User competency is still a major factor affecting analytical performance of glucose meters in patient service

Using geographic rescue time contours, point-of-care strategies, and spatial care paths to prepare island communities for global warming, rising oceans, and weather disasters

OBJECTIVES

To perform geographic contour analysis of sea and land ambulance rescue times in an archipelago subject to super typhoons; to design point-of-care testing strategies for medical emergencies and weather disasters made more intense by global warming and rising oceans; and to assess needs for prehospital testing on spatial care paths that accelerate decision making, increase efficiency, improve outcomes, and enhance standards of care in island nations.

METHODS

We performed needs assessments, inspected healthcare facilities, and collected ambulance rescue times from professionals in the Bantayan Archipelago, Philippines. We mapped sea/land ambulance rescue routes and time contours. To reveal gaps, we statistically compared the fastest and slowest patient rescue times from islands/islets and barangays to the District Hospital on Bantayan Island. We developed spatial care paths (the fastest routes to care) for acute myocardial infarction, community care, and infectious diseases. We generated a compendium of prehospital diagnostic testing and integrated outcomes evidence, diagnostic needs, and public health goals to recommend point-of-care strategies that build geographic health resilience.

RESULTS

We observed limited access to COVID-19 assays, absence of blood gas/pH testing for critical care support, and spatial gaps in land and airborne rescues that worsened during inclement weather and sea swells. Mean paired differences (slowest-fastest) in ambulance rescue times to the District Hospital for both islands and barangays were significant (P < 0.0001). Spatial care path analysis showed where point-of-care cardiac troponin testing should be implemented for expedited care of acute myocardial infarction. Geospatial strengths comprised distributed primary care that can be facilitated by point-of-care testing, logical interisland transfers for which decision making and triage could be accelerated with onboard diagnostics, and healthcare networks amenable to medical advances in prehospital testing that accelerate treatment.

CONCLUSIONS

Point-of-care testing should be positioned upstream close to homes and island populations that have prolonged rescue time contours. Geospatially optimized point-of-need diagnostics and distributed prehospital testing have high potential to improve outcomes. These improvements will potentially decrease disparities in mortality among archipelago versus urban dwellers, help improve island public health, and enhance resilience for increasingly adverse and frequent climate change weather disasters that impact vulnerable coastal areas. [350 words].

Analysis of Quality Indicators of the Pre-Analytical Phase on Blood Gas Analyzers, Point-Of-Care Analyzer in the Period of the COVID-19 Pandemic

Aim of the study: We evaluated and compared blood gas analysis (EGA) non-conformities (NC) considered operator-dependent performed in Point-Of-Care (POC) analyzer as quality indicators (IQ) of the pre-analytical phase. To this end, four different NC registered in the resuscitation departments of the Hospital Polyclinic Bari from the beginning of the pandemic (March 2020) until February 2022 were evaluated. The results obtained were compared with those recorded in the pre-COVID period (March 2018–February 2020) to check if there were differences in number and type. Material and methods: GEM 4000 series blood gas analyzers (Instrumentation Laboratory, Bedford, MA, United States) are installed with integrated Intelligent Quality Management (iQM®), which automatically identify and log pre-analytical errors. All blood gas analyzers are connected to the company intranet and interfaced with the GEM Web Plus (Werfen Instrumentation Laboratory, Bedford, MA, United States) data management information system, which allows the core laboratory to remotely supervise all decentralized POC stations. The operator-dependent process NC were expressed in terms of absolute and relative proportions (percentiles and percentage changes). For performance evaluation, the Mann–Whitney U test, Chi-squared test and Six-Sigma Metric calculation for performance classification were performed. Results: In the COVID period, 31,364 blood gas tests were performed vs. 16,632 tests in the pre-COVID period. The NC related to the suitability of the EGA sample and manageable by the operators were totals of 652 (3.9%) and 749 (2.4%), respectively, in the pre-COVID and COVID periods. The pre-analytical phase IQs used did not show statistically significant differences in the two periods evaluated. The Sigma evaluation did not show an increase in error rates. Conclusions: Considering the increase in the number of EGAs performed in the two periods, the training procedures performed by the core laboratory staff were effective; the clinical users of the POC complied with the indications and procedures shared with the core laboratory without increasing the operator-dependent NCs. Furthermore, the core laboratory developed monitoring activities capable of guaranteeing the maintenance of the pre-analytical quality.

Lot variation and inter-device differences contribute to poor analytical performance of the DCA Vantage™ HbA1c POCT instrument in a true clinical setting

OBJECTIVES

The glycated haemoglobin fraction A_1c (HbA_1c) is widely used in the management of diabetes mellitus, and the Siemens DCA Vantage™ point-of-care testing (POCT) instrument offers rapid HbA_1c results even far from a clinical laboratory. However, the analytical performance has been questioned, and not much is known about effects of changing reagent lot, instrument and operator. We therefore compared the analytical performance of the DCA Vantage™ with established routine methods (Tosoh G8/G11 ion exchange HPLC) in a true clinical setting at two Danish hospitals.

METHODS

We extracted all routine clinical HbA_1c results incidentally drawn from the same patient within 48 h (n=960 pairs) and evaluated the effect of reagent lot, operator and instrument. We also performed a prospective method comparison in our diabetes out-patient clinic (n=97).

RESULTS

The critical difference (CD) between two POCT results varied between 5.14 and 6.61 mmol/mol (0.47-0.55%), and the analytical imprecision of the DCA Vantage™ (CV_A) was >3%. Significant effect of reagent lot and inter-instrument differences were found, whereas no effect of operator was seen.

CONCLUSIONS

The DCA Vantage™ HbA_1c analysis does not fulfil the prevailing analytical performance specifications, but rigorous validation of new reagent lots and continuous recalibration of instruments may potentially improve the precision substantially. Our findings, therefore, clearly emphasise the necessity of a close collaboration between clinicians and laboratory professionals in the POCT field. Finally, POCT HbA_1c results should always be interpreted together with other measures of glycaemic control to avoid inappropriate change of patient treatments due to measurement uncertainty.

What We Should Consider in Point of Care Blood Glucose Test; Current Quality Management Status of a Single Institution

Background and Objectives: Point of care test (POCT) is generally performed by non-laboratory staff who often lack an understanding on the quality control and quality assurance programs. The purpose of this study was to understand the current status of quality management of point of care (POC) blood glucose testing in a single institution where non-laboratory staff perform the tests. Materials and Methods: From July to August 2020, management status of glucometer, test strips, quality control (QC) materials, quality assurance program, and operators’ response to processing of displayed results was monitored in all Soonchunhyang University Bucheon hospital departments that performed POC blood glucose test. Results of the POC blood glucose test conducted from January 2019 to May 2020 were analyzed retrospectively. Results: A total 124 glucometers were monitored in 47 departments. Insufficient management of approximately 50% of blood sugar, test strips, and QC materials was observed. Although daily QC was conducted by 95.7% of the departments, the QC records were inaccurate. The method of recording test results varied with departments and operators. Various judgments and troubleshooting were performed on the unexpected or out of measurable range results, including some inappropriate processes. In POC blood glucose test results review, 4568 atypical results were identified from a total of 572,207 results. Conclusions: Sufficient training of the non-laboratory staff and ongoing assessment of competency through recertification is needed to maintain acceptable levels of POCT quality. In this study, various problems were identified in glucometer and reagent management, QC and post-analytic phase. We believe that these results provide meaningful basal information for planning effective operators’ training and competency evaluation, and the development of an efficient POCT quality management system.

Quality assurance practices for point of care testing programs: Recommendations by the Canadian society of clinical chemists point of care testing interest group

Point of Care Testing (POCT) refers to clinical laboratory testing performed outside the central laboratory, nearer to the patient and sometimes at the patient bedside. The testing is usually performed by clinical staff, such as physicians or nurses, who are not laboratory trained. This document was developed by the POCT Interest group of the Canadian Society of Clinical Chemists (CSCC) as practical guidance for quality assurance practices related to POCT performed in hospital and outside hospital environments. The aspects of quality assurance addressed in this document include: (1) device selection, (2) initial device verification, (3) ongoing device verification, (4) ongoing quality assurance including reagent and quality control (QC) lot changes, and (5) quality management including operator and document management.