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Introduction: End-stage renal disease (ESKD) is a life-threatening complication of diabetes that can be prevented or delayed through intervention.
Therefore, early detection of high-risk groups is crucial.
Pixabay.
com In the past few decades, the incidence of end-stage renal disease (ESKD) in patients with type 1 diabetes has gradually stabilized or declined, which may be related to the use of renin-angiotensin system (RAS) blockers.
Increase related.
However, compared with other common diabetes-related complications such as cardiovascular disease (CVD), the risk reduction of ESKD is not significant, and ESKD is still a life-threatening complication in diabetic patients.
Fortunately, ESKD can be prevented or delayed by intensive hypoglycemic and antihypertensive therapy, so early detection is necessary.
ESKD often occurs in patients with complex and poorly controlled type 1 diabetes, and this population also faces a high degree of pre-ESKD death, especially in elderly patients.
Therefore, an accurate estimation of individual ESKD risk is crucial.
There are few prediction models of ESKD for type 1 diabetes, and three prediction models have been established for people with type 2 diabetes.
The difference between type 1 diabetes and type 2 diabetes is that patients with type 1 diabetes are usually younger at diagnosis and therefore are exposed to diabetes-related ESKD risk factors (such as hyperglycemia and hypertension) for a longer period of time.
In addition, although elevated blood pressure, chronic kidney disease (CKD), and smoking appear to be risk factors for ESKD in both types of diabetes, obesity seems to play a greater role in type 2 diabetes, and the age at the time of diabetes diagnosis is type 1 diabetes The main risk factors for
This indicates that there are differences in the pathophysiology of ESKD between type 1 and type 2 diabetes, and a prediction model for the type 1 diabetes population is needed.
Based on this, some researchers have established an ESKD risk prediction model that takes into account the competitive risk of death and uses a representative large cohort of type 1 diabetic patients who have a large amount of clinical data and national registers.
Information about ESKD events and mortality.
The results of the study were recently published in the journal Diabetes Care.
In a population cohort study of 5460 Danish adults who were clinically diagnosed with type 1 diabetes from 2001 to 2016, researchers developed an ESKD prediction model to explain the risk of competitive death.
Poisson regression analysis is used to estimate the model based on information collected regularly.
The effects of an extended set of predictors (lipids, alcohol intake, etc.
) were further evaluated, and the potential interactions identified in the survival tree analysis were tested.
The final model was externally verified in 9175 adults from Denmark and Scotland.
The results of the study showed that during a median follow-up of 10.
4 years, 303 (5.
5%) subjects developed ESKD, and 764 (14.
0%) died without ESKD.
The final ESKD prediction model includes age, male, duration of diabetes, estimated glomerular filtration rate, micro and macro proteinuria, systolic blood pressure, hemoglobin A1c, smoking, and previous cardiovascular disease.
The differential diagnosis of the risk of ESKD events occurring within 5 years is very good, with a C statistic of 0.
888.
In this study, researchers have deduced and verified an efficient model based on the predictors collected routinely in clinical practice to predict the individual risk of ESKD in adults with type 1 diabetes.
The extension of the model (including less measured factors) did not significantly improve predictions, indicating that a more parsimonious core model is more feasible in a clinical setting and more suitable for assessing the individual 5-year ESKD risk of patients with type 1 diabetes.
In the deduction (A) and validation (C) cohorts, follow up the C statistics of ESKD events for several years, and in the deduction (B) and validation (D) cohorts to test the P value of the appropriate fit of the model.
The dotted lines in B and C indicate the acceptable model correction threshold (acceptable above the dotted line).
The cumulative incidence of ESKD in the Danish derivation and validation cohort is twice that of the Scottish validation cohort, which is much higher than previously reported in Sweden and Finland.
The annual incidence rate of the derivative cohort only decreased slightly during the follow-up period from 2001 to 2016.
The referral criteria for patients with type 1 diabetes in Denmark and Scotland are comparable.
The selection criteria for the two cohorts are similar, except that the Scottish cohort does not include patients diagnosed with type 1 diabetes after the age of 50.
One possible explanation for the difference in ESKD risk may be more aggressive treatment with RAS blockers and lower smoking rates in Scotland.
In short, this new, high-performance ESKD prediction model can be used for risk stratification of adult type 1 diabetes population.
And may improve clinical decision-making and guide early intervention.
Reference: A Validated Prediction Model for End-Stage Kidney Disease in Type 1 Diabetes Dorte Vistisen, Gregers S.
Andersen, Adam Hulman, Stuart J.
McGurnaghan, Helen M.
Colhoun, Jan E.
Henriksen, Reimar W.
Thomsen, Frederik Persson , Peter Rossing, Marit E.
Jørgensen Diabetes Care Apr 2021, 44 (4) 901-907; DOI: 10.
2337/dc20-2586
Therefore, early detection of high-risk groups is crucial.
Pixabay.
com In the past few decades, the incidence of end-stage renal disease (ESKD) in patients with type 1 diabetes has gradually stabilized or declined, which may be related to the use of renin-angiotensin system (RAS) blockers.
Increase related.
However, compared with other common diabetes-related complications such as cardiovascular disease (CVD), the risk reduction of ESKD is not significant, and ESKD is still a life-threatening complication in diabetic patients.
Fortunately, ESKD can be prevented or delayed by intensive hypoglycemic and antihypertensive therapy, so early detection is necessary.
ESKD often occurs in patients with complex and poorly controlled type 1 diabetes, and this population also faces a high degree of pre-ESKD death, especially in elderly patients.
Therefore, an accurate estimation of individual ESKD risk is crucial.
There are few prediction models of ESKD for type 1 diabetes, and three prediction models have been established for people with type 2 diabetes.
The difference between type 1 diabetes and type 2 diabetes is that patients with type 1 diabetes are usually younger at diagnosis and therefore are exposed to diabetes-related ESKD risk factors (such as hyperglycemia and hypertension) for a longer period of time.
In addition, although elevated blood pressure, chronic kidney disease (CKD), and smoking appear to be risk factors for ESKD in both types of diabetes, obesity seems to play a greater role in type 2 diabetes, and the age at the time of diabetes diagnosis is type 1 diabetes The main risk factors for
This indicates that there are differences in the pathophysiology of ESKD between type 1 and type 2 diabetes, and a prediction model for the type 1 diabetes population is needed.
Based on this, some researchers have established an ESKD risk prediction model that takes into account the competitive risk of death and uses a representative large cohort of type 1 diabetic patients who have a large amount of clinical data and national registers.
Information about ESKD events and mortality.
The results of the study were recently published in the journal Diabetes Care.
In a population cohort study of 5460 Danish adults who were clinically diagnosed with type 1 diabetes from 2001 to 2016, researchers developed an ESKD prediction model to explain the risk of competitive death.
Poisson regression analysis is used to estimate the model based on information collected regularly.
The effects of an extended set of predictors (lipids, alcohol intake, etc.
) were further evaluated, and the potential interactions identified in the survival tree analysis were tested.
The final model was externally verified in 9175 adults from Denmark and Scotland.
The results of the study showed that during a median follow-up of 10.
4 years, 303 (5.
5%) subjects developed ESKD, and 764 (14.
0%) died without ESKD.
The final ESKD prediction model includes age, male, duration of diabetes, estimated glomerular filtration rate, micro and macro proteinuria, systolic blood pressure, hemoglobin A1c, smoking, and previous cardiovascular disease.
The differential diagnosis of the risk of ESKD events occurring within 5 years is very good, with a C statistic of 0.
888.
In this study, researchers have deduced and verified an efficient model based on the predictors collected routinely in clinical practice to predict the individual risk of ESKD in adults with type 1 diabetes.
The extension of the model (including less measured factors) did not significantly improve predictions, indicating that a more parsimonious core model is more feasible in a clinical setting and more suitable for assessing the individual 5-year ESKD risk of patients with type 1 diabetes.
In the deduction (A) and validation (C) cohorts, follow up the C statistics of ESKD events for several years, and in the deduction (B) and validation (D) cohorts to test the P value of the appropriate fit of the model.
The dotted lines in B and C indicate the acceptable model correction threshold (acceptable above the dotted line).
The cumulative incidence of ESKD in the Danish derivation and validation cohort is twice that of the Scottish validation cohort, which is much higher than previously reported in Sweden and Finland.
The annual incidence rate of the derivative cohort only decreased slightly during the follow-up period from 2001 to 2016.
The referral criteria for patients with type 1 diabetes in Denmark and Scotland are comparable.
The selection criteria for the two cohorts are similar, except that the Scottish cohort does not include patients diagnosed with type 1 diabetes after the age of 50.
One possible explanation for the difference in ESKD risk may be more aggressive treatment with RAS blockers and lower smoking rates in Scotland.
In short, this new, high-performance ESKD prediction model can be used for risk stratification of adult type 1 diabetes population.
And may improve clinical decision-making and guide early intervention.
Reference: A Validated Prediction Model for End-Stage Kidney Disease in Type 1 Diabetes Dorte Vistisen, Gregers S.
Andersen, Adam Hulman, Stuart J.
McGurnaghan, Helen M.
Colhoun, Jan E.
Henriksen, Reimar W.
Thomsen, Frederik Persson , Peter Rossing, Marit E.
Jørgensen Diabetes Care Apr 2021, 44 (4) 901-907; DOI: 10.
2337/dc20-2586
