y g o o m e d p E l i i 27 Perhaps the most significant criticism of EBM is that it has never itself been subject to t he sort of rigorous evaluation that it espouses for other thera- pies. Specifically, we do not know if physicians who adhere closely to the principles of EBM have better patient outc omes than those who do not. Proponents of EBM point to studies showing that expert opinions voiced in traditional review articles may lag behind evidence from the latest stud- ies, a nd that patients receiving therapies for which there is good evidence do better than those who do not. But these are indirect measures of the effectiveness of EBM; direct comparisons of EBM practitioners with non- EBM practitioners, either in the form of RCTs or observatio nal studies, are lacking. Associations and causality Association Epidemiological research is concerned with comparing rates of disease in populations with different exposures (e.g., rates of lung cancer in a population of men who smoke, compared with men who do not). A difference in rates suggests an association between the disease and the expo- sure (in this case, smoking). The search f or meaningful associations may be complicated by the presence of confounders , or factors t hat are related to both the exposure and the disease. For example, an apparent association between alcohol use and lung cancer may simply reflect the fact that both are related to to bacco use. Associations may also be due simply to chance. The strength of an association is expressed as the risk ratio \200\224 the risk of developing the disease among those with t he exposure, divided by the risk of developing the disease among those without the exposure , or the odds ratio, a similar but not identical measure. Prevalence and incidence To compa re rates of disease between groups, standard measures of disease occurrence are used. Preva lence is the number of cases of disease present in a defined population divided by the tota l num- ber of people in that population. Since it is not usually practical to count an enti re population at once, the period prevalence is often given, indicating that the measuremen t took place over a period of time. For example, the prevalence of obesity (body mass index [BMI] >30) in the United States during the 2009 \200\22310 National Health and Nutrition E xamination Survey (NHANES) survey period was 35.7%. Lifetime prevalence is the proportion o f a population that has ever experienced the condition of interest, and point prevalence is the proportion with the condition at a given point in time. For example, the lifetime prev alence of headache in women has been reported at 99%, and the point prevalence at 22%. Inci dence is the number of new cases occurring in a defined population that is initially free o f the disease, over a specified time period (e.g., the incidence of breast cancer among U.S . women from 2008 was 127 per 100,000 [Surveillance Epidemiology and End Results (SEER) can cer statistics review]). Observational studies Associations are often discovered using obse rva- tional studies . Such studies are distinguished from experimental studies or clinical trials in that the investigator does not control the exposure; she or he merely observes th e subjects who are exposed and those who are not and records the outcomes occurring in each group. Observational studies usually take one of three forms: 1 Cohort studies: The group, or cohort, consisting of subjects exposed to the suspected causal factor (e.g., smoking) i s followed alongside a control group consisting of subjects not exposed. The incidence of t he disease is compared between the groups over the duration of the study. Because the expos ure information is recorded as it occurs, prospective cohort studies do not 28 have recall bias. However, they can be very expensive and time-consuming, particularly for diseases with low incidence rates. y g o o m e d p E 2 Case \200\223control studies: The study group consists of those with the disease (e.g., l ung cancer); the control group consists of those without the dis- ease. The previous occurr ence of the exposure (e.g., smoking) is com- pared between each group. Case \200\223control studies are particularly useful for the investigation of rare diseases, but they suffer fr om recall bias: inaccuracy in the subjects \200\231 reporting of the exposure (e.g., smokin g). 3 Cross-sectional studies: The population of interest is surveyed for the pres- ence of both the exposure and the disease at the same time. This design is usually quick and inexp ensive, and provides prevalence data, but it may not enable the investigator to determine w hether the exposure preceded development of the disease, since both are measured at the sam e time. Establishing causality The finding of an association between an expo- sure and a di sease does not prove that the exposure causes the disease. Reports abound in the media of s upposed links between various foods and worsened or improved health; which will turn out to be valid? Sir Austin Bradford Hill, a British epidemiologist, recognized this problem in t he context of evaluating occupational hazards, and proposed nine criteria for establishing causality: 1 Strength of the association: Does the disease occur much more frequently among the exposed than among the unexposed, such as with lung cancer, or is it only slightly mor e common? l i i 29 2 Consistency: Does the relation hold across different studies in different populations? 3 Specificity: Is the exposure the only potential cause of the disease, or vise versa? This is not required for causality, but when present it strengthens the argument. 4 Temporality: Does the cause precede the effect? 5 Dose response: Does a greater exposure lead to a greater risk of disease? 6 Plausibility: Is there a plausible biologic mechanism to explain the causality? 7 Coherence: Does the cause-and-effect relation fit with histopathology and other information about the disease? 8 Experiment: Does alteration of the exposure, if possible, result in a change in frequency of outcome? 9 Analogy: Is there a similar disease known to be caused by a similar exposure? Risk of disease and its outcomes As physicians, we are frequently called on to prognosticat e. Our patients want to know: Should I be worried about this cough? Is my cholesterol low e nough? How long am I going to be in the hospital? Application of epide- miologic principles can help answer these questions. Which is more likely: An uncommon manifestation of a comm on disease or a common manifestation of an uncommon disease? When we consider the significa nce of a new symptom reported by a patient, we are engaging in a process of risk stratifica tion. Is the sudden, stabbing mid-back pain reported by this 65-yr-old diabetic with hypert ension likely to be from muscle strain? Myocardial infarction? Aortic dissection? In for- m ulating a diagnosis, we estimate the likelihood of various diseases in our patient and also consider how commonly each disease would present with the symptoms we see in front of us. Proper use of epidemiology can guide our application of this information to the patient. Co nsider the develop- ment of angioedema in a person taking an angiotensin-converting enzyme (ACE) inhibitor. Angioedema is a rare symptom with ACE inhibitor use (seen in 0.1 \200\2230.2% of patients), but is commonly seen in patients with C1 inhibitor deficiency (perhaps 90% of such patients). C1 inhibitor deficiency itself is rare, occurrin g in only about 1 in 50,000 in the general population. If a patient on an ACE-inhibitor dev elops angioedema, should we check for C1 inhibitor deficiency? We may intuitively feel that it is a reasonable part of a comprehensive evaluation of angioedema. However, the probabil ity of our patient having ACE inhibitor-induced angioedema is 0.1 \200\2230.2%, whereas the probability that he or she has C1 inhibitor is 0.9 \227 1/50,000 or 0.0018%. It is thus 5 0 \200\223100 times more likely that our patient \200\231s angioedema is due to the ACE inh ibitor. Medical decision-making as a type of gambling A busy physician might see 20 patient s in an afternoon session. How should she or he decide which symptoms to monitor expectantl y and which to investigate until a cause is found? Some patients may offer five separate sy mptom groups in the course of a single visit. Full elucidation of these symptoms might reve al additional complaints, leading to a potentially endless cycle of investigation. Certainl y, some of these complaints might not seem too serious ( \200\234this pain in my toe \200\235). But on reflection, even toe pain might be dangerous if caused by emboli or oste omyelitis. Similarly, fingernail problems with a slight rash might mean arsenic poisoning, lethargy may mean cancer, and so on. Since almost any symptom can be seen as a manifestatio n of some fatal illness, an attitude of excessive pessimism is to be avoided, as it would l ead to many sleepless nights for the doctor and much excessive testing for the patient. On the other hand, an attitude of complacency or blind optimism would be no better and might b e worse, resulting in missed diagnoses. 30 Table 2.1 Calculation of the risk of disease outcomes Epidemiologic investigations have enabled the practitioner to calculate a patient \200\231s overall risk based on the presence or absence of several individ- ual risk factors. For ex ample, the Framingham Risk Calculator permits an assessment of cardiac risk. It uses age, t otal cholesterol, high-density lipoprotein (HDL) cholesterol, blood pressure, diabetes, and smoking status to calculate the 10-yr risk of coronary heart disease . Patient: 62-yr-old male smoker with total cholesterol of 242, HDL cholesterol of 41, blood pressure (BP) 148/9 0, not diabetic, on no medications. Age: 60 \200\22364 Total cholesterol: 240 \200\223279 HDL cholesterol: 35 \200\22344 BP: 140 \200\223159/90 \200\22399 Diabetes: No Smoking: Yes 10-yr CHD risk 2 3% This patient thus has a 23% chance of myocardial infarction in the next 10 yrs. http://hp2010.nhlbihin.net/atpiii/calculator.asp Rather, the physician should be a shrewd gambler, able to use subtle clues to change his or her outlook from pessimism to optimism and vise versa. Sometimes the approach is scientifi c, relying on quantitative estimates of the probability of disease, just as the poker playe r calculates the probability of filling an inside straight. But sometimes it is intuitive, as when the gambler reads the face of his opponent, or the physician relies on her gut sens e that something is \200\234not right \200\235 about the appearance of a patient, a sense that may be invaluable even if it cannot be expressed in words or numbers. y g o o m e d p E l i i Evaluation of diagnostic tests Only rarely does a single test provide a definitive diagnosi s. More often, tests alter our assessment of the likelihood of a condition being present. W hen taking a history and examining patients, we estimate (consciously or unconsciously) how likely various diagnoses are. The results of testing modify those estimates. A test is wor thwhile if it alters the post-test prob- ability (i.e., the likelihood that a patient has d isease, given the test results) and if the results affect our management of the patient. Ba sic test statistics All tests have false positives and false negatives, as summarized below : 31 Patient has the condition Patient does not have the condition False positive ( b ) True negative ( d ) Test result is positive Test result is negative True positive ( a ) False negative ( c ) For tests with a dichotomous outcome (i.e., positive or negative), the following statistics are commonly used: \212 Sensitivity: The proportion of people with disease who test posit ive: (a/a + c). \212 Specificity: The proportion of people without disease who test negative: (d/d + b). \212 Positive predictive value: The proportion of people testing positive who have the disease: (a/a + b). \212 Negative predictive value: The proportion of people testing negative who do not have the disease: (c/c + d). Likelihood ratio In addition to the above, the likelihood ratio (LR) is quite useful, becau se it does not require that the test have a dichotomous out- come (i.e., be limited to one of two values). The LR is calculated as the probability of having the given test result if the patient has disease, divided by the probability of having the same result if the patien t does not have disease. The mnemonic WOWO (With Over WithOut) may be helpful. For example, the LR for iron deficiency anemia with a serum ferritin of 15 \200\22324 is 8.8. This mean s that the likelihood of seeing a ferritin level in the range of 15 \200\22324 is 8.8 times greater in patients with iron deficiency than in those without. Furthermore, the LR for a ferritin level of 45 \200\22399 is 0.54. This means that it is almost 50% less common to ob tain a ferritin level of 45 \200\22399 in a patient with iron deficiency than in a patient without iron deficiency. Advantages of the likelihood ratio: There are two major advantages to using LRs. The first is that they enable us to take into account the exact value of a t est result, rather than simply classifying it as positive or negative. This is in accord wi th how we practice medicine. For example, a patient with chest pain and a troponin I level of 20 ng/mL will certainly get our attention more than one with a troponin of 0.6 ng/mL, de spite the fact that both would be reported as \200\234positive \200\235 in a lab that uses a cutoff value of 0.5. In this case, the LR for myocardial infarction associated with a troponin of 20 ng/mL would be much higher th an the LR for a troponin of 0.5 ng/mL. The traditional test statistics (sensitivity, specif icity, and positive and negative predictive values) treat all tests as either positive or n egative and lose the important informa- tion carried by extreme values. The second advantag e to LRs is that they can be used to calculate a post- test probability, even when multiple tests are used in sequence. This uses the formula: Pre-test odds LR(test result) = post-test odds To use this formula, one must convert bet ween probability and odds using the formulas: Odds = probability/(1 \200\223probability) Probability = odds/(1+odds) Handy pocket-card nomograms are available to apply these formulas with- out having to do th e actual calculations. Using likelihood ratios: Assume you are faced with a patient with as cites and abdominal discomfort. You are considering the diagnosis of spontaneous bacterial peritonitis (SBP). After examining the patient, you estimate the chance that she has SBP to be low, about 20%. You perform a paracentesis, and find 600 polymorphonuclear leukocytes ( PMNs)/ L. How does this affect the likelihood that she has SBP? Number of PMNs in asciti c fluid >1,000 501 \200\2231,000 251 \200\223500 0 \200\223250 32 Likelihood ratio 22.3 2.78 1.14 0.08 Pre-test odds = 0.2/(1 \200\223 0.2) = 0.25 Post-test odds = 0.25 2.78 = 0.695 Post-tes t probability = 0.695/(1 + 0.695) = 0.41 Thus, our new estimate is that she has about a 40% probability of SBP. Had the fluid shown >1,000 PMNs, the post-test probability would have been 85%. The effectiveness of therapy While some therapies have gained general acceptance through dr amatic uncontrolled demonstrations (e.g., insulin for diabetes or penicillin for streptococ cal infection), most modern therapies require a comparison with a control group to demonstr ate safety and effectiveness. This can some- times be accomplished using an observational s tudy, such as a cohort study. However, to minimize bias, controlled clinical trials are pre ferred. Experimental studies In contrast to observational studies, experimental studies or clinical trials involve active assignment by the investigator of sub- jects to the treatmen t under consideration. Randomized controlled trials: Assignment to the treatment and contro l groups is performed randomly to avoid selection bias and help ensure that the two groups do not differ with respect to confounding factors. This is generally felt to be the optimal design for assessing the effect of therapeutic interventions. Comparability of groups: In both cohort studies and RCTs, it is desirable that the two groups be similar to one another with respect to all factors that could affect the outcome, except for the intervention bei ng studied. In large RCTs, random assignment makes this likely to occur, but the reader sho uld still check to see that the two groups are comparable. If they are not, adjustment procedur es, such as multivariate analysis, may be used. Allocation concealment: In RCTs, assignment to treatment or control is done without advance knowledge by the investigator or the subje ct. Blinding: Following randomization, it is often desirable to prevent the subjects and me mbers of the study team from knowing who is receiving treatment and who is receiving placeb o. This helps minimize bias in the measurement of the outcome, and it equalizes the placebo effect between the two groups. Outcome measures: If the risk of dying from an myocardial i nfarction (MI) after \200\234standard treatment \200\235 is 10%, and a new treatment reduc es this to 8%, then the relative risk is 0.8 (i.e., 8/10) and the relative risk reduction i s 20% [(10 \200\223 8/10) 100%]. While this may sound impressive, it is important to al so consider the absolute risk reduction (ARR), which is 10% \200\223 8% = 2%. Thus, if 100 people with MI receive the drug, only 2 would be expected to derive benefit. This may als o be expressed as the number needed to treat, which is calculated as (1/ARR), with ARR expr essed as a fraction rather than a percent. In this example the number needed to treat (NNT) would be 1/0.02 = 50; thus, one would need to treat 50 people to prevent one death from MI . If the outcome being studied is rare, the NNT may be quite high. For example, in some pre ventive studies of mild hypertension in the young, 800 people may need to be treated to pr event one stroke. Furthermore, if one were to compare a new antihypertensive regimen to an older one, with the NNT for the older treatment being 800 and the new treatment only margin ally better, the NNT to prevent one death or stroke by adopting the new regimen in place of the old might be several thousand . The implication is that adoption of the new treatment, if it is more expensive than the old, is not likely to be cost-effective. Intention-to-tre at analysis: Despite the best efforts of the investigators, some subjects inevitably fail t o follow the study protocol. What should be done with patients assigned to the control grou p who wind up taking the treat- ment, and vise versa? One option is to analyze the subjects according to what they actually did in the study (i.e., if a subject did not adhere to the treatment plan, he or she should be lumped in with the control group). Similarly, if a sub ject who was assigned to control opted to use the therapy, he or she should be analyzed as part of the treatment group. This is called on-treatment analysis, and it has a certain pla usibility. However, it is now common practice to analyze subjects according to the group to which they were randomized, regardless of whether they stuck with the study plan. This is known as intention-to-treat analysis , and offers the following advantages: \212 The initi al comparability of groups produced by randomization is y g o o m e d p E l i i 33 preserved. \212 The results reflect imperfect adherence and are therefore more similar to what will happen when the intervention is used in actual practice. \212 Outcomes such as death, which may or may not be due to the therapy or control, will not be overlooked. Table 2.2 Examples of NNTs Study Statins for 1 prevention Death (MI) Statins for 2 prevention Death (MI) Mild hype rtension Systolic hypertension in Outcome NNT 931 (78) for 5 yrs 30 (15) for 5.4 yrs 850 for 1 yr 43 for 4.5 yrs Stroke Stroke elderly Aspirin in acute MI A CE-inhibitor for CHF Death Death 40 6 for 1 yr (NYHA class IV) Screening Screening is defined as the application of a test to detect a potential disease o r condition in a person who has no known signs or symptoms of that disease, so that treatme nt might be more effective, less expensive, or both; or with the aim of identifying and mod ifying risk factors to prevent disease. 1 Because screening is applied to a generally healt hy population, most of whom will not benefit from the screening (because they would not hav e developed the disease regardless), extra care must be taken to ensure that the screening is worthwhile. Wilson and Jungner criteria for screening 2 1 The condition being sought sho uld be an important health problem, for 34 the individual and the community. 2 There should be an acceptable form of treatment for patients with identified disease. 3 The natural history of the condition, including its development from latent to declared disease, should be adequately understood. 4 There should be a recognizab le latent or early symptomatic stage. 5 There should be a suitable screening test or examin ation for detecting the disease at the latent or early symptomatic stage, and this test sho uld be acceptable to the population. 6 The facilities required for diagnosis and treatment of patients revealed by the screening program should be available. 7 There should be an agreed policy on whom to treat as patients. 8 Treatment at the presymp tomatic, borderline stage of a disease should favorably influence its course and prognosis. 9 The cost of case-finding (which would include the cost of diagnosis and treatment) needs to be economically balanced in relation to possible expenditure on medical care as a whole. 10 Case-finding should be a continuing process, not a \200\234once and for all \200\235 project. Biases Assessment of screening programs may reveal the following problems: \212 Lead-time bias: Patients who are screened for asymptomatic disease may simply find that they are bein g diagnosed earlier, even if the early diagno- sis does nothing to improve their ultimate p rognosis. If the success of the screening program is measured in 5-yr survival from the tim e of diagnosis, 1 Modified from Eddy D. How to think about screening. In D. Eddy, ed., Screening for Diseas es: Prevention in Primary Care . acp Press, 1991. 2 Wilson JMG, Jungner G. Principles and P ractice of Screening for Disease . Geneva: World Health Organization, 1968. y g o o m e d p E Table 2.3 Screening effectiveness Examples of effective screening Unproven/ineffective screening Chest radiography for lung l i Papanicolaou smears for cervical cancer cancer Mammography for breast cancer Colonoscopy for colorectal CA-125 for ovarian cancer Digital rectal exam and PSA for i adenomas prostate cancer Blood pressure for hypertension Total body CT scanning NB: Screenings for cervical cancer and mammography (p. 425) are far from perfect \200\224 b oth are susceptible to false negatives, and a negative result might lead that patient to ta ke risks or be inattentive to signs of disease occurring between screenings. one may get the incorrect impression that screening is helping patients when it is not. \212 Length bias: Cases of indolent disease, such as slow-growing prostate cancers, are mo re likely to be discovered on routine screening because they remain in the population for a longer time. Because such cases have a generally better prognosis, studies that use surviv al from time of diag- nosis as the outcome will find benefit among patients who are screene d over those who are not. 35 \212 Overdiagnosis bias: In the extreme case, some patients may harbor cancers (e.g., of t he prostate, kidney, or thyroid) that would never have caused symptoms during their natural life. Identification and removal of such \200\234cancers \200\235 is not helpful to the p atient and may cause harm, yet inclu- sion of such cases in survival statistics may give th e false impression of improved survival among those undergoing screening. Clinical skills L. David Martin, M.D. s l l i k s Contents Taking a history 37 Review of systems 40 Physical examination 42 The cardiovascular system 44 Jugular venous pressure 47 Pulses 48 Heart sounds 50 Heart murmurs 52 The respiratory sy stem 54 Examining the chest 56 GI history 58 Examining the GI system 58 Genitourinary histo ry 59 The neurological system 61 Cranial nerve examination 63 Speech and higher mental func tion 65 Psychiatric assessment 68 l a c i n i l C 3 36 Clinicians who are skilled at the bedside clinical evaluation make better use of diagnostic tests and order fewer unnecessary tests. The way to learn physical signs is at the bedside , with guidance from an experienced col- league. This chapter is not intended as a substitu te for this process: It is simply an aid and an introduction to the important science behin d the art of clinical examination. * We ask questions to get information to help with the d ifferential diagnosis. But we also ask questions to find out about the inner life and past exploits of our patients so that we can respect them as individuals and understand them as a whole. In the words of Sir William Osler, \200\234The good physician treats the disease; the great physician treats the patient who has the dis- ease. \200\235 Knowing our patient s as individuals helps build the mutual respect and trust that are the foundation of effect ive doctor-patient relationships. Optimal patient care requires understanding patients \200 \231 preferences, abilities, resources, and family and social circumstances. Beyond the mec hanics of delivering medical care lies the art of healing. See Table 3.1. Principal sources for this chapter are Talley NJ, O \200\231Connor S. Clinical Examination : A Systematic Guide to Physical Diagnosis, 5th ed. London: Elsevier, 2005; Burton JL, Burt on JBL. Aids to Undergraduate Medicine, 6th ed. New York: Churchill Livingstone, 1997; McGe e SR. Evidence-Based Physical Diagnosis . New York: Elsevier, 2007; Simel DL, Rennie D (eds .). The Rational Clinical Examination: Evidence-Based Clinical Diagnosis. New York: McGraw- Hill, 2007; and The Stanford 25: An initiative to revive the culture of bedside medicine. h ttp://stanford25.wordpress.com. s l l i k s Table 3.1 The patient now waiting for you in ro om 9 1 The first news of your next patient will often be via a phone call: \200\234There \200\231 s an MI on the way in, \200\235 or \200\234There \200\231s someone delirious in room 9, \200\235 or \200\234Can you take the overdose in room 12? \200\235 On hearing such dehuman - ized descriptions, our minds will start painting pictures, colored by the tone of these m essages. So, when we arrive at the bedside, our mind is far from a tabula rasa or blank can vas on which the patient can paint his woes. The mind is always painting pictures, filling in gaps \200\224and falling into traps. Perception is an active process, for, as Marcel Pro ust, that life-long all-knowing patient, observed 1 : l a c i n i l C We never see the people who are dear to us save in the animated system, the perpetual motio n of our incessant love for them, which before allowing the images that their faces present to reach us catches them in its vortex, flings them back upon the idea that we have always had of them, makes them adhere to it, coincide with it. So, if you want to know your patient, take snapshots of him from vari- ous angles and brief ly contemplate him in the round before Proust \200\231s vortex whisks you off track. You ca n prepare for these snapshots in the blink of an eye, saying to yourself: \200\234When I o pen my eyes, I \200\231m going to see my patient face to face \200\235 and in that clinical blink, divest yourself of those prejudices and expectations that all good diagnosticians s omehow ignore. When you open your eyes, you will be all set for a Gestalt rec- ognition of incipient myxedema (the cause of the delirium in room 9), jaundice, anemia, or, perhaps mor e important, the recognition that the person in front of you is frightened, failing, or dyi ng. 37 1 Proust M. The Guermantes Way, trans. Scott Moncrieff. Taking a history Taking histories is what most of us spend much of our professional life do ing: It is worth doing it well. An accurate history is the biggest step in making the corre ct diagnosis. History-taking, examination, and treatment of a patient begin the moment one reaches the bedside. (The divisions imposed by our page titles are somewhat misleading.) Tr y to put the patient at ease: A good rapport may relieve distress on its own. Always introd uce yourself and explain your role (student, resident, attending, etc.). It often helps to shake hands. Please do so gently, for if they have an underlying arthritis, a \200\234norm al \200\235 grasp may be quite uncomfortable. Check whether the patient is comfortable and ask permission to minimize distractions (such as a loud television). Be conversational rath er than interrogative in tone. General questions ( \200\234Before we get into the story of your symptoms, can you tell me about yourself? \200\235) help break the ice and help assess mental functions. Chief complaint (CC) \200\234What has been the trouble recently? \200 \235 Record the patient \200\231s own words rather than medical shorthand (e.g., short of b reath rather than \200\234dyspnea \200\235). Set the agenda Elicit the patient \200\231s a dditional presenting concerns, if any, beyond the chief complaint. Style of questioning At first, questions should be open-ended. Once a differential diagnosis begins to crystallize, ask specific directed questions about the diagnoses you have in mind and associated risk factors (e.g., travel history; p. 546). History of presenting illness (HPI) \200\234Tell me more about your symp- toms/illne ss. \200\235 Establish chronology. When did it start? What was the first thing noticed? Pro gress since then. \200\234Ever had it before? \200\235 PQRST Questions: Provocative/pallia tive factors, Quality of pain (sharp/ burning/deep/aching), Region/radiation, Severity (sca le of 1 \200\22310, 10 being most severe), Timing (duration and sequence with other symptom s). Appreciate the patient \200\231s perspective What does the patient think is going on? W hat are the patient \200\231s worries or fears about his or her symp- toms/health? How is t his illness affecting his or her life? Past medical history (PMH) Ever hospitalized? Illnes ses? Operations? Ask specifically about common conditions such as diabetes, asthma, hyper- tension, heart disease, cancer. Allergies Ask the features of allergies; often patients rep ort significant medication side effects that are not true allergies. Medications Prescribed medications? Any \200\234over-the-counter \200\235 drugs, includ- ing aspirin? Herbal rem edies, oral contraceptives, insulin or other injections? Family history (FH) Age, health, a nd cause of death, if known, of parents, siblings, and children; ask about common condition s and conditions rel- evant to the patient \200\231s illness. Areas of the FH may need deta iled questioning (e.g., to determine if there is a significant FH of heart disease, you nee d to specify the age at which an affected first-degree relative had his or her first heart attack or stroke [prior to age 50 in men, prior to age 60 in women is considered significan t]). See Table 3.2 and Figure 3.1. Social history (SH) Probe without prying. \200\234Who e lse is there at home? \200\235 Include elements of education, language, and health literacy ; relationships, marital status, family, and support (including faith/religion); important life events, job, lifestyle, and home situation; sexual history; dietary habits, sub- stanc e use and abuse (see below), and other risk behaviors. Alcohol, illicit drugs, tobacco How much? How long? When stopped? There are several ways to screen for unhealthy alcohol use, i ncluding the CAGE questionnaire (p. 69). Ever injected drugs? Shared needles? Quantify smok ing in terms of pack-years : 20 cigarettes smoked per day for 1 yr equals 1 pack-year. Don \200\231t make assumptions about substance use based on age or other demographic informatio n: All patients should be screened. Review of systems (ROS) To uncover undeclared symptoms. Some of this may already have been incorporated into the history. Don \200\231t hesitate t o retake the history after a few days: Recollections change. 38 Table 3.2 Drawing family trees to reveal domi nantly inherited disease Advances in genetics touch all branches of medicine. It is increasingly important for docto rs to identify patients at high risk of genetic disease and to make appropriate referrals. The key skill is drawing a family tree to help you structure a family history as follows: 1 Start with your patient : Draw a square for a male and a circle for a female. Add a small arrow (see below) to show that this person is the propositus (the person through whom the f amily tree is ascertained). 2 Add your patient \200\231s parents, brothers, and sisters: Re cord basic informa- tion only (e.g., age, and if alive and well [a&w]). If dead, note age a nd cause of death, and pass a diagonal line through that person \200\231s symbol. (Continued) s l l i k s Table 3.2 (Continued) 3 Ask the key question \200\234Has anybody else in your family had a similar problem as yo urself ? \200\235 (e.g., heart attack/angina/stroke/cancer). Ask only about the family of d iseases that relate to your patient \200\231s main problem. Do not record a full medical hi story for each family member: Time is too short. l a c i n i l C 4 Extend the family tree upward to include grandparents: If you haven \200\231t revealed a problem by now, go no further \200\224you are unlikely to miss important familial disease. If your patient is elderly, it may be impos- sible to obtain good information about grandpa rents. If so, fill out the family tree with your patient \200\231s uncles and aunts on both the mother \200\231s and father \200\231s sides. 5 Shade those in the family tree affected by the disease: \212= an affected female; \200 = an affected male. This helps to show any genetic problem and, if there is one, will help demonstrate the pattern of inheritance. 6 Extend the tree: If you have identified a famili al susceptibility, or your patient has a recognized genetic disease, extend the family tree down to include children, to identify others who may be at risk and who may benefit from s creening. You must find out who is pregnant in the family, or may soon be, and arrange appr opriate genetic counseling. The family tree in Figure 3.1 shows these ideas at work and indicates that there is evidenc e for genetic risk of colon cancer. 39 \200\234This section owes much to Dr. Helen Firth, whom we thank. \200\235 Review of systems The review of systems is a compendium of medical symptoms, grouped by bod y system. It is an important safety net to identify issues the clinician may have forgotten to ask about or the patient forgot to mention. It is typically presented as a series of cl osed yes-or-no questions, heralded by a transition statement such as, \200\234Now I \200 \231d like to ask you a series of questions about whether you \200\231ve recently experienc ed different types of medical symptoms, and you can simply answer yes or no. \200\235 Obtai n enough detail about posi- tive responses to determine whether positive symptoms are signi ficant or trivial (the more severe and chronic the symptom, the more likely it is to be imp ortant). With increasing experience, the review of systems can often be parsed down and gre atly expedited. Some experienced clinicians systemati- cally review systems as they are per forming the physical examination. Elements of selected system reviews are illustrated below . General questions may be the most significant (e.g., in identifying cancer, endocrine pro blems, or tuberculosis [TB]): \212 \212 \212 \212 \212 \212 \212 \212 \212 Cardior espiratory symptoms Chest pain. Palpitations (awareness of heartbeats). Exertional dyspnea (= breathlessness; quantify exercise toler- ance and how it has changed : E.g., stairs clim bed or distance walked before onset of breathlessness). Paroxysmal nocturnal dyspnea (sensa tion of short- ness of breath, often with coughing/choking, that awakens the patient). Orth opnea (breathlessness on lying flat: Quantify the number of pillows the patient must sleep on to prevent dyspnea). Edema. Intermittent claudication (reproducible leg pain that occurs with exercise and is relieved by rest). Cough. Sputum. Hemoptysis (coughing up blood). Whe ezing. GI symptoms Think of symptoms throughout the GI tract, from mouth to anus: \212 \212 \212 \212 \212 Tenesmus is a feeling of incomplete defecation (e.g., due to rectal inflam- mation or tumor). Hematemesis is vomiting blood. Melena refers to black, tarry, fou l-smelling stools, and hematochezia refers to passage of red blood per rectum, usually in o r with stools. Genitourinary symptoms \212 \212 \212 Weight loss Night sweats Any lumps Fatigue Sleeping pattern Appetite Fevers Itching Recent trauma 40 Swallowing Indigestion Nausea/vomiting Abdominal pain Bowel habit Incontinence (stress or urge) Dysuria (painful micturition) Hematuria (bloody urine) \212 \212 Nocturia (needing to urinate at night) Frequency (frequent urination) or polyuria (passing excessive amounts of urine) Hesitancy (difficulty starting urination) Terminal dribbling Va ginal discharge Menses: Frequency, regularity, heavy or light, duration, painful First day of last menstrual period (LMP) Number of pregnancies Menarche Menopause Any chance of pregn ancy now? s l l i k s l a c i n \212 \212 \212 \212 \212 \212 \212 \212 \212 Neurological symptoms \212Special se nses: Sight, hearing, smell, and taste \212 \212 \212 \212 \212 \212 \212 The import ant thing is to assess function: What the patient can and cannot do at home, work, etc. Mus culoskeletal symptoms \212 Pain, stiffness, swelling of joints \212 Trauma \212 Function al deficit Thyroid symptoms \212Hyperthyroidism: Prefers cold weather, ill-tempered, sweat y, diarrhea, oli- gomenorrhea, weight loss (although often \206\221 appetite), tremor, pal pita- tions, visual problems i l C Seizures, fainting Headache \200\234Pins and needles \200\235 (paresthesias) or numbness W eakness ( \200\234Do your arms and legs work? \200\235), poor balance Speech problems Sphin cter disturbance Higher mental function and psychiatric symptoms 41 \212Hypothyroidism: Depressed, slow, tired, thin hair, hoarseness, heavy peri- ods, constipation, dry skin, prefers warm weather History-taking may seem deceptively easy, as if the patient knew the hard facts and the onl y problem was extracting them; but what a patient says is a mixture of hearsay ( \200\234sh e said I looked very pale \200\235), innuendo ( \200\234you know, doctor: down below \200 \235), legend ( \200\234I suppose I bit my tongue; it was a real seizure, you know \200\235 ), exaggeration ( \200\234I haven \200\231t slept for 2 weeks \200\235), and improbabilitie s ( \200\234the Pope put a transmitter in my brain \200\235). The great skill (and pleasure ) in taking a history lies not in ignoring these garbled mes- sages, but in making sense of them. Collateral history taken from family members and other caregivers can also be very i mportant in developing the story of a patient \200\231s illness.