Differences between capillary and venous blood counts in children—A data mining approach

Abstract

Background Capillary sampling of blood counts is a well‐established alternative to venipuncture in paediatrics. However, the sampling method has to be considered when interpreting test results, as measurements differ. Ethical and practical considerations prevent simultaneous venous and capillary sample acquisition in comprehensive paediatric cohorts that span all ages for the purpose of a direct method comparison, resulting in uncertainty regarding the interpretation of capillary test results. Methods We applied a data mining method to calculate the differences between capillary and venous blood count analytes using laboratory data collected during patient care. We examined 486 401 blood counts performed between 2010 and 2017 in two German paediatric tertiary care centers in children from birth to 18 years analysed on SYSMEX XE‐2100 and SYSMEX XE‐5000 devices, and analysed the differences between capillary and venous test results in 15 218 paired samples performed within 24 h. Results We identified the mean systematic differences between capillary and venous (capillary–venous) test results for haemoglobin (+6.5 g/L), haematocrit (+2.38%), platelet count (−7.01 × 109/l), red cell count (+0.18 × 1012/L), white cell count (−0.64 × 109/L), mean corpuscular cell volume (+2.07 fl), mean corpuscular haemoglobin (+0.33 pg), mean corpuscular haemoglobin concentration (−4.4 g/L) and red cell distribution width (+0.40%). The effect of age on these mean deltas is negligible, while the levels of test results influence the difference between capillary and venous test results in most analytes. Conclusions Our results improve guidance regarding the interpretation of capillary test results for children of all ages and in both physiological and pathological ranges.

Stability and comparison of complete blood count parameters between capillary and venous blood samples

“…Bias analysis indicated acceptable variation between capillary and venous measurements for the majority of CBC parameters when desirable TEa was selected as a decision threshold. Our results on comparability between venous and capillary measurements are in line with results of previous studies 8,11,13,15,18 …”

“…Our results on comparability between venous and capillary measurements are in line with results of previous studies. 8,11,13,15,18 To determine whether CBC parameters are potentially suitable for remote sampling and delayed analysis, we aimed to extend previous research and assess long-term CBC stability. Red cell indices, such as erythrocyte counts, hematocrit, hemoglobin, MCH, MCV, MPV, RDW, and platelet counts were generally most stable (up to 168 h) when stored at 4 C, in agreement with previous studies on stability up to 72 h. 10,16,19 Hematocrit, MCV, and RDW measurements gradually increased over time at room temperature (21-22 C) and 30 C.…”

“…Previous research regarding venous and capillary blood sampling to measure CBC parameters has primarily focused on comparability of results and, to a lesser extent, on short‐term stability (up to 24–72 h) 8–16 . Data on comparability, longer term stability, and influence of sample storage conditions for current generations of hematology analyzers is limited.…”

Research on User Demand Determination Method and Marketing Strategy for Home Broadband Business Based on Data Mining

“…The detection results showed that the difference between the hemoglobin content of the two was 6.5g/L, the difference between the hematocrit was 2.38%, and the difference between the distribution width of red blood cells was 0.4%. Therefore, capillary blood sampling can replace venous blood sampling, with certain feasibility [8]. Zhang et al screened structural materials in peroxide solar cells using data mining techniques and stored their structural information in a structural database.…”

“…After obtaining the initial position of the target user, KNN precise positioning is performed using the MR fingerprint algorithm, and the precise positioning of the target user is obtained using the cosine distance algorithm. The expression formula is as follows (8).…”