A new testing modality is being considered for measuring blood glucose levels. If a patient’s true blood glucose level is 86 mg/dL and the new test returns three consecutive results (101 mg/dL, 99 mg/dL, and 100 mg/dL), what can you conclude about this new test?
Reliable but not accurate. Distinguishing reliability from accuracy is very important for study analysis. A reliable test essentially gives very close (almost the same) results on repeat measurement. Reliability is also synonymous with reproducibility and precision. The test generated three very similar blood glucose levels, therefore the test is reliable. However, the patient’s true level is 86 mg/dL. An accurate test is one that measures the true value of a test. This test is not close to the true level of 86 mg/dL; therefore the test is not accurate.
A study of total cholesterol levels in patients with familial hyperlipidemia shows that the data is normally distributed with a mean of 215 mg/dL and a standard deviation (SD) of 15 mg/dL.
Based on these results, 95% of total cholesterol levels in these patients fall in which of the following ranges?
185 and 245 mg/dL. Normally distributed results are symmetric and bell-shaped. Normal distributions are helpful for predicting the percentage of observations that fall within certain limits from the average or mean. This level of deviation from the mean is called standard deviation. 95% of all observations fall within two standard deviations from the mean. In this case, the standard deviation is 15, so 2 × 15 = 30, and 215 ± 30 = 185 and 245. Using the 68-95-99.7 rule (one SD in each direction from the mean creates a 68% confidence interval, two SDs create a 95% confidence interval, and three SDs create a 99.7% confidence interval), there is a 95% chance that this population will have a reading within the range of 185 to 245 mg/dL (Figure below). (C) 200 and 230 mg/dL would be the answer if the questions were asking for 68% of observations (or one standard deviation from the mean).
A study was designed to evaluate a new serologic marker for diagnosing ovarian cancer. Hundred patients with strong family histories of ovarian cancer were selected randomly from the population, screened using the new test, and screened again (using biopsy) to determine the true diagnosis. The findings are summarized in table below.
Which of the following is the sensitivity of the new screening test for ovarian cancer?
5/15. Sensitivity tells you of all patients with TRUE disease state, how many test positive. It is the measure of true positives divided by (true positives + false negatives). In this case it is 5 divided by 15, or 33%. Sensitivity is used to rule diseases out and high sensitivity indicates superior screening value. (B) 40/85 is this test’s specificity. Specificity tells you of all patients who do NOT have disease state, how many truly test negative. It is useful for ruling disease in. (C) 5/50 is this test’s positive predictive value, which tells you of all patients who test positive, how many truly have the disease state; it is measured by taking the true positives and dividing by (true positives + false positives). (D) 40/50 is this test’s negative predictive value, which tells you of all patients who test negative, how many do not have the disease state. It is important to remember that the positive and negative predictive values depend on the prevalence of the disease in the population, whereas sensitivity and specificity are characteristics of the diagnostic test itself and are thus unchanged by the prevalence of the disease.
A study was conducted to evaluate a new test intended for confirmation of tuberculosis infection after a positive purified protein derivative (PPD). The study data revealed a sensitivity of 95%, a specificity of 25%, a positive predictive value of 90%, and a negative predictive value of 85%.
Which of the following can be said about this new test?
Accurate test for screening but not for confirmation of disease. This test generates a high sensitivity (95%) and a low specificity (25%). Sensitivity is a valuable metric for screening diseases, and specificity is a valuable metric for the confirmation of diseases (sensitivity rules out and specificity rules in—“sn-out” and “sp-in”). Therefore, the new test in this question would be excellent for screening tuberculosis infection but not for final confirmation of the disease. (B) This would be the answer if both sensitivity and specificity were low. (C) This would be the answer if this test had a high specificity and a low sensitivity. (D) This would be the answer if both sensitivity and specificity were high.
A 49-year-old man presents for routine physical examination. The patient has no active complaints today other than occasional constipation and increased frequency of urination. The patient reports that he has a diagnosis of “pre-diabetes” given to him by his previous physician in another city. His family history is significant for hypertension. The patient denies alcohol or drug use and works as a long-distance truck driver. Physical examination is unremarkable except for a body mass index of 31 kg/m2 . The patient’s fasting laboratory values reveal the following:
Which of the following is the best next step in the management of this patient?
Atorvastatin and lifestyle modification. This patient is presenting with a new diagnosis of type 2 diabetes mellitus (T2DM) given his symptoms (frequency of urination), increased fasting glucose, and elevated hemoglobin A1c. Patients with diabetes are at significant risk for cardiovascular atherosclerotic events. Therefore, patients who are greater than the age of 40 with diabetes should be on a statin medication (atorvastatin) and begin intensive lifestyle modification (exercise, diet). (C) A sulfonylurea (e.g., glimepiride) is not the initial drug of choice with new-onset diabetes. Metformin therapy is recommended after diagnosing T2DM. (D) Niacin is helpful in raising high-density lipoprotein, but can actually worsen glucose control in diabetics. Fish oil is helpful in reducing serum triglycerides but does not have an effect on improving cardiovascular outcomes.
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