Population segmentation of type 2 diabetes mellitus patients and its clinical applications - a scoping review

Evaluating Patient and Provider Preferences for a Once-Weekly Basal Insulin in Adults with Type 2 Diabetes

Purpose

The global burden of disease of type 2 diabetes (T2D) is significant, and insulin currently plays a central role in T2D management. This study sought to assess the preferences of patients with T2D and healthcare providers (HCPs) involved in T2D care regarding a hypothetical once-weekly basal insulin in comparison to current basal insulin options.

Patients and Methods

In a survey-based study in the United States that included a discrete choice experiment (DCE), patients with T2D (insulin naïve and current insulin users) and providers who treat individuals with T2D were asked to evaluate current basal insulins and identify attributes of importance regarding a hypothetical once-weekly basal insulin. A regression analysis was conducted to identify drivers of preference by relevant demographics, attitudes, and behaviors.

Results

Most respondents (91% of patients with T2D and 89% of HCPs in the base case scenario) would choose a once-weekly basal insulin product over another type of basal insulin. Both patients with T2D and HCPs rated insulin type and delivery method to be attributes of highest importance in the discrete choice exercise. Current basal insulin users ("insulin experienced") reported higher levels of confidence that a once-weekly insulin would help them to achieve their desired blood sugar levels compared to their current basal insulin (5.7 vs 5.2 on a 7-point Likert scale). Most insulin-experienced respondents (88%) were likely to inquire about once-weekly basal insulin, and most HCPs (85%) indicated willingness to educate patients on management of their T2D using a once-weekly basal insulin.

Conclusion

Discussing preferences for T2D medication management is important for patients and HCPs to ensure treatments are offered for patients based on their preferences. This study showed that patient and provider preferences are similar towards a once-weekly basal insulin over current basal insulin preparations.

Visit-to-visit HbA1c variability is associated with poor prognosis in peritoneal dialysis patients with type 2 diabetes mellitus

BACKGROUND

The prognosis of diabetic peritoneal dialysis patients is poor. HbA1c serves as a crucial indicator for monitoring blood glucose control in patients with diabetes. Nevertheless, the relationship between visit-to-visit HbA1c variability and prognosis in peritoneal dialysis with diabetes remains unclear.

METHODS

All participants were categorized into 3 groups based on the HbA1c variability score (HVS), which is the frequency of 0.5% (5.5 mmol/mol) alter in visit-to-visit HbA1c values. Then, the hazard ratio to HVS with all-cause mortality was analyzed using the Cox hazard model, followed by the Fine-Gray competing risk model for major adverse cardiovascular events. Subgroup and sensitivity analysis were conducted to ascertain the robustness of the findings.

RESULTS

Eight hundred twenty patients with type 2 diabetes were finally enrolled in this study from 2,855 participants with a mean age of 56.9 ± 14.6 years and a median follow-up time of 44 months [IQR: 27-70], death occurred in 496 (60.2%) individuals. Compared with the lowest category (HVS < 1/3) after being adjusted by potential confounding factors, the hazard ratio for all-cause mortality was 4.59 (3.74-5.64) and the sub-distribution hazard ratio for major adverse cardiovascular events was 1.91 (1.46-2.51) of the highest category (HVS ≥ 2/3). Subgroup interaction and sensitivity analysis, including the adjustment for variables such as time-weighted average HbA1c, HbA1c measurement times and expansion, confirmed the reliability of the results.

CONCLUSION

The HVS is related to the risk of poor prognosis in peritoneal dialysis with type 2 diabetes mellitus, independently of clinical multiple variables, and is a novel indicator with clinical guidance.

Role of Caregivers in Remote Management of Patients With Type 2 Diabetes Mellitus: Systematic Review of Literature

BACKGROUND

With the growing use of remote monitoring technologies in the management of patients with type 2 diabetes mellitus (T2DM), caregivers are becoming important resources that can be tapped into to improve patient care.

OBJECTIVE

This review aims to summarize the role of caregivers in the remote monitoring of patients with T2DM.

METHODS

We performed a systematic review in MEDLINE, Embase, Scopus, PsycINFO, and Web of Science up to 2022. Studies that evaluated the role of caregivers in remote management of adult patients with T2DM were included. Outcomes such as diabetes control, adherence to medication, quality of life, frequency of home glucose monitoring, and health care use were evaluated.

RESULTS

Of the 1198 identified citations, 11 articles were included. The majority of studies were conducted in North America (7/11, 64%) and South America (2/11, 18%). The main types of caregivers studied were family or friends (10/11, 91%), while the most common remote monitoring modalities evaluated were interactive voice response (5/11, 45%) and phone consultations (4/11, 36%). With regard to diabetes control, 3 of 6 studies showed improvement in diabetes-related laboratory parameters. A total of 2 studies showed improvements in patients' medication adherence rates and frequency of home glucose monitoring. Studies that evaluated patients' quality of life showed mixed evidence. In 1 study, increased hospitalization rates were noted in the intervention group.

CONCLUSIONS

Caregivers may play a role in improving clinical outcomes among patients with T2DM under remote monitoring. Studies on mobile health technologies are lacking to understand their impact on Asian populations and long-term patient outcomes.

Segmenting Patients With Diabetes With the Navigator Service in Primary Care and a Description of the Self-Acting Patient Group: Cross-Sectional Study

BACKGROUND

The aim of patient segmentation is to recognize patients with similar health care needs. The Finnish patient segmentation service Navigator segregates patients into 4 groups, including a self-acting group, who presumably manages their everyday life and coordinates their health care. Digital services could support their self-care. Knowledge on self-acting patients' characteristics is lacking.

OBJECTIVE

The study aims are to describe how Navigator assigns patients with diabetes to the 4 groups at nurses' appointments at a health center, the self-acting patient group's characteristics compared with other patient groups, and the concordance between the nurse's evaluation of the patient's group and the actual group assigned by Navigator (criterion validity).

METHODS

Patients with diabetes ≥18 years old visiting primary care were invited to participate in this cross-sectional study. Patients with disability preventing informed consent for participation were excluded. Nurses estimated the patients' upcoming group results before the appointment. We describe the concordance (%) between the evaluation and actual groups. Nurses used Navigator patients with diabetes (n=304) at their annual follow-up visits. The self-acting patients' diabetes care values (glycated hemoglobin [HbA1c], urine albumin to creatinine ratio, low-density lipoprotein cholesterol, blood pressure, BMI), chronic conditions, medication, smoking status, self-rated health, disability (World Health Organization Disability Assessment Schedule [WHODAS] 2.0), health-related quality of life (EQ-5D-5L), and well-being (Well-being Questionnaire [WBQ-12]) and the patients' responses to Navigator's question concerning their digital skills as outcome variables were compared with those of the other patients. We used descriptive statistics for the patients' distribution into the 4 groups and demographic data. We used the Mann-Whitney U test with nonnormally distributed variables, independent samples t test with normally distributed variables, and Pearson chi-square tests with categorized variables to compare the groups.

RESULTS

Most patients (259/304, 85.2%) were in the self-acting group. Hypertension, hyperlipidemia, and joint ailments were the most prevalent comorbidities among all patients. Self-acting patients had less ischemic cardiac disease (P=.001), depression or anxiety (P=.03), asthma or chronic obstructive pulmonary disease (P<.001), long-term pain (P<.001), and related medication. Self-acting patients had better self-rated health (P<.001), functional ability (P<.001), health-related quality of life (P<.001), and general well-being (P<.001). All patients considered their skills at using electronic services to be good.

CONCLUSIONS

The patients in the self-acting group had several comorbidities. However, their functional ability was not yet diminished compared with patients in the other groups. Therefore, to prevent diabetic complications and disabilities, support for patients' self-management should be emphasized in their integrated care services. Digital services could be involved in the care of patients willing to use them. The study was performed in 1 health center, the participants were volunteers, and most patients were assigned to self-acting patient group. These facts limit the generalizability of our results.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/20570.

Exploring the influencing factors of unmet palliative care needs in Chinese patients with end-stage renal disease undergoing maintenance hemodialysis: a cross-sectional study

BACKGROUND

The role of palliative care for end-stage renal disease (ESRD) patients have been proven in some developed countries, but it is still unclear in the mainland of China. In fact, patients with ESRD experience many unmet palliative care needs, such as physical, psychological, social and spiritual needs, but the factors influencing these needs have not investigated.

METHODS

A cross-sectional study was conducted at two hemodialysis centers in the mainland of China from January to September 2022. Convenience sampling was used to collect data on the participants' socio-demographics, clinical characteristics, the Palliative Care Outcome Scale (POS), the Dialysis Symptom Index (DSI), the Karnofsky Performance Status Scale (KPS), the Patient Health Questionnaire-9 item (PHQ-9), and the Social Support Rate Scale (SSRS). Data were analyzed using latent profile analysis, Kruskal-Wallis test, one-way analysis of variance (ANOVA), the chi-square test and multinomial logistic regression analysis.

RESULTS

Three hundred five participants were included in this study, and divided palliative care needs into three categories: Class 1, mild palliative care needs (n = 154, 50.5%); Class 2, moderate palliative care needs (n = 89, 29.2%); Class 3, severe palliative care needs (n = 62, 20.3%). Based on the analysis of three profiles, the influencing factors of unmet needs were further analyzed. Compared with Class 3, senior high school education, the household per capita monthly income < 2,000, low KPS scores, high PHQ-9 scores, and low SSRS scores were less likely to be in Class 1 (OR = 0.03, P = 0.012; OR = 0.003, P < 0.001; OR = 1.15, P < 0.001; OR = 0.55, P < 0.001; OR = 1.35, P = 0.002; respectively) and Class 2 (OR = 0.03, P = 0.007; OR = 0.05, P = 0.011; OR = 1.10, P = 0.001; OR = 0.60, P = 0.001; OR = 1.32, P = 0.003; respectively), and high symptom severity were less likely to be in Class 1 (OR = 0.82, P = 0.001). Moreover, compared with Class 1, the household per capita monthly income < 2,000 (OR = 16.41, P < 0.001), high symptom severity scores (OR = 1.12, P = 0.002) and low KPS scores (OR = 0.95, P = 0.002) were more likely to be in Class 2.

CONCLUSIONS

This study showed that almost half of ESRD patients receiving MHD presented moderate to severe palliative care needs, and the unmet needs were mainly affected by education level, financial pressure, functional status, symptom burden and social support. In the future, it is important to identify the populations with the greatest need for palliative care and consider the influencing factors of unmet needs from a comprehensive perspective, so as to help them improve health-related quality of life.

Evaluation of Machine Learning Methods Developed for Prediction of Diabetes Complications: A Systematic Review

BACKGROUND

With the rising prevalence of diabetes, machine learning (ML) models have been increasingly used for prediction of diabetes and its complications, due to their ability to handle large complex data sets. This study aims to evaluate the quality and performance of ML models developed to predict microvascular and macrovascular diabetes complications in an adult Type 2 diabetes population.

METHODS

A systematic review was conducted in MEDLINE®, Embase®, the Cochrane® Library, Web of Science®, and DBLP Computer Science Bibliography databases according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist. Studies that developed or validated ML prediction models for microvascular or macrovascular complications in people with Type 2 diabetes were included. Prediction performance was evaluated using area under the receiver operating characteristic curve (AUC). An AUC >0.75 indicates clearly useful discrimination performance, while a positive mean relative AUC difference indicates better comparative model performance.

RESULTS

Of 13 606 articles screened, 32 studies comprising 87 ML models were included. Neural networks (n = 15) were the most frequently utilized. Age, duration of diabetes, and body mass index were common predictors in ML models. Across predicted outcomes, 36% of the models demonstrated clearly useful discrimination. Most ML models reported positive mean relative AUC compared with non-ML methods, with random forest showing the best overall performance for microvascular and macrovascular outcomes. Majority (n = 31) of studies had high risk of bias.

CONCLUSIONS

Random forest was found to have the overall best prediction performance. Current ML prediction models remain largely exploratory, and external validation studies are required before their clinical implementation.

PROTOCOL REGISTRATION

Open Science Framework (registration number: 10.17605/OSF.IO/UP49X).

Multimorbidity prevalence and patterns in chronic kidney disease: findings from an observational multicentre UK cohort study

PURPOSE

Multimorbidity [defined as two or more long-term conditions (LTCs)] contributes to increased treatment and medication burden, poor health-related quality of life, and worse outcomes. Management strategies need to be patient centred and tailored depending on existing comorbidities; however, little is known about the prevalence and patterns of comorbidities in people with chronic kidney disease (CKD). We investigated the prevalence of multimorbidity and comorbidity patterns across all CKD stages.

METHODS

Multimorbidity was assessed, using a composite of self-report and clinical data, across four CKD groups stratified by eGFR [stage 1-2, stage 3a&b, stage 4-5, and kidney transplant (KTx)]. Principal component analysis using varimax rotation was used to identify comorbidity clusters across each group.

RESULTS

Of the 978 participants (mean 66.3 ± 14 years, 60% male), 96.0% had multimorbidity. In addition to CKD, the mean number of comorbidities was 3.0 ± 1.7. Complex multimorbidity (i.e. ≥ 4 multiple LTCs) was identified in 560 (57.3%) participants. When stratified by CKD stage, the two most prevalent comorbidities across all stages were hypertension (> 55%) and musculoskeletal disorders (> 40%). The next most prevalent comorbidity for CKD stages 1-2 was lung conditions and for CKD stages 3 and 4-5 it was heart problems. CKD stages 1-2 showed different comorbidity patterns and clustering compared to other CKD stages.

CONCLUSION

Most people across the spectrum of CKD have multimorbidity. Different patterns of multimorbidity exist at different stages of CKD, and as such, clinicians should consider patient comorbidities to integrate care and provide effective treatment strategies.

Ambulatory Healthcare Use Profiles of Patients With Diabetes and Their Association With Quality of Care: A Cross-Sectional Study

BACKGROUND

Despite the growing burden of diabetes worldwide, evidence regarding the optimal models of care to improve the quality of diabetes care remains equivocal. This study aimed to identify profiles of patients with distinct ambulatory care use patterns and to examine the association of these profiles with the quality of diabetes care.

METHODS

We performed a cross-sectional study of the baseline data of 550 non-institutionalized adults included in a prospective, community-based, cohort study on diabetes care conducted in Switzerland. Clusters of participants with distinct patterns of ambulatory healthcare use were identified using discrete mixture models. To measure the quality of diabetes care, we used both processes of care indicators (eye and foot examination, microalbuminuria screening, blood cholesterol and glycated hemoglobin measurement [HbA1c], influenza immunization, blood pressure measurement, physical activity and diet advice) and outcome indicators (12-Item Short-Form Health Survey [SF-12], Audit of Diabetes-Dependent Quality of Life [ADDQoL], Patient Assessment of Chronic Illness Care [PACIC], Diabetes Self-Efficacy Scale, HbA1c value, and blood pressure <140/90 mmHg). For each profile of ambulatory healthcare use, we calculated adjusted probabilities of receiving processes of care and estimated adjusted outcomes of care using logistic and linear regression models, respectively.

RESULTS

Four profiles of ambulatory healthcare use were identified: participants with more visits to the general practitioner [GP] than to the diabetologist and receiving concomitant podiatry care ("GP & podiatrist", n=86); participants visiting almost exclusively their GP ("GP only", n=195); participants with a substantially higher use of all ambulatory services ("High users", n=96); and participants reporting more visits to the diabetologist and less visits to the GP than other profiles ("Diabetologist first", n=173). Whereas participants belonging to the "GP only" profile were less likely to report most processes related to the quality of diabetes care, outcomes of care were relatively comparable across all ambulatory healthcare use profiles.

CONCLUSIONS

Slight differences in quality of diabetes care appear across the four ambulatory healthcare use profiles identified in this study. Overall, however, results suggest that room for improvement exists in all profiles, and further investigation is necessary to determine whether individual characteristics (like diabetes-related factors) and/or healthcare factors contribute to the differences observed between profiles.