you have to think of for a 50 yr old male, severe chest pain? Aortic dissection or dissecting aortic aneurysm; but if they come in and the said that they have chest pain while brushing their chest up- and down, then think of GERD; it cannot be dissecting aortic aneurysm; the diagnosis is very different with acute setting than the setting in the clinic
Major Influences on Clinical Decision-making1. Physicians’ personal characteristics and practice style – training, opinion leaders, risk of malpractice (defensive medicine) – ‘Filipinos tend
to create small kingdoms’; the practice of Neurology in UERM is different from the practice of neurology in UST and different from the practice of neurology in PGH; why is the practice so different in terms of what test to do and what treatment to use? This is because there are different persons who influenced the practice of medicine among the residents and fellows in the different institution; however, this should not be the case, it should not be the same – these people are so proud of themselves that they refuse to talk to each other; they fail to think what is right; they just think what in their view is right; the training will often affect what decision the clinician will do; this is also affected by the risk of malpractice – defensive medicine is commonly practiced in the US that they do not rely anymore on history and physical diagnosis because they are so scared; they would immediately order CT Scan for a patient who complain of headache even though the chance of finding an abnormality in the brain is 1 out of a million; they will still do CT Scan because if they miss it, they will be sued; in the Philippine (except for commercial hospitals) will do otherwise; however, in commercial hospitals, they tend to be like doctors in US too; story about the lecturer’s niece who tend to collapse but never hit her head – this occurs twice a year; they went to St. Luke’s and they wanted to do a CT Scan without even knowing the history – these people practice defensive medicine in which the patient will end up spending so much for something that is not even significant; another example: lecturer’s classmate ask him to check his 85 yr old aunt who has epigastric pain; the aunt was already worked up in the US wherein every test was done; the lecturer then ordered for a simple lumbosacral spine x-ray and he saw compression – it was radicular pain; other doctors tend to do so many things and they end up so confused with what their diagnosis should be
2. Practice setting – relate to available resources (medicines, tests and specialists); the setting will also have an effect in the clinical decision making; if you are practicing in Tawi-Tawi, you probably would not order MRI because there is no MRI there anyway; but if you are in Manila and you are thinking of radicular pain but the X-ray is normal, then you may order for MRI because this is the only way for you to see the exit of the spinal nerve; you will not do this when you are in Tawi-Tawi; the availability of the test will determine what you will do; even the availability of a specialist; you are a generalist and you are thinking about the spine problem but there are no neurologists available; but since you know what to do and you are thinking of radicular pain, you just ask the patient to rest the back and order for simple analgesic – this will work in most cases
3. Economic incentives - sometimes doctor do things not because they think it is the best for their patient but because they will earn from it; example: In gastroenterology, it is the a rule that that chances of gastric cancer is very low in patients with epigastric pain for the first time and less than 45 yrs of age; if your patient is more than 45 yrs old, then there is now a possibility; if you go to Makati Med, the
patients lined up for gastroscopy are all young; if you look at the patients in UERM lined up for gastroscopy, they are all old – you do gastroscopy not to diagnose peptic ulcer but to exclude the possibility of gastric cancer; in Medical City, gastroscopy and colonoscopy is done at the same time; this is very rare in UERM; when you have a patient with a certain problem, you usually concentrate on his problem and do the gastroscopy or colonoscopy, but not both – why will you do both? Is there a disease which will make you confuse whether it is colonic or gastric pathology? There is no such thing; it is either gastric or colonic but not both; it is very rare that you see a patient that will complain of hypogastric and epigastric pain at the same time complaining of post-prandial vomiting and at the same time constipation
Quantitative Methods Diagnostic tests – will include history, physical examination, blood tests, special procedures – diagnostic test does not only pertains to
laboratory tests or imaging modalities, we also think about physical exams and history as diagnostic test; the physician will only palpate the liver and this will already serve as their diagnostic test - he will be able to diagnose that the patient has a chronic passive congestion; why chronic passive congestion? Because the edge of the liver is smooth, not sharp (dull), and it is a bit tender – this is the classic picture of chronic passive congestion; he was using the physical examination finding as a diagnostic test
Validation of diagnostic tests should have a ‘gold standard’ – 100% sensitivity and 100% specificity – when we are thinking of a diagnostic test (remember: diagnostic test would include physical exam and history), we think of how we could validate a diagnostic test; when you validate a diagnostic test, there must be a comparison to what you call a gold standard; a gold standard is something with 100% sensitivity and 100% specificity; for example, you invented a test that will detect liver cancer (alpha feto protein examination – a level of more than 500 nanogram/L) – you wanted to test this in the diagnosis of liver cancer; you compare this to patients who all underwent CT scan of the upper abdomen including the liver; if you compare whether the alpha feto protein is positive or negative to the CT scan, is that a validation test? No because there should be a gold standard? Is the ability of the CT scan to detect liver cancer 100%? No. It means that the sensitivity of the CT scan is not 100%; it cannot be the gold standard – this is not a validation test; this is what you call correlation test (you correlate the tools); only when you have a test that has 100% sensitivity and 100% specificity would you say that it is a validation test which is very important in diagnosis
Diagnostic IssuesWhen we make clinical decision, we want to put it in a structure so that you can actually apply it; to put in a structure:
Clinical decisions are based on the probability of the disease in a particular clinical scenario – when your scenario is in the Philippines or Manila, the scenario would be probably different when you are in the US, Tawi Tawi or Baguio; this is what we call pre-test probability – the probability if the disease in a particular clinical scenario
Clinicians use prior knowledge, experience and clinical intuitions in making these decisions – but this is the old style; although it works, they do not know how it works; therefore, for you to learn it faster, then you must learn how it works so that you can apply it in a structured manner
Determine the probability of the disease in the particular clinical scenario (pre-test probability) Determine the treatment and testing thresholds – below the testing threshold, you stop testing; above the treatment threshold, you start
treating Select a validation study of a particular test - make sure that it is a validation study; it has to have a gold standard Compute for the likelihood ratios – this is sometimes published in some journal Determine the post-test probability of disease – then you can make some decision
Diagnostic Accuracy
True positive (TP) – the test is positive and it is indeed positive (there is a disease)False negative (FN)– the test is negative but actually it is positive False positive (FP) – the test is positive but actually it is negativeTrue negative (TN) – the test is negative and it is indeed negative
Sensitivity – TP / TP + FN
TP FP
FN TN
(+) Disease (-) Disease
(+) Test
(-) Test
o You look at the patient with the disease; how many patients with the disease were detected by the test; how many of these people were detected by the test?
o Among those with the disease, how many of these patients were detected not to have the diseaseo Sensitivity is the ability of the test to detect those patients with the disease to have the diseaseo Among the patients with the disease, the ability of a test to detect or to come out to be positive is sensitivityo False negative rate – FN/TP + FN; take note that it has the same denominator as sensitivity therefore: false negative rate = 1 –
sensitivity False negative rate + sensitivity = 1 (they have the same denominator)
Specificity – TN / TN + FPo False positive rate = 1 – specificityo Among the patients without the disease, the ability of a test to detect those without the disease (negative) is specificity
PPV – TP / TP + FPo Positive predictive value: If your test is positive, what is the chance that you have the disease?o Among patients who are positive, how many have the disease?o Among those patient found to be positive, the chances that the patient really have the disease is Positive predictive value
NPV – TN / TN + FNo If your test is negative, how many do not have the disease?o Among those patient without the disease, the chances or probability that the patient really don’t have the disease is negative
predictive value
Diagnostic Issue 0 – there is no probability of the disease; 100 – surely, you have the disease
You first set the treatment thresholdo At what level will you put the treatment threshold? This is purely a clinical decisiono The riskier the treatment, the higher it will beo The more expensive the treatment, the higher it will beo Example 1: if you put the treatment threshold at 90% and then you are talking about open heart surgery or coronary artery
bypass – would you risk the 10% of the patients operated on though they do not have the disease? Then, do not set it at 90%, you set it at maybe 98% since you do not want to risk 10% of the patient undergoing such a very risky procedure and you are not even so sure
o Example 2: what if it is a simple abdominal surgery but it will cost 2M pesos, will you set it at 90%? No, since it is so expensive hence you put it higher
o The riskier the treatment and the more expensive the treatment, the higher the treatment threshold will beo you only start treating if the probability reaches the point (or the treatment threshold
On the other side is the testing threshold – the testing threshold is that when you go lower than that you stop testing; you just observeo If you are dealing with a disease that is curable but life-threatening, then the threshold must be lowero Example 1: you are dealing with gastric cancer; if you missed out on gastric cancer, your patient is going to die (especially of he
did not go to surgery immediately) – are you willing to risk 10% of your gastric cancer patients to be missed? No hence you set it at lower level.
o If it is a curable that is life threatening, then it should be lowero if you say life-threatening disease, sometimes it is incurable anyway (such as pancreatic cancer); whether you diagnose it early
or not, the patient is going to die; also, liver cancer – depending on how big it is) – if this is the case, never mind to put it at 10% In between the testing and the treatment threshold, you keep on testing
Suppose the patient has a probability of disease which is 50% - a 30 yr old male patient with painless jaundice; the probability that he has liver cancer is 50% (since we are in the Philippines wherein hepatitis B is very prevalent)
o Before you move on, first, you have to convert the probability in to odds
Convert Probability to Odds Odds is different from probability; if the probability is 50%, then what is the odds? Odds is often used in gambling; the odds are 1:1 or 1:2 or 2:1; it means that if it is 1:2, if there are three fights, Pacquiao will win in one
fight and he will lose in 2 fights; gamblers do not think of probability, they think about odds So convert first the probability into odds; it is very easy when the numbers are easy to remember – if the probability is 50%, then the odds
will be 1:1; when the probability is 33%, then the odds will be 1:2; if the probability is 67%, then the odds will be 2:1; if the probability is 75%, then the odds will be 3:1; if the probability is 80%, then the odds will be 4:1 – however, if you cannot do it mentally, then you must have a system of converting it
Odds = probability / 1 – probability = p : 1 – p = A : B (Odds is equal to probability is to 1 – probability)o Example: probability is 50%; 1 – 50% is 50%; hence 50% is to 50% so it is 1:1o If it is 33%; 1 – probability (33%) is 67%; hence 33.3:66.6 so it is 1:2o You keep on changing it until either A or B becomes 1 – instead of 2:8 you convert it into 1:4o If you have the odds and the number on the left is bigger than the right number then that means that you have the edge; if the
number in the right is higher than the left then the opponent has the edgeo Again, to get the odds – probability: 1 – probability o Example: probability of 85% - 85:15 (5.6:1 – one must always be 1)o You do not say 1:0.5; you instead state it as 2:1
Convert until A or B is 1
Validation Test After converting it to pre-test odds, you now determine the likelihood ratio
o Sensitivity = A/A+Co Specificity = D/D+Bo PPV = A/A+Bo NPV = D/D+Co PLR = SEN/1-SPEC – positive likelihood ratio
Positive likelihood ratio – just focus on the positives (A/A+C ) ÷ (B/B+D) A/A+C is same as sensitivity (B/B+D) is actually equal to 1-specificity (specificity = D/B+D); so B/B+D is 1-specificity Hence PLR = SEN/1-SPEC
o NLR = 1-SEN/SPEC Negative likelihood ration – focus on all the negatives (C/A+C) ÷ (D/D+B)
If (+) Odds = 1:1 X 15 = 15:1o If the test is positive, then you multiply the first number by 15 (Positive likelihood ratio)o The post test odds will now become 15:1 – if the test was positive
Probability = 15/15+1 = 94%o To compute for probability: A/A+Bo Whatever test was done, since the post-test probability was 94%, you start treating since the treatment threshold is 90%
If (-) Odds = 1:1 X 0.1 = 0.1 :1 o If the test was negative, we multiply the 1st number with 0.1 (negative likelihood rationo It now becomes 0.1:1 – you then convert it to probability (A/A+B)
A B
C D
(+) Disease (-) Disease
(+) Test Positive
(-) Test Negative
Probability = 0.1/0.1+1 = 9% o Hence, if the test is negative then we stop testing and we just observeo Many clinician had been doing this but they just didn’t know that they were doing it
If the test is positive (94%), you start treating; if the test is negative (9%), you stop testing and you start observing
What if you do another test because it’s between 10 to 90? If you do another test, do not convert it to probability first; whatever is the likelihood ratio, you multiply it (again and again) until you reach the treatment threshold or the testing threshold
o Example: let’s say your test is positive – 15:1 but the treatment threshold is 98; you cannot treat yet since the probability is 94%; you then do another test wherein that test has a positive likelihood ratio of 10; you again multiply the 10 with the 15, it now becomes 150:1; when you get the probability, it will now be very high so you can now treat – this is base theorem: the probability of something is equal to the probability of the two things combined
o 50% of the students in the room is female; out of their population 50% have cars, what is the probability in the group that you will find females with cars? 25%
Clinical Epidemiology Clinical epidemiologist do research and understand research too
EVIDENCE-BASED MEDICINE
Advocates of evidence based medicine are not expected to be doers but they are expected to be users; they use the research of other people
The process of systematically reviewing, appraising and using clinical research findings to aid the delivery of optimum clinical care to patients
The only way you can deliver the best result is that if you are compiling your results systematically
If you do not believe your clinician, you go to the internet and you will find the findings of the other 1 million doctors (whose finding are printed in clinical trial) – this is not easy though because you have to have the paradigm shift first
“A PARADIGM SHIFT IN MEDICAL PRACTICE …” – you have to change the way you thinko The Old Paradigm – this is what they had been doing since time in memorial
1. Pathophysiologic Understanding2. Common Sense3. Experience4. Expert’s Opinion
Example: What drug will I give to my patient? Carbocysteine is marketed as solmux; it sells about 1 Billion pesos a year; why do you use this? First, you look at the pathophysiology of sputum when you have bronchitis (sputum is very viscid – this is due to the presence of disulfide bridges which will make allow the proteins to fold and link upon itself (disulfide bridges); the more convoluted it is, the more convoluted it is, the more viscid is the sputum; if you want the sputum to be less viscid, you have to break the disulfide bridges; if you give a drug like carbocysteine that will donate a hydrogen atom (since it has SH group), then the S-S bond will now break therefore it will now become less viscid (this is common sense); experience thought you that if you give antibiotic + carbocysteine to patient with acute bacterial bronchitis, the patient will get better very fast; when you ask the pulmonologist (expert’s opinion) they too were using this hence Luviscol (solmux) became a very big market – EBM is saying that you do not discount or discard all those four, it is just that you need to consider one more thing which is medical evidence (in 1996, a blinded RCT was done wherein carbocysteine was compared to the placebo which looked like carbocysteine in patient with acute exacerbation of bronchitis; one week later, when they analyzed the patient according to ease of expectoration, it was exactly the same – it was repeated by another institution in 1997 (RCT); again, they found out that it was exactly the same: hence it is a drug that does not really work; whatever the experts were saying and whatever your experience about it, even though it is common sense and even thought there is a pathophysiologic rationale behind it, evidence is saying that it doesn’t work; will you use a drug wherein you know how it works but you do not know if it works? There are drugs that most doctors know how it works but they do not know if it works; what if you have a drug that works but you do not know how it works, will you use it? Aspirin was discovered in 1807; they know for a fact that it lower the temperature but they did not know how it works until 1971 when they discovered that it works by inhibiting cyclooxygenase (this won a noble prize); for 70 years, people were using it not because they know how it works but they know it works; another example: acupuncture – surgeons don’t want to use it because they do not know how it works until a group of Americans went to China in 1970 wherein the surgeons in China showed them that they operated a patient who is wide awake and they open the skull; they were operating the brain only with acupuncture – with this, the Americans started to believe because they saw that it works; they do not know how it works but they do know that it works (this is better than showing you hoe a drug works but there is no evidence that it actually worked)
o The New Paradigm1. Medical Evidence 2. Pathophysiologic Understanding 3. Common Sense4. Experience5. Expert’s Opinion
Where can we find information? o Personal experienceo Reasoning and intuitiono Colleagueso Published evidence
- the only way to reduce ineffective, dangerous or costly interventions Evidence - Based Medicine (Definition)
o Integration of…1. Best research evidence 2. Clinical expertise 3. Patient values
o …In clinical decisions
INTEGRATIVE STUDY DESIGNS Reviews Overviews Meta-Analysis Guideline Development
Types of Integrative Studies REVIEW - any 2 articles (or more) OVERVIEW - comprehensive, systematic and objective search META-ANALYSIS - results of trials are combined statistically GUIDELINES - actual recommendations on management are made
Critical Appraisal A method of assessing and interpreting the evidence by systematically considering its validity, results and relevance
Grading of Evidence At least one meta-analysis of multiple RCTs At least one properly designed RCT of appropriate size Well-designed non-randomized clinical trial, cohort study, matched case-control study, or cross-sectional study Well-designed descriptive study Expert’s opinion
Therapeutic Issue Determine the 95 % confidence intervals of the relative risks from all critically appraised articles Integrate the data using meta-analysis Determine the summary relative risk and its 95% confidence interval Determine the numbers needed to treat
Therapeutic Issue Why use the 95% confidence interval? How do we relate this to the p value?
Test of Statistical Significance Used in hypothesis testing Start with the Null Hypothesis and determine if this will be rejected If the Null Hypothesis is rejected – there is enough evidence to say that there is a difference If the Null Hypothesis is NOT rejected – there is NOT enough evidence to say there is a difference the time it will fall within the specified
range Test of significance is measured by the p value p value represents the random error – p is the probability of chance error; when you say that two things are different, the chances that
you are wrong when you say such thing is the p-value; you are wrong because it happened by chance (but you use this to say that they are different) – this is the probability of chance error: when you say that the two things are different when in fact you are wrong
“the chance that when you say there is a difference that you are wrong” less than 5% is the acceptable error, so if you are 95% sure, then there is a difference
Confidence Intervals Preferable to p values as the range of possible effect sizes can be determined Usual estimate used – 95% confidence interval (CI) If the measurement is done 100 times, in 95% of the time the true value will fall within the specified range What is the relationship between the confidence interval and P value? See discussion below (figures)
Effect Size Risk without therapy (control) = X Risk with therapy (experimental) = Y ARR = X – Y RR = Y / X RRR = (X-Y) / X or 1-RR
o Unity for relative risk is 1o Unity for relative risk reduction 0 (1 – unity of relative risk reduction which is 1)o Unity for absolute risk reduction (because 0 divided by any number is 0
Continuation of the discussion about relative risk; in relative risk, if it is to the left, then it is good since the risk is lower; if your risk is higher, then it is bad; 1)point and interval estimate is good; it is definitely good (what is the p value of the trial? What is the chance that it is bad? Less than 5%; is this statistically significant? Yes, it is definitely significant or good; 2) point estimate is good; however interval estimate crosses the line 1: sometimes it is good, sometimes it is bad (what do you think is the p value of this? More than or equal to 5% - you do not know if it is good and you do not know if it is bad); 3) it crosses the line again; you do not know if it is good and you do not know if it is bad; this means that the p-value is more than or equal to 5
Example about the suitor; you do not judge him because he slapped you; you judged him by how many times he slapped you
Estimate of error vs Test of significance Range of Values
Rela tiv e R
is k1
2
This is the confidence interval; this is what we call the interval estimate; For example, you did a trial on 1 million patients to determine the response on aspirin in patients with possibility of myocardial infarction in 50 yr old males; you found that aspirin will decrease the chance of MI by 50%; is this true? You gave aspirin to half and placebo to the other half and you found that the incidence of MI was reduced by 50%; is this the truth?
It is not the truth, why? This is just an estimate of the truth; when will you know that it is the truth? When you have tested everybody in the world; since you did not test everybody and you only tested 1 million; the truth is somewhere near this but this is not the truth; the truth is somewhere in 95% of the time; if you would like to find out where the truth is, it is between the confidence interval in 95% of the time
What is the chance therefore that the truth is before the confidence interval? Less than 5% (the same is true after the confidence interval); chances that it is before or after is less than 5%
Estimate of error vs Test of significance
If the 95% CI does not include unity (no effect), then the p value must be less than 5% The bigger the sample size the smaller is the p and the narrower is the confidence interval and vice versa
Therapeutic Issue NNT= 1/ARR if ARR = 10% or 0.1 NNT = 1/0.1 = 10 Need to treat 10 patients to prevent 1 patient from developing the outcome
Errors in Scientific Investigation Alpha errors are usually due to methodologic defects e.g. existence of biases Beta errors are usually due to insufficient sample size “Alpha error is graver than beta error”
