Minimal mathematical model for glycation of albumin.

BACKGROUND: Glycated albumin (GA) is often described as a reflection of glucose exposure over the past 2-4 weeks. We examined the scale of the operative interval for changes in %GA from the perspective of a theoretical model for GA formation, by simulating the time course of changes in %GA after changes in glucose. METHODS: Probability of survival of albumin (A) was according to first-order elimination based on t1/2 of 17 days. Probability of formation of GA from A per unit time was proportional to glucose (G) and a glycation rate constant, k, deduced from reference values for %GA vs. G. We then simulated the kinetics of changes in %GA for conditions in which a prior steady-state (constant G) was followed by a step change in G. RESULTS: The glycation rate constant k was 9.79e-4/d/(mmol/L). We simulated changes in %GA for two scenarios involving step changes in G at time = 0: A. from 10 mmol/L to 15 mmol/L (%GA ultimately moves from 19.3% to 26.4%); B. from 15 mmol/L to 10 mmol/L (%GA ultimately moves from 26.4% to 19.3%). For both scenarios, the fractional transition of %GA between respective starting points and ultimate endpoints was after 30 days approximately 80% of the ultimate full transition. CONCLUSIONS: Model-based calculations support the description of %GA as a reflection of G over the past 4-6 weeks, longer than the period of 2-4 weeks that is commonly cited.

Jerk (d(acceleration)/dt) as an operative variable in pneumatic tube transport (PTT).

BACKGROUND: Jerk, the rate of change of acceleration (d(acceleration)/dt), is a known operative variable in public transportation safety, but this term has never appeared in the literature regarding pneumatic tube transport (PTT) and specimen integrity. We investigated profiles of acceleration and jerk for 2 PTT routes within our hospital system. METHODS: Acceleration data were collected for PTT for 2 routes (A, B) using an accelerometer. Acceleration vectors (a) were analyzed in terms of distributions of jerk (da/dt), and distributions of θ, the angle between successive acceleration vectors. RESULTS: Routes A and B had transit times of approximately 300 s. Acceleration vectors (a) ranged in magnitude from 0 to 8 g. For B, a > 1.2 g comprised 29.0% of results, compared to 13.5% of results for A (ratio = 2.1). Jerk ranged from 0 to 94 g/s. For B, jerk > 0.5 g/s comprised 71.9% of results, compared to 32.5% of results for A (ratio = 2.2). θ ranged from 0 to 180 degrees. For B, θ > 5 degrees comprised 59.3% of results, compared to 26.6% of results for A (ratio = 2.2). CONCLUSION: Differences in distribution in acceleration, jerk, and θ ran in parallel as variables for comparison between 2 PTT routes. Jerk and θ are likely to be operative variables in effects of PTT.

Evaluation of the ARK Diagnostics immunoassay for qualitative detection of xylazine in urine.

Xylazine exposure is common in some US cities, but a commercial assay for routine laboratory testing for xylazine is not currently available. We evaluated a pre-release version of the ARK Diagnostics immunoassay for qualitative detection of xylazine/4-hydroxyxylazine in urine. Studies were conducted using either the semi-quantitative assay application (A. Roche Cobas 503 analyzer) or the qualitative assay application (B. Beckman Coulter AU480 analyzer). Study specimens consisted of deidentified patient urine samples submitted for routine drugs-of-abuse testing. Measurements of xylazine (X) were performed by LC-MS-MS to obtain X-NEGATIVE (X <10 ng/mL) and X-POSITIVE (X ≥10 ng/mL). The semi-quantitative ARK assay was calibrated with a 10 ng/mL cutoff for ARK-POSITVE. For (A): among 74 X-POSITIVE samples, there was 1 ARK-NEGATIVE result (false-negative rate = 1.4%); among 78 X-NEGATIVE samples by LC-MS-MS, there were 0% ARK-POSITIVE results (false-positive rate = 0%). For (B), among 74 X-POSITIVE samples, there were 0 ARK-NEGATIVE results (false-negative rate = 0%); among 78 X-NEGATIVE samples there was 1 ARK-POSITIVE sample (false-positive rate = 1.3%). Common sources of interferences were investigated without evidence of interference. The ARK xylazine/4-OH-xylazine immunoassay was found to be suitable for routine use in screening patient urine samples for presence of xylazine >10 ng/mL.

Pharmacokinetics of apixaban in patients undergoing pancreaticoduodenectomy (PAP-UP).

OBJECTIVE: The impact of pancreaticoduodenectomy on absorption of drugs in the duodenum remains largely unknown. We aim to characterize the pharmacokinetics of apixaban in patients who had previously undergone pancreaticoduodenectomy. MATERIALS AND METHODS: A single 10-mg dose of apixaban was administered to 4 volunteers who underwent pancreaticoduodenectomy at least 6 months prior. The maximum plasma apixaban concentration (Cmax) and area under the plasma concentration time-curve (AUC0-24, AUC0-inf) were compared against healthy historical control subjects (N = 12). Geometric mean ratios (GMR) with 90% confidence interval (CI) were calculated for determination of comparative bioequivalence. RESULTS: In pancreaticoduodenectomy patients, AUC0-24 and AUC0-inf were 1,861 and 2,080 ng×h/mL, respectively. The GMRs of AUC0-24 and AUC0-inf between study subjects and healthy controls were 1.27 (90% CI 0.88 - 1.83) and 1.18 (90% CI 0.82 - 1.72). The mean Cmax of apixaban was 201 ng/mL (SD 15.6) occurring at a median tmax of 3.25 hours (range 2.5 - 4 hours). The GMR of Cmax between study subjects and healthy controls was 1.12 (90% CI 0.77 - 1.63). CONCLUSION: The pharmacokinetic characteristics of apixaban in subjects who had undergone pancreaticoduodenectomy are not significantly different from those of healthy controls. Though the sample size of this study is small, results suggest that no change to apixaban dose regimen is needed in patients who have had a pancreaticoduodenectomy.

Time-of-day dependence of running averages for some components of metabolic panels at our hospital: Relation to strategy for patient-based quality control.

BACKGROUND AND OBJECTIVES: We assessed properties of running averages for our hospital's most common chemistry analytes, for use in real-time patient-based quality control (PBQC). We determined whether there was dependence of any running averages on 24-h clock time (time-of-day, TOD). MATERIALS AND METHODS: We analyzed 3-months' data for measurements of 13 metabolic panel components. Running averages for 20 consecutive results (20-mers) were computed for data restricted to results within reference intervals. This produced an overall mean (X) and standard-deviation (SD) of 20-mers for each analyte. We then computed the average 20-mer result (Y) reported within 1-h bins across 24-hour clock time (t). Y(t) was regarded as having TOD-dependence if either nadir or apex values for |Y-X| exceeded 0.5 SD, occurring within a contiguous series of at least 4 Y(t) values on one side of the mean. RESULTS: Seven analytes (albumin, aspartate aminotransferase, calcium, chloride, CO2, potassium, total protein) demonstrated TOD-dependence of running means for 20-mers. CONCLUSIONS: At our hospital, TOD-dependence of running means was identified for 7 of 13 metabolic panel analytes. TOD-dependence is likely to be hospital-specific. Utilization of TOD-dependent targets for PBQC, rather than fixed targets, would be appropriate in these cases.

Example of use of clock time-dependent targets for patient-based quality control.

BACKGROUND: Running means for total calcium (Ca) results at our laboratory exhibit a stable time-of-day (TOD) periodic pattern. We examined use of TOD-dependent targets for running means in patient-based quality control (PBQC) for Ca. METHODS: Primary data were Ca results over a 3 month interval, restricted to weekday data within the Ca reference interval (8.5-10.3 mg/dL; 2.12-2.57 mmol/L). Running means were evaluated as sliding averages of 20 samples (20-mers). RESULTS: Data comprised 39,629 consecutive Ca measurements (75.3% inpatient (IP)) for which Ca was 9.29±0.47 mg/dL. The all data average for 20-mers was 9.29 ± 0.18 mg/dL. When parsed in 1 h TOD intervals, however, averages among 20-mers ranged from 9.1 to 9.5 mg/dL, with blocs of contiguous results above (0800-2300 h; 53.3% of results; IP = 75.3%) and below (2300-0800 h; 46.7% of results; IP = 99.9%) the all-data mean. There was thus an inherent TOD-dependent pattern of deviation of means from target when using a fixed PBQC target. Using Fourier series analysis as an example approach, characterization of the pattern to produce TOD-dependent PBQC targets eliminated this inherent inaccuracy. CONCLUSIONS: In circumstances of periodic variation in running means, simple characterization of that variation can reduce the probability of both false positive and false negative flags in PBQC.

Characterizing ability of serum potassium (K) and the serum K reference interval to flag hypokalemia or hyperkalemia as observed in plasma: A simulation study.

OBJECTIVES: Serum potassium (K) exhibits a positive shift relative to plasma K due to a variable amount of K release associated with clotting. Because of this variation, plasma K results outside of the reference interval (RI) for plasma (hypokalemia or hyperkalemia) in individual samples may not produce classification-concordant results in serum according to the serum RI. We examined this premise from a theoretical standpoint by simulation. DESIGN & METHODS: We used textbook K reference intervals for plasma (PRI = 3.4-4.5 mmol/L) and serum (SRI = 3.5-5.1 mmol/L). The difference between PRI and SRI is characterized by a normal distribution: serum K = plasma K + 0.35 ± 0.308 mmol/L. This transformation was applied by simulation to an observed patient data distribution for plasma K to generate a corresponding theoretical serum K distribution. Individual samples were tracked for comparison with respect to classification (below, within, above RI) for plasma and serum. RESULTS: Primary data were an all-comers plasma K patient distribution (n = 41,768; median = 4.1 mmol/L; 7.1% below PRI (hypokalemia); 15.5% above PRI (hyperkalemia)). Simulation to obtain the associated serum K yielded a right-shifted distribution (median = 4.4 mmol/L; 4.8% below SRI; 10.8% above SRI). Sensitivity for detection in serum (flagged below SRI) for samples originating as hypokalemic in plasma was 45.7% (specificity = 98.3%). Sensitivity for detection in serum (flagged above SRI) for samples originating as hyperkalemic in plasma was 56.6% (specificity = 97.6%). CONCLUSIONS: Simulation results indicate that serum K should best be thought of as an inferior substitute marker for plasma K. These results follow simply from the variable component of serum K compared to plasma K. Plasma should be the preferred specimen type for K assessment.

Quantitation of hemoglobins using Sebia Capillarys-2 capillary electrophoresis (CE) for A1c: Comparison to results using CE for hemoglobins.

Background: Measurement of A1c using the Sebia Capillarys-2 capillary electrophoresis (A1c CE) involves relative quantitative measurements of peaks for hemoglobins A1c, A, A2. We examined correlation of A1c CE results with results of CE analysis for hemoglobins (Hb CE) for homozygous A and S-trait patients. We specifically examined whether abnormalities in A2 or the A/S ratio by A1c CE alone would reasonably be the basis for recommendation of red cell indices for evaluation of possible thalassemia. Methods: Selection of patients was from results for A1c CE, exhibiting either a normal pattern or a pattern consistent with S-trait. We then examined correlation of results of quantitation for A, S and A2 between A1c CE and Hb CE. Results: %A2 by A1c CE (y) had high correlation with %A2 by Hb CE (x): y = 0.88 x; r = 0.948. %A2 in S-trait patients was right-shifted in comparison to normals by 0.5%. For S-trait patients, the A/S ratio by A1c CE (y) had high correlation with the A/S ratio by Hb CE (x): y = 1.02 x; r = 0.995. Conclusions: Given high correlation of results between A1c CE and Hb CE, patent elevation of A2 by A1c CE for either normal or S-trait patients is a reasonable basis for recommendation of red cell indices for evaluation of possible beta thalassemia. For S-trait patients, patent abnormality in the A/S ratio by A1c CE is a reasonable basis for recommendation of red cell indices for evaluation of possible alpha or beta thalassemia.

Characteristics and calculations of paired vancomycin measurements for determination of 24-h area-under-curve (AUC).

Background: Current pharmacy practice guidelines recommend 24-h area-under-curve (AUC24) targets for use of vancomycin against methicillin-resistant Staphylococcus aureus (MRSA). AUC protocol-specific vancomycin orders were begun recently (2022) at our institution. We reviewed initial AUC protocol-associated data and calculations. Methods: AUC24 calculations are derived from timed, paired measurements of vancomycin (V1,V2). We retrieved paired (V1,V2) measurements for a 90-day interval. Calculations to obtain AUC24 were performed according to two accepted methods (A, B) that assume first-order kinetics for vancomycin elimination between V1 and V2. Results: 44 (V1,V2) measurement pairs were from among 27 patients. Dosing intervals were 8, 12, or 24 h. The first-order rate constant k was normally distributed (k = 0.096 ± 0.046 1/h); t1/2 ranged from 3 to 30 h. For target AUC24 = 400-600 h × µg/mL, 55% of calculated AUC24 results were within target. Imprecision for calculated k was predicted to be least when V2 is a trough level. Method B results were greater than Method A results by a factor of 1.07. Conclusions: 45% of AUC24 results indicated need for change in dosage. Recommendations are that average results from A and B methods of calculation should be used, and that V1 and V2 should be as widely separated as possible.

Characterization by image analysis of the dose vs response curve for a qualitative serum hCG lateral flow immunoassay.

BACKGROUND: As an adjunct to verification of performance characteristics of a qualitative serum hCG lateral flow immunoassay (LFI), we performed image analysis to characterize the dose vs response curve (visibility of the test line), as a means of understanding the transition from negative to positive as a function of increasing [hCG]. METHODS: Using serum samples of known [hCG], device images were obtained using a scanner at the prescribed reading time (5 min). Image analysis (using Python and R) was used to obtain the integral (S) of the test-line color as a function of [hCG]. RESULTS: Data for S as a function of [hCG] were well characterized by a simple hyperbola: S = Smax [hCG]/([hCG] + K), where K = 202 mIU/ml (r = 0.997). Replicates of S at K had CV of 7.3 %. By eye, uncertainty of test results among users occurred only below the assay's stated sensitivity of 10 mIU/ml, in region of S < 3 % of Smax, and signal:noise ratio < 3. CONCLUSIONS: By image analysis, the dose vs response (Test line integral) for this qualitative serum hCG LFI was a simple hyperbola. Characterization of the dose vs response curve was useful in verification of the assay's performance characteristics.

Corrigendum to "Observation of a positive interference in LC-MS/MS measurement of d6-25-OH-vitamin D3" [Clin. Mass Spectrom. 3 (2017) 22-24].

[This corrects the article DOI: 10.1016/j.clinms.2017.06.001.].

Intraperitoneal pharmacokinetics of vancomycin in patients on automated peritoneal dialysis.

It is unclear if the pharmacokinetics of vancomycin are the same during automated peritoneal dialysis (APD), where cycler exchanges may affect the systemic, peritoneal, and urinary disposition of drug. We conducted a prospective pharmacokinetic study evaluating the pharmacokinetics of vancomycin in plasma, dialysis fluid, and urine in peritonitis-negative patients on APD. Patients underwent four drug-free exchanges with 1.5% or 2.5% dextrose following the initial dwell period. Plasma, dialysis fluid, and urine was collected over the course of 7 days for pharmacokinetic analysis. Four patients completed the study with no adverse events. Following a median (range) dwell of 14.6 (14.2-17.6 h), the mean (±SD) observed maximum plasma concentration was 28.7 ± 4.9 mg/L with a mean bioavailability of 98.5 ± 1.4% prior to starting the cycler. The overall mean total plasma clearance estimated from study start to completion was 7.6 ± 1.2 ml/min. Mean total clearance during the dialytic exchange was 13.6 ± 4.9 ml/min. In patients with residual renal function, the mean vancomycin renal clearance was 3.1 ± 1.5 ml/min, representing 21.4%-58.9% of the overall total plasma clearance during the study period. Despite the small sample size, this pilot study suggests that the dwell time has important implications for systemic vancomycin exposure, time to therapeutic plasma concentration, and dosing. Dose is driven by dwell time, whereas the cycler determines the dosing interval. Rapid exchanges from APD will determine the frequency of dosing rather than the adequacy of absorption when vancomycin is given in the peritoneum.

High prevalence of xylazine among fentanyl screen-positive urines from hospitalized patients, Philadelphia, 2021.

BACKGROUND: Xylazine is an α-2 adrenoreceptor agonist used as a sedative/analgesic in veterinary medicine. Xylazine is known to be present within the street supply of opiates in urban Philadelphia. Medical staff at our hospital asked if we could test for xylazine in fentanyl screen-positive urine samples. We developed an LC-MS/MS assay for this purpose, and determined prevalence of xylazine among fentanyl screen-positive urine samples at our hospital. METHODS: The LC-MS/MS assay utilized d5-norfentanyl as internal standard (IS). One hundred microliter samples were extracted with 200 µl of MeOH/IS. LC was performed using a Phenomenex Kinetix C18 column (100 A, 5 µm, 50 × 4.6 mm) at 40 °C. Time-variable mobile phases (A = H2O, 0.1% formic acid; B = MeOH, 0.1% formic acid) were used at a fixed flow rate of 0.5 ml/min. MS/MS used positive electrospray ionization, monitoring m/z transitions of 221 > 164 for xylazine (primary), 221 > 90 for xylazine (qualifier), and 238 > 84 for d5-norfentanyl (IS). Retention time was 3.9 min for both xylazine and IS. RESULTS: Calibration curve was linear (0-500 ng/ml; r > 0.99). Inter-assay CVs (n = 20) were 5.2% (18 ng/ml) and 6.6% (95 ng/ml). Lower limit of detection was set at 10 ng/ml (CV = 15%). Among 81 urine samples that were screen-positive for fentanyl (Ark Diagnostics immunoassay), 63 (78%) were positive for xylazine (>10 ng/ml). CONCLUSIONS: By LC-MS/MS, there was high prevalence (78%) of xylazine in fentanyl screen-positive urine samples submitted to the laboratory. Because α-2 adrenoreceptor agonists may be used in treatment of opioid addiction, knowledge of xylazine exposure may be clinically useful to guide patient management.

Drug-Drug Interaction Study of Apixaban with Cyclosporine and Tacrolimus in Healthy Volunteers.

Apixaban is metabolized by cytochrome P450 (CYP) 3A4 in the liver and intestine, undergoes direct intestinal excretion, and is a substrate to permeability glycoprotein (P-gp) and breast cancer resistance protein (BCRP) transporters. We examined the drug interactions between cyclosporine and tacrolimus (combined inhibitors of CYP3A4, P-gp, and BCRP) with apixaban in 12 healthy adult male volunteers. Apixaban 10 mg was administered orally alone, in combination with 100 mg cyclosporine or 5 mg tacrolimus. Co-administration with cyclosporine resulted in increase in apixaban maximum plasma concentration (Cmax ) and area under the plasma concentration-time curve from time zero to the last quantifiable concentration (AUC(0-tlast) ) with associated geometric mean ratios (GMRs) and 90% confidence intervals (CIs) of 143% (112, 183) and 120% (97, 148), respectively. Co-administration with tacrolimus resulted in reduction in apixaban Cmax and AUC(0-tlast) with associated GMRs (90% CI) of 87% (69, 112) and 78% (63, 97), respectively. The observed changes in apixaban exposure margins with cyclosporine or tacrolimus are within the range of the historical clinical development program, therefore, apixaban dose adjustments are not warranted.

Use of a model for A1c formation to estimate average glucose operative between short-interval measurements of %A1c.

BACKGROUND: Estimated average glucose (AG) is generally reported along with hemoglobin A1c measurements according to a standard calculation. Given a normal red blood cell lifetime of 120 days, serial A1c measurements at intervals <120 days are not completely independent. For short interval measurements, a change in AG (ΔAG) necessarily underestimates the change in average glucose operative during the interval (ΔG). We use a model for kinetics of HbA1c to evaluate the theoretical relationship between ΔAG and ΔG for HbA1c measurements made at intervals between 0 and 120 days. METHODS: From any given starting point for A1c, step changes in G were simulated using model calculations to determine the extent to which A1c could change as a function of the interval of exposure. Values for ΔAG were compared to the operative ΔG as a function of the interval between A1c measurements. RESULTS: Results of model simulations are a single graph for relationship of ΔAG to ΔG as a function of the interval between A1c measurements. ΔAG for (15, 30, 45, 60, 76, and 90) day intervals underestimated operative ΔG by (73, 51, 34, 21, 11, and 5)%, respectively. CONCLUSIONS: Model calculations predict the relationship between changes in estimated average glucose to changes in operative glucose for serial A1c measurements made at intervals <120 days. Given that serial measurements of A1c made at short intervals are not uncommon in practice, physicians may find this information to be useful.

Distribution of serum concentrations reported for macroenzyme aspartate aminotransferase (macro-AST).

BACKGROUND: The presence of macroenzyme (M) is often the explanation of an isolated elevation of aspartate aminotransferase (AST). Where M is identified, it is reasonable for the clinician to ask where an individual patient's result fits in with known concentrations of M. In this context, we conducted a survey of literature to examine the distribution of reported serum concentrations of macro-AST. We also analyzed the distribution data to examine whether elevations were consistent with simple alteration of circulatory half-life (t1/2) of M relative to normal AST. METHODS: Distributions of M were compiled from the literature. These distributions were compared to predictions based on fixed changes in t1/2 applied to the reference interval for AST. RESULTS: There was a bimodal distribution of literature values for M (n =51), comprised roughly of populations A (M <200 U/L; 60% of total) and B (M >200 U/L; 40% of total). The two distributions were reasonably well characterized by a simple projection to the right of the reference interval for AST according to increased t1/2 (A: t1/2 =3.3 days; B: t1/2 =19.8 days) relative to AST (t1/2 =0.7 days). CONCLUSIONS: Knowledge of distributions for M may be useful in discussion with clinicians regarding significance of M for individual patients. Distributions for M were consistent with the simplest explanation for elevated AST due strictly to an extended circulatory lifetime for M. Caveats to analysis, however, include selection within literature data mainly for patients with various co-morbidities.

Average glucose from hemoglobin A1c for altered red blood cell lifetimes: Predictions based on a model for hemoglobin A1c formation.

BACKGROUND: A model for hemoglobin A1c (HbA1c) formation was used to predict the relationship between average glucose (AG) and %HbA1c under conditions of altered red blood cell lifetime (RCL). METHODS: Using a kinetic mass balance model for formation of HbA1c in red blood cells as a function of age (time in circulation), whole blood %HbA1c vs. glucose was calculated based on the nonlinear distribution of red blood cells as a function of age across RCL. RESULTS: Model calculations provided a close fit to the standard relationship of estimated average glucose to %HbA1c for normal RCL (r>0.999). Results for altered RCL were calculated assuming simple time-scale compression or expansion of the distribution of red blood cells as a function of RCL. For a given %HbA1c, the operative average glucose needed to have achieved a given %HbA1c was predicted to be altered by RCL according to average glucose×RCL=constant. CONCLUSIONS: Model calculations estimate the extent to which standard reporting of AG vs. HbA1c underestimates or overestimates AG under conditions of altered RCL. Conditions of altered RCL may often be operative in patients with certain hemoglobin variants.

Model analysis of bidirectional interference in two-stage labeled-ligand immunoassays.

OBJECTIVES: Immunoassays involving sample incubation followed by a wash step prior to introduction of labeled analyte are potentially subject to both positive and negative interference (bidirectional interference) by a competing ligand. We examine this phenomenon from a theoretical standpoint using a mathematical model for sequential-step immunoassays in the presence of interferent. DESIGN & METHODS: Competitive binding to antibody between analyte and interferent was modeled for sequential-step immunoassays. A primary assumption was that the ratio of affinity constants between the intended analyte and the interferent reflected the ratio of dissociation rate constants, with the higher dissociation rate constant for the lesser affinity ligand. RESULTS: Relationships of parameters (relative affinity constants, relative concentrations) for analyte and interferent were determined for conditions in which bidirectional interference can occur, for both steady-state and non-steady-state sample incubation conditions. Non-steady state sample incubation conditions can enhance the effects of an interferent. Homogeneous assay formats utilizing labeled ligand without a wash step can also demonstrate bidirectional interference, but positive interference is favored under such formats. CONCLUSIONS: Model calculations demonstrate the theoretical basis for bidirectional interference in two-stage immunoassays. Results delineate constraints on conditions in which bidirectional interference can occur.

Estimation of hematocrit in filter paper dried bloodspots by potassium measurement: advantage of use of perimeter ring samples over circular center sub-punch samples.

BACKGROUND: Quantitative assays using dried filter paper bloodspots (DBS) may be adversely affected by hematocrit (HCT) as an unknown variable. Studies have demonstrated the utility of the measurement of potassium (K) from DBS punches to estimate HCT. Because there is significant accrual of RBCs at the DBS perimeter, we investigated whether K measurement from ring-shaped specimens inclusive of the perimeter might provide an advantage over conventional interior circular sub-punch samples to estimate HCT. METHODS: Primary samples were Li-heparin whole blood, with HCT as measured on concurrently-drawn K-EDTA specimens. DBS were formed by bolus addition of 40 µL whole blood to filter paper cards. Total bloodspot area was determined by image analysis. Removal of center sub-punch (P) samples of fixed area produced remainder ring (R) samples inclusive of the perimeter. Samples were extracted in K-EDTA (2.5 mmol/L) and measured for diluent-corrected K per area (α, µmol K/cm2). RESULTS: Forty-three patient samples were utilized. α was normally distributed: α(P)=1.23±0.26 µmol K/cm2; α(R)=1.86±0.41 µmol K/cm2; α(R)/α(P)=1.51±0.15. α was correlated with HCT: α(P)=0.030 HCT(%)+0.015 µmol K/cm2 (r2=0.795); α(R)=0.052 HCT(%)+0.010 µmol K/cm2 (r2 = 0.912), but with higher resolution and lesser error for α(R). CONCLUSIONS: K per area (α) was significantly higher in R samples vs. P samples, with higher resolution for α(R) vs. HCT. Use of ring samples inclusive of the perimeter to estimate HCT for DBS via K measurement can provide an advantage over use of center sub-punch samples.