Comparison of Point-of-Care and Laboratory Glycated Hemoglobin A1c and Its Relationship to Time-in-Range and Glucose Variability: A Real-World Study.

Introduction The main objective of the current study was to perform a comparison of point-of-care testing for hemoglobin A1c (POCT-HbA1c) versus the standard laboratory method (Lab HbA1c) and their relationship to time-in-range (TIR) and glucose variability (GV) among patients with diabetes mellitus (DM) presented to the outpatient diabetes clinics. Methods This single-center cross-sectional study was carried out on diabetic patients (aged ≥14 years of both genders) who undergo routine follow-up at our institution and whose physicians ordered HbA1c analysis for routine care. The included patients were those using the intermittently scanned continuous glucose monitoring (isCGM) Abbott's FreeStyle Libre system for at least three months and regular CGM users with at least 70% use. Results We included 97 diabetic patients (41 female and 56 male), with a median age of 25 years (Interquartile range= 18) and a mean DM duration of 10.33±5.48 years. The mean values of Lab-HbA1c and POCT HbA1c were 8.82%±0.85% and 8.53%±0.89%, respectively. The TIR, time below range, and time above range were 33.47±14.38 minutes (47.78%±14.32%), 5.44±2.58 minutes (8.41%±4.42%), and 28.8±8.27 minutes (43.81%±13.22%), respectively. According to the Bland-Altman plot analysis, the POCT-HbA1c values are consistent with the standard Lab-HbA1c values (SD of bias= 0.55, and 95% CI= -0.78 to 1.4). The univariate linear regression analysis showed a statistically significant relationship between laboratory HbA1c and POCT HbA1c (R2= 0.637, p <0.001), TIR (R2= 0.406, p <0.001), and GV (R2= 0.048, p= 0.032). After adjusting for age, gender, disease duration, diabetes type, and percentage of sensor data in a multivariable linear regression model, the linear associations remained significant (all p < 0.05). Conclusion The current findings show that TIR and GV can be used as endpoints and valuable parameters for the therapy of DM.

Evaluation of Patient Reported Satisfaction and Clinical Efficacy of Insulin Glargine 300 U/mL Versus 100 U/mL in Patients With Type 1 Diabetes Using Flash Glucose Monitoring System.

Background and Aims: To analyze patient-reported satisfaction and clinical effectiveness of concentrated insulin glargine 300 U/mL (Gla-300) among patients with type 1 diabetes (T1D) using a flash glucose monitoring (FGM) system. Methods: This comparative study was conducted among 86 patients with T1D (aged 14-40 years), who were treated with Glargine 100 U/mL (Gla-100) and switched to Gla-300 at day 1 (baseline). The following data were collected from each patient: demographic information, clinical parameters, and glycemic control markers. All patients completed the Diabetes Treatment Satisfaction Questionnaire (Arabic version), first at baseline and then after 12 weeks. A comparison was done for all the data recorded at baseline (on Gla-100) and after 12 weeks (on Gla-300) and subjected to analysis. Results: Compared to patients treated with Gla-100, significant improvements were observed in the Gla-300 group, in terms of the ambulatory glucose profile (AGP) markers, such as percentage of time spent within the target range of the glucose levels (70-180 mg/dL) (P = .037), percentage which fell below the target (<70 mg/dL) (P = .027), and percentage of time spent (<54 mg/dL) (P = .043). Compared to Gla-100, patients treated with Gla-300 experienced significant improvements in the current treatment satisfactions (P = .047), convenient finding treatment recently (P = .034), and flexible finding treatment recently (P = .041), recommend the current treatment (P = .042) and satisfied to continue the current treatment (P = .035). Conclusion: Compared to the patients on Gla-100, patients treated with Gla-300 exhibited significant improvements in the AGP markers and degree of treatment satisfaction.

Level of Agreement and Correlation Between the Estimated Hemoglobin A1c Results Derived by Continuous or Conventional Glucose Monitoring Systems Compared with the Point-of-Care or Laboratory-Based Measurements: An Observational Study.

INTRODUCTION: Hemoglobin A1C (HbA1c) is an important marker for diabetes care management. With the increasing use of new technologies such as continuous glucose monitoring (CGM) and point-of-care testing (POCT), patients and their physicians have been able to monitor and continuously check their blood glucose levels in an efficient and timely manner. This study aimed to investigate the level of agreement between the standard laboratory test for HbA1c (Lab-HbA1c) with point-of-care testing (POCT-HbA1c) and glucose monitoring index (GMI) derived by intermittently scanned CGM (isCGM) or estimated average glucose (eAG) derived by conventional self-monitored blood glucose (SMBG) devices. METHODS: A cross-sectional study was conducted at the Diabetes Treatment Center, Prince Sultan Military Medical City, Saudi Arabia, between May and December 2020 with 81 patients with diabetes who used the isCGM system (n = 30) or conventional finger-pricking SMBG system (n = 51). At the same visit, venous and capillary blood samples were taken for routine HbA1c analysis by the standard laboratory and POCT methods, respectively. Also, for isCGM users, the GMI data for 28 days (GMI-28) and 90 days (GMI-90) were obtained, while for SMBG users, eAG data for 30 days (eAG-30) and 90 days (eAG-90) were calculated. The limits of agreement in different HbA1c measurements were evaluated using a Bland-Altman analysis. Pearson correlation and multivariate linear regression analyses were also performed. RESULTS: Based on the Bland-Altman analysis, HbA1c levels for 96.7% and 96.1% of the patients analyzed by the POCT and the standard laboratory methods were within the range of the 95% limit of agreement in both isCGM and conventional SMBG users, respectively. About 93.3% of the GMI measurements were within the 95% limit of agreement. Also, about 94.12% of the eAG-30 and 90.2% of the eAG-90 measurements were within the 95% limit of agreement. Moreover, the correlation analysis revealed a statistically significant positive correlation and linear regression among Lab-HbA1c, POCT-HbA1c, GMI, and eAG in both conventional SMBG and isCGM users (all p < 0.001). These positive results persisted significantly after adjusting for different factors (all p < 0.001). CONCLUSION: GMI derived by isCGM or eAG derived by conventional SMBG systems, as well as the POCT-HbA1c measurements, showed a high level of agreement; therefore, we recommend them as potential methods for diabetes monitoring, especially when a rapid result is needed or with patients with uncontrolled diabetes or on intensive insulin therapy.

Assessment of Patient Satisfaction with On-Site Point-of-Care Hemoglobin A1c Testing: An Observational Study.

INTRODUCTION: Poor glycemic control is a serious challenge in successful diabetes management. Given the low adherence and compliance with HbA1c testing frequency and the corresponding delay in the appropriate medication adjustment, point-of-care testing (POCT) for HbA1c provides an opportunity for better control of diabetes and higher patient satisfaction. The data with this regard are limited in Saudi Arabia. Therefore, we aimed to assess the level of patient satisfaction associated with the POCT service implementation for HbA1c and evaluate the differences between the number of requested and conducted HbA1c tests before and after POCT implementation and its effect on glycemic control in Saudi clinical practice. METHODS: We conducted a single-center ambispective descriptive cohort study in Riyadh, Saudi Arabia. This study had two phases: the retrospective phase (January 2017 to December 2017) and the prospective phase (January 2018 to December 2018). Patient satisfaction was assessed using the patient satisfaction questionnaire short form (PSQ-18) and on-site HbA1c point-of-care testing (HbA1c-POCT) satisfaction questionnaire. RESULTS: This study included 75 patients with diabetes (37% type 1, 63% type 2) with a mean age of 44.35 (± 17.97) years. The adherence to physician recommendations for HbA1c testing frequency increased from 24% to 85% (before and after POCT implementation, respectively). High levels of satisfaction across seven dimensions of PSQ-18 (77-88%) were reported towards the provided healthcare service after POCT implementation. Furthermore, a high level of agreement on the statements of the on-site HbA1c-POCT satisfaction questionnaire was also observed. Finally, the mean HbA1c level has significantly improved after POCT implementation compared to the traditional HbA1c laboratory testing before POCT implementation [8.34 ± 0.67 and 8.06 ± 0.62, respectively, p < 0.001). CONCLUSION: HbA1c testing at POCT improved adherence to recommendations for HbA1c testing frequency for better glycemic control and higher patient satisfaction. POCT reduces turnaround time, improves glycemic control, and facilitates the decision-making process. HbA1c measurement with POC devices is recommended to be implemented in diabetes treatment centers. All of the described benefits of POCT come together to make HbA1c testing the most common procedure for diabetes management at the point of care.

Comment on: Impact of a multidisciplinary intensive education program on type 2 diabetes mellitus patients' glycemic control and cardiovascular risk factors.

[No Abstract Available].

Level of diabetes knowledge among adult patients with diabetes using diabetes knowledge test.

[No Abstract Available].

An Inverse Relationship Between Age of Type 2 Diabetes Onset and Complication Risk and Mortality: The Impact of Youth-Onset Type 2 Diabetes.

OBJECTIVE: This study compared the prevalence of complications in 354 patients with T2DM diagnosed between 15 and 30 years of age (T2DM15-30) with that in a duration-matched cohort of 1,062 patients diagnosed between 40 and 50 years (T2DM40-50). It also examined standardized mortality ratios (SMRs) according to diabetes age of onset in 15,238 patients covering a wider age-of-onset range. RESEARCH DESIGN AND METHODS: Complication status was assessed according to a standard protocol and extracted from our electronic database. Survival status was ascertained by data linkage with the Australian National Death Index. SMRs were calculated in comparison with the background Australian population and analyzed according to age of onset. RESULTS: After matching for duration, despite their younger age, T2DM15-30 had more severe albuminuria (P = 0.004) and neuropathy scores (P = 0.003). T2DM15-30 were as commonly affected by metabolic syndrome factors as T2DM40-50 but less frequently treated for hypertension and dyslipidemia (P < 0.0001). An inverse relationship between age of diabetes onset and SMR was seen, which was the highest for T2DM15-30 (3.4 [95% CI 2.7-4.2]). SMR plots adjusting for duration show that for those with T2DM15-30, SMR is the highest at any chronological age, with a peak SMR of more than 6 in early midlife. In contrast, mortality for older-onset groups approximates that of the background population. CONCLUSIONS: The negative effect of diabetes on morbidity and mortality is greatest for those diagnosed at a young age compared with T2DM of usual onset. These results highlight the growing imperative to direct attention toward young-onset T2DM and for effective interventions to be applied before middle age.

Assessment of health-related quality of life among adolescents with type 1 diabetes mellitus in Saudi Arabia.

OBJECTIVE: To evaluate the health-related quality of life (HRQoL) for adolescents with type 1 diabetes mellitus (T1DM). METHODS: A cross-sectional study was conducted among 214 adolescents (13-18 years) with T1DM at the Diabetes Treatment Center, Prince Sultan Military Medical City, Riyadh, Saudi Arabia from June to September 2013. Respondents were selected by their availability during routine visits to outpatient clinics, and interviewed using the Arabic translated version of the Pediatric Quality of Life Inventory 3.0 Diabetes Module (PedsQL 3.0 DM) independently. RESULTS: Female gender, multiple daily injection (MDI), longer duration of T1DM (>7 years), diabetic ketoacidosis (DKA) and adolescents with >7 HbA1c level had at least one poor HRQoL outcome. Multivariate linear regression analysis showed that age, treatment type (MDI), DKA, and >7 HbA1c were independent influencing factors for subscale diabetes symptoms, whereas >7 HbA1c was the independent influencing factor for treatment barriers and overall HRQoL. The DKA was the independent influencing factor for the subscale of worry, and female gender was the independent influencing factor for the subscale of communication. CONCLUSION: Female gender, age, treatment type, >7 HbA1c, and DKA are the strongest determinants for lower HRQoL for at least one subscale of the PedsQL 3.0 DM.