Diabetes treatments and risk of amputation, blindness, severe kidney failure, hyperglycaemia, and hypoglycaemia: open cohort study in primary care

Objective: To assess the risks of amputation, blindness, severe kidney failure, hyperglycaemia, and hypoglycaemia in patients with type 2 diabetes associated with prescribed diabetes drugs, particularly newer agents including gliptins or glitazones (thiazolidinediones). Design: Open cohort study in primary care. Setting: 1243 practices contributing data to the QResearch database in England. Participants: 469 688 patients with type 2 diabetes aged 25-84 years between 1 April 2007 and 31 January 2015. Exposures: Hypoglycaemic agents (glitazones, gliptins, metformin, sulphonylureas, insulin, and other) alone and in combination. Main outcome measures: First recorded diagnoses of amputation, blindness, severe kidney failure, hyperglycaemia, and hypoglycaemia recorded on patients’ primary care, mortality, or hospital records. Cox models estimated hazard ratios for diabetes treatments adjusting for potential confounders. Results: 21 308 (4.5%) and 32 533 (6.9%) patients received prescriptions for glitazones and gliptins during follow-up, respectively. Compared with non-use, glitazones were associated with a decreased risk of blindness (adjusted hazard ratio 0.71, 95% confidence interval 0.57 to 0.89; rate 14.4 per 10 000 person years of exposure) and an increased risk of hypoglycaemia (1.22, 1.10 to 1.37; 65.1); gliptins were associated with a decreased risk of hypoglycaemia (0.86, 0.77 to 0.96; 45.8). Although the numbers of patients prescribed gliptin monotherapy or glitazones monotherapy were relatively low, there were significantly increased risks of severe kidney failure compared with metformin monotherapy (adjusted hazard ratio 2.55, 95% confidence interval 1.13 to 5.74). We found significantly lower risks of hyperglycaemia among patients prescribed dual therapy involving metformin with either gliptins (0.78, 0.62 to 0.97) or glitazones (0.60, 0.45 to 0.80) compared with metformin monotherapy. Patients prescribed triple therapy with metformin, sulphonylureas, and either gliptins (adjusted hazard ratio 5.07, 95% confidence interval 4.28 to 6.00) or glitazones (6.32, 5.35 to 7.45) had significantly higher risks of hypoglycaemia than those prescribed metformin monotherapy, but these risks were similar to those involving dual therapy with metformin and sulphonylureas (6.03, 5.47 to 6.63). Patients prescribed triple therapy with metformin, sulphonylureas, and glitazones had a significantly reduced risk of blindness compared with metformin monotherapy (0.67, 0.48 to 0.94). Conclusions: We have found lower risks of hyperglycaemia among patients prescribed dual therapy involving metformin with either gliptins or glitazones compared with metformin alone. Compared with metformin monotherapy, triple therapy with metformin, sulphonylureas, and either gliptins or glitazones was associated with an increased risk of hypoglycaemia, which was similar to the risk for dual therapy with metformin and sulphonylureas. Compared with metformin monotherapy, triple therapy with metformin, sulphonylureas, and glitazones was associated with a reduced risk of blindness. These results, while subject to residual confounding, could have implications for the prescribing of hypoglycaemic drugs

Development and validation of QDiabetes-2018 risk prediction algorithm to estimate future risk of type 2 diabetes: cohort study

Objectives: To derive and validate updated QDiabetes-2018 prediction algorithms to estimate the 10 year risk of type 2 diabetes in men and women, taking account of potential new risk factors, and to compare their performance with current approaches. Design: Prospective open cohort study. Setting: Routinely collected data from 1457 general practices in England contributing to the QResearch database: 1094 were used to develop the scores and a separate set of 363 were used to validate the scores. Participants: 11.5 million people aged 25-84 and free of diabetes at baseline: 8.87 million in the derivation cohort and 2.63 million in the validation cohort. Methods: Cox proportional hazards models were used in the derivation cohort to derive separate risk equations in men and women for evaluation at 10 years. Risk factors considered included those already in QDiabetes (age, ethnicity, deprivation, body mass index, smoking, family history of diabetes in a first degree relative, cardiovascular disease, treated hypertension, and regular use of corticosteroids) and new risk factors: atypical antipsychotics, statins, schizophrenia or bipolar affective disorder, learning disability, gestational diabetes, and polycystic ovary syndrome. Additional models included fasting blood glucose and glycated haemoglobin (HBA1c). Measures of calibration and discrimination were determined in the validation cohort for men and women separately and for individual subgroups by age group, ethnicity, and baseline disease status. Main outcome measure: Incident type 2 diabetes recorded on the general practice record. Results: In the derivation cohort, 178 314 incident cases of type 2 diabetes were identified during follow-up arising from 42.72 million person years of observation. In the validation cohort, 62 326 incident cases of type 2 diabetes were identified from 14.32 million person years of observation. All new risk factors considered met our model inclusion criteria. Model A included age, ethnicity, deprivation, body mass index, smoking, family history of diabetes in a first degree relative, cardiovascular disease, treated hypertension, and regular use of corticosteroids, and new risk factors: atypical antipsychotics, statins, schizophrenia or bipolar affective disorder, learning disability, and gestational diabetes and polycystic ovary syndrome in women. Model B included the same variables as model A plus fasting blood glucose. Model C included HBA1c instead of fasting blood glucose. All three models had good calibration and high levels of explained variation and discrimination. In women, model B explained 63.3% of the variation in time to diagnosis of type 2 diabetes (R2), the D statistic was 2.69 and the Harrell’s C statistic value was 0.89. The corresponding values for men were 58.4%, 2.42, and 0.87. Model B also had the highest sensitivity compared with current recommended practice in the National Health Service based on bands of either fasting blood glucose or HBA1c. However, only 16% of patients had complete data for blood glucose measurements, smoking, and body mass index. Conclusions: Three updated QDiabetes risk models to quantify the absolute risk of type 2 diabetes were developed and validated: model A does not require a blood test and can be used to identify patients for fasting blood glucose (model B) or HBA1c (model C) testing. Model B had the best performance for predicting 10 year risk of type 2 diabetes to identify those who need interventions and more intensive follow-up, improving on current approaches. Additional external validation of models B and C in datasets with more completely collected data on blood glucose would be valuable before the models are used in clinical practice

Development and validation of risk prediction equations to estimate survival in patients with colorectal cancer: cohort study

Objective: To develop and externally validate risk prediction equations to estimate absolute and conditional survival in patients with colorectal cancer. Design: Cohort study. Setting: General practices in England providing data for the QResearch database linked to the national cancer registry. Participants: 44 145 patients aged 15-99 with colorectal cancer from 947 practices to derive the equations. The equations were validated in 15 214 patients with colorectal cancer from 305 different QResearch practices and 437 821 patients with colorectal cancer from the national cancer registry. Main outcome measures: The primary outcome was all cause mortality and secondary outcome was colorectal cancer mortality. Methods: Cause specific hazards models were used to predict risks of colorectal cancer mortality and other cause mortality accounting for competing risks, and these risk estimates were combined to obtain risks of all cause mortality. Separate equations were derived for men and women. Several variables were tested: age, ethnicity, deprivation score, cancer stage, cancer grade, surgery, chemotherapy, radiotherapy, smoking status, alcohol consumption, body mass index, family history of bowel cancer, anaemia, liver function test result, comorbidities, use of statins, use of aspirin, clinical values for anaemia, and platelet count. Measures of calibration and discrimination were determined in both validation cohorts at 1, 5, and 10 years. Results: The final models included the following variables in men and women: age, deprivation score, cancer stage, cancer grade, smoking status, colorectal surgery, chemotherapy, family history of bowel cancer, raised platelet count, abnormal liver function, cardiovascular disease, diabetes, chronic renal disease, chronic obstructive pulmonary disease, prescribed aspirin at diagnosis, and prescribed statins at diagnosis. Improved survival in women was associated with younger age, earlier stage of cancer, well or moderately differentiated cancer grade, colorectal cancer surgery (adjusted hazard ratio 0.50), family history of bowel cancer (0.62), and prescriptions for statins (0.77) and aspirin (0.83) at diagnosis, with comparable results for men. The risk equations were well calibrated, with predicted risks closely matching observed risks. Discrimination was good in men and women in both validation cohorts. For example, the five year survival equations on the QResearch validation cohort explained 45.3% of the variation in time to colorectal cancer death for women, the D statistic was 1.86, and Harrell’s C statistic was 0.80 (both measures of discrimination, indicating that the scores are able to distinguish between people with different levels of risk). The corresponding results for all cause mortality were 42.6%, 1.77, and 0.79. Conclusions: Risk prediction equations were developed and validated to estimate overall and conditional survival of patients with colorectal cancer accounting for an individual’s clinical and demographic characteristics. These equations can provide more individualised accurate information for patients with colorectal cancer to inform decision making and follow-up

Protocol for the development and validation of risk prediction equations to estimate absolute and conditional survival in patients with cancer

This is a protocol to describe the development and validation of a set of prediction equations to quantify absolute survival for patients with different types of cancers taking account of other clinical factors available through routine linkage of cancer registry data to primary care electronic health records. We will also include estimates of conditional survival since it may be a more accurate measure of survival among those surviving the first year, especially when the initial prognosis is poor, such as with advanced stage colorectal cancer. Such estimates can be used to provide better information for patients and doctors to help inform treatment and other life decisions

Diabetes treatments and risk of heart failure, cardiovascular disease and all-cause mortality: cohort study in primary care

Objective: To assess associations between risks of cardiovascular disease, heart failure, and all cause mortality and different diabetes drugs in people with type 2 diabetes, particularly newer agents, including gliptins and thiazolidinediones (glitazones). Design: Open cohort study. Setting: 1243 general practices contributing data to the QResearch database in England. Participants: 469 688 people with type 2 diabetes aged 25-84 years between 1 April 2007 and 31 January 2015. Exposures: Diabetes drugs (glitazones, gliptins, metformin, sulphonylureas, insulin, other) alone and in combination. Main outcome measure: First recorded diagnoses of cardiovascular disease, heart failure, and all cause mortality recorded on the patients’ primary care, mortality, or hospital record. Cox proportional hazards models were used to estimate hazard ratios for diabetes treatments, adjusting for potential confounders. Results: During follow-up, 21 308 patients (4.5%) received prescriptions for glitazones and 32 533 (6.9%) received prescriptions for gliptins. Compared with non-use, gliptins were significantly associated with an 18% decreased risk of all cause mortality, a 14% decreased risk of heart failure, and no significant change in risk of cardiovascular disease; corresponding values for glitazones were significantly decreased risks of 23% for all cause mortality, 26% for heart failure, and 25% for cardiovascular disease. Compared with no current treatment, there were no significant associations between monotherapy with gliptins and risk of any complications. Dual treatment with gliptins and metformin was associated with a decreased risk of all three outcomes (reductions of 38% for heart failure, 33% for cardiovascular disease, and 48% for all cause mortality). Triple treatment with metformin, sulphonylureas, and gliptins was associated with a decreased risk of all three outcomes (reductions of 40% for heart failure, 30% for cardiovascular disease, and 51% for all cause mortality). Compared with no current treatment, monotherapy with glitazone was associated with a 50% decreased risk of heart failure, and dual treatment with glitazones and metformin was associated with a decreased risk of all three outcomes (reductions of 50% for heart failure, 54% for cardiovascular disease, and 45% for all cause mortality); dual treatment with glitazones and sulphonylureas was associated with risk reductions of 35% for heart failure and 25% for cardiovascular disease; triple treatment with metformin, sulphonylureas, and glitazones was associated with decreased risks of all three outcomes (reductions of 46% for heart failure, 41% for cardiovascular disease, and 56% for all cause mortality). Conclusions: There are clinically important differences in risk of cardiovascular disease, heart failure, and all cause mortality between different diabetes drugs alone and in combination. Overall, use of gliptins or glitazones was associated with decreased risks of heart failure, cardiovascular disease, and all cause mortality compared with non-use of these drugs. These results, which do not account for levels of adherence or dosage information and which are subject to confounding by indication, might have implications for prescribing of diabetes drugs

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

Study question: Is it possible to develop and externally validate risk prediction equations to estimate the 10 year risk of blindness and lower limb amputation in patients with diabetes aged 25-84 years? Methods: This was a prospective cohort study using routinely collected data from general practices in England contributing to the QResearch and Clinical Practice Research Datalink (CPRD) databases during the study period 1998-2014. The equations were developed using 763 QResearch practices (n=454 575 patients with diabetes) and validated in 254 different QResearch practices (n=142 419) and 357 CPRD practices (n=206 050). Cox proportional hazards models were used to derive separate risk equations for blindness and amputation in men and women that could be evaluated at 10 years. Measures of calibration and discrimination were calculated in the two validation cohorts. Study answer and limitations: Risk prediction equations to quantify absolute risk of blindness and amputation in men and women with diabetes have been developed and externally validated. In the QResearch derivation cohort, 4822 new cases of lower limb amputation and 8063 new cases of blindness occurred during follow-up. The risk equations were well calibrated in both validation cohorts. Discrimination was good in men in the external CPRD cohort for amputation (D statistic 1.69, Harrell’s C statistic 0.77) and blindness (D statistic 1.40, Harrell’s C statistic 0.73), with similar results in women and in the QResearch validation cohort. The algorithms are based on variables that patients are likely to know or that are routinely recorded in general practice computer systems. They can be used to identify patients at high risk for prevention or further assessment. Limitations include lack of formally adjudicated outcomes, information bias, and missing data. What this study adds: Patients with type 1 or type 2 diabetes are at increased risk of blindness and amputation but generally do not have accurate assessments of the magnitude of their individual risks. The new algorithms calculate the absolute risk of developing these complications over a 10 year period in patients with diabetes, taking account of their individual risk factors

Development and validation of QMortality risk prediction algorithm to estimate short term risk of death and assess frailty: cohort study

Objectives: To derive and validate a risk prediction equation to estimate the short term risk of death, and to develop a classification method for frailty based on risk of death and risk of unplanned hospital admission. Design: Prospective open cohort study. Participants: Routinely collected data from 1436 general practices contributing data to QResearch in England between 2012 and 2016. 1079 practices were used to develop the scores and a separate set of 357 practices to validate the scores. 1.47 million patients aged 65-100 years were in the derivation cohort and 0.50 million patients in the validation cohort. Methods: Cox proportional hazards models in the derivation cohort were used to derive separate risk equations in men and women for evaluation of the risk of death at one year. Risk factors considered were age, sex, ethnicity, deprivation, smoking status, alcohol intake, body mass index, medical conditions, specific drugs, social factors, and results of recent investigations. Measures of calibration and discrimination were determined in the validation cohort for men and women separately and for each age and ethnic group. The new mortality equation was used in conjunction with the existing QAdmissions equation (which predicts risk of unplanned hospital admission) to classify patients into frailty groups. Main outcome measure: The primary outcome was all cause mortality. Results: During follow-up 180 132 deaths were identified in the derivation cohort arising from 4.39 million person years of observation. The final model included terms for age, body mass index, Townsend score, ethnic group, smoking status, alcohol intake, unplanned hospital admissions in the past 12 months, atrial fibrillation, antipsychotics, cancer, asthma or chronic obstructive pulmonary disease, living in a care home, congestive heart failure, corticosteroids, cardiovascular disease, dementia, epilepsy, learning disability, leg ulcer, chronic liver disease or pancreatitis, Parkinson’s disease, poor mobility, rheumatoid arthritis, chronic kidney disease, type 1 diabetes, type 2 diabetes, venous thromboembolism, anaemia, abnormal liver function test result, high platelet count, visited doctor in the past year with either appetite loss, unexpected weight loss, or breathlessness. The model had good calibration and high levels of explained variation and discrimination. In women, the equation explained 55.6% of the variation in time to death (R2), and had very good discrimination—the D statistic was 2.29, and Harrell’s C statistic value was 0.85. The corresponding values for men were 53.1%, 2.18, and 0.84. By combining predicted risks of mortality and unplanned hospital admissions, 2.7% of patients (n=13 665) were classified as severely frail, 9.4% (n=46 770) as moderately frail, 43.1% (n=215 253) as mildly frail, and 44.8% (n=223 790) as fit. Conclusions: We have developed new equations to predict the short term risk of death in men and women aged 65 or more, taking account of demographic, social, and clinical variables. The equations had good performance on a separate validation cohort. The QMortality equations can be used in conjunction with the QAdmissions equations, to classify patients into four frailty groups (known as QFrailty categories) to enable patients to be identified for further assessment or interventions

Predicting risk of upper gastrointestinal bleed and intracranial bleed with anticoagulants: cohort study to derive and validate the QBleed scores

Objective: To develop and validate risk algorithms (QBleed) for estimating the absolute risk of upper gastrointestinal and intracranial bleed for patients with and without anticoagulation aged 21-99 years in primary care. Design: Open cohort study using routinely collected data from general practice linked to hospital episode statistics data and mortality data during the five year study period between 1 January 2008 and 1 October 2013. Setting: 565 general practices in England contributing to the national QResearch database to develop the algorithm and 188 different QResearch practices to validate the algorithm. All 753 general practices had data linked to hospital episode statistics and mortality data at individual patient level. Endpoint: Gastrointestinal bleed and intracranial bleed recorded on either the linked mortality data or the linked hospital records. Participants: We studied 4.4 million patients in the derivation cohort with 16.4 million person years of follow-up. During follow-up, 21 641 patients had an incident upper gastrointestinal bleed and 9040 had an intracranial bleed. For the validation cohort, we identified 1.4 million patients contributing over 4.9 million person years of follow-up. During follow-up, 6600 patients had an incident gastrointestinal bleed and 2820 had an intracranial bleed. We excluded patients without a valid Townsend score for deprivation and those prescribed anticoagulants in the 180 days before study entry. Risk factors: Candidate variables recorded on the general practice computer system before entry to the cohort, including personal variables (age, sex, Townsend deprivation score, ethnicity), lifestyle variables (smoking, alcohol intake), chronic diseases, prescribed drugs, clinical values (body mass index, systolic blood pressure), and laboratory test results (haemoglobin, platelets). We also included previous bleed recorded before entry to the study. Results: The final QBleed algorithms incorporated 21 variables. When applied to the validation cohort, the algorithms in women explained 40% of the variation for upper gastrointestinal bleed and 58% for intracranial bleed. The corresponding D statistics were 1.67 and 2.42. The receiver operating curve statistic values were 0.77 and 0.86. The sensitivity values for the top 10th of men and women at highest risk were 38% and 51%, respectively. There were similar results for men. Conclusion: The QBleed algorithms provided valid measures of absolute risk of gastrointestinal and intracranial bleed in patients with and without anticoagulation as shown by the performance of the algorithms in a separate validation cohort. Further research is needed to evaluate the clinical outcomes and the cost effectiveness of using these algorithms in primary care

Development and validation of risk prediction algorithm (QThrombosis) to estimate future risk of venous thromboembolism: prospective cohort study

Objectives To derive and validate a new clinical risk prediction algorithm (QThrombosis, www.qthrombosis.org) to estimate individual patients’ risk of venous thromboembolism