Point of care testing to improve glycemic control

HBA1C point-of-care testing for diabetes control in a low-income population: A before and after study and cost-parity analysis HbA1c point-of-care testing for diabetes control

OBJECTIVE

To evaluate the results of a program that offered access to HbA1c POC tests for the glycemic control of patients with diabetes in small and poor municipalities of Minas Gerais, Brazil.

METHODS

Using a before and after study, we compared four groups: patients submitted to (i) POC tests; (ii) conventional tests; (iii) both tests; and (iv) neither test. The analysis considered three periods: before the program; before the pandemic; and during the pandemic. A cost comparison was conducted under the societal perspective and a cost-parity model was designed.

RESULTS

1349 patients previously diagnosed with diabetes were included in the analysis. The rate of consultations and the rate of HbA1c testing were significantly different between all periods and groups. Group iii had a much higher consultation and testing rate. The costs were around 89.45 PPP-USD for POC tests and between 32.44 and 54.66 PPP-USD for conventional tests. Cost-parity analysis suggests that the technology would be acceptable if the annual number of tests was between 247 and 771.

CONCLUSION

Using POC devices improved access to HbA1c testing but not glycemic control. Even in small towns, the number of tests necessary to achieve cost-parity is low enough to enable their incorporation into the public health system.

Novel use of menstrual blood for monitoring glycaemic control in patients with diabetes: a proof-of-concept study

BACKGROUND

Glycated haemoglobin (HbA1c) is the diagnostic and prognostic standard for clinical management of diabetes mellitus (DM). Unfortunately, patient adherence to guidelines for routine testing can be poor and there are significant gender-based disparities in DM management and outcomes. Recent evidence suggests that menstrual blood may be comparable to systemic blood for monitoring of common biomarkers. The objective of the present study was to assess the concordance of HbA1c levels between menstrual and systemic blood in healthy women and women with diabetes of reproductive age.

METHODS

In this prospective, observational cohort study, we enrolled healthy and diabetic (type 1 and type 2 DM) reproductive-age women (aged ≥18 and ≤45 years). Menstrual blood and venous systemic blood specimens were simultaneously obtained at time of menstruation, and analysed for HbA1c levels. Participants self-collected menstrual blood using a QPad, a novel, modified menstrual pad with an embedded dried blood spot strip.

RESULTS

Among 172 participants, 57.6% were healthy and 42.4% had a diagnosis of either type 1 or type 2 DM. There were no significant differences in mean HbA1c values in menstrual and systemic blood across the overall cohort or within the diabetic subgroup. Furthermore, HbA1c levels between blood sources were robustly correlated and demonstrated a significant linear relationship.

CONCLUSIONS

There is a strong concordance in HbA1c levels between menstrual and systemic blood. Empowered by self-collection technologies, these findings suggest that menstrual blood may serve as a reliable, non-invasive and potentially cost-effective alternative to serum for HbA1c monitoring among reproductive-age women with DM.

Utilizing point-of-care A1c to impact outcomes - can we make it happen in primary care?

PURPOSE OF REVIEW

Hemoglobin A1c testing provides a marker of glycemic control and is the standard for diabetes risk assessment. According to the Centers for Disease Control (CDC), only 67.3-71.4% of diabetic patients between 2011 and 2016 had at least two A1c levels tested per year. Moreover, 27.8% had uncontrolled diabetes with an A1c of ≥8.0%, increasing the risk of microvascular complications. Lack of monitoring presents a significant barrier, and improving ease of testing could improve glycemic control.

RECENT FINDINGS

Point-of-care (POC) A1c testing, obtained via capillary blood with results provided in 5-6 min, is available and used frequently in endocrine clinics. However, POC A1c testing is not standard in primary care, where 90% of type 2 diabetes patients are treated. Barriers include access and costs of POC A1c machines and standardization of testing in the primary care setting. Review of literature, however, suggests that POC A1c testing in primary care may lead to A1c reduction whereas being potentially cost-effective and strengths the patient-physician relationship.

SUMMARY

POC A1c testing in primary care, if widely available and integrated into workflow, has the potential to positively impact diabetes care. Real-time feedback may change patient and physician behaviors, allowing earlier therapeutic intensification.

Current Status of Clinical Application of Point-of-Care Testing

CONTEXT.—

The clinical applications of point-of-care testing (POCT) are gradually increasing in many health care systems. Recently, POCT devices using molecular genetic method techniques have been developed. We need to examine clinical pathways to see where POCT can be applied to improve them.

OBJECTIVE.—

To introduce up-to-date POCT items and equipment and to provide the content that should be prepared for clinical application of POCT.

DATA SOURCES.—

Literature review based on PubMed searches containing the terms point-of-care testing, clinical chemistry, diagnostic hematology, and clinical microbiology.

CONCLUSIONS.—

If medical resources are limited, POCT can help clinicians make quick medical decisions. As POCT technology improves and menus expand, areas where POCT can be applied will also increase. We need to understand the limitations of POCT so that it can be optimally used to improve patient management.

Availability and analytical quality of hemoglobin A1c point-of-care testing in general practitioners’ offices are associated with better glycemic control in type 2 diabetes

Background

It is not clear if point-of-care (POC) testing for hemoglobin A1c (HbA1c) is associated with glycemic control in type 2 diabetes.

Methods

In this cross-sectional study, we linked general practitioner (GP) data on 22,778 Norwegian type 2 diabetes patients to data from the Norwegian Organization for Quality Improvement of Laboratory Examinations. We used general and generalized linear mixed models to investigate if GP offices’ availability (yes/no) and analytical quality of HbA1c POC testing (average yearly “trueness score”, 0–4), as well as frequency of participation in HbA1c external quality assurance (EQA) surveys, were associated with patients’ HbA1c levels during 2014–2017.

Results

Twenty-eight out of 393 GP offices (7%) did not perform HbA1c POC testing. After adjusting for confounders, their patients had on average 0.15% higher HbA1c levels (95% confidence interval (0.04–0.27) (1.7 mmol/mol [0.5–2.9]). GP offices participating in one or two yearly HbA1c EQA surveys, rather than the maximum of four, had patients with on average 0.17% higher HbA1c levels (0.06, 0.28) (1.8 mmol/mol [0.6, 3.1]). For each unit increase in the GP offices’ HbA1c POC analytical trueness score, the patients’ HbA1c levels were lower by 0.04% HbA1c (−0.09, −0.001) (−0.5 mmol/mol [−1.0, −0.01]).

Conclusions

Novel use of validated patient data in combination with laboratory EQA data showed that patients consulting GPs in offices that perform HbA1c POC testing, participate in HbA1c EQA surveys, and maintain good analytical quality have lower HbA1c levels. Accurate HbA1c POC results, available during consultations, may improve diabetes care.

Implementation of A1C Point-of-Care Testing: Serving Under-Resourced Adults With Type 2 Diabetes in a Public Health Department

IN BRIEF A1C point-of-care testing (POCT) paired with face-to-face education potentially improves glycemic control in under-resourced populations. In this study, A1C POCT was implemented with same-day face-to-face medication management and education for adults with type 2 diabetes in a public health department in southeastern North Carolina. The combination of POCT, medication management, and education provided together improved glycemic control and decreased clinical inertia in a setting in which access to health care is limited.

The value proposition for point-of-care testing in healthcare: HbA1c for monitoring in diabetes management as an exemplar

Point-of-care testing (POCT) is a key enabling technology for disruptive and transformative innovation in healthcare, allowing tests to be performed quickly and close to the patient. This results in faster clinical decision making and new, more efficient models of care, with clinical, process and economic benefits potentially accruing to all stakeholders. Recognised barriers to the adoption of new technology such as POCT include poor understanding of current practice and thus the unmet need, the challenges of process change, and reluctance to disinvest in redundant resources resulting from improved pathway efficiency. Major contributors to this problem include a background of funding, organisation and management of healthcare that fails to recognise the complexity of a multiple stakeholder health economy seeking to become more outcomes-based and value driven. We examine the concept of a structured value proposition as a generic tool to achieve better adoption of POCT using as an example, the evidence that is available for the rapid measurement of glycated haemoglobin (HbA1c) in the management of diabetes. We highlight the key components of the value proposition, identifying the impact of the test result on all stakeholders and the metrics which are required to define current practice (e.g. a laboratory-based HbA1c testing service), in order to develop the business case and the implementation plan required to demonstrate effective adoption of a POCT-based service. We conclude that the value proposition helps to identify the potential benefits to be gained from using POCT, and the stakeholders to whom they accrue.

Impact of HbA1c Testing at Point of Care on Diabetes Management

Diabetes is a highly prevalent disease also implicated in the development of several other serious complications like cardiovascular or renal disease. HbA1c testing is a vital step for effective diabetes management, however, given the low compliance to testing frequency and, commonly, a subsequent delay in the corresponding treatment modification, HbA1c at the point of care (POC) offers an opportunity for improvement of diabetes care. In this review, based on data from 1999 to 2016, we summarize the evidence supporting a further implementation of HbA1c testing at POC, discuss its limitations and propose recommendations for further development.

A1c Gear: Laboratory quality HbA1c measurement at the point of care

INTRODUCTION

HbA1c is an important part of assessing the diabetic control and since the use of point-of-care devices for monitoring HbA1c is increasing, it is important to determine how these devices compare to the central laboratory.

METHODS

One hundred and twenty patient samples were analyzed on the Bio-Rad Variant™II and one POC analyzer (Sakae A1c Gear). Three patient sample pools containing ~5%, ~7%, and ~10% HbA1c levels were run over 20 days. Three reagent lots and three instruments were evaluated for the A1c Gear.

RESULTS

The 120 patient samples showed strong correlation (R(2)>0.989) when compared to the Variant™II with means=8.06% and 7.81%, for Variant IIand A1c Gear, respectively. Changing reagent lots or instruments had no impact for the A1c Gear. The ~5%, ~7%, and ~10% pools within-run and between-run imprecision was between 0.87-1.33% and 1.03-1.32%, and 1.41-2.35% and 1.24-1.89% with total imprecision of 1.67-2.35% and 1.61-2.31% for the A1c Gear and Variant II, respectively. The A1c Gear showed a small negative bias (0.25% HbA1c) across HbA1c measurement ranges of <11.5%. This bias was, however, acceptable and not considered to be clinically significant.

CONCLUSIONS

The A1c Gear meets the criteria of total CV <3% leading us to the conclusion that the A1c Gear can give results as precise as the laboratory at the POC.

The state of point-of-care testing: a European perspective

Point-of-care testing (POCT) refers to any diagnostic test administered outside the central laboratory at or near the location of the patient. By performing the sample collection and data analysis steps in the same location POCT cuts down on transport and processing delays, resulting in the rapid feedback of test results to medical decision-makers. Over the past decades the availability and use of POCT have steadily increased in Europe and throughout the international community. However, concerns about overall utility and the reliability of benefits to patient care have impeded the growth of POCT in some areas. While there is no agreed-upon standard for how success should be judged, the increases in speed and mobility provided by POCT can lead to substantial advantages over traditional laboratory testing. When properly utilized, POCT has been shown to yield measurable improvements in patient care, workflow efficiency, and even provide significant financial benefits. However, important organizational and quality assurance challenges must be addressed with the implementation of POCT in any health care environment. To ensure maximal benefits it may be necessary to evaluate critically and restructure existing clinical pathways to capitalize better on the rapid test turnaround times provided by POCT.

Reducing analytical variation between point-of-care and laboratory HbA1c testing

BACKGROUND

Point-of-care (POC) HbA1c testing allows for timely treatment changes, improved glycemic control, and patient and provider satisfaction. Substantial variation between POC and laboratory HbA1c results has been reported. At our university hospital diabetes clinic, we observed significant negative bias in HbA1c with the DCA Vantage (Siemens Healthcare Diagnostics, Tarrytown, NY, USA) compared with the Tosoh G8 HPLC laboratory analyzer (Tosoh Bioscience, San Francisco, CA, USA). This led us to systematically analyze the bias with the goal of recalibrating the DCA to minimize bias.

METHODS

We analyzed 45 patient samples, with HbA1c ranging between 5% and 10.8%, concurrently on two DCA analyzers and on the Tosoh G8 machine. The bias for each sample was the difference between the value on the DCA and the Tosoh G8 analyzer. Based on regression equations derived from the data, a correction factor for each DCA analyzer was calculated. The analyzers were recalibrated and retested for bias.

RESULTS

At baseline, the mean bias (range) was -0.5229 (+0.1 to -1.3) for Analyzer 1 and -0.5348 (0.0 to -1.6) for Analyzer 2. After recalibration, the mean bias (range) was 0.000 (+0.6 to -0.6) and 0.0003 (+0.5 to -0.5) for Analyzers 1 and 2, respectively, and the systematic negative bias seen prior to the calibration was almost eliminated.

CONCLUSIONS

We recommend periodic recalibration of POC analyzers to eliminate systematic unidirectional bias and to harmonize results between the POC and central laboratory analyzers within a healthcare system. Calibration may need to be repeated with any change in the reagent lot.

Point-of-care testing for Hb A1c in the management of diabetes: a systematic review and metaanalysis

BACKGROUND

The measurement of hemoglobin A(1c) (Hb A(1c)) is employed in monitoring of patients with diabetes. Use of point-of-care testing (POCT) for Hb A(1c) results at the time of the patient consultation potentially provides an opportunity for greater interaction between patient and caregiver, and more effective care.

OBJECTIVE

To perform a systematic review of current trials to determine whether POCT for Hb A(1c), compared with conventional laboratory testing, improves outcomes for patients with diabetes.

METHODS

Searches were undertaken on 4 electronic databases and bibliographies from, and hand searches of, relevant journal papers. Only randomized controlled trials were included. The primary outcome measures were change in Hb A(1c) and treatment intensification. Metaanalyses were performed on the data obtained.

RESULTS

Seven trials were found. There was a nonsignificant reduction of 0.09% (95% CI -0.21 to 0.02) in the Hb A(1c) in the POCT compared to the standard group. Although data were collected on the change in proportion of patients reaching a target Hb A(1c) of <7.0%, treatment intensification and heterogeneity in the populations studied and how measures were reported precluded pooling of data and metaanalysis. Positive patient satisfaction was also reported in the studies, as well as limited assessments of costs.

CONCLUSIONS

There is an absence of evidence in clinical trial data to date for the effectiveness of POCT for Hb A(1c) in the management of diabetes. In future studies attention to trial design is needed to ensure appropriate selection and stratification of patients, collection of outcome measures, and action taken upon Hb A(1c) results when produced.

A High Level of Patient Activation Is Observed But Unrelated to Glycemic Control Among Adults With Type 2 Diabetes

OBJECTIVE

To measure patient activation and its relationship to glycemic control among adults with type 2 diabetes who had not participated in a formal diabetes self-management education program as a baseline assessment for tailoring diabetes education in a primary care setting.

RESEARCH DESIGN AND METHODS

Patient activation was assessed in a stratified, cross-sectional study of adults with controlled (n = 21) and uncontrolled (n = 27) type 2 diabetes, who were receiving primary care at a unique family practice center of Baylor Health Care System in Dallas, Tex.

RESULTS

The mean patient activation was 66.0 (95% confidence interval [CI] 60.8-71.2) among patients with uncontrolled diabetes and 63.7 (55.9-71.5) among those with controlled diabetes (P = 0.607). A significant association was observed between the self-management behavior score and activation among patients whose glycemia was under control (ρ = 0.73, P = 0.01) as well as among patients with uncontrolled glycemia (ρ = 0.48, P < 0.001).

CONCLUSIONS

Although activation is correlated with self-management and may be important in tailored patient-centered approaches to improving diabetes care outcomes, the highest stage of activation may be necessary to achieve glycemic control. These findings reinforce the importance of conducting prerequisite needs assessments so diabetes educators are able to tailor their educational interventions to individual patients' needs and readiness to take action.

Usefulness of point-of-care testing in the treatment of diabetes in an underserved population

BACKGROUND

The purpose of this article was to communicate our experience with point-of-care testing (POCT) using Bayer's A1CNow+ device to test glycated hemoglobin (A1C) in the management of diabetes and to share the observations of our quality control efforts.

METHODS

Forty-seven patients' POCT samples were compared with laboratory samples to determine the validity of the POCT sample being drawn. Data collected represent a 10-month time period that were drawn on-site with the following distribution: 36 samples were drawn the same day, 7 samples were drawn 1 day later, 3 samples were drawn within 3 days, and 1 sample was drawn 4 days later. Although all samples were collected on-site, some of the samples were sent to other local branches of nationally recognized laboratories for analysis.

RESULTS

The range of A1C results for the POCT group was 5.6 to >13%. The range of A1C results for the laboratory-drawn group was 5 to 12.6%. Twenty-four patients had laboratory results that read lower than the result obtained in the clinic, with an A1C range of 5 to 12.6%, and two patients had laboratory results that read exactly the same as the result obtained in the clinic when using POCT. These two individuals had A1C results of 9.1 and 12.6%. Analysis of data collected determined an r value of 0.918 demonstrating agreement between the POCT samples and the laboratory samples.

CONCLUSIONS

POCT with the A1CNow+ is an effective, economical tool for use in a pharmacist-based diabetes clinic that serves a high-risk underserved population. POCT allows the pharmacist the ability to use on-site results to inform patients of their progress, modify their therapy immediately with an immediate face-to-face opportunity to assure understanding, and provide a self-management goal.