A Comparison Study Between Point-of-Care Testing Systems and Central Laboratory for Determining Blood Glucose in Venous Blood

Correlational analysis between salivary and blood glucose levels in individuals with and without diabetes mellitus: a cross-sectional study

OBJECTIVE

To estimate the association of patient-related demographic, socioeconomic status, physical activity, stress, and dietary factors influencing the relationship between salivary and blood glucose levels in individuals with and without diabetes mellitus (DM).

METHOD

This cross-sectional study was conducted on 166 participants with and without DM. Saliva and blood were collected to estimate the glucose levels. Age, gender, occupation, socioeconomic and education level, BMI, hip to waist circumference, stress, dietary pattern, lifestyle, physical activity, family history of diabetes, and type of diabetes were recorded. The association of saliva to predict blood glucose levels was analysed using Spearman Rank Correlation and how these patient-related factors influence the correlation was estimated for future machine learning models. The difference in medians for various groups was calculated using the Mann-Whitney U Test or Kruskal Wallis Test.

RESULTS

Blood glucose level is not significantly correlated to salivary glucose level. However, a statistically significant difference in the median blood glucose levels for diabetic participants (median = 137) compared to healthy controls (p-value < .05) was noted. The correlation between blood and salivary glucose was more positive for higher levels of glucose (Spearman 0.4). Age, alcohol consumption, monthly wages, intake of vegetables, and socioeconomic status affect blood glucose levels.

CONCLUSION

A correlation between saliva and blood glucose levels in healthy individuals was weak. Saliva should only be used as a monitoring tool rather than a diagnostic tool and is more reliable for patients with poorly controlled diabetes mellitus.

Predicting hypoglycemia after treatment of hyperkalemia with insulin and glucose (Glu-K60 score)

Background

Hyperkalemia can lead to fatal cardiac arrhythmias. Ten units of intravenous (IV) regular insulin with 25 g of glucose is the mainstay for treating hyperkalemia. However, the most important complication of this treatment is hypoglycemia. We aimed to develop a scoring model to predict hypoglycemia after the treatment of hyperkalemia.

Methods

A retrospective study was conducted at a university-based hospital between January 2013 and June 2021. We included the hyperkalemic patients (> 5.3 mmol/L) who were ≥ 18 years old and treated with 10 units of IV regular insulin with 25 g of glucose. Incomplete data on posttreatment blood glucose, pregnancy, and diabetes mellitus were excluded. Endpoint was posttreatment hypoglycemia (≤ 70 mg/dL or ≤ 3.9 mmol/L). Multivariable logistic regression was used to establish a full model and a subsequently reduced model using the backward elimination method. We demonstrated the model performance using the area under the receiver operating characteristic curve (AuROC), calibration plot, and Hosmer–Lemeshow goodness-of-fit test. Internal validation was done with a bootstrap sampling procedure with 1000 replicates. Model optimism was estimated.

Results

Three hundred and eighty-five patients were included, with 97 posttreatment hypoglycemia (25.2%). The predictive model comprised the following three criteria: age > 60 years old, pretreatment blood glucose ≤ 100 mg/dL (≤ 5.6 mmol/L), and pretreatment potassium > 6 mmol/L. The AuROC of this model was 0.671 (95% confidence interval [CI] 0.608 to 0.735). The calibration plot demonstrated consistency with the original data. Hosmer–Lemeshow goodness-of-fit test showed no evidence of lack-of-fit (p 0.792); therefore, the model was also fit to the original data. Internal validation via bootstrap sampling showed a consistent AuROC of 0.670 (95% CI 0.660 to 0.670) with minimal model optimism. A high risk for posttreatment hypoglycemia was indicated if the patient met at least one of those criteria. Sensitivity and specificity were 95.9% and 14.9%, respectively.

Conclusion

High risk was indicated when at least one of the criteria was met: age > 60 years old, pretreatment blood glucose ≤ 100 mg/dL (≤ 5.6 mmol/L), and pretreatment potassium > 6 mmol/L. Blood glucose levels should frequently check in the high-risk group.

Trial registration

TCTR20210225002 (www.thaiclinicaltrials.org).

Performance of commercially-available cholesterol self-tests

BACKGROUND

Hypercholesterolemia (plasma cholesterol concentration ≥5.2 mmol/L) is a risk factor for cardiovascular disease and stroke. Many different cholesterol self-tests are readily available at general stores, pharmacies and web shops. However, there is limited information on their analytical and diagnostic performance.

METHODS

We included 62 adult patients who required a lipid panel measurement (cholesterol, high-density lipoprotein (HDL), triglycerides and LDL_calc) for routine care. The performance of five different cholesterol self-tests, three quantitative meters (Roche Accutrend Plus, Mission 3-in-1 and Qucare) and two semi-quantitative strip tests (Veroval and Mylan MyTest), was assessed according to the manufacturers' protocol.

RESULTS

The average plasma cholesterol concentration was 5.2 ± 1.2 mmol/L. The mean absolute relative difference (MARD) of the five cholesterol self-tests ranged from 6 ± 5% (Accutrend Plus) to 20 ± 12% (Mylan Mytest). The Accutrend Plus cholesterol meter showed the best diagnostic performance with a 92% sensitivity and 89% specificity. The Qucare and Mission 3-in-1 are able to measure HDL concentrations and can thus provide a cholesterol:HDL ratio. The Passing-Bablok regression analyses for the ratio showed poor performance in both self-tests (Mission 3-in-1: y = 1.62x-1.20; Qucare: y = 0.61x + 1.75). The Accutrend Plus is unable to measure the plasma high-density lipoprotein concentration.Conclusions/interpretation: The Accutrend Plus cholesterol meter (Roche) had excellent diagnostic and analytic performance. However, several of the commercially-available self-tests had considerably poor accuracy and diagnostic performance and therefore do not meet the required qualifications, potentially leading to erroneous results. Better regulation, standardization and harmonization of cholesterol self-tests is warranted.

Utility of point-of-care vs reference laboratory testing for the evaluation of glucose levels

AIMS

To assess the level of agreement between point-of-care and laboratory reference glucose values in defining glycaemic status.

METHODS

We analysed 1292 overweight/obese, non-institutionalized participants, aged 40-65 years, in the San Juan Overweight Adults Longitudinal Study. Fasting venous blood glucose was determined using a point-of-care Bayer Contour Blood Glucose Meter and by Vitros System 250 instrument (laboratory). American Diabetes Association thresholds were used to classify participants into normoglycaemia (< 5.6 mmol/l), prediabetes (5.6 to 6.9 mmol/l), or diabetes groups (≥ 7 mmol/l).

RESULTS

Bland-Altman plot analysis showed a slope of 0.04 (P=0.002) for the regression between the mean difference and the average of the two methods. The slopes were significantly different from zero among people with normoglycaemia (β=-0.57, P<0.001), and prediabetes (β=-0.75, P<0.001) but not among people with diabetes (β=-0.02, P=0.68). When the prediabetes and diabetes groups were merged into one group, the slope was 0.01, and the glucose values remained similar using the two methods (P=0.76).

CONCLUSION

Point-of-care blood glucose measurement may be useful to screen people with diabetes, and to assess glucose among individuals with diabetes where blood can be drawn, but laboratory tests are unavailable or untimely.

Traceability to a primary reference measurement procedure (ID-LCMS); A key step in validating the clinical accuracy and safety of hospital blood glucose monitoring systems

OBJECTIVE

A key step in the evaluation of the accuracy of blood glucose monitoring systems (BGMS) is using a comparator method aligned to a high order definitive reference method. We describe how we achieved traceability to an isotope dilution liquid chromatography mass spectrometry (ID-LCMS) method. We used ID-LCMS to evaluate the accuracy and specificity of two hospital BGMS used in China.

METHOD

ID-LCMS was used to verify the calibration alignment of the laboratory plasma hexokinase reference method using NIST standard reference material and clinical samples. The ID-LCMS aligned hexokinase method was used to evaluate the clinical accuracy of two BGMS in hospitalized patients. System accuracy was evaluated using Chinese consensus guidelines. BGMS accuracy was also assessed with interference factors known to be present in critically ill patients' blood.

RESULTS

The laboratory plasma hexokinase reference method was shown to calibrate closely with ID-LCMS. Two BGMS demonstrated good correlation with this reference method. Only one BGMS met the Chinese guidelines. The interference factors didn't influence this BGMS but adversely affected the clinical accuracy of the other.

CONCLUSIONS

We advocate that our IDMS calibration alignment approach for ensuring the accuracy of the glucose reference method should be adopted in evaluations assessing the accuracy of blood glucose monitoring systems.

Assessment of the performance of blood glucose monitoring systems for monitoring dysglycaemia in neonatal patients

OBJECTIVE

To validate a three-step protocol that assesses the clinical risk associated with using blood glucose monitoring systems (BGMS) in neonates for the management of dysglycaemia.

METHOD

The three-step validation approach included confirmation of the accuracy of the reference method using National Institute of Standards and Technology (NIST) glucose standards, assessment of analytical risk performed on whole blood collected from paediatric patients routinely tested for glucose and a clinical risk assessment performed using heel stick capillary samples collected from 147 new-born babies and neonates admitted to intensive care. BGMS glucose measurements were compared with the NIST aligned laboratory reference method.

RESULTS

The accuracy of the laboratory reference method was confirmed with the NIST standards. Specificity studies demonstrated that the accuracy of one of the BGMS was affected, particularly, in the hypoglycaemic range, by known interference factors including haematocrit, ascorbic acid, lactose, galactose, N-acetylcysteine and glutathione. The accuracy of the other BGMS was unaffected. The clinical performance of this BGMS in neonates met the system accuracy criteria of Clinical and Laboratory Standards Institute (CLSI) POCT 12-A3 standard for evaluating hospital BGMS with 95.1% of glucose measurements within±0.67 mmol/L for samples ≤5.55 mmol/L and 95.6% within±12.5% for samples>5.55 mmol/L.

CONCLUSIONS

This three-step validation protocol provides a challenging approach for determining the accuracy and reliability of BGMS for managing dysglycaemia in neonates. StatStrip BGMS achieved analytical and clinical performance criteria confirming its suitability for use in neonates. We advocate that this validation approach should be considered for performance evaluations of both BGMS and continuous glucose monitoring systems going forward.