Personalized and Population-Based Reference Intervals for 48 Common Clinical Chemistry and Hematology Measurands: A Comparative Study.

BACKGROUND: Personalized reference intervals (prRIs) have the potential to improve individual patient follow-up as compared to population-based reference intervals (popRI). In this study, we estimated popRI and prRIs for 48 clinical chemistry and hematology measurands using samples from the same reference individuals and explored the effect of using group-based and individually based biological variation (BV) estimates to derive prRIs. METHODS: 143 individuals (median age 28 years) were included in the study and had fasting blood samples collected once. From this population, 41 randomly selected subjects had samples collected weekly for 5 weeks. PopRIs were estimated according to Clinical Laboratory Standards Institute EP28 and within-subject BV (CVI) were estimated by CV-ANOVA. Data were assessed for trends and outliers prior to calculation of individual prRIs, based on estimates of (a) within-person BV (CVP), (b) CVI derived in this study, and (c) publically available CVI estimates. RESULTS: For most measurands, the individual prRI ranges were smaller than the popRI range, but overall about half the study participants had a prRI wider than the popRI for 5 or more out of 48 measurands. The dispersion of prRIs based on CVP was wider than that of prRIs based on CVI. CONCLUSION: The prRIs derived in our study varied significantly between different individuals, especially if based on CVP. Our results highlight the limitations of popRIs in interpreting test results of individual patients. If sufficient data from a steady-state situation are available, using prRI based on CVP estimates will provide a RI most specific for an individual patient.

Personalized reference intervals - statistical approaches and considerations.

For many measurands, physicians depend on population-based reference intervals (popRI), when assessing laboratory test results. The availability of personalized reference intervals (prRI) may provide a means to improve the interpretation of laboratory test results for an individual. prRI can be calculated using estimates of biological and analytical variation and previous test results obtained in a steady-state situation. In this study, we aim to outline statistical approaches and considerations required when establishing and implementing prRI in clinical practice. Data quality assessment, including analysis for outliers and trends, is required prior to using previous test results to estimate the homeostatic set point. To calculate the prRI limits, two different statistical models based on 'prediction intervals' can be applied. The first model utilizes estimates of 'within-person biological variation' which are based on an individual's own data. This model requires a minimum of five previous test results to generate the prRI. The second model is based on estimates of 'within-subject biological variation', which represents an average estimate for a population and can be found, for most measurands, in the EFLM Biological Variation Database. This model can be applied also when there are lower numbers of previous test results available. The prRI offers physicians the opportunity to improve interpretation of individuals' test results, though studies are required to demonstrate if using prRI leads to better clinical outcomes. We recommend that both popRIs and prRIs are included in laboratory reports to aid in evaluating laboratory test results in the follow-up of patients.

Personalized Reference Intervals in Laboratory Medicine: A New Model Based on Within-Subject Biological Variation.

BACKGROUND: The concept of personalized medicine has received widespread attention in the last decade. However, personalized medicine depends on correct diagnosis and monitoring of patients, for which personalized reference intervals for laboratory tests may be beneficial. In this study, we propose a simple model to generate personalized reference intervals based on historical, previously analyzed results, and data on analytical and within-subject biological variation. METHODS: A model using estimates of analytical and within-subject biological variation and previous test results was developed. We modeled the effect of adding an increasing number of measurement results on the estimation of the personal reference interval. We then used laboratory test results from 784 adult patients (>18 years) considered to be in a steady-state condition to calculate personalized reference intervals for 27 commonly requested clinical chemistry and hematology measurands. RESULTS: Increasing the number of measurements had little impact on the total variation around the true homeostatic set point and using ≥3 previous measurement results delivered robust personalized reference intervals. The personalized reference intervals of the study participants were different from one another and, as expected, located within the common reference interval. However, in general they made up only a small proportion of the population-based reference interval. CONCLUSIONS: Our study shows that, if using results from patients in steady state, only a few previous test results and reliable estimates of within-subject biological variation are required to calculate personalized reference intervals. This may be highly valuable for diagnosing patients as well as for follow-up and treatment.

Biological Variation Estimates for Plasma Copeptin and Clinical Implications.

BACKGROUND: Plasma copeptin measurement is useful for the differential diagnoses of polyuria-polydipsia syndrome. It has also been proposed as a prognostic marker for cardiovascular diseases. However, limited information is available about the within- (CVI) and between-subject (CVG) biological variation (BV). This study presents BV estimates for copeptin in healthy individuals. METHODS: Samples were collected weekly from 41 healthy subjects over 5 weeks and analyzed using the BRAHMS Copeptin proAVP KRYPTOR assay after at least 8 h of food and fluid abstinence. Outlier detection, variance homogeneity, and trend analysis were performed followed by CV-ANOVA for BV and analytical variation (CVA) estimation with 95% confidence intervals. Reference change values (RCVs), index of individuality (II), and analytical performance specification (APS) were also calculated. RESULTS: The analysis included 178 results from 20 males and 202 values from 21 females. Copeptin concentrations were significantly higher in males than in females (mean 8.5 vs 5.2 pmol/L, P < 0.0001). CVI estimates were 18.0% (95% CI, 15.4%-21.6%) and 19.0% (95% CI, 16.4%-22.6%), for males and females, respectively; RCVs were -35% (decreasing value) and 54% (increasing value). There was marked individuality for copeptin. No result exceeded the diagnostic threshold (>21.4 pmol/L) for arginine vasopressin resistance. CONCLUSIONS: The availability of BV data allows for refined APS and associated II, and RCVs applicable as aids in the serial monitoring of patients with specific diseases such as heart failure. The BV estimates are only applicable in subjects who abstained from oral intake due to the rapid and marked effects of fluids on copeptin physiology.

Relationship among plasma secretory phospholipase A2, oxidized low density lipoprotein & paraoxonase activities in hypertensive subjects treated with angiotensin converting enzyme inhibitors.

BACKGROUND & OBJECTIVE: Secretory phospholipase A(2) (sPLA(2)) and oxidized low density lipoprotein (oxLDL) are considered as oxidative and inflammatory markers. The effects of oxLDL have been shown to be inhibited by paraoxonase (PON1). This study was undertaken to investigate the relationship between oxidative and inflammatory markers in hypertensive patients with or without antihypertensive drug treatment. METHODS: Newly diagnosed hypertensive patients (n=35) and hypertensive patients who had been taking angiotensin converting enzyme (ACE) inhibitors as antihypertensive therapy (10 or 20 mg/day for 9 +/- 2 wk; n=35) and age-matched normotensive subjects (controls; n=20) were included in this study. Plasma sPLA(2), oxLDL and PON1 activities were determined. RESULTS: Hypertensives had higher plasma oxLDL and sPLA(2) levels (P<0.01) and lower PON1 levels than the controls (P<0.01). Treated hypertensives had lower plasma sPLA< and oxLDL levels and higher PON1 activities than hypertensives (P<0.01). sPLA(2) was positively correlated with oxLDL (r=0.433, P<0.01) and negatively correlated with plasma PON1 (r=-0.540, P<0.01) in untreated hypertensives. In controls and treated hypertensives, plasma PON1 was positively correlated with oxLDL (r=0.455, r=0.429, P<0.01, respectively) and sPLA(2) (r=0.450, r=0.506, P<0.01, respectively). INTERPRETATION & CONCLUSION: Reduction in PON1 activity and elevation in both sPLA(2) activities and oxLDL levels might be involved in elevated oxidative stress and inflammation. ACE inhibitor treatment may help reduce inflammation and oxidative stress in hypertensives.