Dried blood spot collection, sample quality, and fieldwork conditions: Structural validations for conversion into standard values

Börsch‐Supan, Axel; Weiss, Luzia M.; Börsch‐Supan, Martina; Potter, Alan; Cofferen, Jake; Kerschner, Elizabeth

doi:10.1002/ajhb.23517

Cited by 15 publications

(25 citation statements)

References 33 publications

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“…Further research is also needed for comparing the degree of bias present in the segmentation across countries. Future studies should compare the assessments resulting from the application of the CCSST to either (a) the assessments of an international group of clinicians or (b) entirely objective measures such as biomarkers, potentially including analyses of dried blood spot assays 29 . Additional further research includes applying the CCSST to related international surveys, 13 including the US Health and Retirement Study, to accelerate a deeper understanding needed to improve population health globally.…”

Section: Discussionmentioning

confidence: 99%

Validation and application of a needs‐based segmentation tool for cross‐country comparisons

Duminy

Sivapragasam

Matchar

et al. 2021

Health Services Research

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Objective To compare countries' health care needs by segmenting populations into a set of needs‐based health states. Data sources We used seven waves of the Survey of Health, Aging and Retirement in Europe (SHARE) panel survey data. Study design We developed the Cross‐Country Simple Segmentation Tool (CCSST), a validated clinician‐administered instrument for categorizing older individuals by distinct, homogeneous health and related social service needs. Using clinical indicators, self‐reported physician diagnosis of chronic disease, and performance‐based tests conducted during the survey interview, individuals were assigned to 1–5 global impressions (GI) segments and assessed for having any of the four identifiable complicating factors (CFs). We used Cox proportional hazard models to estimate the risk of mortality by segment. First, we show the segmentation cross‐sectionally to assess cross‐country differences in the fraction of individuals with different levels of medical needs. Second, we compare the differences in the rate at which individuals transition between those levels and death. Data collection/extraction methods We segmented 270,208 observations (from Austria, Belgium, Czech Republic, Denmark, France, Germany, Greece, Israel, Italy, the Netherlands, Poland, Spain, Sweden, and Switzerland) from 96,396 individuals into GI and CF categories. Principal findings The CCSST is a valid tool for segmenting populations into needs‐based states, showing Switzerland with the lowest fraction of individuals in high medical needs segments, followed by Denmark and Sweden, and Poland with the highest fraction, followed by Italy and Israel. Comparing hazard ratios of transitioning between health states may help identify country‐specific areas for analysis of ecological and cultural risk factors. Conclusions The CCSST is an innovative tool for aggregate cross‐country comparisons of both health needs and transitions between them. A cross‐country comparison gives policy makers an effective means of comparing national health system performance and provides targeted guidance on how to identify strategies for curbing the rise of high‐need, high‐cost patients.

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Section: Discussionmentioning

confidence: 99%

Validation and application of a needs‐based segmentation tool for cross‐country comparisons

Duminy

Sivapragasam

Matchar

et al. 2021

Health Services Research

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show abstract

“…Differences are primarily due to capillary samples being derived from a dermal puncture, which results in contamination of venous blood with arterial blood, interstitial fluid, and intracellular fluid [103]. One study comparing capillary and venepuncture samples found that spot size is the most significant factor when applying a correction formula to account for differences in metabolite concentrations [113]. The formula derived by the researchers was Y = f (X, T, H) which calculates the estimated standard value (Y) from DBS treatment (X), the treatment conditions such as temperature, humidity, drying time, shipment time, spot size (T), and donor characteristics such as health, age, and sex (H) reflecting potential interaction effects [113].…”

Section: Determining An Equivalent Concentration Factormentioning

confidence: 99%

“…One study comparing capillary and venepuncture samples found that spot size is the most significant factor when applying a correction formula to account for differences in metabolite concentrations [113]. The formula derived by the researchers was Y = f (X, T, H) which calculates the estimated standard value (Y) from DBS treatment (X), the treatment conditions such as temperature, humidity, drying time, shipment time, spot size (T), and donor characteristics such as health, age, and sex (H) reflecting potential interaction effects [113]. Use of newly developed microsampling devices that collect a fixed volume of blood may eliminate this consideration of spot size and punching [27].…”

Section: Determining An Equivalent Concentration Factormentioning

confidence: 99%

Advanced Microsamples: Current Applications and Considerations for Mass Spectrometry-Based Metabolic Phenotyping Pipelines

et al. 2022

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Microsamples are collections usually less than 50 µL, although all devices that we have captured as part of this review do not fit within this definition (as some can perform collections of up to 600 µL); however, they are considered microsamples that can be self-administered. These microsamples have been introduced in pre-clinical, clinical, and research settings to overcome obstacles in sampling via traditional venepuncture. However, venepuncture remains the sampling gold standard for the metabolic phenotyping of blood. This presents several challenges in metabolic phenotyping workflows: accessibility for individuals in rural and remote areas (due to the need for trained personnel), the unamenable nature to frequent sampling protocols in longitudinal research (for its invasive nature), and sample collection difficulty in the young and elderly. Furthermore, venous sample stability may be compromised when the temperate conditions necessary for cold-chain transport are beyond control. Alternatively, research utilising microsamples extends phenotyping possibilities to inborn errors of metabolism, therapeutic drug monitoring, nutrition, as well as sport and anti-doping. Although the application of microsamples in metabolic phenotyping exists, it is still in its infancy, with whole blood being overwhelmingly the primary biofluid collected through the collection method of dried blood spots. Research into the metabolic phenotyping of microsamples is limited; however, with advances in commercially available microsampling devices, common barriers such as volumetric inaccuracies and the ‘haematocrit effect’ in dried blood spot microsampling can be overcome. In this review, we provide an overview of the common uses and workflows for microsampling in metabolic phenotyping research. We discuss the advancements in technologies, highlighting key considerations and remaining knowledge gaps for the employment of microsamples in metabolic phenotyping research. This review supports the translation of research from the ‘bench to the community’.

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“…Differences are primarily due to capillary samples being derived from a dermal puncture which results in contamination of venous blood with arterial blood, interstitial fluid, and intracellular fluid [102]. One study comparing capillary and venipuncture samples found that spot size is the most significant factor when applying a correction formula to account for differences in metabolite concentrations [113]. The formula derived by the researchers was Y = f(X, T, H).…”

Section: Determining An Equivalent Concentration Factormentioning

confidence: 99%

“…The formula derived by the researchers was Y = f(X, T, H). Which calculates the estimated standard value (Y) from DBS treatment (X), the treatment conditions such as temperature, humidity, drying time, shipment time, spot size (T), and donor characteristics such as health, age, and sex (H) reflecting potential interaction effects [113]. Use of newly developed microsampling devices which collect a fixed volume of blood may eliminate this consideration of spot size and punching [27].…”

Section: Determining An Equivalent Concentration Factormentioning

confidence: 99%

Advanced Microsamples: Current Applications and Considerations for Mass Spectrometry-Based Metabolic Phenotyping Pipelines

Roberts¹,

Whiley²,

Gray³

et al. 2022

Preprint

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Microsamples (collections usually less than 50 µL) have been introduced in pre-clinical, clinical, and research settings to overcome obstacles in sampling via traditional venipuncture. However, venipuncture remains the sampling gold standard for metabolic phenotyping of blood. This pre-sents several challenges in metabolic phenotyping workflows: accessibility for individuals in ru-ral and remote underserved areas (due to the need for trained personnel), the unamenable nature to frequent sampling protocols in longitudinal research (for its invasive nature), and sample col-lection difficulty in the young and elderly. Furthermore, venous sample stability may be compro-mised when temperate conditions necessary for cold-chain transport are beyond control. Alter-natively, research utilising microsamples extends phenotyping possibilities to inborn errors of metabolism, therapeutic drug monitoring, nutrition, as well as sport and anti-doping. Although the application of microsamples in metabolic phenotyping exists, it is still in its infancy, with whole blood being overwhelmingly the primary biofluid collected through the collection method of dried blood spots. Research into metabolic phenotyping of microsamples is limited; however, with advances in commercially available microsampling devices, common barriers such as volumetric inaccuracies and the ‘haematocrit effect’ in dried blood spot microsampling can be overcome. In this review, we provide an overview of the common uses and workflows for mi-crosampling in metabolic phenotyping research. We discuss the advancements in technologies, highlighting key considerations and remaining knowledge gaps for employment of microsamples in metabolic phenotyping research. Supporting the translation of research from the ‘bench to the community’.

show abstract

Dried blood spot collection, sample quality, and fieldwork conditions: Structural validations for conversion into standard values

Cited by 15 publications

References 33 publications

Validation and application of a needs‐based segmentation tool for cross‐country comparisons

Validation and application of a needs‐based segmentation tool for cross‐country comparisons

Advanced Microsamples: Current Applications and Considerations for Mass Spectrometry-Based Metabolic Phenotyping Pipelines

Advanced Microsamples: Current Applications and Considerations for Mass Spectrometry-Based Metabolic Phenotyping Pipelines

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