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Background: The value of the ratio of plasma aldosterone concentration (aldosterone) to plasma renin activity (renin) to identify patients at risk for primary aldosteronism is controversial. We determined the sensitivity, specificity, and predictive value of the ratio to identify combinations of renin and aldosterone compatible with primary aldosteronism., Methods: The ratio was calculated in 505 adults with essential hypertension (143 black women, 82 black men, 122 white women, and 158 white men). We used a conventional cutpoint for an increased ratio (i.e., 20 mL/dL x h). The primary combination of renin and aldosterone considered compatible with primary aldosteronism was increased aldosterone for the concomitant renin, defined as aldosterone in the highest quartile predicted by linear regression on renin. Renin was considered low if it was in the lowest quartile of the sample distribution., Results: The sensitivity of the ratio to identify individuals with increased aldosterone for the concomitant renin was 66% (80% in blacks and 56% in whites; P = 0.009), and the specificity of the ratio was 67% (46% in blacks and 84% in whites; P <0.0001). In 36% of instances of an increased ratio, it was a measure of low renin alone without increased aldosterone for renin (32% in blacks and 45% in whites; P = 0.072). The positive predictive value of the ratio to identify individuals with increased aldosterone for the concomitant renin was 34% (49% in whites and 27% in blacks; P <0.002)., Conclusion: The aldosterone/renin ratio lacks sensitivity and specificity and has only a modest predictive value for combinations of renin and aldosterone that are compatible with primary aldosteronism.

Pharmacogenomics is a term recently coined to embody the concept of individualized and rational drug selection based on the genotype of a particular patient. Customization of drug therapy offers the potential for optimal safety and efficacy in an individual patient. Such a process contrasts current prescribing practices, which use medications shown to be safe and effective in patient populations or based on anecdotal experiences. Within patient populations, medications vary in their efficacy among individual patients. More importantly, a medication that is safe and effective in one patient may be ineffective or even harmful in another. Underlying many of these phenotypic differences are genotypic variants (polymorphisms) of key enzymes and proteins that affect the safety and efficacy of a drug in an individual patient. An understanding of these polymorphisms has the potential to enhance patient care by allowing physicians to customize the selection of medication to meet individual patient needs. Pharmacogenomics may also lead to improved compliance and shorter time to optimal disease management, thereby reducing morbidity and mortality. Significant cost savings could result from reductions in polypharmacy as well as from fewer physician encounters and hospitalizations for exacerbations of underlying illness and because of adverse drug reactions.

Laboratory tests are key indicators for certain practice guidelines, and analytic bias can significantly alter the performance of these guidelines. Three clinical paradigms are described: serum cholesterol testing for risk assessment of cardiac disease, serum thyroid-stimulating hormone (TSH) measurement for the detection of hypothyroidism, and serum prostate-specific antigen (PSA) testing for prostate cancer risk assessment. Maximum tolerance limits for analytic bias are calculated by assessing the subgroup population fluctuations in the number of patients exceeding the guideline threshold values and limiting the analytic bias to one-half of these fluctuations. Our calculated maximum bias limits are +/-1% for cholesterol and +/-6% for TSH and PSA. Our recommended +/-1% bias limit for cholesterol allows for a -6.5% to + 5.8% change in the number of patients designated as at risk for cardiac disease, whereas the +/-3% National Cholesterol Education Program limits permit a -18.4% to +16.7% variation. Similarly, our +/-6% bias limits for TSH allow a -17.7% to +26.6% change in patients flagged for hypothyroidism, whereas the +/-10% bias values found with many commercial reagents permit a -28.2% to +49.2% variation in patient classification. Our +/-6% PSA bias limits correspond to changes from -14.2% to +11.4% in the number of men classified as at risk for prostate cancer. The +/-10% bias ranges for PSA correspond to -19.9% to +20.4% variation in patient classification. The larger tolerance limits of the CLIA-88 standards for proficiency testing would cause even wider variations in patient classifications.