Long-term quality-of-care score predicts incident chronic kidney disease in patients with type 2 diabetes

Long-term quality-of-care score for predicting the occurrence of acute myocardial infarction in patients with type 2 diabetes mellitus

BACKGROUND

Cardiovascular disease (CVD) is the leading cause of death globally, and diabetes mellitus (DM) is a well-established risk factor. Among the risk factors for CVD, DM is a major modifiable factor. In the fatal CVD outcomes, acute myocardial infarction (AMI) is the most common cause of death.

AIM

To develop a long-term quality-of-care score for predicting the occurrence of AMI among patients with type 2 DM on the basis of the hypothesis that good quality of care can reduce the risk of AMI in patients with DM.

METHODS

Using Taiwan's Longitudinal Cohort of Diabetes Patient Database and the medical charts of a medical center, we identified incident patients diagnosed with type 2 DM from 1999 to 2003 and followed them until 2011. We constructed a summary quality-of-care score (with values ranging from 0 to 8) with process indicators (frequencies of HbA1c and lipid profile testing and urine, foot and retinal examinations), intermediate outcome indicators (low-density lipoprotein, blood pressure and HbA1c), and co-morbidity of hypertension. The associations between the score and the incidence of AMI were evaluated using Cox regression models.

RESULTS

A total of 7351 patients who had sufficient information to calculate the score were enrolled. In comparison with participants who had scores ≤ 1, those with scores between 2 and 4 had a lower risk of developing AMI [adjusted hazard ratio (AHR) = 0.71; 95% confidence interval (95%CI): 0.55-0.90], and those with scores ≥ 5 had an even lower risk (AHR = 0.37; 95%CI: 0.21-0.66).

CONCLUSION

Good quality of care can reduce the risk of AMI in patients with type 2 DM. The quality-of-care score developed in this study had a significant association with the risk of AMI and thus can be applied to guiding the care for these patients.

Opportunities To Improve Diabetes Care in the Hemodialysis Unit: A Cohort Study in Ontario, Canada

Background

Patients with diabetes receiving chronic, in-center hemodialysis face healthcare challenges. We examined the prevalence of gaps in their diabetes care, explored regional differences, and determined predictors of care gaps.

Methods

We conducted a population-based, retrospective study between January 1, 2016 and January 1, 2018 in Ontario, Canada. We included adults with prevalent diabetes mellitus receiving in-center hemodialysis as of January 1, 2018 and examined the proportion with (1) insufficient or excessive glycemic monitoring, (2) suboptimal screening for diabetes-related complications (retinopathy and cardiovascular screening), (3) hospital encounters for hypo- or hyperglycemia, and (4) hospital encounters for hypertension in the 2 years prior (January 1, 2016 to January 1, 2018). We then identified patient, provider, and health-system factors associated with more than one care gap and used multivariable logistic regression to determine predictors. Further, we used geographic information systems to explore spatial variation in gaps.

Results

There were 4173 patients with diabetes receiving in-center hemodialysis; the mean age was 67 years, 39% were women, and the majority were of lower socioeconomic status. Approximately 42% of patients had more than one diabetes care gap, the most common being suboptimal retinopathy screening (53%). Significant predictors of more than one gap included younger age, female sex, shorter duration of diabetes, dementia, fewer specialist visits, and not seeing a physician for diabetes. There was evidence of spatial variation in care gaps across our region.

Conclusions

There are opportunities to improve diabetes care in patients receiving in-center hemodialysis, particularly screening for retinopathy. Focused efforts to bring diabetes support to high-risk individuals might improve their care and outcomes.

Integrating risk prediction models into chronic kidney disease care

PURPOSE OF REVIEW

Although the concept of risk prediction in chronic kidney disease (CKD) is not new, how to integrate risk prediction models into CKD care remains largely unknown, particularly in the prevention and early management of CKD. The present review presents a timely overview of recent CKD risk prediction models and conceptualizes how these may be integrated into the care of patients with CKD.

RECENT FINDINGS

In recent literature, prediction of time-to-ESKD has been thoroughly validated in multiple international cohorts, new models focused on CKD incidence, morbidity, and mortality have been developed, and ongoing work will determine the impact of integrating risk prediction models into CKD care on patients, nephrologists, and health systems.

SUMMARY

With the availability of new models focused on CKD incidence, the United States Preventive Task Force should reconsider its determination of insufficient evidence for primary screening of CKD, which was due in part to the absence of validated risk models to guide CKD screening. Models predicting CKD morbidity and mortality present a new opportunity to standardize the intensity and frequency of care across nephrology practices.