Diabetes Care Management Research Articles

1152-P: Achieving Diabetic Management Targets in People with Type 1 Diabetes

Background: Optimal diabetes care and risk factor management are important to delay micro- and macrovascular complications in individuals with type 1 diabetes (T1D) . Ongoing improvement of management strategies requires the evaluation of target achievement and identification of determinants in individuals who do (or do not) achieve these targets. Methods: Cross-sectional data were collected from individuals with T1D visiting Diabeter and the University Medical Center Groningen, the Netherlands, in 2018. Targets were defined as HbA1c <7%, LDL-c <2.6mmol/l (no CVD present) or <1.8mmol/l (CVD present) , or blood pressure (BP) <140/90mmHg. Determinants between individuals who did (or did not) achieve targets were compared by presence of CVD. Results: Data from 1,737 individuals were available. Mean age was 27 [IQR 22-43] years and 50% female. Mean HbA1c was 63 mmol/mol (7.9%) , LDL-c was 2.67mmol/l, and BP 131/76mmHg. In individuals with CVD, 24%, 33%, and 46% achieved HbA1c, LDL-c, and BP targets respectively. In individuals without CVD these percentages were, 29%, 54%, and 77%, respectively. Individuals with CVD who did or did not achieve HbA1c targets did not differ significantly. Individuals who achieved LDL-c targets had a longer diabetes duration (42 vs. 37 years, p=0.03) , lower eGFR (eGFR 56 vs. 70 ml/min/kg2) and were prescribed more lipid lowering medication (94% vs. 77%, p=0.05) . Individuals who achieved BP targets were younger (58 vs. 62 years, p=0.05) . In comparison, individuals without CVD who achieved HbA1c targets differed in age (28 vs. 25 years, p=<.001) , sex (45% vs. 53% female, p=0.02) , and insulin pump use (60% vs 52%, p=0.01) amongst others. Those who achieved LDL-c targets had lower BMI (24.1 vs. 26.1kg/m2, p=<.001) . BP targets were achieved by younger individuals with less microvascular complications (14% vs. 33%, p=<.001) . Conclusions: Opportunities for improvement diabetes management exist for the achievement of glycaemic, lipid and BP targets but differ between individuals with and without CVD. Disclosure R.D.M.Varkevisser: None. E.Birnie: None. C.E.Vollenbrock: None. D.Mul: None. P.Van dijk: None. M.M.Van der klauw: Advisory Panel; Pfizer Inc. H.J.Veeze: Employee; Medtronic. H.Aanstoot: Consultant; Lilly Diabetes, Medtronic. B.H.Wolffenbuttel: Advisory Panel; Novo Nordisk A/S, Other Relationship; Novo Nordisk.

Impact of Pharmacist-Led Interventions to Improve Clinical Outcomes for Adults With Type 2 Diabetes at Risk of Developing Cardiovascular Disease: A Systematic Review and Meta-analysis

Objective The aim of this systematic review and meta-analysis of randomised controlled trials is to evaluate the impact of pharmacist-led interventions on cardiovascular disease (CVD) risk factors among patients with type 2 diabetes. Method: A literature review was conducted according to PRISMA guidelines using 4 electronic databases: Embase, MEDLINE, CINHAL and the Cochrane Central Register of Controlled Trials. We searched for pharmacist interventions among adults with type 2 diabetes and cardiovascular disease in randomised controlled trials from inception to May 2021 in primary care, diabetes clinics and hospitals. The clinical outcomes measured glycosylated haemoglobin (HbA1c), blood pressure (BP) and lipid profile. The non-clinical outcomes included medication adherence, smoking, health-related quality of life and the cost of the intervention. For the meta-analysis, clinical outcomes were pooled with the random effect model in RevMan 5.3. The Cochrane risk-of-bias tool was used to assess the quality of the included studies. Results: We retrieved 223 studies,141 of which were included in the review. Ten published articles met the inclusion criteria and were included in the meta-analysis. The pharmacists delivered the interventions alone or collaboratively with other healthcare professionals in hospitals or similar settings. The overall result showed a significant reduction in HbA1c (n = 10; standard deviation in mean value [SDM]: −.53%, 95% CI: −.84, −.23) and systolic BP (n = 10; [SDM]: −.35 mmHg, 95% CI: −.51, −.20) in pharmacist intervention groups. For the non-clinical outcomes, the review revealed variable results from pharmacist intervention compared with those standard care. Conclusion: Pharmacy interventions provide evidence for pharmacists’ decisive role in diabetes care management and reducing cardiovascular risk factors among adults with type 2 diabetes.

Patient and Parent Well-Being and Satisfaction With Diabetes Care During a Comparative Trial of Mobile Self-Monitoring Blood Glucose Technology and Family-Centered Goal Setting.

Patient engagement in the process of developing a diabetes treatment plan is associated with person-centered care and improved treatment outcomes. The objective of the present study was to evaluate the self-reported patient and parent-centered satisfaction and well-being outcomes associated with the three treatment strategies utilized in a comparative effectiveness trial of technology-enhanced blood glucose monitoring and family-centered goal setting. We evaluated data from 97 adolescent-parent pairs at baseline and 6-months during the randomized intervention. Measures included: Problem Areas in Diabetes (PAID) child and parent scales, pediatric diabetes-related quality of life, sleep quality, and satisfaction with diabetes management. Inclusion criteria were 1) ages 12-18 years, 2) a T1D diagnosis for at least six months and 3) parent/caregiver participation. Longitudinal changes in survey responses were measured at 6 months from baseline. Differences between and within participant groups were evaluated using ANOVA. The average age of youth participants was 14.8 ± 1.6 years with half of the participants being female (49.5%). The predominant ethnicity/race was Non-Hispanic (89.9%) and white (85.9%). We found that youth perceived 1) greater of diabetes-related communication when using a meter capable of transmitting data electronically, 2) increased engagement with diabetes self-management when using family-centered goal setting, and 3) worse sleep quality when using both strategies together (technology-enhanced meter and family-centered goal setting). Throughout the study, scores for self-reported satisfaction with diabetes management were higher in youth than parents. This suggests that patients and parents have different goals and expectations regarding their diabetes care management and care delivery. Our data suggest that youth with diabetes value communication via technology and patient-centered goal setting. Strategies to align youth and parent expectations with the goal of improving satisfaction could be utilized as a strategy to improve partnerships in diabetes care management.

DIET EDUCATION SEBAGAI UPAYA UNTUK MENINGKATKAN PENGETAHUAN DAN KETERAMPILAN DIABETESI DALAM PERENCANAAN DIET SEIMBANG

Diabetes awareness and care management is still a major challenge faced by stakeholders around the world. Lack of knowledge about diabetes management is still widely reported in many studies, especially from developing countries. Knowledge of diabetes, especially in terms of food management, can improve the quality of life of patients and reduce the burden on the family. Diet Education is a program to increase knowledge and skills of diabetics, especially with regard to planning the diet consumed by diabetics every day so that self-care increases and ultimately the quality of life of diabetics and their families can also increase. The purpose of this community service is to increase the knowledge and skills of diabetes in diabetes management by planning a balanced diet. This service is carried out in the form of counseling and training on procedures for managing diabetes with a balanced diet planning approach. Health education methods used are lectures, questions and answers, and demonstrations. The results of the activity showed that the goal was achieved where participants experienced an increase in knowledge and understanding as well as skills in planning a balanced diet. The results of the evaluation showed that 85% of the participants had the right knowledge and skills. Participants are able to choose and adjust the diet according to the dietary rules for people with diabetes mellitus.Keywords: diet education, diabetes mellitus, diabetesi

IDF21-0544 Artificial Intelligence in Diabetes Care

Background: Artificial intelligence (AI) is making a paradigm shift in traditional diabetes care in terms of target achieving efficacy, excellent safety, best quality care, effective monitoring and improved quality of life for both patients with Type-1 diabetes (T1D) and Type-2 diabetes (T2D). All guidelines for adult and pediatric T1D suggest that target Glycated hemoglobin (HbA1c) should be less than 53 mmol/mol (7%) but very less percent population with T1D can achieve this target due to many factors namely individual variability of insulin requirements, fear of hypoglycemia, errors in insulin dose calculations, etc. These lead to poor glycemic control, diabetes complications and a major source of stress for families and care givers especially in pediatric populations. All these affects quality of life (QoL), mental well-being and productivity of patients. So, the emergence of AI in diabetes care has revolutionized the management of diabetes care in optimizing the glycemic control by extending the ‘Target In Range’ (TIR), reducing ‘Target Above Range’ (TAR), ‘Target Below Range’ (TBR) and minimizing severe hypoglycemia. Glucose monitoring by Self Monitoring Blood Glucose (SMBG) using smart glucometer or Ambulatory glucose profile (AGP), Continuous Glucose Monitoring System (CGMS), to name a few. AI is being used in delivering insulin by smart pens, Continuous Subcutaneous Insulin Infusion (CSII), recently introduced Hybrid Artificial Pancreas and with prolonged CGMS. Smart insulin pens namely InPen, Novo Pen 6, Novo Pen Echo Plus, etc. can calculate the bolus dose considering current glucose reading, target, carb intake, probable exercise, etc. by algorithm. Furthermore, it can record the amount and timing of each insulin dose, display the last dose and insulin onboard, and wirelessly transmit the information via Bluetooth to a dedicated mobile application and transfers data automatically on the cloud for sharing with health care professionals as well. Novo Pen 6 and Novo Pen Echo Plus use near-field communication technology. CGMS uses sensor to monitor blood glucose continuously for 14 days and shows results in real-time without using a needle which made the life of young children much comfortable. It calculates TIR, TAR, TBR and probable HbA1c in a graphic presentation. Now new models are in pipeline for much longer duration. Smart glucometers are incorporated with bolus advisors to calculate insulin dosages, algorithm-driven message response to each glucose reading. The Hybrid Close Loop artificial pancreas is the new generation of CSII which automatically determines the basal rates, can determine the bolus dose as per CGMS but this feature is still under trial and having other old generation basic and advanced features. The ongoing trials results will guide further improvements and eventually will lead complete AI based diabetes management. Aim: To see the scope, feasibility and critical role of AI in diabetes care. Method: Contemporary literature is being searched to get the latest information on AI use in diabetes care. Results: PubMed, MEDLINE, CINAHL Plus, EMBASE, Cochrane and Google Scholar is being searched with search words 'Artificial Intelligence AND Diabetes', 'Artificial Pancreas', Close Loop system', 'Smart Insulin Pen'. 312 articles were found and finally 34 were selected.

IDF21-0131 What Is Driving Diabetes and Diabetes Complications in British Columbia (BC), Canada?

Background: British Columbia (BC) is a Western Canadian province with 5.02 million people, ethnic diversity (Aboriginal, Chinese, South Asian) & >70% rural/remote/isolated geography. For 40 years, intermittent efforts in BC & Canada to improve chronic disease & diabetes management had little positive effect. Ideally, healthcare services & planning should be driven by strong data & evidence. No integrated system exists in BC to track, study, understand, manage & improve diabetes care. No comprehensive BC diabetes environmental scan has ever been conducted before. Aim: The BC Provincial Diabetes Evaluation is a quality improvement project to identify & better understand major gaps/barriers; & practical solutions for data-driven diabetes care management redesign in BC; so that all 470,000 diabetes people receive improved, timely, appropriate, cost-effective care that is locally/culturally/ethnically-sensitive, no matter where they live in BC. Method:1.Retrospective, open quantitative multi-source data analysis2.Prospective, open qualitative data analysis3.Knowledge Translation phase including Diabetes Heat Maps with diabetes performance measures; co-morbidities (*CKD, CAD, CHF); major procedures/surgeries (*renal dialysis, cardiac PCI, lower limb amputation) Results: BC’s geographical, socio-economic & lifestyle factors are drivers of diabetes & complications (by each of BC geographical health authority): • Fraser Health Authority (FHA): with BC’s highest population density, diabetes incidence & prevalence driven by BC's 2nd highest immigrant, non-English-speaking (ESL); highest South Asian population; & unhealthy lifestyle (diet, obesity, physical inactivity)#; associated with BC’s 2nd highest diabetes complications*, hospitalization cost & premature mortality; with an overall younger FHA population. • Northern Health Authority (NHA): with BC’s 2nd highest diabetes incidence & prevalence driven by highest rurality (64% of BC’s land mass), Aboriginal, low-education population; lifestyle# & smoking; associated with BC’s 2nd highest diabetes complications* & highest premature mortality; with an overall younger NHA population. • Vancouver Coastal Health Authority (VCHA): with BC’s highest immigrant, ESL, Chinese, low-income population; 2nd highest South Asian & BC population density; offset by a younger, healthier VCH population; associated with mid-range diabetes incidence & prevalence; & BC’s highest hospitalization cost. • Vancouver Island Health Authority (VIHA): BC’s highest elderly population; mid-range rurality, Aboriginal, immigrant, low-income population; associated with 2nd lowest incidence & prevalence; & BC’s lowest hospitalization cost. • Interior Health Authority (IHA): with BC’s 2nd highest elderly, Aboriginal, low-education population living alone; 2nd highest rurality; lowest ESL population; associated with BC’s lowest diabetes incidence & prevalence. Discussion: Substantial variation exists between BC's 5 geographical health authorities for diabetes outcomes, health determinants & cost. The current state of diabetes as an ambulatory-care-sensitive-condition indicates an inadequate primary healthcare system throughout BC & Canada. Barriers to accessing diabetes resources contributed to 80% of diabetes care provided by GPs in BC & Canada. BC has a higher reliance on physicians & lower usage of allied healthcare providers for diabetes care than other provinces. Without coordinated BC provincial diabetes care planning or services, a fragmented, non-standardized, patchwork system has resulted with negative health outcomes & high impact on BC's entire healthcare system (similar across Canada). Our BC Provincial Diabetes Evaluation Knowledge Translation findings will help with diabetes care management redesign; & improve the trajectory of diabetes, outcomes & healthcare utilization in BC.

IDF21-0214 COVID-19 & Diabetes in Canada: Two Global Pandemics Colliding & An Opportunity For Optimizing Data-Driven Diabetes Care Delivery & Outcomes in British Columbia (BC)

Background: Diabetes is a key ambulatory care sensitive condition to measure the effectiveness of primary care. In Canada, diabetes is a serious, prevalent, complex & costly problem affecting the entire healthcare system spectrum. Global healthcare systems, including diabetes & primary care, have been sidelined by COVID-19 (SARS-CoV2 corona virus) after initial December 2019 reports from China & official mid-March 2020 pandemic declaration by WHO. In the province of British Columbia (BC) & throughout Canada, travel restrictions, home-isolation & other pandemic-safe strategies finally began mid-March 2020. By July-Oct 2020, COVID-19 resurgence & escalation were evident in BC, Canada & world-wide. By July 2021, there were contagious SARS-CoV2 variants globally, fuelled by delayed vaccinations & other factors. Worse COVID-19 prognosis & increased mortality have been associated with diabetes, advanced age and/or major co-morbidities. Purpose: The BC Provincial Diabetes Evaluation is a quality improvement environmental scan project (now in its Knowledge Translation Phase) to better understand BC's diabetes risk factors; major gaps, barriers & impact on diabetes care services; & best practice solutions for data-driven, diabetes care management redesign so that people with diabetes receive improved, timely, appropriate, cost-effective diabetes care that is locally/culturally/ethnically-sensitive, no matter where they live in BC; with emphasis now on the COVID-19 global pandemic as a driver for change. Methods: 1. Retrospective, open quantitative multi-source data analysis 2. Prospective, open qualitative data analysis 3. Diabetes performance measures; co-morbidities (CKD, CAD, CHF); major procedures/surgeries (renal dialysis, PCI, lower limb amputation) Results: BC has 5.02 million people, ethnic diversity & >70% rural/remote/isolated geography. Aboriginal, Chinese & South Asian people are at highest risk of diabetes & account for the highest rates of diabetes, diabetes complications, hospitalizations, major surgeries/procedures & premature mortality in BC. For over 40 years, intermittent efforts in BC & Canada to improve chronic disease & diabetes management had little positive effect, unfortunately. Ideally, healthcare services & planning should be driven by strong data & strong evidence. No provincial/centralized diabetes program/service/strategy exists for BC’s 470,000 diabetes people. Eighty-precent of diabetes care in BC is provided by family physicians (primary care). To assist with pandemic responses such as COVID-19, our BC Provincial Diabetes Evaluation innovative tools such as Diabetes Heat Maps, Diabetes Snapshot Reports & geographically-specific, multi-source, quantitative & qualitative data analysis could be useful for improving data-driven decisions & care delivery; plus accurately locating & profiling BC’s diabetes high COVID-19 risk populations:•Elderly, cardiac, renal disease: poor COVID-19 prognosis•By age groups, gender, health determinants & premature mortality•Insulin users•No onsite, reliable lab testing (e.g. HbA1c, SCr)•Insufficient physician & healthcare•By urban, rural/remote/isolated areas Conclusion: Without coordinated BC provincial diabetes care planning or services, a fragmented, non-standardized, patchwork system has resulted with negative health outcomes & high impact on the entire healthcare system in BC (similar across Canada). Our BC Provincial Diabetes Evaluation Knowledge Translation findings will help with diabetes care management redesign; & improve the trajectory of diabetes, diabetes outcomes & healthcare utilization in BC. The time is now for data-driven improvements in BC’s diabetes care, with COVID-19 as a driver for change.

Development and validation of a risk prediction model for chronic kidney disease among individuals with type 2 diabetes

Many studies had established the chronic kidney disease (CKD) prediction models, but most of them were conducted on the general population and not on patients with type 2 diabetes, especially in Asian populations. This study aimed to develop a risk prediction model for CKD in patients with type 2 diabetes from the Diabetes Care Management Program (DCMP) in Taiwan. This research was a retrospective cohort study. We used the DCMP database to set up a cohort of 4,601 patients with type 2 diabetes without CKD aged 40–92 years enrolled in the DCMP program of a Taichung medical center in 2002–2016. All patients were followed up until incidences of CKD, death, and loss to follow-up or 2016. The dataset for participants of national DCMP in 2002–2004 was used as external validation. The incident CKD cases were defined as having one of the following three conditions: ACR data greater than or equal to 300 (mg/g); both eGFR data less than 60 (ml/min/1.73 m2) and ACR data greater than or equal to 30 (mg/g); and eGFR data less than 45 (ml/min/1.73 m2). The study subjects were randomly allocated to derivation and validation sets at a 2:1 ratio. Cox proportional hazards regression model was used to identify the risk factors of CKD in the derivation set. Time-varying area under receiver operating characteristics curve (AUC) was used to evaluate the performance of the risk model. After an average of 3.8 years of follow-up period, 3,067 study subjects were included in the derivation set, and 786 (25.63%) were newly diagnosed CKD cases. A total of 1,534 participants were designated to the validation set, and 378 (24.64%) were newly diagnosed CKD cases. The final CKD risk factors consisted of age, duration of diabetes, insulin use, estimated glomerular filtration rate, albumin-to-creatinine ratio, high-density lipoprotein cholesterol, triglyceride, diabetes retinopathy, variation in HbA1c, variation in FPG, and hypertension drug use. The AUC values of 1-, 3-, and 5-year CKD risks were 0.74, 0.76, and 0.77 in the validation set, respectively, and were 0.76, 0.77, and 0.76 in the sample for external validation, respectively. The value of Harrell’s c-statistics was 0.76 (0.74, 0.78). The proposed model is the first CKD risk prediction model for type 2 diabetes patients in Taiwan. The 1-, 3-, and 5-year CKD risk prediction models showed good prediction accuracy. The model can be used as a guide for clinicians to develop medical plans for future CKD preventive intervention in Chinese patients with type 2 diabetes.

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.

IntroductionHemoglobin 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.MethodsA 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.ResultsBased 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).ConclusionGMI 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.

DEPRESSION AMONG TYPE ONE DIABETES PATIENTS IN BURAYDAH IN 2019.

Background:Type 1 diabetes most commonly seen in the younger age group,during this period young age group may not be matured enough to manage their sugar levels as well as other diabetes care management generally.As a result of this event, tend fall in depression. Present study was aimed to find the prevalence of depression and some risk factors association with type 1 Diabetes mellitus patients. Methodology: A cross sectional study was conducted among the patients attending at Diabetic centre,King Fahad Specialist hospital among 177 participants,for the sample recruitment was done through self administered questionnaire.Data analyzed and necessary statistical tests were applied.Results:In the present study,mean age and standard deviation was 24.42± 9.256.A total of 177 patients were participated and there was 59.3% of females were participated. In the current study, there was depression of prevalence among the type 1 Diabetes patients was 20.3% (36/177). There was significant association was observed with depression among type 1 diabetes with low education level, female gender and high HbA1C level (P&lt;0.05). Conclusions: Based on the study findings, there was decrease prevalence of depression was noticed. This could be due to PHQ-9 tool, also self administered questionnaire and self reported responses. Need to be maintained good diabetic education and communication to the participants

Influence of Care Delivery Models on Quality of Diabetes Care Among Individuals With Schizophrenia.

Objective: Managing chronic conditions in individuals with severe mental illnesses is critical for amending health disparities in this vulnerable group. The study aimed to compare the management and outcomes of diabetes care under different care models in individuals with schizophrenia in Taiwan.Methods: A population-based retrospective cohort comprising incident cases of diabetes in individuals (N = 9,109) with schizophrenia (ICD-9-CM code 295) in Taiwan between 2008 and 2015 was selected using the National Health Insurance Research Database. Generalized estimating equation (GEE) modeling was used to compare 3 care models: the sole-physician model, the colocation model, and the different-facilities model. Each individual was followed up for 3 years. Propensity score matching was used to address potential selection bias.Results: Patients in the sole-physician model had the highest number of recommended routine examinations (incident rate ratio [IRR] = 1.2; 95% CI, 1.1-1.2) and the highest likelihood of having regular diabetes-related visits as recommended (odds ratio [OR] = 2.6; 95% CI, 2.1-3.2), followed by those in the colocation model (number of recommended routine examinations: IRR = 1.1; 95% CI, 1.1-1.2; likelihood of regular visits: OR = 1.6; 95% CI, 1.3-1.9) and those in the different-facilities model. However, the sole-physician group had a significantly higher likelihood of admission for diabetes-related ambulatory care sensitive conditions within 1 year (OR = 1.9; 95% CI, 1.3-2.8) and 3 years (OR = 1.6; 95% CI, 1.2-2.1) than its counterparts. Within the sole-physician group, patients of psychiatrists had more favorable disease outcomes than those of non-psychiatrists.Conclusions: The sole-physician and colocation models may significantly improve the process quality of diabetes care; however, such models alone are not sufficient to improve diabetes outcomes.

Effect of blood pressure trajectory and variability on new-onset chronic kidney disease in patients with type 2 diabetes.

This study aimed to evaluate the effects of BP trajectory and variability on chronic kidney disease (CKD) incidence in patients with type 2 diabetes. This retrospective longitudinal study included 4,560 participants with type 2 diabetes, aged ≥30 years, free of CKD, with ≥3 years of follow-up, and who attended the Diabetes Care Management Program in 2001-2013. The follow-up period ended in 2016. The adverse outcome was a new-onset CKD event, which was determined using eGFR and albuminuria. Cox proportional hazards models were used to assess the associations. At the end of the follow-up, 1255 participants had developed CKD, with a mean follow-up of 4.3 ± 3.2 years. Three trajectory subgroups of BP, i.e., Cluster 1: "moderate-stable" for SBP and "moderate-downward" for DBP, Cluster 2: "low-upward-downward" for both SBP and DBP, and Cluster 3: "high-downward-upward" for both SBP and DBP, were generated. The BP variability was grouped into three classes on the basis of tertiles. For the BP trajectory, patients in Cluster 3 of DBP had a higher CKD risk than those in Cluster 1 (HR = 1.24, 95% CI = 1.03-1.50). For the BP variability, patients in Tertile 3 had a significantly higher CKD risk than those in Tertile 1 (SBP: 1.28, 1.11-1.47; DBP: 1.17, 1.02-1.34). Persons with type 2 diabetes who achieved a small reduction in DBP after participating in the education program but rebounded and those who had the highest variation in both SBP and DBP faced the highest increase in CKD risk.

Clinical Application Effect of Whole-course Health Education Model in Diabetes Care Management and Its Influence on Blood Glucose

Clinical Application Effect of Whole-course Health Education Model in Diabetes Care Management and Its Influence on Blood Glucose

Care Management Challenges for Diabetic Foot Ulcers: A Qualitative Study

Background: Diabetes mellitus, if is not well taken care and treated, may exacerbate complications such as ulcers, infections, and amputations. Accordingly, preventive measures and timely treatment are of paramount importance. Objectives: This study sought to collect profound experiences about diabetic ulcer care management with an emphasis on community-based care. Methods: The study was carried out using the qualitative content analysis method during 2017 - 2018 in Semnan Province, Iran. The study encompassed 14 participants, including six caregivers, two family members, one nurse, one nursing supervisor, one manager of counseling and nursing care service centers (CNCSCs), one head nurse, and two physicians, who were selected using the purposive sampling method. The analysis process was performed based on Graneheim and Lundma’s approach. Results: Two main categories were as follows: (1) "Dispersion in preventive measures," with subcategories of ‘vague perception of disease symptoms’, ‘inability in self-management’ and ‘low-efficient preventive care’, and (2) "Gradual access to outpatient services" with subcategories of ‘gradual expansion of outpatient ulcer care’, ‘complexity of financial transactions’, ‘improving inter-sectoral cooperation’ and ‘ambiguous professional boundaries’. Conclusions: Care facilities offering services to diabetic foot ulcers, especially community-based nursing care centers, are gradually expanding, and new wound care methods are being adopted. However, lack of self-care management, insufficient support, treatment-oriented approaches, and all clients’ insufficient access to these services are some of the problems that still exist in the care management of diabetic ulcers.

Health-Related Quality of Life and Associated Factors Among Adults with Type-2 Diabetes Mellitus: A Community-Based Cross-Sectional Study in Rural Kerala, India.

India is the second-largest country in the world with an estimated 77 million people living with diabetes. Persons with diabetes reported lower quality of life when compared to non-diabetes persons. There are significant associations between poor health-related quality of life (HRQoL) and adverse health outcomes among diabetes people. However, evidence documenting the same in India is scarce. To assess the health-related quality of life and its determinants among diabetic patients in rural Kerala, India. We conducted a community-based cross-sectional study among 425 adults type-2 diabetic patients from rural Kerala. The World Health Organization Quality of Life questionnaire was used to measure HRQoL. Multiple logistic regression analysis was used to study the association between HRQoL and independent variables. More than half of the respondents were men (52%), and 45% were older adults (≥60 years). Poor quality of life was reported by 38% of the respondents. Medium [Odds ratio (OR):4.70, 95%CI: 2.61-8.46] and low socio-economic status (SES) group (OR: 4.59, CI: 2.43-8.66) had poorer HRQoL compared to the higher SES group. Those with multi-morbidity (OR: 2.91, CI: 1.63-5.19), unemployed (OR: 2.54, CI: 1.46-4.42), and less educated (OR: 2.28, CI: 1.34-3.88) and older adults (OR: 2.11, CI: 1.28-3.45) were more likely to have poor HRQoL compared to their counterparts. More than one-third of the diabetes patients in rural Kerala reported poor HRQoL. The study also identified age, socio-economic status, education, and occupation as the important predictors of HRQoL among diabetes patients. The findings highlighted the need for assessing HRQoL as part of the routine management of diabetes care in similar settings.

A Proposed Multi-Criteria Optimization Approach to Enhance Clinical Outcomes Evaluation for Diabetes Care: A Commentary.

There are several challenges in diabetes care management including optimizing the currently used therapies, educating patients on selfmanagement, and improving patient lifestyle and systematic healthcare barriers. The purpose of performing a systems approach to implementation science aided by artificial intelligence techniques in diabetes care is two-fold: 1) to explicate the systems approach to formulate predictive analytics that will simultaneously consider multiple input and output variables to generate an ideal decision-making solution for an optimal outcome; and 2) to incorporate contextual and ecological variations in practicing diabetes care coupled with specific health educational interventions as exogenous variables in prediction. A similar taxonomy of modeling approaches proposed by Brennon et al (2006) is formulated to examining the determinants of diabetes care outcomes in program evaluation. The discipline-free methods used in implementation science research, applied to efficiency and quality-of-care analysis are presented. Finally, we illustrate a logically formulated predictive analytics with efficiency and quality criteria included for evaluation of behavioralchange intervention programs, with the time effect included, in diabetes care and research.

Barriers and facilitators of providing standard of care diabetes management at primary care level in geriatric population.

Geriatric population is gradually increasing and is expected to grow till 20% by 2050 from the current 8.6%, and so is diabetes prevalence and other comorbidities. To improve diabetes control, better coordination of provider, patient and health system is needed. It has been found that almost half of the diabetes patients do not achieve treatment targets. So, it is essential to assess the perceived barriers as well as facilitators from patients' and physicians' perspectives. The aim of the study was assessment of needs, care, barriers and facilitators to achieve treatment goals for patients and physicians. This observational, cross-sectional study was conducted by the Department of Geriatrics among 100 elderly diabetes patients and 50 physicians after obtaining ethical approval. All participants were interviewed based on a predefined, structured questionnaire with multiple options to grade or choose from. Polypharmacy, mobility issues and dementia were the most common geriatric issues. Hypertension (HTN), arthritis and coronary artery disease (CAD) were the commonest comorbidities. Also, 73% reported that they followed dietary advice, but only 22% accepted that they were doing exercise regularly. Moreover, 5% mentioned that they were taking alcohol, and 15% confirmed of smoking. Also, 47% of patients felt that diabetes was well controlled. Remaining 53% patients gave reasons for poor diabetes control; the most common reason was not following a proper diabetic diet plan in 42% of patients and lack of exercise as instructed was reported by 22% as the reason for their poor diabetic control. Nineteen percent of patients accepted of not measuring their blood glucose as instructed. Among urban doctors, the three most common factors were not being consistent with lifestyle interventions, followed by no regular self-monitoring of blood glucose and no regular follow-up. Top three interventions suggested were to engage or encourage the family members of patients to become involved in diabetes care, provide more convenient diabetes brochures or education materials to patients and improve multidisciplinary and multispeciality collaboration in diabetes control. Diabetes in elderly needs proactive health system and coordinated care. Doctor-patient relationship with good communication skills, family support and multidisciplinary care is needed to improve diabetes care. Health education with a focus on diet control, exercise and other lifestyle modifications are essential factors in improving diabetes care.

Clinical Practice Experiences Using a Professional Diabetes Management Ecosystem During COVID.

Challenges of patient care in diabetes were exacerbated by COVID, undermining the ability of patients to engage in-person with health care professionals (HCPs). To combat this, there has been accelerated adoption of telemedicine to support patient and provider connectivity. We collated survey information regarding telemedicine from 21 European clinical institutions. Health care professionals joined virtual meetings focusing on the OneTouch Reveal (OTR) ecosystem and its utility for conducting telemedicine. Selected HCPs provided clinical case studies to explain how the OTR ecosystem supported patient care. Remote consultations increased by nearly 50% in 21 European clinics during the pandemic (Belgium [24%], Iberia [65%], Germany [34%], Italy [54%]). In all, 52% of people with diabetes using OTR app to connect remotely with HCPs had type 1 diabetes and 48% had type 2 diabetes. Remote connection methods included telephone (60%), email (19%), video chat (10%), text only (3%), or a mix of these methods (8%). Health care professionals usually reviewed patient data during consultations (45%) rather than before consultations (25%). Fifty-five percent of HCPs indicated digital ecosystems like OTR ecosystem would become their standard of care for diabetes management. In-depth conversations with HCPs provided a deeper understanding of how a digital ecosystem integrated into clinical practice and population management. In addition, five patient case studies using OTR ecosystem were provided by a selection of our HCPs. Diabetes management solutions, such as OTR ecosystem, supported telemedicine during the pandemic and will continue to play a valuable role in patient care beyond the pandemic.

Type 2 diabetes self-management: spirituality, coping and responsibility

BackgroundIn England, although The National Institute of Health and Care Excellence recommends that patients’ religious beliefs should be incorporated into individual healthcare plans, these components are often neglected in diabetes management care plans. A literature review identified a paucity of research regarding how the spirituality of British people may influence their approach to their self-management of type 2 diabetes (T2D).AimsTo explore how the spirituality of a small group of adults with T2D, living in England, influenced their coping strategies and self-management of diet and exercise.MethodsBiographic Narrative Interpretive Method of two interviews per participant and thematic analysis for data interrogation (n = 8). Data as glycated haemoglobin, living situation, age, length of time since T2D diagnosis, body mass index and diabetic medicines contextualised the interview data.ResultsParticipants’ spirituality, health beliefs, coping and sense of responsibility for T2D self-management overlapped in complex layers. Three themes were generated: (a) spirituality influences expectations in life; (b) beliefs influence coping styles of diabetes self-management; and (c) responsibility influences diabetes self-management. A model was created to assist nurses in addressing these components.ConclusionNurses should consider how patients’ self-management of T2D may be influenced by their spirituality, health beliefs, coping and sense of responsibility.

Effect of blood lipid variability on mortality in patients with type 2 diabetes: a large single-center cohort study

BackgroundDyslipidemia is a major cardiovascular risk factor and common in diabetes patients. Most guidelines focus on optimal lipid levels, while variation of lipid profiles is far less discussed. This study aims to investigate the association of visit-to-visit variability in blood lipids with all-cause, cardiovascular, and non-cardiovascular mortality in patients with type 2 diabetes.MethodsWe identified 10,583 type 2 diabetes patients aged ≥ 30 years with follow-up ≥ 3 years and who participated in the Diabetes Care Management Program at a medical center in Taiwan. Variability in lipid profiles within 3 years after entry was calculated using coefficient of variation. Cox proportional hazard models were used to evaluate lipid variability in relation to subsequent mortality.ResultsOver a mean follow-up of 6.4 years, 1838 all-cause deaths (809 cardiovascular deaths) were observed. For each 10% increase in variability in high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and total cholesterol, the hazard ratios (95% confidence intervals) of all-cause mortality were 1.30 (1.22–1.37), 1.05 (1.01–1.09), and 1.10 (1.03–1.16), respectively; those of cardiovascular mortality were 1.27 (1.16–1.39), 1.08 (1.02–1.15), and 1.16 (1.07–1.27), respectively. Each 10% increase in high-density lipoprotein cholesterol variability conveyed 31% greater risk of non-cardiovascular mortality. High variability in total cholesterol and low-density lipoprotein cholesterol increased all-cause mortality in subgroups of nonsmoking, regular exercising, non-dyslipidemia, and more severe status of diabetes at baseline.ConclusionsBlood lipid variability except for triglyceride variability was associated with all-cause and cardiovascular mortality in patients with type 2 diabetes.