Abstract
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.
Abstract
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.
Using laboratory test results for diagnosis and monitoring requires a reliable reference to which the results can be compared. Currently, most reference data is derived from the population, and ...patients in this context are considered members of a population group rather than individuals. However, such reference data has limitations when used as the reference for an individual. A patient's test results preferably should be compared with their own, individualized reference intervals (RI), i.e. a personalized RI (prRI).
The prRI is based on the homeostatic model and can be calculated using an individual's previous test results obtained in a steady-state situation and estimates of analytical (CV
A
) and biological variation (BV). BV used to calculate the prRI can be obtained from the population (within-subject biological variation, CV
I
) or an individual's own data (within-person biological variation, CV
P
). Statistically, the prediction interval provides a useful tool to calculate the interval (i.e. prRI) for future observation based on previous measurements. With the development of information technology, the data of millions of patients is stored and processed in medical laboratories, allowing the implementation of personalized laboratory medicine. PrRI for each individual should be made available as part of the laboratory information system and should be continually updated as new test results become available.
In this review, we summarize the limitations of population-based RI for the diagnosis and monitoring of disease, provide an outline of the prRI concept and different approaches to its determination, including statistical considerations for deriving prRI, and discuss aspects which must be further investigated prior to implementation of prRI in clinical practice.
Biological variation (BV) data have many applications for diagnosing and monitoring disease. The standard statistical approaches for estimating BV are sensitive to "noisy data" and assume homogeneity ...of within-participant CV. Prior knowledge about BV is mostly ignored. The aims of this study were to develop Bayesian models to calculate BV that (
) are robust to "noisy data," (
) allow heterogeneity in the within-participant CVs, and (
) take advantage of prior knowledge.
We explored Bayesian models with different degrees of robustness using adaptive Student
distributions instead of the normal distributions and when the possibility of heterogeneity of the within-participant CV was allowed. Results were compared to more standard approaches using chloride and triglyceride data from the European Biological Variation Study.
Using the most robust Bayesian approach on a raw data set gave results comparable to a standard approach with outlier assessments and removal. The posterior distribution of the fitted model gives access to credible intervals for all parameters that can be used to assess reliability. Reliable and relevant priors proved valuable for prediction.
The recommended Bayesian approach gives a clear picture of the degree of heterogeneity, and the ability to crudely estimate personal within-participant CVs can be used to explore relevant subgroups. Because BV experiments are expensive and time-consuming, prior knowledge and estimates should be considered of high value and applied accordingly. By including reliable prior knowledge, precise estimates are possible even with small data sets.
Biological variation (BV) data have many important applications in laboratory medicine. Concerns about quality of published BV data led the European Federation of Clinical Chemistry and Laboratory ...Medicine (EFLM) 1st Strategic Conference to indicate need for new studies to generate BV estimates of required quality. In response, the EFLM Working Group on BV delivered the multicenter European Biological Variation Study (EuBIVAS). This review summarises the EuBIVAS and its outcomes. Serum/plasma samples were taken from 91 ostensibly healthy individuals for 10 consecutive weeks at 6 European centres. Analysis was performed by Siemens ADVIA 2400 (clinical chemistry), Cobas Roche 8000, c702 and e801 (proteins and tumor markers/hormones respectively), ACL Top 750 (coagulation parameters), and IDS iSYS or DiaSorin Liaison (bone biomarkers). A strict preanalytical and analytical protocol was applied. To determine BV estimates with 95% CI, CV-ANOVA after analysis of outliers, homogeneity and trend analysis or a Bayesian model was applied. EuBIVAS has so far delivered BV estimates for 80 different measurands. Estimates for 10 measurands (non-HDL cholesterol, S100-β protein, neuron-specific enolase, soluble transferrin receptor, intact fibroblast growth-factor-23, uncarboxylated-unphosphorylated matrix-Gla protein, human epididymis protein-4, free, conjugated and %free prostate-specific antigen), prior to EuBIVAS, have not been available. BV data for creatinine and troponin I were obtained using two analytical methods in each case. The EuBIVAS has delivered high-quality BV data for a wide range of measurands. The BV estimates are for many measurands lower than those previously reported, having an impact on the derived analytical performance specifications and reference change values.
Acute intermittent porphyria (AIP) is an autosomal dominant inherited disease with low clinical penetrance, caused by mutations in the hydroxymethylbilane synthase (
) gene, which encodes the third ...enzyme in the haem biosynthesis pathway. In susceptible
mutation carriers, triggering factors such as hormonal changes and commonly used drugs induce an overproduction and accumulation of toxic haem precursors in the liver. Clinically, this presents as acute attacks characterised by severe abdominal pain and a wide array of neurological and psychiatric symptoms, and, in the long-term setting, the development of primary liver cancer, hypertension and kidney failure. Treatment options are few, and therapies preventing the development of symptomatic disease and long-term complications are non-existent. Here, we provide an overview of the disorder and treatments already in use in clinical practice, in addition to other therapies under development or in the pipeline. We also introduce the pathomechanistic effects of
mutations, and present and discuss emerging therapeutic options based on HMBS stabilisation and the regulation of proteostasis. These are novel mechanistic therapeutic approaches with the potential of prophylactic correction of the disease by totally or partially recovering the enzyme functionality. The present scenario appears promising for upcoming patient-tailored interventions in AIP.
Concern has been raised about the quality of available biological variation (BV) estimates and the effect of their application in clinical practice. A European Federation of Clinical Chemistry and ...Laboratory Medicine Task and Finish Group has addressed this issue. The aim of this report is to (
) describe the Biological Variation Data Critical Appraisal Checklist (BIVAC), which verifies whether publications have included all essential elements that may impact the veracity of associated BV estimates, (
) use the BIVAC to critically appraise existing BV publications on enzymes, lipids, kidney, and diabetes-related measurands, and (
) apply metaanalysis to deliver a global within-subject BV (CV
) estimate for alanine aminotransferase (ALT).
In the BIVAC, publications were rated as A, B, C, or D, indicating descending compliance for 14 BIVAC quality items, focusing on study design, methodology, and statistical handling. A D grade indicated that associated BV estimates should not be applied in clinical practice. Systematic searches were applied to identify BV studies for 28 different measurands.
In total, 128 publications were identified, providing 935 different BV estimates. Nine percent achieved D scores. Outlier analysis and variance homogeneity testing were scored as C in >60% of 847 cases. Metaanalysis delivered a CV
estimate for ALT of 15.4%.
Application of BIVAC to BV publications identified deficiencies in required study detail and delivery, especially for statistical analysis. Those deficiencies impact the veracity of BV estimates. BV data from BIVAC-compliant studies can be combined to deliver robust global estimates for safe clinical application.
Abstract The porphyrias are a group of rare inborn errors of metabolism associated with various clinical presentations and long‐term complications, making them relevant differential diagnoses to ...consider for many clinical specialities, especially hepatologists, gastroenterologists and dermatologists. To diagnose a patient with porphyria requires appropriate biochemical investigations, as clinical features alone are not specific enough. Furthermore, it is important to be aware that abnormalities of porphyrin accumulation and excretion occur in many other disorders that are collectively far more common than the porphyrias. In this review, we provide an overview of porphyria‐related tests with their strengths and limitations, give recommendations on requesting and diagnostic approaches in non‐expert and expert laboratories for different clinical scenarios and discuss the role of genetic testing in the porphyrias. To diagnose porphyria in a currently symptomatic patient requires analysis of biochemical markers to demonstrate typical patterns of haem precursors in urine, faeces and blood. The use of genomic sequencing in diagnostic pathways for porphyrias requires careful consideration, and the demonstration of increased porphyrin‐related markers is necessary prior to genomic testing in symptomatic patients. In the acute porphyrias, genomic testing is presently a useful adjunct for genetic counselling of asymptomatic family members and the most common cutaneous porphyria, porphyria cutanea tarda, is usually a sporadic, non‐hereditary disease. Getting a correct and timely porphyria diagnosis is essential for delivering appropriate care and ensuring best patient outcome.
PDF
