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Clinical Care/Education/Nutrition/Psychosocial Research

Overall Quality of Care Predicts the Variability of Key Risk Factors for Complications in Type 2 Diabetes: An Observational, Longitudinal Retrospective Study

  1. Antonio Ceriello1⇑,
  2. Maria Chiara Rossi2,
  3. Salvatore De Cosmo3,
  4. Giuseppe Lucisano2,
  5. Roberto Pontremoli4,
  6. Paola Fioretto5,
  7. Carlo Giorda6,
  8. Antonio Pacilli3,
  9. Francesca Viazzi4,
  10. Giuseppina Russo7 and
  11. Antonio Nicolucci2, on behalf of the AMD-Annals Study Group*
  1. 1Department of Cardiovascular and Metabolic Diseases, IRCCS MultiMedica, Sesto San Giovanni, Milan, Italy
  2. 2Center for Outcomes Research and Clinical Epidemiology (CORESEARCH), Pescara, Italy
  3. 3Department of Medical Sciences, Scientific Institute “Casa Sollievo della Sofferenza,” San Giovanni Rotondo, Foggia, Italy
  4. 4Department of Cardionephrology, IRCCS Azienda Ospedaliera Universitaria San Martino-IST, Genoa, Italy
  5. 5Department of Medicine, University of Padua, Padua, Italy
  6. 6Diabetes and Metabolism Unit, Department of Internal Medicine, ASL Turin 5, Chieri, Turin, Italy
  7. 7Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
  1. Corresponding author: Antonio Ceriello, antonio.ceriello{at}hotmail.it
Diabetes Care 2019 Apr; 42(4): 514-519. https://doi.org/10.2337/dc18-1471
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  • Erratum. Overall Quality of Care Predicts the Variability of Key Risk Factors for Complications in Type 2 Diabetes: An Observational, Longitudinal Retrospective Study. Diabetes Care 2019;42:514–519 - February 01, 2020

Abstract

OBJECTIVE An association between variability in clinical parameters (HbA1c, blood pressure, cholesterol, and uric acid) and risk of complications in type 2 diabetes has been reported. In this analysis, we investigated to what extent such variability is associated with overall quality of care.

RESEARCH DESIGN AND METHODS The quality of care summary score (Q-score) represents a validated, overall quality of care indicator ranging between 0 and 40; the higher the score, the better the quality of care provided by the diabetes center. We identified patients with five or more measurements of clinical parameters after the assessment of the Q-score. Multiple linear regression analyses assessed the role of the Q-score in predicting the variability of the different parameters.

RESULTS Overall, 273,888 patients were analyzed. The variability of all the parameters systematically increased with decreasing Q-score values. At multivariate linear regression analysis, compared with a Q-score >25, a score <15 was associated with a significantly larger variation in HbA1c, blood pressure, uric acid, total cholesterol, and LDL cholesterol and a lower variation in HDL cholesterol. The analysis of standardized β coefficients show that the Q-score has a larger impact on the variability of HbA1c (0.34; P < 0.0001), systolic blood pressure (0.21; P < 0.0001), total cholesterol (0.21; P < 0.0001), and LDL cholesterol (0.20; P < 0.0001).

CONCLUSIONS The variability of risk factors for diabetic complications is associated with quality of care. Quality of care improvement initiatives should be targeted to increase the achievement of the recommended target while reducing such variability.

Introduction

In Italy, a continuous improvement effort implemented by a network of diabetes clinics, i.e., AMD (Associazone Medici Diabetologi) Annals, has been in place since 2006 (1,2). After 8 years from the launch of the initiative, half of the diabetes clinics in Italy participated in the AMD-Annals initiative, caring for over one-sixth of all diagnosed patients. Process and intermediate outcome measures consistently improved, in parallel with a more intensive and appropriate use of pharmacologic treatments (3).

Among the indicators included in AMD-Annals reports, a quality of care summary score (Q-score) is calculated. The Q-score has been developed and validated in two previous studies (4,5) and integrated in the AMD-Annals initiative since the 2009 edition (3). The score is based on a combination of process and outcome indicators relative to HbA1c, blood pressure, LDL cholesterol, and microalbuminuria. The score ranges between 0 and 40, with a higher score indicating better quality of care (Fig. 1). In the two previous studies (4,5), the Q-score was closely related to long-term outcomes. In fact, the risk of developing a new cardiovascular event was 80% higher in patients with a score <15 and 20% higher in those with a score between 15 and 25, as compared with those with a score >25.

Figure 1
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Figure 1

The Q-score.

Recently, much attention has been paid to the possibility that a variability in time of blood glucose (HbA1c, fasting, and postprandial hyperglycemia) (6), blood pressure (7), lipids (8,9), uric acid (10), and body weight (11) may be a risk factor for the development of complications in subjects with or without diabetes.

The mechanisms underlying these findings are, at the moment, largely speculative. It should be argued that when these factors vary over time, they could favor an increase in oxidative stress, either increasing the generation of free radicals (glucose, blood pressure, plasma cholesterol, and uric acid) (6,12,13) or decreasing the defense against them (HDL) (14).

The reasons for intraindividual variability in different clinical parameters are largely unknown. Nevertheless, such variability could reflect at least partially the quality/efficacy of treatments used. Following this hypothesis, the aim of the present analysis was to evaluate whether overall quality of care at baseline, as summarized by the Q-score, was able to predict the variability of HbA1c, blood pressure, serum uric acid, and lipid profile in patients with type 2 diabetes.

Research Design and Methods

This was an observational, longitudinal retrospective study. The data were derived from the AMD-Annals initiative, i.e., from electronic medical records of all patients attending 300 diabetes clinics between 2005 and 2011 (3). The database contains information on over 1,000,000 patients with type 2 diabetes with at least one visit during the years at the participating centers.

Data relative to the 1st year of observation for each patient were considered for the calculation of the Q-score (baseline). Baseline values of the different parameters were represented by the last value recorded during the 1st year of observation of each patient. Baseline descriptive variables included age, sex, duration of diabetes, smoking, weight, BMI, HbA1c, blood pressure, serum uric acid, lipid profile, estimated glomerular filtration rate (eGFR; Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI] formula), albuminuria, diabetes treatment scheme, antihypertensive treatment, lipid-lowering treatment, and aspirin.

To ensure a robust estimate of variability, we identified those subjects having at least five measurements of HbA1c, systolic and diastolic blood pressure, serum uric acid, total cholesterol, HDL cholesterol, LDL cholesterol, and/or triglycerides in the years after the baseline. The availability of a minimum of five measures for each parameter after the baseline in each patient was required in the selection procedure to ensure a robust estimate of variability (Fig. 2).

Figure 2
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Figure 2

Study flow chart.

No patients were asked for input in the creation of this article. The article is an honest, accurate, and transparent account of the study being reported; no important aspects of the study have been omitted.

Statistical Methods

Patients were grouped according to the Q-score values (<15, 15–25, and >25). Descriptive data are summarized as mean and SD for continuous variables and percentages for categorical variables and compared among the three groups through Kruskal-Wallis one-way ANOVA test.

Variability of HbA1c, systolic and diastolic blood pressure, uric acid, total cholesterol, HDL cholesterol, LDL cholesterol, and triglycerides was expressed as the SD of the measures during the follow-up. Variability in the three groups was compared through Kruskal-Wallis one-way ANOVA test.

Multiple linear regression analyses were performed to evaluate the role of the Q-score in predicting the variability of the different parameters. Each measure of variability was tested as a dependent variable in a separate regression model adjusted for age, sex, and diabetes duration. Results of the linear regression models are expressed as β parameters along with their associated P value. To assess which measures of variability the Q-score had a greater effect on, standardized β coefficients were also calculated. Since greater variability can be associated with larger baseline values of the parameters of interest, additional regression models were performed, including the baseline value of the parameter of interest in the model. Finally, to take the hierarchical nature of the data into account (patients clustered within centers), a multilevel model was also applied and the intracluster correlation (ICC) was calculated.

Tests were two sided, and a P value <0.05 was considered statistically significant. Statistical analyses were performed with SAS software, version 9.4 (SAS Institute Inc., Cary, NC).

Results

Overall, 273,888 patients with type 2 diabetes cared for by 300 diabetes clinics were included in the cohort (Fig. 2). Baseline characteristics according to the three Q-score classes are reported in Table 1. A low Q-Score was associated with slightly older age, higher prevalence of female sex, longer diabetes duration, and poorer control of all the parameters investigated, with the only exception of GFR.

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Table 1

Baseline patient characteristics according to Q-score classes

Patients were followed up for an average of 2.5 ± 0.9 years. Mean values and SDs of the different parameters of interest according to Q-score classes are reported in Table 2. The average value and the variability of all the parameters investigated systematically increased with decreasing Q-score values.

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Table 2

Mean values and variability (SD) of clinical parameters during the follow-up by baseline Q-score classes

At multivariate linear regression analysis, the Q-score was an independent predictor of the variability of all the parameters considered (Table 3). In fact, compared with a Q-score >25, a score <15 was associated with a significantly larger variation in HbA1c, blood pressure, uric acid, total cholesterol, and LDL cholesterol and a lower variation in HDL cholesterol. Furthermore, compared with a Q-score >25, having a Q-score between 15 and 25 was also associated with a significantly larger variation in HbA1c, systolic blood pressure, uric acid, total cholesterol, and LDL cholesterol.

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Table 3

Association between baseline Q-score and variability of clinical parameters during the follow-up

The analysis of standardized β coefficients show that the Q-score has a larger impact on the variability of HbA1c, systolic blood pressure, total cholesterol, and LDL cholesterol (Table 3).

When baseline values of the parameters of interest were included as covariates in the multivariate models, standardized β coefficients decreased, but the association between poorer quality of care and higher variability remained highly statistically significant for all the parameters considered, with the only exception of triglycerides (Table 3). Finally, taking the clustered nature of the data into account did not substantially modify study results. All ICC values were largely <0.20, suggesting a modest center effect (Table 3).

Conclusions

Our data show that the higher the overall quality of care as expressed by the Q-score, the lower the variability in the key clinical parameters for diabetes care, i.e., HbA1c, blood pressure, lipid profile, uric acid, and weight. These new data show that the variability in HbA1c levels, systolic blood pressure levels, and total and LDL cholesterol levels is particularly influenced by a Q-score <15 and to a lesser extent by a Q-score between 15 and 25.

The interaction between glucose variability/blood pressure/lipids variability in favoring the appearance of diabetic complications, as well as the relationship between glucose variability and major outcomes, has been documented in an increasing number of studies in recent years (6–11). Evidence linking glucose variability to adverse consequences in hospitalized people without diabetes is also available and noteworthy (6,15,16).

As previously mentioned, in terms of physiopathological mechanisms, a huge body of experimental studies, both in vitro and in vivo, suggests that variability in glucose, blood pressure, and uric acid levels favors an increase in oxidative stress, increasing the generation of free radicals (6,12,13,17,18); other mechanisms as possible triggers of diabetes complications deserve consideration (19–22). On the other hand, in glucose-oscillating conditions, the intracellular antioxidant system seems to be less effective (23), and the protective antioxidant action of HDL cholesterol seems reduced if its levels are suboptimal and unstable over time (14). These are the possible reasons why variability in these parameters can be considered the key pathogenic factor for diabetic complications and also a cardiovascular risk factor (24,25).

To the best of our knowledge, this is the first study assessing the relationship between quality of care and variability in risk factors for complications. It remains to be established by which mechanisms poor quality of care can increase the risk of variability in such a large array of different parameters. A low Q-score, which is mainly driven by the difficulty in reaching satisfactory HbA1c, blood pressure, and lipid levels, can reflect poor patient compliance to medical recommendations, as well as clinical inertia, with a substantial delay in the intensification of treatment. These factors can be responsible for elevated baseline levels of the different parameters, as well as their intraindividual variability over time.

In terms of implications for clinical practice, these findings suggest that variability in the clinical parameters can derive from poor quality of care or poor compliance with medical recommendations; therefore, any effort should be devoted to keeping the parameters constantly within ranges recommended by guidelines over time, with timely therapy intensification whenever needed. Our data also suggest the need to focus on stabilizing not only HbA1c levels but all relevant parameters in order to reduce the risk of long-term complications.

Our study has limitations. First, information on medication adherence was not available. Adherence could at least partially explain the relationship between diabetes care quality and variability in risk factors. Second, the study population was selected based on the availability of at least five measurements during follow-up for each of the parameters of interest. In other words, the study population possibly represents a “compliant” patient group. Patients with low Q-scores (due to less than five measurements during follow-up, probably related to less or no measurements in the baseline year) had a higher chance of being excluded. Nevertheless, despite this limitation, a clear association between poorer quality of care and higher variability of the different parameters was clearly documented in our study. Third, we only evaluated glycemic variability in terms of HbA1c variability despite the evidence that many patients with similar levels of HbA1c may have very different degrees of glycemic variability, as measured by continuous glucose monitoring. Glycemic variability covers two predominant categories of measurements: short-term glycemic variability, represented by both within-day and between-day glycemic variability, and long-term glycemic variability, usually involving HbA1c. However, it should be noted that measures of short-term variability are impossible to obtain in very large numbers of patients with type 2 diabetes followed for a long period of time. For this reason, we focused on long-term glycemic variability, based on serial determinations of HbA1c over a long period of time.

Finally, the results regarding the variability of the risk parameters were all highly statistically significant due to the high numbers of patients and measurements. Additional research is needed to explain the meaning of these levels of variability and their clinical relevance.

In conclusion, the AMD-Annals database documented for the first time that intraindividual variability in risk factors can be at least partially related to the quality of care provided by centers. These data suggest that parameter variability could represent additional quality indicators, in the light of the mounting body of evidence linking variability of risk factors to long-term diabetes complications.

Article Information

Funding. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Duality of Interest. No potential conflicts of interest relevant to this article were reported.

Author Contributions. A.C. substantially contributed to the conception and design, drafted the manuscript, provided final approval of the version to be published, and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. M.C.R. acquired, analyzed, and interpreted data; drafted the manuscript; and provided final approval of the version to be published. S.D.C., R.P., P.F., C.G., A.P., F.V., and G.R. critically revised the manuscript for important intellectual content and provided final approval of the version to be published. G.L. acquired, analyzed, and interpreted data and provided final approval of the version of the manuscript to be published. A.N. substantially contributed to conception and design, drafted the manuscript, provided final approval of the version to be published, and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. A.C. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Footnotes

  • This article contains Supplementary Data online at http://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc18-1471/-/DC1.

  • ↵* A complete list of the members of the AMD-Annals Study Group can be found in the Supplementary Data online.

  • Received July 10, 2018.
  • Accepted January 15, 2019.
  • © 2019 by the American Diabetes Association.
http://www.diabetesjournals.org/content/license

Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at http://www.diabetesjournals.org/content/license.

References

  1. ↵
    1. Rossi MC,
    2. Nicolucci A,
    3. Arcangeli A, et al.; Associazione Medici Diabetologi Annals Study Group
    . Baseline quality-of-care data from a quality-improvement program implemented by a network of diabetes outpatient clinics. Diabetes Care 2008;31:2166–2168pmid:18694979
    OpenUrlAbstract/FREE Full Text
  2. ↵
    1. Nicolucci A,
    2. Rossi MC,
    3. Arcangeli A, et al.; AMD-Annals Study Group
    . Four-year impact of a continuous quality improvement effort implemented by a network of diabetes outpatient clinics: the AMD-Annals initiative. Diabet Med 2010;27:1041–1048pmid:20722678
    OpenUrlCrossRefPubMed
  3. ↵
    1. Rossi MC,
    2. Candido R,
    3. Ceriello A, et al
    . Trends over 8 years in quality of diabetes care: results of the AMD Annals continuous quality improvement initiative. Acta Diabetol 2015;52:557–571pmid:25528003
    OpenUrlPubMed
  4. ↵
    1. De Berardis G,
    2. Pellegrini F,
    3. Franciosi M, et al.; QuED (Quality of Care and Outcomes in Type 2 Diabetes) Study Group
    . Quality of diabetes care predicts the development of cardiovascular events: results of the QuED study. Nutr Metab Cardiovasc Dis 2008;18:57–65pmid:16860547
    OpenUrlCrossRefPubMedWeb of Science
  5. ↵
    1. Rossi MC,
    2. Lucisano G,
    3. Comaschi M, et al.; AMD-QUASAR Study Group
    . Quality of diabetes care predicts the development of cardiovascular events: results of the AMD-QUASAR study. Diabetes Care 2011;34:347–352pmid:21270192
    OpenUrlAbstract/FREE Full Text
  6. ↵
    Ceriello A, Monnier L, Owens D. Glycaemic variability in diabetes: clinical and therapeutic implications. Lancet Diabetes Endocrinol. 13 August 2018 [Epub ahead of print]. DOI: 10.1016/S2213-8587(18)30136-0
  7. ↵
    1. Di Flaviani A,
    2. Picconi F,
    3. Di Stefano P, et al
    . Impact of glycemic and blood pressure variability on surrogate measures of cardiovascular outcomes in type 2 diabetic patients. Diabetes Care 2011;34:1605–1609pmid:21610126
    OpenUrlAbstract/FREE Full Text
  8. ↵
    1. Clark D III,
    2. Nicholls SJ,
    3. St John J, et al
    . Visit-to-visit cholesterol variability correlates with coronary atheroma progression and clinical outcomes. Eur Heart J 2018;39:2551–2558pmid:29688308
    OpenUrlPubMed
  9. ↵
    1. Waters DD,
    2. Bangalore S,
    3. Fayyad R, et al
    . Visit-to-visit variability of lipid measurements as predictors of cardiovascular events. J Clin Lipidol 2018;12:356–366pmid:29310989
    OpenUrlPubMed
  10. ↵
    1. Ceriello A,
    2. De Cosmo S,
    3. Rossi MC, et al.; AMD-Annals Study Group
    . Variability in HbA1c, blood pressure, lipid parameters and serum uric acid, and risk of development of chronic kidney disease in type 2 diabetes. Diabetes Obes Metab 2017;19:1570–1578pmid:28432733
    OpenUrlPubMed
  11. ↵
    1. Nam GE,
    2. Cho KH,
    3. Han K, et al
    . Impact of body mass index and body weight variabilities on mortality: a nationwide cohort study. Int J Obes 2019;43:412–423
    OpenUrl
  12. ↵
    1. Morelli NR,
    2. Scavuzzi BM,
    3. Miglioranza LHDS,
    4. Lozovoy MAB,
    5. Simão ANC,
    6. Dichi I
    . Metabolic syndrome components are associated with oxidative stress in overweight and obese patients. Arch Endocrinol Metab 2018;62:309–318pmid:29791650
    OpenUrlPubMed
  13. ↵
    1. Maxwell AJ,
    2. Bruinsma KA
    . Uric acid is closely linked to vascular nitric oxide activity. Evidence for mechanism of association with cardiovascular disease. J Am Coll Cardiol 2001;38:1850–1858pmid:11738284
    OpenUrlFREE Full Text
  14. ↵
    1. Karlsson H,
    2. Kontush A,
    3. James RW
    . Functionality of HDL: antioxidation and detoxifying effects. Handb Exp Pharmacol 2015;224:207–228pmid:25522989
    OpenUrlPubMed
  15. ↵
    1. Sato H,
    2. Hosojima M,
    3. Ishikawa T, et al
    . Glucose variability based on continuous glucose monitoring assessment is associated with postoperative complications after cardiovascular surgery. Ann Thorac Cardiovasc Surg 2017;23:239–247pmid:28717057
    OpenUrlPubMed
  16. ↵
    1. Krinsley JS,
    2. Maurer P,
    3. Holewinski S, et al
    . Glucose control, diabetes status, and mortality in critically ill patients: the continuum from intensive care unit admission to hospital discharge. Mayo Clin Proc 2017;92:1019–1029pmid:28645517
    OpenUrlPubMed
  17. ↵
    1. Ohara M,
    2. Fukui T,
    3. Ouchi M, et al
    . Relationship between daily and day-to-day glycemic variability and increased oxidative stress in type 2 diabetes. Diabetes Res Clin Pract 2016;122:62–70pmid:27810687
    OpenUrlPubMed
  18. ↵
    1. Costantino S,
    2. Paneni F,
    3. Battista R, et al
    . Impact of glycemic variability on chromatin remodeling, oxidative stress, and endothelial dysfunction in patients with type 2 diabetes and with target HbA1c levels. Diabetes 2017;66:2472–2482pmid:28634176
    OpenUrlAbstract/FREE Full Text
  19. ↵
    1. Xia J,
    2. Qu Y,
    3. Yin C,
    4. Xu D
    . Optical coherence tomography assessment of glucose fluctuation impact on the neointimal proliferation after stent implantation in a diabetic/hypercholesterolemic Swine model. Int Heart J 2017;58:608–614pmid:28690299
    OpenUrlPubMed
    1. Ying C,
    2. Liu T,
    3. Ling H, et al
    . Glucose variability aggravates cardiac fibrosis by altering AKT signalling path. Diab Vasc Dis Res 2017;14:327–335pmid:28301953
    OpenUrlPubMed
    1. Hoffman RP,
    2. Dye AS,
    3. Huang H,
    4. Bauer JA
    . Glycemic variability predicts inflammation in adolescents with type 1 diabetes. J Pediatr Endocrinol Metab 2016;29:1129–1133pmid:27658133
    OpenUrlPubMed
  20. ↵
    1. Kuricová K,
    2. Pácal L,
    3. Šoupal J,
    4. Prázný M,
    5. Kaňková K
    . Effect of glucose variability on pathways associated with glucotoxicity in diabetes: evaluation of a novel in vitro experimental approach. Diabetes Res Clin Pract 2016;114:1–8pmid:27103362
    OpenUrlPubMed
  21. ↵
    1. La Sala L,
    2. Cattaneo M,
    3. De Nigris V, et al
    . Oscillating glucose induces microRNA-185 and impairs an efficient antioxidant response in human endothelial cells. Cardiovasc Diabetol 2016;15:71pmid:27137793
    OpenUrlPubMed
  22. ↵
    1. Tsuboi A,
    2. Takenouchi A,
    3. Kurata M,
    4. Fukuo K,
    5. Kazumi T
    . Postmeal triglyceridemia and variability of HbA1c and postmeal glycemia were predictors of annual decline in estimated glomerular filtration rate in type 2 diabetic patients with different stages of nephropathy. J Diabetes Metab Disord 2017;16:1pmid:28097106
    OpenUrlPubMed
  23. ↵
    1. Takao T,
    2. Suka M,
    3. Yanagisawa H,
    4. Matsuyama Y,
    5. Iwamoto Y
    . Predictive ability of visit-to-visit variability in HbA1c and systolic blood pressure for the development of microalbuminuria and retinopathy in people with type 2 diabetes. Diabetes Res Clin Pract 2017;128:15–23pmid:28432895
    OpenUrlPubMed
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Overall Quality of Care Predicts the Variability of Key Risk Factors for Complications in Type 2 Diabetes: An Observational, Longitudinal Retrospective Study
Antonio Ceriello, Maria Chiara Rossi, Salvatore De Cosmo, Giuseppe Lucisano, Roberto Pontremoli, Paola Fioretto, Carlo Giorda, Antonio Pacilli, Francesca Viazzi, Giuseppina Russo, Antonio Nicolucci
Diabetes Care Apr 2019, 42 (4) 514-519; DOI: 10.2337/dc18-1471

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Overall Quality of Care Predicts the Variability of Key Risk Factors for Complications in Type 2 Diabetes: An Observational, Longitudinal Retrospective Study
Antonio Ceriello, Maria Chiara Rossi, Salvatore De Cosmo, Giuseppe Lucisano, Roberto Pontremoli, Paola Fioretto, Carlo Giorda, Antonio Pacilli, Francesca Viazzi, Giuseppina Russo, Antonio Nicolucci
Diabetes Care Apr 2019, 42 (4) 514-519; DOI: 10.2337/dc18-1471
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  • Epidemiology-Diabetes Complications

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