Search

Aims: To determine differences in the management of diabetic kidney disease (DKD) relevant to patient sex, ethnicity and socio-economic group in UK primary care., Methods: A cross-sectional analysis as of January 1, 2019 was undertaken using the IQVIA Medical Research Data dataset, to determine the proportion of people with DKD managed in accordance with national guidelines, stratified by demographics. Robust Poisson regression models were used to calculate adjusted risk ratios (aRR) adjusting for age, sex, ethnicity and social deprivation., Results: Of the 2.3 million participants, 161,278 had type 1 or 2 diabetes, of which 32,905 had DKD. Of people with DKD, 60% had albumin creatinine ratio (ACR) measured, 64% achieved blood pressure (BP, <140/90 mmHg) target, 58% achieved glycosylated haemoglobin (HbA1c, <58 mmol/mol) target, 68% prescribed renin-angiotensin-aldosterone system (RAAS) inhibitor in the previous year. Compared to men, women were less likely to have creatinine: aRR 0.99 (95% CI 0.98-0.99), ACR: aRR 0.94 (0.92-0.96), BP: aRR 0.98 (0.97-0.99), HbA 1c : aRR 0.99 (0.98-0.99) and serum cholesterol: aRR 0.97 (0.96-0.98) measured; achieve BP: aRR 0.95 (0.94-0.98) or total cholesterol (<5 mmol/L) targets: aRR 0.86 (0.84-0.87); or be prescribed RAAS inhibitors: aRR 0.92 (0.90-0.94) or statins: aRR 0.94 (0.92-0.95). Compared to the least deprived areas, people from the most deprived areas were less likely to have BP measurements: aRR 0.98 (0.96-0.99); achieve BP: aRR 0.91 (0.8-0.95) or HbA 1c : aRR 0.88 (0.85-0.92) targets, or be prescribed RAAS inhibitors: aRR 0.91 (0.87-0.95). Compared to people of white ethnicity; those of black ethnicity were less likely to be prescribed statins aRR 0.91 (0.85-0.97)., Conclusions: There are unmet needs and inequalities in the management of DKD in the UK. Addressing these could reduce the increasing human and societal cost of managing DKD., (© 2023 The Authors. Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UK.)

Aims: People with pre-diabetes are at high risk of progressing to type 2 diabetes. This progression is not well characterised by ethnicity, deprivation and age, which we describe in a large cohort of individuals with pre-diabetes., Methods: A retrospective cohort study with The Health Improvement Network (THIN) database was conducted. Patients aged 18 years and over and diagnosed with pre-diabetes [HbA1c 42 mmol/mol (6.0%) to 48 mmol/mol (6.5%) were included]. Cox proportional hazards regression was used to calculate adjusted hazard rate ratios (aHR) for the risk of progression from pre-diabetes to type 2 diabetes for each of the exposure categories [ethnicity, deprivation (Townsend), age and body mass index (BMI)] separately., Results: Of the baseline population with pre-diabetes (n = 397,853), South Asian (aHR 1.31; 95% CI 1.26-1.37) or Mixed-Race individuals (aHR 1.22; 95% CI 1.11-1.33) had an increased risk of progression to type 2 diabetes compared with those of white European ethnicity. Likewise, deprivation (aHR 1.17; 95% CI 1.14-1.20; most vs. least deprived) was associated with an increased risk of progression. Both younger (aHR 0.63; 95% CI 0.58-0.69; 18 to <30 years) and older individuals (aHR 0.85; 95% CI 0.84-0.87; ≥65 years) had a slower risk of progression from pre-diabetes to type 2 diabetes, than middle-aged (40 to <65 years) individuals., Conclusions: South Asian or Mixed-Race individuals and people with social deprivation had an increased risk of progression from pre-diabetes to type 2 diabetes. Clinicians need to recognise the differing risk across their patient populations to implement appropriate prevention strategies., (© 2022 The Authors. Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UK.)

Aims: Type 1 diabetes is characterised by the destruction of pancreatic β-cells. Significant levels of β-cells remain at diagnosis. Preserving these cells improves glucose control and protects from long-term complications. We undertook a systematic review and meta-analyses of all randomised controlled trials (RCTs) of interventions to preserve β-cell function in people newly diagnosed with type 1 diabetes. This paper reports the results of interventions for improving glucose control to assess whether they preserve β-cell function., Methods: Searches for RCTs in MEDLINE, Embase, Cochrane CENTRAL, ClinicalTrials.gov and WHO International Clinical Trials Registry. Eligible studies included newly diagnosed patients with type 1 diabetes, any intervention to improve glucose control and at least 1 month of follow-up. Data were extracted using a pre-defined data-extraction sheet with 10% of extractions checked by a second reviewer., Results: Twenty-eight studies with 1662 participants were grouped by intervention into six subgroups (alternative insulins, subcutaneous and intravenous insulin delivery, intensive therapy, glucose sensing, adjuncts). Only three studies demonstrated an improvement in glucose control as well as β-cell function. These interventions included intensive insulin therapy and use of an alternative insulin., Conclusions: This is the largest comprehensive review of RCTs in this area. It demonstrates a lack of robust evidence that interventions to improve glucose control preserve β-cell function in new onset type 1 diabetes, although analysis was hampered by low-quality evidence and inconsistent reporting of studies. Development of guidelines to support the design of trials in this field is a priority., (© 2021 Diabetes UK.)

Background: People with diabetes have longer hospital stays and poorer clinical outcomes. Diabetes inpatient specialist nurses have been introduced to improve care., Aims: To assess the evidence for the benefit of diabetes specialist nurses in the inpatient setting., Methods: A systematic search of MEDLINE (ovid), Embase (ovid), CINAHL (EBSCO) and Web of Science core collection from January 1998 to September 2019 was performed using key terms for diabetes specialist nurses and hospital setting. Studies measuring patient care using any standardised or validated outcome measures after introduction of a dedicated diabetes specialist nurse or nursing team were eligible for inclusion and findings reported by narrative synthesis., Results: There were 10 studies which met the inclusion criteria. One was a randomised controlled study and the remaining nine studies were before and after studies with three of them using a time series analysis methodology. The majority reported length of stay (LOS) and showed a reduction in median LOS by between 0.5 and 3 days. Reductions in bed occupancy ranged from 39% to 47%. There was a paucity of evidence for outcomes related to patient care with some measures limited to single studies. These included a 52% reduction in total drug errors, improved patient knowledge, higher patient satisfaction and improved glycaemic control post-discharge. There was no reduction of mortality observed., Conclusions: These studies suggest a reduction in LOS and improved clinical care for patients with diabetes after the introduction of diabetes inpatient specialist nurses. Future research should examine a range of benefits associated with diabetes inpatient specialist nurse delivered services, including reduction of inpatient complications such as infections and cardiovascular events., (© 2021 The Authors. Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UK.)

Aim: To determine whether the Diabetes Inpatient Care and Education (DICE) programme, a whole-systems approach to managing inpatient diabetes, reduces length of stay, in-hospital mortality and readmissions., Research Design and Methods: Diabetes Inpatient Care and Education initiatives included identification of all diabetes admissions, a novel DICE care-pathway, an online system for prioritizing referrals, use of web-linked glucose meters, an enhanced diabetes team, and novel diabetes training for doctors. Patient administration system data were extracted for people admitted to Ipswich Hospital from January 2008 to June 2016. Logistic regression was used to compare binary outcomes (mortality, 30-day readmissions) 6 months before and after the intervention; generalized estimating equations were used to compare lengths of stay. Interrupted time series analysis was performed over the full 7.5-year period to account for secular trends., Results: Before-and-after analysis revealed a significant reduction in lengths of stay for people with and without diabetes: relative ratios 0.89 (95% CI 0.83, 0.97) and 0.93 (95% CI 0.90, 0.96), respectively; however, in interrupted time series analysis the change in long-term trend for length of stay following the intervention was significant only for people with diabetes (P=0.017 vs P=0.48). Odds ratios for mortality were 0.63 (0.48, 0.82) and 0.81 (0.70, 0.93) in people with and without diabetes, respectively; however, the change in trend was not significant in people with diabetes, while there was an apparent increase in those without diabetes. There was no significant change in 30-day readmissions, but interrupted time series analysis showed a rising trend in both groups., Conclusion: The DICE programme was associated with a shorter length of stay in inpatients with diabetes beyond that observed in people without diabetes., (© 2019 Diabetes UK.)

Aims: To assess whether glycaemic control is associated with a lifelong increased risk of fracture in people with newly diagnosed Type 1 diabetes., Methods: People with newly diagnosed Type 1 diabetes between 1 January 1995 and 10 May 2016 were identified in The Health Improvement Network database. Longitudinal HbA 1c measurements from diagnosis to fracture or study end or loss to follow-up were collected. A Cox proportional hazards model with HbA 1c included as a time-dependent variable was fitted to these data., Results: Some 5368 people with newly diagnosed Type 1 diabetes were included. The estimated adjusted hazard ratio (aHR) for HbA 1c was statistically significant [aHR 1.007; 95% confidence interval (CI) 1.002-1.011 (mmol/mol) and aHR 1.07; 95% CI 1.03-1.12 (%)]. An incremental higher risk of fracture was observed with increasing levels of HbA 1c ., Conclusions: In people with newly diagnosed Type 1 diabetes, higher HbA 1c is associated with an increased risk for fractures., (© 2019 Diabetes UK.)

Aim: To temporally and externally validate our previously developed prediction model, which used data from University Hospitals Birmingham to identify inpatients with diabetes at high risk of adverse outcome (mortality or excessive length of stay), in order to demonstrate its applicability to other hospital populations within the UK., Methods: Temporal validation was performed using data from University Hospitals Birmingham and external validation was performed using data from both the Heart of England NHS Foundation Trust and Ipswich Hospital. All adult inpatients with diabetes were included. Variables included in the model were age, gender, ethnicity, admission type, intensive therapy unit admission, insulin therapy, albumin, sodium, potassium, haemoglobin, C-reactive protein, estimated GFR and neutrophil count. Adverse outcome was defined as excessive length of stay or death., Results: Model discrimination in the temporal and external validation datasets was good. In temporal validation using data from University Hospitals Birmingham, the area under the curve was 0.797 (95% CI 0.785-0.810), sensitivity was 70% (95% CI 67-72) and specificity was 75% (95% CI 74-76). In external validation using data from Heart of England NHS Foundation Trust, the area under the curve was 0.758 (95% CI 0.747-0.768), sensitivity was 73% (95% CI 71-74) and specificity was 66% (95% CI 65-67). In external validation using data from Ipswich, the area under the curve was 0.736 (95% CI 0.711-0.761), sensitivity was 63% (95% CI 59-68) and specificity was 69% (95% CI 67-72). These results were similar to those for the internally validated model derived from University Hospitals Birmingham., Conclusions: The prediction model to identify patients with diabetes at high risk of developing an adverse event while in hospital performed well in temporal and external validation. The externally validated prediction model is a novel tool that can be used to improve care pathways for inpatients with diabetes. Further research to assess clinical utility is needed., (© 2018 Diabetes UK.)

Aims: To explore whether a quantitative approach to identifying hospitalized patients with diabetes at risk of hypoglycaemia would be feasible through incorporation of routine biochemical, haematological and prescription data., Methods: A retrospective cross-sectional analysis of all diabetic admissions (n=9584) from 1 January 2014 to 31 December 2014 was performed. Hypoglycaemia was defined as a blood glucose level of <4 mmol/l. The prediction model was constructed using multivariable logistic regression, populated by clinically important variables and routine laboratory data., Results: Using a prespecified variable selection strategy, it was shown that the occurrence of inpatient hypoglycaemia could be predicted by a combined model taking into account background medication (type of insulin, use of sulfonylureas), ethnicity (black and Asian), age (≥75 years), type of admission (emergency) and laboratory measurements (estimated GFR, C-reactive protein, sodium and albumin). Receiver-operating curve analysis showed that the area under the curve was 0.733 (95% CI 0.719 to 0.747). The threshold chosen to maximize both sensitivity and specificity was 0.15. The area under the curve obtained from internal validation did not differ from the primary model [0.731 (95% CI 0.717 to 0.746)]., Conclusions: The inclusion of routine biochemical data, available at the time of admission, can add prognostic value to demographic and medication history. The predictive performance of the constructed model indicates potential clinical utility for the identification of patients at risk of hypoglycaemia during their inpatient stay., (© 2017 Diabetes UK.)

Aim: Accurate assessment of missed discharge codes for diabetes is critical for effective planning of hospital diabetes services. We wished to estimate the frequency of missed discharge diagnostic codes for diabetes and the impact missed codes would have on diabetes-related payments to the hospital., Methods: We linked Patient Administration System data to the Prescribing Information and Communication System. We defined diabetes as those having a discharge code for diabetes in the Patient Administration System and those on anti-diabetic medication in the Prescribing Information and Communication System. Based on the two sources, we calculated the estimated missed discharge codes for diabetes using the capture-recapture technique. We generated the Healthcare Resource Group for a given admission before and after correction for the missed code to estimate the impact that correction would make on payments to the hospital., Results: Among the 171 067 admissions linked, 22 412 (13.1%) had a code for diabetes at discharge. An additional 2706 admissions were classified as having diabetes based on prescription data. The capture-recapture technique estimated there were 4588 (2.7% of all admissions) admissions with diabetes missed by current coding, of which 2706 (60%) would be obtained from prescription data. After adding a diabetes diagnostic code, 12.8% of the missed admissions with diabetes resulted in a change to the Healthcare Resource Group tariff code and payment., Conclusion: The use of electronic prescription data is a simple solution to correct for missed discharge diagnostic codes., (© 2012 The Authors. Diabetic Medicine © 2012 Diabetes UK.)

Aim: To study the length of stay and inpatient mortality of patients with diabetes who had an episode of hypoglycaemia in a non critical care setting at University Hospital Birmingham, UK., Methods: Retrospective analysis of routinely available electronic data of 6374 admissions with a recording of either laboratory or point-of-care blood glucose value. Based on the lowest recorded blood glucose values, patients were categorized into a group without hypoglycaemia (> 3.9 mmol/l), a group with mild to moderate hypoglycaemia (2.3-3.9 mmol/l) and a group with severe hypoglycaemic (≤ 2.2 mmol/l). Length of stay and inpatient mortality were compared between the three groups, adjusting for age, gender, ethnicity, deprivation, admission type, use of insulin and modified Charlson co-morbidity score., Results: There were 148 admissions (2.3%) with severe hypoglycaemia (≤ 2.2 mmol/l), 500 admissions (7.8%) with mild to moderate hypoglycaemia (2.2-3.9 mmol/l) and 5726 admissions with no recorded hypoglycaemic episode (> 3.9 mmol/l). After adjustment, length of stay, when compared with those without a recorded hypoglycaemic episode, was 1.51 (95% CI 1.35-1.68) times higher in the group with blood glucose values of 2.3-3.9 mmol/l and 2.33 (95% CI 1.91-2.84) higher in the group with blood glucose values ≤ 2.2 mmol/l. Adjusted odds ratio of inpatient mortality when compared with the group without hypoglycaemia was 1.62 (95% CI 1.16-2.27) in the group with blood glucose values of 2.3-3.9 mmol/l and 2.05 (95% CI 1.24-3.38) in the group with blood glucose values ≤ 2.2 mmol/l., Conclusion: Hypoglycaemia is associated with increased length of stay and inpatient mortality. Whilst causative evidence is lacking, our data are consistent with the need to avoid hypoglycaemia in our current and continued approach for optimal glycaemic control in people with diabetes admitted to hospital., (© 2012 The Authors. Diabetic Medicine © 2012 Diabetes UK.)

Background: Computerized clinical decision support systems have been claimed to reduce prescription errors and improve patient care. They may play an important role in the care of hospitalized patients with diabetes., Aim: To collate evidence for the use of clinical decision support systems in improving the care of hospitalized patients with diabetes in a non-critical care setting and to assess their effectiveness., Methods: We searched four databases from 1980 to 2010 without language restrictions. All types of studies other than case reports were included. Data extraction and quality assessment were carried out based on the Centre for Review and Dissemination guidance. A narrative synthesis was conducted., Results: Fourteen studies met the inclusion criteria, including two cluster randomized controlled trials, eight before-and-after studies and four other descriptive studies. Generally, the quality of the studies was not very high. Nine out of 10 studies reported reduction in mean blood glucose or similar measures (patient-day-weighted mean blood glucose) during inpatient stay. The reduction using computerized physician order entry system in patient-day-weighted mean blood glucose ranged from 0.6 to 0.8 mmol/l (10.8-15.6 mg/dl). Other beneficial effects during inpatient stay included reduced use of sliding scale insulin and greater use of basal-bolus insulin regimen. Only one study found a significant increase in hypoglycaemic events., Conclusions: Clinical decision support systems have been used, often as part of a complex programme, to improve the care of hospitalized patients with diabetes. There is some evidence that they may have a beneficial effect, but this needs further confirmation., (© 2011 The Authors. Diabetic Medicine © 2011 Diabetes UK.)