Development and validation of risk prediction equations to estimate future risk of blindness and lower limb amputation in patients with diabetes: cohort study

Evolving understanding of autoimmune mechanisms and new therapeutic strategies of autoimmune disorders

Autoimmune disorders are characterized by aberrant T cell and B cell reactivity to the body's own components, resulting in tissue destruction and organ dysfunction. Autoimmune diseases affect a wide range of people in many parts of the world and have become one of the major concerns in public health. In recent years, there have been substantial progress in our understanding of the epidemiology, risk factors, pathogenesis and mechanisms of autoimmune diseases. Current approved therapeutic interventions for autoimmune diseases are mainly non-specific immunomodulators and may cause broad immunosuppression that leads to serious adverse effects. To overcome the limitations of immunosuppressive drugs in treating autoimmune diseases, precise and target-specific strategies are urgently needed. To date, significant advances have been made in our understanding of the mechanisms of immune tolerance, offering a new avenue for developing antigen-specific immunotherapies for autoimmune diseases. These antigen-specific approaches have shown great potential in various preclinical animal models and recently been evaluated in clinical trials. This review describes the common epidemiology, clinical manifestation and mechanisms of autoimmune diseases, with a focus on typical autoimmune diseases including multiple sclerosis, type 1 diabetes, rheumatoid arthritis, systemic lupus erythematosus, and sjögren's syndrome. We discuss the current therapeutics developed in this field, highlight the recent advances in the use of nanomaterials and mRNA vaccine techniques to induce antigen-specific immune tolerance.

Prediction model for lower limb amputation in hospitalized diabetic foot patients using classification and regression trees

BACKGROUND

The decision to perform amputation of a limb in a patient with diabetic foot ulcer (DFU) is not an easy task. Prediction models aim to help the surgeon in decision making scenarios. Currently there are no prediction model to determine lower limb amputation during the first 30 days of hospitalization for patients with DFU.

METHODS

Classification And Regression Tree analysis was applied on data from a retrospective cohort of patients hospitalized for the management of diabetic foot ulcer, using an existing database from two Orthopaedics and Traumatology departments. The secondary analysis identified independent variables that can predict lower limb amputation (mayor or minor) during the first 30 days of hospitalization.

RESULTS

Of the 573 patients in the database, 290 feet underwent a lower limb amputation during the first 30 days of hospitalization. Six different models were developed using a loss matrix to evaluate the error of not detecting false negatives. The selected tree produced 13 terminal nodes and after the pruning process, only one division remained in the optimal tree (Sensitivity: 69%, Specificity: 75%, Area Under the Curve: 0.76, Complexity Parameter: 0.01, Error: 0.85). Among the studied variables, the Wagner classification with a cut-off grade of 3 exceeded others in its predicting capacity.

CONCLUSIONS

Wagner classification was the variable with the best capacity for predicting amputation within 30 days. Infectious state and vascular occlusion described indirectly by this classification reflects the importance of taking quick decisions in those patients with a higher compromise of these two conditions. Finally, an external validation of the model is still required.

LEVEL OF EVIDENCE

III.

Plasma Metabolomics Identifies Key Metabolites and Improves Prediction of Diabetic Retinopathy: Development and Validation across Multinational Cohorts

PURPOSE

To identify longitudinal metabolomic fingerprints of diabetic retinopathy (DR) and to evaluate their usefulness in predicting DR development and progression.

DESIGN

Multicenter, multiethnic cohort study.

PARTICIPANTS

This study included 17 675 participants from the UK Biobank (UKB) who had baseline prediabetes or diabetes, identified in accordance with the 2021 American Diabetes Association guidelines, and were free of baseline DR and an additional 638 participants with type 2 diabetes mellitus from the Guangzhou Diabetic Eye Study (GDES) for external validation. Diabetic retinopathy was determined by ICD-10 codes in the UKB cohort and revised ETDRS grading criteria in the GDES cohort.

METHODS

Longitudinal DR metabolomic fingerprints were identified through nuclear magnetic resonance (NMR) assay in UKB participants. The predictive value of these fingerprints for predicting DR development were assessed in a fully withheld test set. External validation and extrapolation analyses of DR progression and microvascular damage were conducted in the GDES cohort using NMR technology. Model assessments included the concordance (C) statistic, net classification improvement (NRI), integrated discrimination improvement (IDI), calibration, and clinical usefulness in both cohorts.

MAIN OUTCOME MEASURES

DR development and progression and retinal microvascular damage.

RESULTS

Of 168 metabolites, 118 were identified as candidate metabolomic fingerprints for future DR development. These fingerprints significantly improved the predictability for DR development beyond traditional indicators (C statistic, 0.802 [95% confidence interval (CI), 0.760-0.843] vs. 0.751 [95% CI, 0.706-0.796]; P = 5.56 × 10-4). Glucose, lactate, and citrate were among the fingerprints validated in the GDES cohort. Using these parsimonious and replicable fingerprints yielded similar improvements for predicting DR development (C statistic, 0.807 [95% CI, 0.711-0.903] vs. 0.617 [95% CI, 0.494-0.740]; P = 1.68 × 10-4) and progression (C statistic, 0.797 [95% CI, 0.712-0.882] vs. 0.665 [95% CI, 0.545-0.784]; P = 0.003) in the external GDES cohort. Improvements in NRIs, IDIs, and clinical usefulness also were evident in both cohorts (all P < 0.05). In addition, lactate and citrate were associated with microvascular damage across macular and optic nerve head regions among Chinese GDES (all P < 0.05).

CONCLUSIONS

Metabolomic profiling may be effective in identifying robust fingerprints for predicting future DR development and progression, providing novel insights into the early and advanced stages of DR pathophysiology.

FINANCIAL DISCLOSURE(S)

The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Role of Systemic Factors in Improving the Prognosis of Diabetic Retinal Disease and Predicting Response to Diabetic Retinopathy Treatment

Topic

To review clinical evidence on systemic factors that might be relevant to update diabetic retinal disease (DRD) staging systems, including prediction of DRD onset, progression, and response to treatment.

Clinical relevance

Systemic factors may improve new staging systems for DRD to better assess risk of disease worsening and predict response to therapy.

Methods

The Systemic Health Working Group of the Mary Tyler Moore Vision Initiative reviewed systemic factors individually and in multivariate models for prediction of DRD onset or progression (i.e., prognosis) or response to treatments (prediction).

Results

There was consistent evidence for associations of longer diabetes duration, higher glycosylated hemoglobin (HbA1c), and male sex with DRD onset and progression. There is strong trial evidence for the effect of reducing HbA1c and reducing DRD progression. There is strong evidence that higher blood pressure (BP) is a risk factor for DRD incidence and for progression. Pregnancy has been consistently reported to be associated with worsening of DRD but recent studies reflecting modern care standards are lacking. In studies examining multivariate prognostic models of DRD onset, HbA1c and diabetes duration were consistently retained as significant predictors of DRD onset. There was evidence of associations of BP and sex with DRD onset. In multivariate prognostic models examining DRD progression, retinal measures were consistently found to be a significant predictor of DRD with little evidence of any useful marginal increment in prognostic information with the inclusion of systemic risk factor data apart from retinal image data in multivariate models. For predicting the impact of treatment, although there are small studies that quantify prognostic information based on imaging data alone or systemic factors alone, there are currently no large studies that quantify marginal prognostic information within a multivariate model, including both imaging and systemic factors.

Conclusion

With standard imaging techniques and ways of processing images rapidly evolving, an international network of centers is needed to routinely capture systemic health factors simultaneously to retinal images so that gains in prediction increment may be precisely quantified to determine the usefulness of various health factors in the prognosis of DRD and prediction of response to treatment.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Diabetes-Related Macrovascular Complications Are Associated With an Increased Risk of Diabetic Microvascular Complications: A Prospective Study of 1518 Patients With Type 1 Diabetes and 20 802 Patients With Type 2 Diabetes in the UK Biobank

BACKGROUND

Diabetic vascular complications share common pathophysiological mechanisms, but the relationship between diabetes-related macrovascular complications (MacroVCs) and incident diabetic microvascular complications remains unclear. We aimed to investigate the impact of MacroVCs on the risk of microvascular complications.

METHODS AND RESULTS

There were 1518 participants with type 1 diabetes (T1D) and 20 802 participants with type 2 diabetes from the UK Biobank included in this longitudinal cohort study. MacroVCs were defined by the presence of macrovascular diseases diagnosed after diabetes at recruitment, including coronary heart disease, peripheral artery disease, stroke, and ≥2 MacroVCs. The primary outcome was incident microvascular complications, a composite of diabetic retinopathy, diabetic kidney disease, and diabetic neuropathy. During a median (interquartile range) follow-up of 11.61 (5.84-13.12) years and 12.2 (9.50-13.18) years, 596 (39.3%) and 4113 (19.8%) participants developed a primary outcome in T1D and type 2 diabetes, respectively. After full adjustment for conventional risk factors, Cox regression models showed significant associations between individual as well as cumulative MacroVCs and the primary outcome, except for coronary heart disease in T1D (T1D: diabetes coronary heart disease: 1.25 [0.98-1.60]; diabetes peripheral artery disease: 3.00 [1.86-4.84]; diabetes stroke: 1.71 [1.08-2.72]; ≥2: 2.57 [1.66-3.99]; type 2 diabetes: diabetes coronary heart disease: 1.59 [1.38-1.82]; diabetes peripheral artery disease: 1.60 [1.01-2.54]; diabetes stroke: 1.50 [1.13-1.99]; ≥2: 2.66 [1.92-3.68]). Subgroup analysis showed that strict glycemic (glycated hemoglobin <6.5%) and blood pressure (<140/90 mm Hg) control attenuated the association.

CONCLUSIONS

Individual and cumulative MacroVCs confer significant risk of incident microvascular complications in patients with T1D and type 2 diabetes. Our results may facilitate cost-effective high-risk population identification and development of precise prevention strategies.

Pooling of primary care electronic health record (EHR) data on Huntington's disease (HD) and cancer: establishing comparability of two large UK databases

OBJECTIVES

To explore whether UK primary care databases arising from two different software systems can be feasibly combined, by comparing rates of Huntington's disease (HD, which is rare) and 14 common cancers in the two databases, as well as characteristics of people with these conditions.

DESIGN

Descriptive study.

SETTING

Primary care electronic health records from Clinical Practice Research Datalink (CPRD) GOLD and CPRD Aurum databases, with linked hospital admission and death registration data.

PARTICIPANTS

4986 patients with HD and 1 294 819 with an incident cancer between 1990 and 2019.

PRIMARY AND SECONDARY OUTCOME MEASURES

Incidence and prevalence of HD by calendar period, age group and region, and annual age-standardised incidence of 14 common cancers in each database, and in a subset of 'overlapping' practices which contributed to both databases. Characteristics of patients with HD or incident cancer: medical history, recent prescribing, healthcare contacts and database follow-up.

RESULTS

Incidence and prevalence of HD were slightly higher in CPRD GOLD than CPRD Aurum, but with similar trends over time. Cancer incidence in the two databases differed between 1990 and 2000, but converged and was very similar thereafter. Participants in each database were most similar in terms of medical history (median standardised difference, MSD 0.03 (IQR 0.01-0.03)), recent prescribing (MSD 0.06 (0.03-0.10)) and demographics and general health variables (MSD 0.05 (0.01-0.09)). Larger differences were seen for healthcare contacts (MSD 0.27 (0.10-0.41)), and database follow-up (MSD 0.39 (0.19-0.56)).

CONCLUSIONS

Differences in cancer incidence trends between 1990 and 2000 may relate to use of a practice-level data quality filter (the 'up-to-standard' date) in CPRD GOLD only. As well as the impact of data curation methods, differences in underlying data models can make it more challenging to define exactly equivalent clinical concepts in each database. Researchers should be aware of these potential sources of variability when planning combined database studies and interpreting results.

Evaluation and Optimization of Diabetic Retinopathy Screenings for Uninsured Latinx Patients in a Resource-Limited Student-Run Free Clinic

Background

Diabetic retinopathy (DR) is a sight-threatening condition that causes progressive retina damage. Student-run free clinics represent a valuable opportunity to provide DR screenings to high-risk populations. We characterized the patient population, evaluated the performance, and conducted a needs assessment of DR screenings at the University of California, San Diego Student-Run Ophthalmology Free Clinic, which provides care to predominantly uninsured, Latino patients.

Methods

Retrospective chart review was conducted of all patients seen at the free clinic since 2019 with a diagnosis of type II diabetes. Date and outcome of all DR-related screenings or visits from 2015 onward, demographics information, and DR risk factors such as A1c and insulin dependence were recorded. Predictors of diabetic retinopathy and frequency of DR screenings for each patient were analyzed using multiple logistic regression, t-test for equality of means, and Pearson's correlation.

Results

Of 179 uninsured diabetic patients receiving care at the free clinic, 71% were female and average age was 59. 83% had hypertension, 93% had hyperlipidemia, and 79% had metabolic syndrome. Prevalence of non-proliferative DR was 34% and that of proliferative DR was 15% in diabetic patients. The free clinic capacity in recent years plateaued at just under 50% of patients seen for DR screening or visit per year, though average wait time was over 2 years between visits. Patients with higher no-show rates had less frequent DR screenings. Chronic kidney disease and poor glycemic control were the strongest predictors of DR.

Conclusion

The student-run free ophthalmology clinic has been effective in providing screening and follow-up care for DR patients. Creation of a protocol to identify which patients are at highest risk of DR and should be seen more urgently, addressing no-shows, and implementation of a tele-retina program are potential avenues for improving clinic efficiency in a resource-limited setting for vulnerable populations.

Bleeding risk in patients prescribed dual antiplatelet therapy and triple therapy after coronary interventions: the ADAPTT retrospective population-based cohort studies

Background

Bleeding among populations undergoing percutaneous coronary intervention or coronary artery bypass grafting and among conservatively managed patients with acute coronary syndrome exposed to different dual antiplatelet therapy and triple therapy (i.e. dual antiplatelet therapy plus an anticoagulant) has not been previously quantified.

Objectives

The objectives were to estimate hazard ratios for bleeding for different antiplatelet and triple therapy regimens, estimate resources and the associated costs of treating bleeding events, and to extend existing economic models of the cost-effectiveness of dual antiplatelet therapy.

Design

The study was designed as three retrospective population-based cohort studies emulating target randomised controlled trials.

Setting

The study was set in primary and secondary care in England from 2010 to 2017.

Participants

Participants were patients aged ≥ 18 years undergoing coronary artery bypass grafting or emergency percutaneous coronary intervention (for acute coronary syndrome), or conservatively managed patients with acute coronary syndrome.

Data sources

Data were sourced from linked Clinical Practice Research Datalink and Hospital Episode Statistics.

Interventions

Coronary artery bypass grafting and conservatively managed acute coronary syndrome: aspirin (reference) compared with aspirin and clopidogrel. Percutaneous coronary intervention: aspirin and clopidogrel (reference) compared with aspirin and prasugrel (ST elevation myocardial infarction only) or aspirin and ticagrelor.

Main outcome measures

Primary outcome: any bleeding events up to 12 months after the index event. Secondary outcomes: major or minor bleeding, all-cause and cardiovascular mortality, mortality from bleeding, myocardial infarction, stroke, additional coronary intervention and major adverse cardiovascular events.

Results

The incidence of any bleeding was 5% among coronary artery bypass graft patients, 10% among conservatively managed acute coronary syndrome patients and 9% among emergency percutaneous coronary intervention patients, compared with 18% among patients prescribed triple therapy. Among coronary artery bypass grafting and conservatively managed acute coronary syndrome patients, dual antiplatelet therapy, compared with aspirin, increased the hazards of any bleeding (coronary artery bypass grafting: hazard ratio 1.43, 95% confidence interval 1.21 to 1.69; conservatively-managed acute coronary syndrome: hazard ratio 1.72, 95% confidence interval 1.15 to 2.57) and major adverse cardiovascular events (coronary artery bypass grafting: hazard ratio 2.06, 95% confidence interval 1.23 to 3.46; conservatively-managed acute coronary syndrome: hazard ratio 1.57, 95% confidence interval 1.38 to 1.78). Among emergency percutaneous coronary intervention patients, dual antiplatelet therapy with ticagrelor, compared with dual antiplatelet therapy with clopidogrel, increased the hazard of any bleeding (hazard ratio 1.47, 95% confidence interval 1.19 to 1.82), but did not reduce the incidence of major adverse cardiovascular events (hazard ratio 1.06, 95% confidence interval 0.89 to 1.27). Among ST elevation myocardial infarction percutaneous coronary intervention patients, dual antiplatelet therapy with prasugrel, compared with dual antiplatelet therapy with clopidogrel, increased the hazard of any bleeding (hazard ratio 1.48, 95% confidence interval 1.02 to 2.12), but did not reduce the incidence of major adverse cardiovascular events (hazard ratio 1.10, 95% confidence interval 0.80 to 1.51). Health-care costs in the first year did not differ between dual antiplatelet therapy with clopidogrel and aspirin monotherapy among either coronary artery bypass grafting patients (mean difference £94, 95% confidence interval -£155 to £763) or conservatively managed acute coronary syndrome patients (mean difference £610, 95% confidence interval -£626 to £1516), but among emergency percutaneous coronary intervention patients were higher for those receiving dual antiplatelet therapy with ticagrelor than for those receiving dual antiplatelet therapy with clopidogrel, although for only patients on concurrent proton pump inhibitors (mean difference £1145, 95% confidence interval £269 to £2195).

Conclusions

This study suggests that more potent dual antiplatelet therapy may increase the risk of bleeding without reducing the incidence of major adverse cardiovascular events. These results should be carefully considered by clinicians and decision-makers alongside randomised controlled trial evidence when making recommendations about dual antiplatelet therapy.

Limitations

The estimates for bleeding and major adverse cardiovascular events may be biased from unmeasured confounding and the exclusion of an eligible subgroup of patients who could not be assigned an intervention. Because of these limitations, a formal cost-effectiveness analysis could not be conducted.

Future work

Future work should explore the feasibility of using other UK data sets of routinely collected data, less susceptible to bias, to estimate the benefit and harm of antiplatelet interventions.

Trial registration

This trial is registered as ISRCTN76607611.

Funding

This project was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 27, No. 8. See the NIHR Journals Library website for further project information.

Precision prognostics for the development of complications in diabetes

Individuals with diabetes face higher risks for macro- and microvascular complications than their non-diabetic counterparts. The concept of precision medicine in diabetes aims to optimise treatment decisions for individual patients to reduce the risk of major diabetic complications, including cardiovascular outcomes, retinopathy, nephropathy, neuropathy and overall mortality. In this context, prognostic models can be used to estimate an individual's risk for relevant complications based on individual risk profiles. This review aims to place the concept of prediction modelling into the context of precision prognostics. As opposed to identification of diabetes subsets, the development of prediction models, including the selection of predictors based on their longitudinal association with the outcome of interest and their discriminatory ability, allows estimation of an individual's absolute risk of complications. As a consequence, such models provide information about potential patient subgroups and their treatment needs. This review provides insight into the methodological issues specifically related to the development and validation of prediction models for diabetes complications. We summarise existing prediction models for macro- and microvascular complications, commonly included predictors, and examples of available validation studies. The review also discusses the potential of non-classical risk markers and omics-based predictors. Finally, it gives insight into the requirements and challenges related to the clinical applications and implementation of developed predictions models to optimise medical decision making.

Development and validation of predictive risk models for sight threatening diabetic retinopathy in patients with type 2 diabetes to be applied as triage tools in resource limited settings

Background

Delayed diagnosis and treatment of sight threatening diabetic retinopathy (STDR) is a common cause of visual impairment in people with Type 2 diabetes. Therefore, systematic regular retinal screening is recommended, but global coverage of such services is challenging. We aimed to develop and validate predictive models for STDR to identify 'at-risk' population for retinal screening.

Methods

Models were developed using datasets obtained from general practices in inner London, United Kingdom (UK) on adults with type 2 Diabetes during the period 2007-2017. Three models were developed using Cox regression and model performance was assessed using C statistic, calibration slope and observed to expected ratio measures. Models were externally validated in cohorts from Wales, UK and India.

Findings

A total of 40,334 people were included in the model development phase of which 1427 (3·54%) people developed STDR. Age, gender, diabetes duration, antidiabetic medication history, glycated haemoglobin (HbA1c), and history of retinopathy were included as predictors in the Model 1, Model 2 excluded retinopathy status, and Model 3 further excluded HbA1c. All three models attained strong discrimination performance in the model development dataset with C statistics ranging from 0·778 to 0·832, and in the external validation datasets (C statistic 0·685 - 0·823) with calibration slopes closer to 1 following re-calibration of the baseline survival.

Interpretation

We have developed new risk prediction equations to identify those at risk of STDR in people with type 2 diabetes in any resource-setting so that they can be screened and treated early. Future testing, and piloting is required before implementation.

Funding

This study was funded by the GCRF UKRI (MR/P207881/1) and supported by the NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology.

Interpretable machine learning-derived nomogram model for early detection of diabetic retinopathy in type 2 diabetes mellitus: a widely targeted metabolomics study

Objective

Early identification of diabetic retinopathy (DR) is key to prioritizing therapy and preventing permanent blindness. This study aims to propose a machine learning model for DR early diagnosis using metabolomics and clinical indicators.

Methods

From 2017 to 2018, 950 participants were enrolled from two affiliated hospitals of Wenzhou Medical University and Anhui Medical University. A total of 69 matched blocks including healthy volunteers, type 2 diabetes, and DR patients were obtained from a propensity score matching-based metabolomics study. UPLC-ESI-MS/MS system was utilized for serum metabolic fingerprint data. CART decision trees (DT) were used to identify the potential biomarkers. Finally, the nomogram model was developed using the multivariable conditional logistic regression models. The calibration curve, Hosmer–Lemeshow test, receiver operating characteristic curve, and decision curve analysis were applied to evaluate the performance of this predictive model.

Results

The mean age of enrolled subjects was 56.7 years with a standard deviation of 9.2, and 61.4% were males. Based on the DT model, 2-pyrrolidone completely separated healthy controls from diabetic patients, and thiamine triphosphate (ThTP) might be a principal metabolite for DR detection. The developed nomogram model (including diabetes duration, systolic blood pressure and ThTP) shows an excellent quality of classification, with AUCs (95% CI) of 0.99 (0.97–1.00) and 0.99 (0.95–1.00) in training and testing sets, respectively. Furthermore, the predictive model also has a reasonable degree of calibration.

Conclusions

The nomogram presents an accurate and favorable prediction for DR detection. Further research with larger study populations is needed to confirm our findings.

Interpretable Machine Learning for the Prediction of Amputation Risk Following Lower Extremity Infrainguinal Endovascular Interventions for Peripheral Arterial Disease

PURPOSE

Severe peripheral artery disease (PAD) may result in lower extremity amputation or require multiple procedures to achieve limb salvage. Current prediction models for major amputation risk have had limited performance at the individual level. We developed an interpretable machine learning model that will allow clinicians to identify patients at risk of amputation and optimize treatment decisions for PAD patients.

METHODS

We utilized the American College of Surgeons National Surgical Quality Improvement Program database to collect preoperative clinical and laboratory information on 14,444 patients who underwent lower extremity endovascular procedures for PAD from 2011 to 2018. Using data from 2011 to 2017 for training and data from 2018 for testing, we developed a machine learning model to predict 30 day amputation in this patient population. We present performance metrics overall and stratified by race, sex, and age. We also demonstrate model interpretability using Gini importance and SHapley Additive exPlanations.

RESULTS

A random forest machine learning model achieved an area under the receiver-operator curve (AU-ROC) of 0.81. The most important features of the model were elective surgery designation, claudication, open wound/wound infection, white blood cell count, and albumin. The model performed equally well on white and non-white patients (Delong p-value = 0.189), males and females (Delong p-value = 0.572), and patients under age 65 and patients age 65 and older (Delong p-value = 0.704).

CONCLUSION

We present a machine learning model that predicts 30 day major amputation events in PAD patients undergoing lower extremity endovascular procedures. This model can optimize clinical decision-making for patients with PAD.

Prognostic models of diabetic microvascular complications: a systematic review and meta-analysis

BACKGROUND

Many prognostic models of diabetic microvascular complications have been developed, but their performances still varies. Therefore, we conducted a systematic review and meta-analysis to summarise the performances of the existing models.

METHODS

Prognostic models of diabetic microvascular complications were retrieved from PubMed and Scopus up to 31 December 2020. Studies were selected, if they developed or internally/externally validated models of any microvascular complication in type 2 diabetes (T2D).

RESULTS

In total, 71 studies were eligible, of which 32, 30 and 18 studies initially developed prognostic model for diabetic retinopathy (DR), chronic kidney disease (CKD) and end stage renal disease (ESRD) with the number of derived equations of 84, 96 and 51, respectively. Most models were derived-phases, some were internal and external validations. Common predictors were age, sex, HbA1c, diabetic duration, SBP and BMI. Traditional statistical models (i.e. Cox and logit regression) were mostly applied, otherwise machine learning. In cohorts, the discriminative performance in derived-logit was pooled with C statistics of 0.82 (0.73‑0.92) for DR and 0.78 (0.74‑0.83) for CKD. Pooled Cox regression yielded 0.75 (0.74‑0.77), 0.78 (0.74‑0.82) and 0.87 (0.84‑0.89) for DR, CKD and ESRD, respectively. External validation performances were sufficiently pooled with 0.81 (0.78‑0.83), 0.75 (0.67‑0.84) and 0.87 (0.85‑0.88) for DR, CKD and ESRD, respectively.

CONCLUSIONS

Several prognostic models were developed, but less were externally validated. A few studies derived the models by using appropriate methods and were satisfactory reported. More external validations and impact analyses are required before applying these models in clinical practice.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42018105287.

T1DMicro: A Clinical Risk Calculator for Type 1 Diabetes Related Microvascular Complications

Development of complications in type 1 diabetes patients can be reduced by modifying risk factors. We used a cross-sectional cohort of 1646 patients diagnosed with type 1 diabetes (T1D) to develop a clinical risk score for diabetic peripheral neuropathy (DPN), autonomic neuropathy (AN), retinopathy (DR), and nephropathy (DN). Of these patients, 199 (12.1%) had DPN, 63 (3.8%) had AN, 244 (14.9%) had DR, and 88 (5.4%) had DN. We selected five variables to include in each of the four microvascular complications risk models: age, age of T1D diagnosis, duration of T1D, and average systolic blood pressure and HbA1C over the last three clinic visits. These variables were selected for their strong evidence of association with diabetic complications in the literature and because they are modifiable risk factors. We found the optimism-corrected R2 and Harrell’s C statistic were 0.39 and 0.87 for DPN, 0.24 and 0.86 for AN, 0.49 and 0.91 for DR, and 0.22 and 0.83 for DN, respectively. This tool was built to help inform patients of their current risk of microvascular complications and to motivate patients to control their HbA1c and systolic blood pressure in order to reduce their risk of these complications.

Prognostic models for predicting the risk of foot ulcer or amputation in people with type 2 diabetes: a systematic review and external validation study

AIMS/HYPOTHESIS

Approximately 25% of people with type 2 diabetes experience a foot ulcer and their risk of amputation is 10-20 times higher than that of people without type 2 diabetes. Prognostic models can aid in targeted monitoring but an overview of their performance is lacking. This study aimed to systematically review prognostic models for the risk of foot ulcer or amputation and quantify their predictive performance in an independent cohort.

METHODS

A systematic review identified studies developing prognostic models for foot ulcer or amputation over minimal 1 year follow-up applicable to people with type 2 diabetes. After data extraction and risk of bias assessment (both in duplicate), selected models were externally validated in a prospective cohort with a 5 year follow-up in terms of discrimination (C statistics) and calibration (calibration plots).

RESULTS

We identified 21 studies with 34 models predicting polyneuropathy, foot ulcer or amputation. Eleven models were validated in 7624 participants, of whom 485 developed an ulcer and 70 underwent amputation. The models for foot ulcer showed C statistics (95% CI) ranging from 0.54 (0.54, 0.54) to 0.81 (0.75, 0.86) and models for amputation showed C statistics (95% CI) ranging from 0.63 (0.55, 0.71) to 0.86 (0.78, 0.94). Most models underestimated the ulcer or amputation risk in the highest risk quintiles. Three models performed well to predict a combined endpoint of amputation and foot ulcer (C statistics >0.75).

CONCLUSIONS/INTERPRETATION

Thirty-four prognostic models for the risk of foot ulcer or amputation were identified. Although the performance of the models varied considerably, three models performed well to predict foot ulcer or amputation and may be applicable to clinical practice.

Associations between attainment of incentivized primary care indicators and incident lower limb amputation among those with type 2 diabetes: a population-based historical cohort study

INTRODUCTION

England has invested considerably in diabetes care through such programs as the Quality and Outcomes Framework (QOF) and National Diabetes Audit (NDA). Associations between program indicators and clinical endpoints, such as amputation, remain unclear. We examined associations between primary care indicators and incident lower limb amputation.

RESEARCH DESIGN AND METHODS

This population-based retrospective cohort study, spanning 2010-2017, was comprised of adults in England with type 2 diabetes and no history of lower limb amputation. Exposures at baseline (2010-2011) were attainment of QOF glycated hemoglobin (HbA1c), blood pressure and total cholesterol indicators, and number of NDA processes completed. Propensity score matching was performed and multivariable Cox proportional hazards models, adjusting for disease-related, comorbidity, lifestyle, and sociodemographic factors, were fitted using matched samples for each exposure.

RESULTS

83 688 individuals from 330 English primary care practices were included. Mean follow-up was 3.9 (SD 2.0) years, and 521 (0.6%) minor or major amputations were observed (1.62 per 1000 person-years). HbA1c and cholesterol indicator attainment were associated with considerably lower risks of minor or major amputation (adjusted HRs; 95% CIs) 0.61 (0.49 to 0.74; p<0.0001) and 0.67 (0.53 to 0.86; p=0.0017), respectively). No evidence of association between blood pressure indicator attainment and amputation was observed (adjusted HR 0.88 (0.73 to 1.06; p=0.1891)). Substantially lower amputation rates were observed among those completing a greater number of NDA care processes (adjusted HRs 0.45 (0.24 to 0.83; p=0.0106), 0.67 (0.47 to 0.97; p=0.0319), and 0.38 (0.20 to 0.70; p=0.0022) for comparisons of 4-6 vs 0-3, 7-9 vs 0-3, and 7-9 vs 4-6 processes, respectively). Results for major-only amputations were similar for HbA1c and blood pressure, though cholesterol indicator attainment was non-significant.

CONCLUSIONS

Comprehensive primary care-based secondary prevention may offer considerable protection against diabetes-related amputation. This has important implications for diabetes management and medical decision-making for patients, as well as type 2 diabetes quality improvement programs.

Sodium-glucose cotransporter protein-2 (SGLT-2) inhibitors and glucagon-like peptide-1 (GLP-1) receptor agonists for type 2 diabetes: systematic review and network meta-analysis of randomised controlled trials

OBJECTIVE

To evaluate sodium-glucose cotransporter-2 (SGLT-2) inhibitors and glucagon-like peptide-1 (GLP-1) receptor agonists in patients with type 2 diabetes at varying cardiovascular and renal risk.

DESIGN

Network meta-analysis.

DATA SOURCES

Medline, Embase, and Cochrane CENTRAL up to 11 August 2020.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

Randomised controlled trials comparing SGLT-2 inhibitors or GLP-1 receptor agonists with placebo, standard care, or other glucose lowering treatment in adults with type 2 diabetes with follow up of 24 weeks or longer. Studies were screened independently by two reviewers for eligibility, extracted data, and assessed risk of bias.

MAIN OUTCOME MEASURES

Frequentist random effects network meta-analysis was carried out and GRADE (grading of recommendations assessment, development, and evaluation) used to assess evidence certainty. Results included estimated absolute effects of treatment per 1000 patients treated for five years for patients at very low risk (no cardiovascular risk factors), low risk (three or more cardiovascular risk factors), moderate risk (cardiovascular disease), high risk (chronic kidney disease), and very high risk (cardiovascular disease and kidney disease). A guideline panel provided oversight of the systematic review.

RESULTS

764 trials including 421 346 patients proved eligible. All results refer to the addition of SGLT-2 inhibitors and GLP-1 receptor agonists to existing diabetes treatment. Both classes of drugs lowered all cause mortality, cardiovascular mortality, non-fatal myocardial infarction, and kidney failure (high certainty evidence). Notable differences were found between the two agents: SGLT-2 inhibitors reduced admission to hospital for heart failure more than GLP-1 receptor agonists, and GLP-1 receptor agonists reduced non-fatal stroke more than SGLT-2 inhibitors (which appeared to have no effect). SGLT-2 inhibitors caused genital infection (high certainty), whereas GLP-1 receptor agonists might cause severe gastrointestinal events (low certainty). Low certainty evidence suggested that SGLT-2 inhibitors and GLP-1 receptor agonists might lower body weight. Little or no evidence was found for the effect of SGLT-2 inhibitors or GLP-1 receptor agonists on limb amputation, blindness, eye disease, neuropathic pain, or health related quality of life. The absolute benefits of these drugs vary substantially across patients from low to very high risk of cardiovascular and renal outcomes (eg, SGLT-2 inhibitors resulted in 3 to 40 fewer deaths in 1000 patients over five years; see interactive decision support tool (https://magicevidence.org/match-it/200820dist/#!/) for all outcomes.

CONCLUSIONS

In patients with type 2 diabetes, SGLT-2 inhibitors and GLP-1 receptor agonists reduced cardiovascular and renal outcomes, with some differences in benefits and harms. Absolute benefits are determined by individual risk profiles of patients, with clear implications for clinical practice, as reflected in the BMJ Rapid Recommendations directly informed by this systematic review.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42019153180.

Predictors for diabetic retinopathy progression-findings from nominal group technique and Evidence review

Objectives

Risk stratification is needed for patients referred to hospital eye services by Diabetic Eye Screening Programme UK. This requires a set of candidate predictors. The literature contains a large number of predictors. The objective of this research was to arrive at a small set of clinically important predictors for the outcome of the progression of diabetic retinopathy (DR). They need to be evidence based and readily available during the clinical consultation.

Methods and analysis

Initial list of predictors was obtained from a systematic review of prediction models. We sought the clinical expert opinion using a formal qualitative study design. A series of nominal group technique meetings to shorten the list and to rank the predictors for importance by voting were held with National Health Service hospital-based clinicians involved in caring for patients with DR in the UK. We then evaluated the evidence base for the selected predictors by critically appraising the evidence.

Results

The source list was presented at nominal group meetings (n=4), attended by 44 clinicians. Twenty-five predictors from the original list were ranked as important predictors and eight new predictors were proposed. Two additional predictors were retained after evidence check. Of these 35, 21 had robust supporting evidence in the literature condensed into a set of 19 predictors by categorising DR.

Conclusion

We identified a set of 19 clinically meaningful predictors of DR progression that can help stratify higher-risk patients referred to hospital eye services and should be considered in the development of an individual risk stratification model.

Study design

A qualitative study and evidence review.

Setting

Secondary eye care centres in North East, Midlands and South of England.

Approaches for combining primary care electronic health record data from multiple sources: a systematic review of observational studies

OBJECTIVE

To identify observational studies which used data from more than one primary care electronic health record (EHR) database, and summarise key characteristics including: objective and rationale for using multiple data sources; methods used to manage, analyse and (where applicable) combine data; and approaches used to assess and report heterogeneity between data sources.

DESIGN

A systematic review of published studies.

DATA SOURCES

Pubmed and Embase databases were searched using list of named primary care EHR databases; supplementary hand searches of reference list of studies were retained after initial screening.

STUDY SELECTION

Observational studies published between January 2000 and May 2018 were selected, which included at least two different primary care EHR databases.

RESULTS

6054 studies were identified from database and hand searches, and 109 were included in the final review, the majority published between 2014 and 2018. Included studies used 38 different primary care EHR data sources. Forty-seven studies (44%) were descriptive or methodological. Of 62 analytical studies, 22 (36%) presented separate results from each database, with no attempt to combine them; 29 (48%) combined individual patient data in a one-stage meta-analysis and 21 (34%) combined estimates from each database using two-stage meta-analysis. Discussion and exploration of heterogeneity was inconsistent across studies.

CONCLUSIONS

Comparing patterns and trends in different populations, or in different primary care EHR databases from the same populations, is important and a common objective for multi-database studies. When combining results from several databases using meta-analysis, provision of separate results from each database is helpful for interpretation. We found that these were often missing, particularly for studies using one-stage approaches, which also often lacked details of any statistical adjustment for heterogeneity and/or clustering. For two-stage meta-analysis, a clear rationale should be provided for choice of fixed effect and/or random effects or other models.

Prediction models for development of retinopathy in people with type 2 diabetes: systematic review and external validation in a Dutch primary care setting

AIMS/HYPOTHESIS

The aims of this study were to identify all published prognostic models predicting retinopathy risk applicable to people with type 2 diabetes, to assess their quality and accuracy, and to validate their predictive accuracy in a head-to-head comparison using an independent type 2 diabetes cohort.

METHODS

A systematic search was performed in PubMed and Embase in December 2019. Studies that met the following criteria were included: (1) the model was applicable in type 2 diabetes; (2) the outcome was retinopathy; and (3) follow-up was more than 1 year. Screening, data extraction (using the checklist for critical appraisal and data extraction for systemic reviews of prediction modelling studies [CHARMS]) and risk of bias assessment (by prediction model risk of bias assessment tool [PROBAST]) were performed independently by two reviewers. Selected models were externally validated in the large Hoorn Diabetes Care System (DCS) cohort in the Netherlands. Retinopathy risk was calculated using baseline data and compared with retinopathy incidence over 5 years. Calibration after intercept adjustment and discrimination (Harrell's C statistic) were assessed.

RESULTS

Twelve studies were included in the systematic review, reporting on 16 models. Outcomes ranged from referable retinopathy to blindness. Discrimination was reported in seven studies with C statistics ranging from 0.55 (95% CI 0.54, 0.56) to 0.84 (95% CI 0.78, 0.88). Five studies reported on calibration. Eight models could be compared head-to-head in the DCS cohort (N = 10,715). Most of the models underestimated retinopathy risk. Validating the models against different severities of retinopathy, C statistics ranged from 0.51 (95% CI 0.49, 0.53) to 0.89 (95% CI 0.88, 0.91).

CONCLUSIONS/INTERPRETATION

Several prognostic models can accurately predict retinopathy risk in a population-based type 2 diabetes cohort. Most of the models include easy-to-measure predictors enhancing their applicability. Tailoring retinopathy screening frequency based on accurate risk predictions may increase the efficiency and cost-effectiveness of diabetic retinopathy care.

REGISTRATION

PROSPERO registration ID CRD42018089122.

Association between glucose variation and lower extremity amputation incidence in individuals with type 2 diabetes: a nationwide retrospective cohort study

AIMS/HYPOTHESIS

Elevated glucose level is one of the risk factors for lower extremity amputation (LEA), but whether glycaemic variability confers independent risks of LEA remains to be elucidated. This study aimed to investigate the association between visit-to-visit glycaemic variability and minor and major LEA risks during 8 years of follow-up in type 2 diabetic individuals aged 50 years and older.

METHODS

This retrospective cohort study included 27,574 ethnic Chinese type 2 diabetic individuals aged ≥50 years from the National Diabetes Care Management Program in Taiwan. Glycaemic variability measures were presented as the CVs of fasting plasma glucose (FPG-CV) and of HbA_1c (A_1c-CV). The effect of glycaemic variability on the incidence of LEA events was analysed using Cox proportional hazards models.

RESULTS

After a median follow-up of 8.9 years, 541 incident cases of LEA with a crude incidence density rate of 2.4 per 1000 person-years were observed. After multivariate adjustment, FPG-CV and A_1c-CV were found to be significantly associated with minor LEA, with corresponding HRs of 1.53 (95% CI 1.15, 2.04) and 1.34 (95% CI 1.02, 1.77) for the third tertiles of FPG-CV and A_1c-CV, respectively. In addition, these associations were stronger amongst older adults with longer diabetes duration (≥3 years) than amongst those with shorter duration (<3 years) (p_interaction < 0.01).

CONCLUSIONS/INTERPRETATION

Our study suggests that visit-to-visit variations in HbA_1c and FPG are important predictors of minor LEA amongst older adults with type 2 diabetes, particularly for those with more than 3 years of diabetes duration.

Innovations in personalised diabetes care and risk management

Type 2 diabetes is associated with an increased risk of developing macro and microvascular complications. Nevertheless, there is substantial heterogeneity between people with type 2 diabetes in their risk of developing such complications. Personalised medicine for people with type 2 diabetes may aid in efficient and tailored diabetes care for those at increased risk of developing such complications. Recently, progress has been made in the development of personalised diabetes care in several areas. Particularly for the risk prediction of cardiovascular disease, retinopathy and nephropathy, innovative methods have been developed for prediction and tailored monitoring or treatment to prevent such complications. For other complications or subpopulations of people with type 2 diabetes, such as the frail elderly, efforts are currently ongoing to develop such methods. In this review, we discuss the recent developments in innovations of personalised diabetes care for different complications and subpopulations of people with type 2 diabetes, their performance and modes of application in clinical practice.

Disparities in glycaemic control, monitoring, and treatment of type 2 diabetes in England: A retrospective cohort analysis

BACKGROUND

Disparities in type 2 diabetes (T2D) care provision and clinical outcomes have been reported in the last 2 decades in the UK. Since then, a number of initiatives have attempted to address this imbalance. The aim was to evaluate contemporary data as to whether disparities exist in glycaemic control, monitoring, and prescribing in people with T2D.

METHODS AND FINDINGS

A T2D cohort was identified from the Royal College of General Practitioners Research and Surveillance Centre dataset: a nationally representative sample of 164 primary care practices (general practices) across England. Diabetes healthcare provision and glucose-lowering medication use between 1 January 2012 and 31 December 2016 were studied. Healthcare provision included annual HbA1c, renal function (estimated glomerular filtration rate [eGFR]), blood pressure (BP), retinopathy, and neuropathy testing. Variables potentially associated with disparity outcomes were assessed using mixed effects logistic and linear regression, adjusted for age, sex, ethnicity, and socioeconomic status (SES) using the Index of Multiple Deprivation (IMD), and nested using random effects within general practices. Ethnicity was defined using the Office for National Statistics ethnicity categories: White, Mixed, Asian, Black, and Other (including Arab people and other groups not classified elsewhere). From the primary care adult population (n = 1,238,909), we identified a cohort of 84,452 (5.29%) adults with T2D. The mean age of people with T2D in the included cohort at 31 December 2016 was 68.7 ± 12.6 years; 21,656 (43.9%) were female. The mean body mass index was 30.7 ± SD 6.4 kg/m2. The most deprived groups (IMD quintiles 1 and 2) showed poorer HbA1c than the least deprived (IMD quintile 5). People of Black ethnicity had worse HbA1c than those of White ethnicity. Asian individuals were less likely than White individuals to be prescribed insulin (odds ratio [OR] 0.86, 95% CI 0.79-0.95; p < 0.01), sodium-glucose cotransporter-2 (SGLT2) inhibitors (OR 0.68, 95% CI 0.58-0.79; p < 0.001), and glucagon-like peptide-1 (GLP-1) agonists (OR 0.37, 95% CI 0.31-0.44; p < 0.001). Black individuals were less likely than White individuals to be prescribed SGLT2 inhibitors (OR 0.50, 95% CI 0.39-0.65; p < 0.001) and GLP-1 agonists (OR 0.45, 95% CI 0.35-0.57; p < 0.001). Individuals in IMD quintile 5 were more likely than those in the other IMD quintiles to have annual testing for HbA1c, BP, eGFR, retinopathy, and neuropathy. Black individuals were less likely than White individuals to have annual testing for HbA1c (OR 0.89, 95% CI 0.79-0.99; p = 0.04) and retinopathy (OR 0.82, 95% CI 0.70-0.96; p = 0.011). Asian individuals were more likely than White individuals to have monitoring for HbA1c (OR 1.10, 95% CI 1.01-1.20; p = 0.023) and eGFR (OR 1.09, 95% CI 1.00-1.19; p = 0.048), but less likely for retinopathy (OR 0.88, 95% CI 0.79-0.97; p = 0.01) and neuropathy (OR 0.88, 95% CI 0.80-0.97; p = 0.01). The study is limited by the nature of being observational and defined using retrospectively collected data. Disparities in diabetes care may show regional variation, which was not part of this evaluation.

CONCLUSIONS

Our findings suggest that disparity in glycaemic control, diabetes-related monitoring, and prescription of newer therapies remains a challenge in diabetes care. Both SES and ethnicity were important determinants of inequality. Disparities in glycaemic control and other areas of care may lead to higher rates of complications and adverse outcomes for some groups.

Limitations for health research with restricted data collection from UK primary care

Purpose

UK primary care provides a rich data source for research. The impact of proposed data collection restrictions is unknown. This study aimed to assess the impact of restricting the scope of electronic health record (EHR) data collection on the ability to conduct research. The study estimated the consequences of restricted data collection on published Clinical Practice Research Datalink studies from high impact journals or referenced in clinical guidelines.

Methods

A structured form was used to systematically analyse the extent to which individual studies would have been possible using a database with data collection restrictions in place: (1) retrospective collection of specified diseases only; (2) retrospective collection restricted to a 6‐ or 12‐year period; (3) prospective and retrospective collection restricted to non‐sensitive data. Outcomes were categorised as unfeasible (not reproducible without major bias); compromised (feasible with design modification); or unaffected.

Results

Overall, 91% studies were compromised with all restrictions in place; 56% studies were unfeasible even with design modification. With restrictions on diseases alone, 74% studies were compromised; 51% were unfeasible. Restricting collection to 6/12 years had a major impact, with 67 and 22% of studies compromised, respectively. Restricting collection of sensitive data had a lesser but marked impact with 10% studies compromised.

Conclusion

EHR data collection restrictions can profoundly reduce the capacity for public health research that underpins evidence‐based medicine and clinical guidance. National initiatives seeking to collect EHRs should consider the implications of restricting data collection on the ability to address vital public health questions.

Incidence and Risk Factors for Blindness in Adults With Diabetes: The Israeli National Diabetes Registry (INDR)

PURPOSE

To estimate the 3-year incidence of blindness among diabetes patients aged ≥18 years; to compare blindness incidence rates of persons with and without diabetes; and to investigate risk factors associated with diabetic retinopathy (DR), age-related macular degeneration (ARMD), glaucoma, and cataract-related blindness.

DESIGN

Cohort study.

METHODS

The Israeli National Diabetes Registry for 2012 was cross-linked with the database of blindness certifications obtained from the National Registry of the Blind. Blindness was defined as the receipt of an official certificate of blindness (a visual acuity of 3/60 or worse, or a visual field loss of <20 degrees in the better eye.) Incidence rates of blindness, overall and by main cause of blindness, were calculated for the years 2013-2015. Standardized morbidity ratios (SMRs) for 2013 were calculated, using the nondiabetic population as a reference. A multinomial logistic model was used to identify covariates associated with the incidence of blindness by main cause of blindness.

RESULTS

The 3-year incidence rates were 31.0 and 8.4 per 10 000 for overall and DR-related blindness, respectively. The SMR for overall blindness in people with diabetes was significantly higher than in the general nondiabetic population (1.39; 95% confidence interval: 1.27-1.53); however, the SMRs for ARMD, glaucoma, and cataract were not statistically significant. Poor metabolic control, insulin treatment, long diabetes duration, and chronic kidney disease were associated with DR-related blindness. Low socioeconomic status (SES) was associated with both cataract and DR-related blindness.

CONCLUSIONS

Optimum metabolic control of diabetes is important for prevention of DR-related blindness. SES-related disparities in blindness risk should be explored and reduced by directing efforts to provide appropriate treatment for all diabetic patients in order to prevent unnecessary blindness.

Prognostic prediction models for diabetic retinopathy progression: a systematic review

With the increasing incidence of diabetic retinopathy and its improved detection, there is increased demand for diabetic retinopathy treatment services. Prognostic prediction models have been used to optimise services but these were intended for early detection of sight-threatening retinopathy and are mostly used in diabetic retinopathy screening services. We wanted to look into the predictive ability and applicability of the existing models for the higher-risk patients referred into hospitals. We searched MEDLINE, EMBASE, COCHRANE CENTRAL, conference abstracts and reference lists of included publications for studies of any design using search terms related to diabetes, diabetic retinopathy and prognostic models. Search results were screened for relevance to the review question. Included studies had data extracted on model characteristics, predictive ability and validation. They were assessed for quality using criteria specified by PROBAST and CHARMS checklists, independently by two reviewers. Twenty-two articles reporting on 14 prognostic models (including four updates) met the selection criteria. Eleven models had internal validation, eight had external validation and one had neither. Discriminative ability with c-statistics ranged from 0.57 to 0.91. Studies ranged from low to high risk of bias, mostly due to the need for external validation or missing data. Participants, outcomes, predictors handling and modelling methods varied. Most models focussed on lower-risk patients, the majority had high risk of bias and doubtful applicability, but three models had some applicability for higher-risk patients. However, these models will also need updating and external validation in multiple hospital settings before being implemented into clinical practice.

Development and validation of prediction models to estimate risk of primary total hip and knee replacements using data from the UK: two prospective open cohorts using the UK Clinical Practice Research Datalink

Objectives

The ability to efficiently and accurately predict future risk of primary total hip and knee replacement (THR/TKR) in earlier stages of osteoarthritis (OA) has potentially important applications. We aimed to develop and validate two models to estimate an individual’s risk of primary THR and TKR in patients newly presenting to primary care.

Methods

We identified two cohorts of patients aged ≥40 years newly consulting hip pain/OA and knee pain/OA in the Clinical Practice Research Datalink. Candidate predictors were identified by systematic review, novel hypothesis-free ‘Record-Wide Association Study’ with replication, and panel consensus. Cox proportional hazards models accounting for competing risk of death were applied to derive risk algorithms for THR and TKR. Internal–external cross-validation (IECV) was then applied over geographical regions to validate two models.

Results

45 predictors for THR and 53 for TKR were identified, reviewed and selected by the panel. 301 052 and 416 030 patients newly consulting between 1992 and 2015 were identified in the hip and knee cohorts, respectively (median follow-up 6 years). The resultant model C-statistics is 0.73 (0.72, 0.73) and 0.79 (0.78, 0.79) for THR (with 20 predictors) and TKR model (with 24 predictors), respectively. The IECV C-statistics ranged between 0.70–0.74 (THR model) and 0.76–0.82 (TKR model); the IECV calibration slope ranged between 0.93–1.07 (THR model) and 0.92–1.12 (TKR model).

Conclusions

Two prediction models with good discrimination and calibration that estimate individuals’ risk of THR and TKR have been developed and validated in large-scale, nationally representative data, and are readily automated in electronic patient records.

Type 1 diabetes

Type 1 diabetes is a chronic autoimmune disease characterised by insulin deficiency and resultant hyperglycaemia. Knowledge of type 1 diabetes has rapidly increased over the past 25 years, resulting in a broad understanding about many aspects of the disease, including its genetics, epidemiology, immune and β-cell phenotypes, and disease burden. Interventions to preserve β cells have been tested, and several methods to improve clinical disease management have been assessed. However, wide gaps still exist in our understanding of type 1 diabetes and our ability to standardise clinical care and decrease disease-associated complications and burden. This Seminar gives an overview of the current understanding of the disease and potential future directions for research and care.

Influence of micro- and macro-vascular disease and Tumor Necrosis Factor Receptor 1 on the level of lower-extremity amputation in patients with type 2 diabetes

BACKGROUND

Patients with type 2 diabetes (T2D) face a high amputation rate. We investigated the relationship between the level of amputation and the presence of micro or macro-vascular disease and related circulating biomarkers, Tumor Necrosis Factor Receptor 1 (TNFR1) and Angiopoietin like-2 protein (ANGPTL2).

METHODS

We have analyzed data from 1468 T2D participants in a single center prospective cohort (the SURDIAGENE cohort). Our outcome was the occurrence of lower limb amputation categorized in minor (below-ankle) or major (above ankle) amputation. Microvascular disease was defined as a history of albuminuria [microalbuminuria: uACR (urinary albumine-to-creatinine ratio) 30-299 mg/g or macroalbuminuria: uACR ≥ 300 mg/g] and/or severe diabetic retinopathy or macular edema. Macrovascular disease at baseline was divided into peripheral arterial disease (PAD): peripheral artery revascularization and/or major amputation and in non-peripheral macrovascular disease: coronary artery revascularization, myocardial infarction, carotid artery revascularization, stroke. We used a proportional hazard model considering survival without minor or major amputation.

RESULTS

During a median follow-up period of 7 (0.5) years, 79 patients (5.5%) underwent amputation including 29 minor and 50 major amputations. History of PAD (HR 4.37 95% CI [2.11-9.07]; p < 0.001), severe diabetic retinopathy (2.69 [1.31-5.57]; p = 0.0073), male gender (10.12 [2.41-42.56]; p = 0.0016) and serum ANGPTL2 concentrations (1.25 [1.08-1.45]; p = 0.0025) were associated with minor amputation outcome. History of PAD (6.91 [3.75-12.72]; p < 0.0001), systolic blood pressure (1.02 [1.00-1.03]; p = 0.004), male gender (3.81 [1.67-8.71]; p = 0.002), and serum TNFR1 concentrations (HR 13.68 [5.57-33.59]; p < 0.0001) were associated with major amputation outcome. Urinary albumin excretion was not significantly associated with the risk of minor and major amputation.

CONCLUSIONS

This study suggests that the risk factors associated with the minor vs. major amputation including biomarkers such as TNFR1 should be considered differently in patients with T2D.

Frequency of reporting on patient and public involvement (PPI) in research studies published in a general medical journal: a descriptive study

OBJECTIVES

While documented plans for patient and public involvement (PPI) in research are required in many grant applications, little is known about how frequently PPI occurs in practice. Low levels of reported PPI may mask actual activity due to limited PPI reporting requirements. This research analysed the frequency and types of reported PPI in the presence and absence of a journal requirement to include this information.

DESIGN AND SETTING

A before and after comparison of PPI reported in research papers published in The BMJ before and 1 year after the introduction of a journal policy requiring authors to report if and how they involved patients and the public within their papers.

RESULTS

Between 1 June 2013 and 31 May 2014, The BMJ published 189 research papers and 1 (0.5%) reported PPI activity. From 1 June 2015 to 31 May 2016, following the introduction of the policy, The BMJ published 152 research papers of which 16 (11%) reported PPI activity. Patients contributed to grant applications in addition to designing studies through to coauthorship and participation in study dissemination. Patient contributors were often not fully acknowledged; 6 of 17 (35%) papers acknowledged their contributions and 2 (12%) included them as coauthors.

CONCLUSIONS

Infrequent reporting of PPI activity does not appear to be purely due to a failure of documentation. Reporting of PPI activity increased after the introduction of The BMJ's policy, but activity both before and after was low and reporting was inconsistent in quality. Journals, funders and research institutions should collaborate to move us from the current situation where PPI is an optional extra to one where PPI is fully embedded in practice throughout the research process.

Individualised variable-interval risk-based screening for sight-threatening diabetic retinopathy: the Liverpool Risk Calculation Engine

AIMS/HYPOTHESIS

Individualised variable-interval risk-based screening offers better targeting and improved cost-effectiveness in screening for diabetic retinopathy. We developed a generalisable risk calculation engine (RCE) to assign personalised intervals linked to local population characteristics, and explored differences in assignment compared with current practice.

METHODS

Data from 5 years of photographic screening and primary care for people with diabetes, screen negative at the first of > 1 episode, were combined in a purpose-built near-real-time warehouse. Covariates were selected from a dataset created using mixed qualitative/quantitative methods. Markov modelling predicted progression to screen-positive (referable diabetic retinopathy) against the local cohort history. Retinopathy grade informed baseline risk and multiple imputation dealt with missing data. Acceptable intervals (6, 12, 24 months) and risk threshold (2.5%) were established with patients and professional end users.

RESULTS

Data were from 11,806 people with diabetes (46,525 episodes, 388 screen-positive). Covariates with sufficient predictive value were: duration of known disease, HbA1c, age, systolic BP and total cholesterol. Corrected AUC (95% CIs) were: 6 months 0.88 (0.83, 0.93), 12 months 0.90 (0.87, 0.93) and 24 months 0.91 (0.87, 0.94). Sensitivities/specificities for a 2.5% risk were: 6 months 0.61, 0.93, 12 months 0.67, 0.90 and 24 months 0.82, 0.81. Implementing individualised RCE-based intervals would reduce the proportion of people becoming screen-positive before the allocated screening date by > 50% and the number of episodes by 30%.

CONCLUSIONS/INTERPRETATION

The Liverpool RCE shows sufficient performance for a local introduction into practice before wider implementation, subject to external validation. This approach offers potential enhancements of screening in improved local applicability, targeting and cost-effectiveness.

Patient perspectives on peripheral neuropathic pain experience within the community

Purpose of this cross-sectional study was to explore the relationship between neuropathic physical complaints (NPC) and quality-of-life (QoL) in community-dwelling patients with diabetes in Broward County, Florida. Adult patients were invited to complete a 10-minute paper questionnaire at a community hospital between October 2014-April 2016. Analysis of data from 124 participants (60 with distal symmetric polyneuropathy (DSPN) diagnosis versus 64 without DSPN diagnosis) with NPC, showed those with DSPN had a longer duration of diabetes, suffered a higher number of NPC, and had a lower QoL (all p≤0.001) with more impediments to performing daily activities. While differences in pain severity and QoL were present in patients with DSPN versus those without DSPN diagnosis, NPC were still reported by those without DSPN diagnosis. Healthcare providers are encouraged to identify possible NPC during earlier stages of glycemic dysregulation and address foot care issues promptly to mitigate the disease's effect on QoL.

Establishment and Validation of a Prediction Equation to Estimate Risk of Intraoperative Hypothermia in Patients Receiving General Anesthesia

Inadvertent intraoperative hypothermia (core temperature <36 °C) is a frequent but preventable complication of general anesthesia. Accurate risk assessment of individual patients may help physicians identify patients at risk for hypothermia and apply preventive approaches, which include active intraoperative warming. This study aimed to develop and validate a risk-prediction model for intraoperative hypothermia. Two independent observational studies in China, the Beijing Regional Survey and the China National Survey, were conducted in 2013 and 2014, respectively, to determine the incidence of hypothermia and its underlying risk factors. In this study, using data from these two studies, we first derived a risk calculation equation, estimating the predictive risk of hypothermia using National Survey data (3132 patients), then validated the equation using the Beijing Regional Survey data (830 patients). Measures of accuracy, discrimination and calibration were calculated in the validation data set. Through validation, this model, named Predictors Score, had sound overall accuracy (Brier Score = 0.211), good discrimination (C-Statistic = 0.759) and excellent calibration (Hosmer-Lemeshow, P = 0.5611). We conclude that the Predictors Score is a valid predictor of the risk of operative hypothermia and can be used in deciding whether intraoperative warming is a cost-effective measure in preventing the hypothermia.

Verification of Decision-Analytic Models for Health Economic Evaluations: An Overview

Decision-analytic models for cost-effectiveness analysis are developed in a variety of software packages where the accuracy of the computer code is seldom verified. Although modeling guidelines recommend using state-of-the-art quality assurance and control methods for software engineering to verify models, the fields of pharmacoeconomics and health technology assessment (HTA) have yet to establish and adopt guidance on how to verify health and economic models. The objective of this paper is to introduce to our field the variety of methods the software engineering field uses to verify that software performs as expected. We identify how many of these methods can be incorporated in the development process of decision-analytic models in order to reduce errors and increase transparency. Given the breadth of methods used in software engineering, we recommend a more in-depth initiative to be undertaken (e.g., by an ISPOR-SMDM Task Force) to define the best practices for model verification in our field and to accelerate adoption. Establishing a general guidance for verifying models will benefit the pharmacoeconomics and HTA communities by increasing accuracy of computer programming, transparency, accessibility, sharing, understandability, and trust of models.

Development and validation of a screening instrument to identify cardiometabolic predictors of mortality in older individuals with cancer: Secondary analysis of the Australian Longitudinal Study of Ageing (ALSA)

OBJECTIVE

The objective of this study was to identify significant cardiometabolic predictors of mortality among older cancer survivors and develop and validate a screening instrument to assess individual risk of mortality.

MATERIALS AND METHODS

Retrospective cohort study used collected data from the ALSA. Cox proportional hazards model was used to derive the risk equation for mortality that could be evaluated at 10years. Measures of discrimination and calibration were calculated in the validation cohort.

RESULTS

The equation was developed using 294 cancer survivors and validated in 127 different cancer survivors. Significant cardiometabolic predictors of mortality included in the final model are age, sex, history of cerebrovascular disease, non-adherence to exercise guidelines (150min moderate activity per week), and smoking. Discrimination and calibration were acceptable with minimal differences in C statistics (0.0442, 95% CI: -0.0149 to 0.103) and adjusted R2 values (0.0407, 95% CI: -0.181 to 0.0998) between the development and validation cohorts, respectively.

CONCLUSION

We have developed and validated the first screening tool to predict cardiometabolic risk of mortality in older cancer survivors and defined centile values for risk classification. Further validation and research on the usability and usefulness of the tool in clinical practice are recommended in order to target cancer survivors for interventions. Cost effectiveness of such an approach should also be examined.

Real-world evidence studies into treatment adherence, thresholds for intervention and disparities in treatment in people with type 2 diabetes in the UK

PURPOSE

The University of Surrey-Lilly Real World Evidence (RWE) diabetes cohort has been established to provide insights into the management of type 2 diabetes mellitus (T2DM). There are 3 areas of study due to be conducted to provide insights into T2DM management: exploration of medication adherence, thresholds for changing diabetes therapies, and ethnicity-related or socioeconomic-related disparities in management. This paper describes the identification of a cohort of people with T2DM which will be used for these analyses, through a case finding algorithm, and describes the characteristics of the identified cohort.

PARTICIPANTS

A cohort of people with T2DM was identified from the Royal College of General Practitioners Research and Surveillance Centre (RCGP RSC) data set. This data set comprises electronic patient records collected from a nationally distributed sample of 130 primary care practices across England with scope to increase the number of practices to 200.

FINDINGS TO DATE

A cohort (N=58 717) of adults with T2DM was identified from the RCGP RSC population (N=1 260 761), a crude prevalence of diabetes of 5.8% in the adult population. High data quality within the practice network and an ontological approach to classification resulted in a high level of data completeness in the T2DM cohort; ethnicity identification (82.1%), smoking status (99.3%), alcohol use (93.3%), glycated haemoglobin (HbA1c; 97.9%), body mass index (98.0%), blood pressure (99.4%), cholesterol (87.4%) and renal function (97.8%). Data completeness compares favourably to other, similarly large, observational cohorts. The cohort comprises a distribution of ages, socioeconomic and ethnic backgrounds, diabetes complications, and comorbidities, enabling the planned analyses.

FUTURE PLANS

Regular data uploads from the RCGP RSC practice network will enable this cohort to be followed prospectively. We will investigate medication adherence, explore thresholds and triggers for changing diabetes therapies, and investigate any ethnicity-related or socioeconomic-related disparities in diabetes management.

PERS&O (PERsistent Sitagliptin treatment & Outcomes): observational retrospective study on cardiovascular risk evolution in patients with type 2 diabetes on persistent sitagliptin treatment

OBJECTIVES

The UK Prospective Diabetes Study (UKPDS) Risk Engine (RE) provides the best risk estimates available for people with type 2 diabetes (T2D), so it was applied to patients on persistent sitagliptin treatment.

DESIGN

A 'real-world' retrospective, observational, single-center study.

SETTING

The study was performed in a general hospital in Northern Italy in order: (1) to validate UKPDS RE in a cohort of Italian participants with T2D without prespecified diabetes duration, with/without cardiovascular (CV) disease, treated with sitagliptin; (2) to confirm CV risk gender difference; (3) to evaluate the effect on metabolic control and on CV risk evolution obtained by 'add-on' persistent sitagliptin treatment.

PARTICIPANTS

Sitagliptin 100 mg once a day was taken by 462 participants with T2D: 170 of them (males: 106; age: 63.6±8.8; T2D duration: 11.58±7.33; females: 64; age: 65.6±7.95; T2D duration 13.5±7.9) were treated for 48 months with the same dosage.

INTERVENTIONS

An analysis of normality was performed both for continuous, and for groups variables on UKPDS RE percentage values, defining the requirement of a base log10 transformation to normalize risk factor values for analysis validation.

RESULTS

The evaluation of CV risk evolution by gender (t-test) confirmed the expected statistical difference (p<0.0001). Sitagliptin obtained significant results after 12 months, and at the end of the observation, both on metabolic control (expressed by glycated hemoglobin) and on UKPDS RE. Analysis of variance test revealed a significant effect on CV risk after 12 months (p=0.003), and after 48 months (p=0.04). A bivariate correlation analysis revealed a correlation index (r)=0.2 between the two variables (p<0.05).

CONCLUSIONS

These 'real-world' data obtained applying UKPDS RE may reflect patients' and clinicians' interest in realizing individual CV risk, and its evolution. Sitagliptin-persistent treatment for a medium-long period obtained an improvement on metabolic control, as well as a reduction on CV risk.