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Registers in Denmark Bendix Carstensen Steno Diabetes Center Gentofte, Denmark http://BendixCarstensen.com Prince of Wales Hospital, Hong Kong 12 May 2014 http://BendixCarstensen.com/SDC/PWH-HK 1/ 30 Use of routine care data in research

  1. Clinical registers (e.g. Danish Adult Diabetes Database) ◮ Data collection (recording) at fixed intervals (once a year, e.g.) ◮ Clinical data on individuals ◮ Data collection independent of patients’ clinical status ◮ Missing data: ◮ a patient was not seen for an entire year ◮ a patient has moved ◮ a patient died (but was not recorded as such) ◮ Used for quality monitoring: ◮ What percentage of pateints have had eyexamination within the last 2 years etc. 5/ 30

  2. Clinical registers (e.g. Danish Adult Diabetes Database) ◮ Data collection (recording) at fixed intervals (once a year, e.g.) ◮ Clinical data on individuals ◮ Data collection independent of patients’ clinical status ◮ Missing data: ◮ a patient was not seen for an entire year ◮ a patient has moved ◮ a patient died (but was not recorded as such) ◮ Used for quality monitoring: ◮ What percentage of pateints have had eyexamination within the last 2 years etc. 5/ 30

  3. Clinical registers (e.g. Danish Adult Diabetes Database) ◮ Data collection (recording) at fixed intervals (once a year, e.g.) ◮ Clinical data on individuals ◮ Data collection independent of patients’ clinical status ◮ Missing data: ◮ a patient was not seen for an entire year ◮ a patient has moved ◮ a patient died (but was not recorded as such) ◮ Used for quality monitoring: ◮ What percentage of pateints have had eyexamination within the last 2 years etc. 5/ 30

  4. Population level registers (Danish National Diabetes Register) ◮ Aims to cover the entire population: ◮ Limited information on each patient: ◮ date of birth ◮ date of diagnosis ◮ date of death ◮ sex ◮ Monitoring of demographics: ◮ prevalence of DM ◮ DM occurrence (incidence rates) ◮ mortality of DM patients ◮ Important because we have: ◮ long term follow-up ◮ no patient drop-out 6/ 30

  5. Population level registers (Danish National Diabetes Register) ◮ Aims to cover the entire population: ◮ Limited information on each patient: ◮ date of birth ◮ date of diagnosis ◮ date of death ◮ sex ◮ Monitoring of demographics: ◮ prevalence of DM ◮ DM occurrence (incidence rates) ◮ mortality of DM patients ◮ Important because we have: ◮ long term follow-up ◮ no patient drop-out 6/ 30

  6. Population level registers (Danish National Diabetes Register) ◮ Aims to cover the entire population: ◮ Limited information on each patient: ◮ date of birth ◮ date of diagnosis ◮ date of death ◮ sex ◮ Monitoring of demographics: ◮ prevalence of DM ◮ DM occurrence (incidence rates) ◮ mortality of DM patients ◮ Important because we have: ◮ long term follow-up ◮ no patient drop-out 6/ 30

  7. Population level registers (Danish National Diabetes Register) ◮ Aims to cover the entire population: ◮ Limited information on each patient: ◮ date of birth ◮ date of diagnosis ◮ date of death ◮ sex ◮ Monitoring of demographics: ◮ prevalence of DM ◮ DM occurrence (incidence rates) ◮ mortality of DM patients ◮ Important because we have: ◮ long term follow-up ◮ no patient drop-out 6/ 30

  8. Population level registers (Danish National Diabetes Register) ◮ Aims to cover the entire population: ◮ Limited information on each patient: ◮ date of birth ◮ date of diagnosis ◮ date of death ◮ sex ◮ Monitoring of demographics: ◮ prevalence of DM ◮ DM occurrence (incidence rates) ◮ mortality of DM patients ◮ Important because we have: ◮ long term follow-up ◮ no patient drop-out 6/ 30

  9. Population level registers (Danish National Diabetes Register) ◮ Aims to cover the entire population: ◮ Limited information on each patient: ◮ date of birth ◮ date of diagnosis ◮ date of death ◮ sex ◮ Monitoring of demographics: ◮ prevalence of DM ◮ DM occurrence (incidence rates) ◮ mortality of DM patients ◮ Important because we have: ◮ long term follow-up ◮ no patient drop-out 6/ 30

  10. Population level registers (Danish National Diabetes Register) ◮ Aims to cover the entire population: ◮ Limited information on each patient: ◮ date of birth ◮ date of diagnosis ◮ date of death ◮ sex ◮ Monitoring of demographics: ◮ prevalence of DM ◮ DM occurrence (incidence rates) ◮ mortality of DM patients ◮ Important because we have: ◮ long term follow-up ◮ no patient drop-out 6/ 30

  11. Population level registers (Danish National Diabetes Register) ◮ Aims to cover the entire population: ◮ Limited information on each patient: ◮ date of birth ◮ date of diagnosis ◮ date of death ◮ sex ◮ Monitoring of demographics: ◮ prevalence of DM ◮ DM occurrence (incidence rates) ◮ mortality of DM patients ◮ Important because we have: ◮ long term follow-up ◮ no patient drop-out 6/ 30

  12. Population level registers (Danish National Diabetes Register) ◮ Aims to cover the entire population: ◮ Limited information on each patient: ◮ date of birth ◮ date of diagnosis ◮ date of death ◮ sex ◮ Monitoring of demographics: ◮ prevalence of DM ◮ DM occurrence (incidence rates) ◮ mortality of DM patients ◮ Important because we have: ◮ long term follow-up ◮ no patient drop-out 6/ 30

  13. Population level registers (Danish National Diabetes Register) ◮ Aims to cover the entire population: ◮ Limited information on each patient: ◮ date of birth ◮ date of diagnosis ◮ date of death ◮ sex ◮ Monitoring of demographics: ◮ prevalence of DM ◮ DM occurrence (incidence rates) ◮ mortality of DM patients ◮ Important because we have: ◮ long term follow-up ◮ no patient drop-out 6/ 30

  14. Population level registers (Danish National Diabetes Register) ◮ Aims to cover the entire population: ◮ Limited information on each patient: ◮ date of birth ◮ date of diagnosis ◮ date of death ◮ sex ◮ Monitoring of demographics: ◮ prevalence of DM ◮ DM occurrence (incidence rates) ◮ mortality of DM patients ◮ Important because we have: ◮ long term follow-up ◮ no patient drop-out 6/ 30

  15. Population level registers (Danish National Diabetes Register) ◮ Aims to cover the entire population: ◮ Limited information on each patient: ◮ date of birth ◮ date of diagnosis ◮ date of death ◮ sex ◮ Monitoring of demographics: ◮ prevalence of DM ◮ DM occurrence (incidence rates) ◮ mortality of DM patients ◮ Important because we have: ◮ long term follow-up ◮ no patient drop-out 6/ 30

  16. Population level registers (Danish National Diabetes Register) ◮ Aims to cover the entire population: ◮ Limited information on each patient: ◮ date of birth ◮ date of diagnosis ◮ date of death ◮ sex ◮ Monitoring of demographics: ◮ prevalence of DM ◮ DM occurrence (incidence rates) ◮ mortality of DM patients ◮ Important because we have: ◮ long term follow-up ◮ no patient drop-out 6/ 30

  17. NDR 1995-2012: Adding population data ◮ Combine with populations data: ◮ population size ◮ population risk time (person-years) ◮ . . . in order to compute: ◮ Prevalence of DM at different dates ◮ Incidence rates of DM in the non-DM population ◮ Mortality of DM patients ◮ Relative mortality of DM patients (SMR) 7/ 30

  18. NDR 1995-2012: Adding population data ◮ Combine with populations data: ◮ population size ◮ population risk time (person-years) ◮ . . . in order to compute: ◮ Prevalence of DM at different dates ◮ Incidence rates of DM in the non-DM population ◮ Mortality of DM patients ◮ Relative mortality of DM patients (SMR) 7/ 30

  19. NDR 1995-2012: Adding population data ◮ Combine with populations data: ◮ population size ◮ population risk time (person-years) ◮ . . . in order to compute: ◮ Prevalence of DM at different dates ◮ Incidence rates of DM in the non-DM population ◮ Mortality of DM patients ◮ Relative mortality of DM patients (SMR) 7/ 30

  20. NDR 1995-2012: Adding population data ◮ Combine with populations data: ◮ population size ◮ population risk time (person-years) ◮ . . . in order to compute: ◮ Prevalence of DM at different dates ◮ Incidence rates of DM in the non-DM population ◮ Mortality of DM patients ◮ Relative mortality of DM patients (SMR) 7/ 30

  21. NDR 1995-2012: Adding population data ◮ Combine with populations data: ◮ population size ◮ population risk time (person-years) ◮ . . . in order to compute: ◮ Prevalence of DM at different dates ◮ Incidence rates of DM in the non-DM population ◮ Mortality of DM patients ◮ Relative mortality of DM patients (SMR) 7/ 30

  22. NDR 1995-2012: Adding population data ◮ Combine with populations data: ◮ population size ◮ population risk time (person-years) ◮ . . . in order to compute: ◮ Prevalence of DM at different dates ◮ Incidence rates of DM in the non-DM population ◮ Mortality of DM patients ◮ Relative mortality of DM patients (SMR) 7/ 30

  23. NDR 1995-2012: Adding population data ◮ Combine with populations data: ◮ population size ◮ population risk time (person-years) ◮ . . . in order to compute: ◮ Prevalence of DM at different dates ◮ Incidence rates of DM in the non-DM population ◮ Mortality of DM patients ◮ Relative mortality of DM patients (SMR) 7/ 30

  24. NDR 1995-2012: Adding population data ◮ Combine with populations data: ◮ population size ◮ population risk time (person-years) ◮ . . . in order to compute: ◮ Prevalence of DM at different dates ◮ Incidence rates of DM in the non-DM population ◮ Mortality of DM patients ◮ Relative mortality of DM patients (SMR) 7/ 30

  25. NDR 1995-2012: Prevalence[1] Men 2012 Women 20 2012 15 DM prevalence (%) 10 1995 1995 5 0 20 30 40 50 60 70 80 90 20 30 40 50 60 70 80 90 Age 8/ 30

  26. NDR 1995-2012: Incidence rates[1] 10 10 5 5 Rate per 1000 PY 2 2 Rate ratio 1 1 ● 0.5 0.5 0.2 0.2 0 20 40 60 80 100 1900 1920 1940 1960 1980 2000 2020 Age Calendar time 9/ 30

  27. NDR 1995-2012: SMR[1] 5.0 Standardized Mortality Ratio (SMR) 2.0 1.0 0.5 30 40 50 60 70 80 90 Age 10/ 30

  28. Mortality among SDC T1 & T2 patients Patients followed 1 Jan 2002 to 31 Dec 2010 [2, 3] T1 T2 Men Women Men Women No. patients 2,614 2,207 3,423 2,421 Annual decrease (%): Mortality 6.6 4.8 5.5 3.3 SMR 4.3 2.6 3.0 1.4 So also in SDC patients mortality has been declining more than in the general population. 11/ 30

  29. Mortality among SDC T1 & T2 patients Patients followed 1 Jan 2002 to 31 Dec 2010 [2, 3] T1 T2 Men Women Men Women No. patients 2,614 2,207 3,423 2,421 Annual decrease (%): Mortality 6.6 4.8 5.5 3.3 SMR 4.3 2.6 3.0 1.4 So also in SDC patients mortality has been declining more than in the general population. 11/ 30

  30. Renal disease, CVD and death SDC T1 patients [4, 5] with DN ◮ Patients with DN (diabetic nephropathy) ◮ Occurrence of: ◮ ESRD (end stage renal disease: dialysis or transplant) ◮ Death ◮ How do rates depend on clinical parameters? ◮ How is long-term outcome dependent on clinical status? 12/ 30

  31. Renal disease, CVD and death SDC T1 patients [4, 5] with DN ◮ Patients with DN (diabetic nephropathy) ◮ Occurrence of: ◮ ESRD (end stage renal disease: dialysis or transplant) ◮ Death ◮ How do rates depend on clinical parameters? ◮ How is long-term outcome dependent on clinical status? 12/ 30

  32. Renal disease, CVD and death SDC T1 patients [4, 5] with DN ◮ Patients with DN (diabetic nephropathy) ◮ Occurrence of: ◮ ESRD (end stage renal disease: dialysis or transplant) ◮ Death ◮ How do rates depend on clinical parameters? ◮ How is long-term outcome dependent on clinical status? 12/ 30

  33. Renal disease, CVD and death SDC T1 patients [4, 5] with DN ◮ Patients with DN (diabetic nephropathy) ◮ Occurrence of: ◮ ESRD (end stage renal disease: dialysis or transplant) ◮ Death ◮ How do rates depend on clinical parameters? ◮ How is long-term outcome dependent on clinical status? 12/ 30

  34. Renal disease, CVD and death SDC T1 patients [4, 5] with DN ◮ Patients with DN (diabetic nephropathy) ◮ Occurrence of: ◮ ESRD (end stage renal disease: dialysis or transplant) ◮ Death ◮ How do rates depend on clinical parameters? ◮ How is long-term outcome dependent on clinical status? 12/ 30

  35. Renal disease, CVD and death SDC T1 patients [4, 5] with DN ◮ Patients with DN (diabetic nephropathy) ◮ Occurrence of: ◮ ESRD (end stage renal disease: dialysis or transplant) ◮ Death ◮ How do rates depend on clinical parameters? ◮ How is long-term outcome dependent on clinical status? 12/ 30

  36. Renal disease, CVD and death SDC T1 patients [4, 5] with DN ◮ Patients with DN (diabetic nephropathy) ◮ Occurrence of: ◮ ESRD (end stage renal disease: dialysis or transplant) ◮ Death ◮ How do rates depend on clinical parameters? ◮ How is long-term outcome dependent on clinical status? 12/ 30

  37. SDC: T1DM patients with kidney compliations ◮ G. Andresdottir, M. L. Jensen, B. Carstensen, H. H. Parving, K. Rossing, T. W. Hansen, and P. Rossing: Improved Survival and Renal Prognosis of Patients With Type 2 Diabetes and Nephropathy With Improved Control of Risk Factors Diabetes Care, Mar 2014. ◮ G. Andresdottir, M. L. Jensen, B. Carstensen, H. H. Parving, P. Hovind, T. W. Hansen, P. Rossing: Improved prognosis of diabetic nephropathy in type 1 diabetes Accepted in Kidney International on 17 April 2014. 13/ 30

  38. SDC: T1DM patients with kidney compliations ◮ G. Andresdottir, M. L. Jensen, B. Carstensen, H. H. Parving, K. Rossing, T. W. Hansen, and P. Rossing: Improved Survival and Renal Prognosis of Patients With Type 2 Diabetes and Nephropathy With Improved Control of Risk Factors Diabetes Care, Mar 2014. ◮ G. Andresdottir, M. L. Jensen, B. Carstensen, H. H. Parving, P. Hovind, T. W. Hansen, P. Rossing: Improved prognosis of diabetic nephropathy in type 1 diabetes Accepted in Kidney International on 17 April 2014. 13/ 30

  39. SDC: T1DM patients with kidney compliations Extract patients with Diabetic Nephropathy (DN) from the SDC patient records and record: ◮ Date of birth ◮ Date of diabetes ◮ Date of DN ◮ Date of CVD ◮ Date of ESRD ◮ Date of death ◮ Clinical parameters at date of DN (baseline) 14/ 30

  40. SDC: T1DM patients with kidney compliations Extract patients with Diabetic Nephropathy (DN) from the SDC patient records and record: ◮ Date of birth ◮ Date of diabetes ◮ Date of DN ◮ Date of CVD ◮ Date of ESRD ◮ Date of death ◮ Clinical parameters at date of DN (baseline) 14/ 30

  41. SDC: T1DM patients with kidney compliations Extract patients with Diabetic Nephropathy (DN) from the SDC patient records and record: ◮ Date of birth ◮ Date of diabetes ◮ Date of DN ◮ Date of CVD ◮ Date of ESRD ◮ Date of death ◮ Clinical parameters at date of DN (baseline) 14/ 30

  42. SDC: T1DM patients with kidney compliations Extract patients with Diabetic Nephropathy (DN) from the SDC patient records and record: ◮ Date of birth ◮ Date of diabetes ◮ Date of DN ◮ Date of CVD ◮ Date of ESRD ◮ Date of death ◮ Clinical parameters at date of DN (baseline) 14/ 30

  43. SDC: T1DM patients with kidney compliations Extract patients with Diabetic Nephropathy (DN) from the SDC patient records and record: ◮ Date of birth ◮ Date of diabetes ◮ Date of DN ◮ Date of CVD ◮ Date of ESRD ◮ Date of death ◮ Clinical parameters at date of DN (baseline) 14/ 30

  44. SDC: T1DM patients with kidney compliations Extract patients with Diabetic Nephropathy (DN) from the SDC patient records and record: ◮ Date of birth ◮ Date of diabetes ◮ Date of DN ◮ Date of CVD ◮ Date of ESRD ◮ Date of death ◮ Clinical parameters at date of DN (baseline) 14/ 30

  45. SDC: T1DM patients with kidney compliations Extract patients with Diabetic Nephropathy (DN) from the SDC patient records and record: ◮ Date of birth ◮ Date of diabetes ◮ Date of DN ◮ Date of CVD ◮ Date of ESRD ◮ Date of death ◮ Clinical parameters at date of DN (baseline) 14/ 30

  46. T1DM patients with kidney compliations DN DN DN 34 (1.4) Dead(DN) Dead(DN) Dead(DN) 2,493.9 2,493.9 2,493.9 34 34 34 393 197 393 197 393 197 70 (2.8) CVD CVD CVD 42 (5.1) Dead(CVD) Dead(CVD) Dead(CVD) 824.7 824.7 824.7 42 42 42 104 76 104 76 104 76 56 (6.8) 92 (3.7) ESRD+CVD ESRD+CVD ESRD+CVD 45 (19.1) Dead(ESRD+CVD) Dead(ESRD+CVD) Dead(ESRD+CVD) 235.5 235.5 235.5 45 45 45 46 46 46 35 (14.0) ESRD ESRD ESRD 14 (5.6) Dead(ESRD) Dead(ESRD) Dead(ESRD) 250.0 250.0 250.0 14 14 14 43 43 43 15/ 30

  47. Covariate effects ● Prior cardiovascular disease ● ● ● Male vs. female ● ● ● Body mass index (kg/m 2 ) ● ● ● Systolic blood pressure (10 mmHg) ● ● ● GFR (−10 ml/min/1.73m 2 ) ● ● DN DN DN 34 (1.4) ● Dead(DN) Dead(DN) Dead(DN) Albuminuria (per 100% incr.) ● 2,493.9 2,493.9 2,493.9 ● 34 34 34 393 197 393 197 393 197 ● Insulin/kg (per 50% incr.) 70 (2.8) ● ● ● Hemoglobin (mmol/l) ● ● CVD CVD CVD 42 (5.1) Dead(CVD) Dead(CVD) Dead(CVD) 824.7 824.7 824.7 42 42 42 104 76 104 76 104 76 ● HbA 1c (%) ● ● 56 (6.8) ● Total cholesterol (mmol/l) ● ● 92 (3.7) ESRD+CVD ESRD+CVD ESRD+CVD 45 (19.1) Dead(ESRD+CVD) Dead(ESRD+CVD) Dead(ESRD+CVD) ● 235.5 235.5 235.5 Smoker vs. non−smoker ● 45 45 45 ● 46 46 46 35 (14.0) 0.4 0.6 1.0 2.0 4.0 RR of pre−ESRD death ESRD ESRD ESRD 14 (5.6) Dead(ESRD) Dead(ESRD) Dead(ESRD) RR of post−ESRD death 250.0 250.0 250.0 14 14 14 43 43 43 RR of ESRD 16/ 30

  48. ● Prior cardiovascular disease ● ● ● Male vs. female ● ● Body mass index (kg/m 2 ) ● ● ● ● Systolic blood pressure (10 mmHg) ● ● ● GFR (−10 ml/min/1.73m 2 ) ● ● ● Albuminuria (per 100% incr.) ● ● ● Insulin/kg (per 50% incr.) ● ● ● Hemoglobin (mmol/l) ● ● ● HbA 1c (%) ● ● ● Total cholesterol (mmol/l) ● ● ● Smoker vs. non−smoker ● ● 0.4 0.6 1.0 2.0 4.0 RR of pre−ESRD death RR of post−ESRD death 17/ 30 RR of ESRD

  49. Example patients Regulation Fair Poor Sex Man Man Age 40/45 40/45 Time since DN 5 5 Diabetes duration 25 25 HbA 1c 7.5 9.0 Systolic blood pr. 130 150 Total cholesterol 4.5 5.5 Albumin 300 1000 Smoking never, < 3 4–20, 20+ BMI 22 22 GFR 70 70 Hemoglobin 8 8 Insulin dose per kg 0.75 0.75 18/ 30

  50. a b 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 DN DN 0.2 0.2 Poor control of risk factors Fair control of risk factors 0.0 0.0 Probability 40 42 44 46 48 50 40 42 44 46 48 50 c d 1.0 1.0 DN DN DN 34 (1.4) Dead(DN) Dead(DN) Dead(DN) 2,493.9 2,493.9 2,493.9 34 34 34 0.8 0.8 393 197 393 197 393 197 70 (2.8) 0.6 0.6 CVD CVD CVD 42 (5.1) Dead(CVD) Dead(CVD) Dead(CVD) 824.7 824.7 824.7 42 42 42 104 76 104 76 104 76 0.4 0.4 56 (6.8) 92 (3.7) ESRD+CVD ESRD+CVD ESRD+CVD DN, CVD DN, CVD 45 (19.1) Dead(ESRD+CVD) Dead(ESRD+CVD) Dead(ESRD+CVD) 0.2 0.2 235.5 235.5 235.5 Poor control of risk factors 45 45 45 Fair control of risk factors 46 46 46 35 (14.0) 0.0 0.0 46 48 50 52 54 46 48 50 52 54 ESRD ESRD ESRD 14 (5.6) Dead(ESRD) Dead(ESRD) Dead(ESRD) 250.0 250.0 250.0 Age at follow−up 14 14 14 43 43 43 19/ 30

  51. a b 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 DN DN 0.2 0.2 Poor control of risk factors Fair control of risk factors 0.0 0.0 Probability 40 42 44 46 48 50 40 42 44 46 48 50 1.0 c 1.0 d 0.8 0.8 0.6 0.6 20/ 30 0.4 0.4

  52. 0.4 0.4 DN DN 0.2 0.2 Poor control of risk factors Fair control of risk factors 0.0 0.0 Probability 40 42 44 46 48 50 40 42 44 46 48 50 c d 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 DN, CVD DN, CVD 0.2 0.2 Poor control of risk factors Fair control of risk factors 0.0 0.0 46 48 50 52 54 46 48 50 52 54 Age at follow−up 21/ 30

  53. Prediction of lifecourse of patients ◮ Only possible if we model the entire lifecourse. ◮ Only events (ESRD, CVD, Death) are modelled ◮ Changes in clinical parameters are ignored — all is conditional on baseline only . ◮ Possible to model rates as a function of current clinical parameters (time-updated variables) ◮ no model for the clinical parameters (HbA 1c , cholesterol, . . . ) ◮ so we lose the ability to predict the lifecourse ◮ This was not done in the Danish kidney-complications study. ◮ . . . but it is possible with the SDC EPR. 22/ 30

  54. Prediction of lifecourse of patients ◮ Only possible if we model the entire lifecourse. ◮ Only events (ESRD, CVD, Death) are modelled ◮ Changes in clinical parameters are ignored — all is conditional on baseline only . ◮ Possible to model rates as a function of current clinical parameters (time-updated variables) ◮ no model for the clinical parameters (HbA 1c , cholesterol, . . . ) ◮ so we lose the ability to predict the lifecourse ◮ This was not done in the Danish kidney-complications study. ◮ . . . but it is possible with the SDC EPR. 22/ 30

  55. Prediction of lifecourse of patients ◮ Only possible if we model the entire lifecourse. ◮ Only events (ESRD, CVD, Death) are modelled ◮ Changes in clinical parameters are ignored — all is conditional on baseline only . ◮ Possible to model rates as a function of current clinical parameters (time-updated variables) ◮ no model for the clinical parameters (HbA 1c , cholesterol, . . . ) ◮ so we lose the ability to predict the lifecourse ◮ This was not done in the Danish kidney-complications study. ◮ . . . but it is possible with the SDC EPR. 22/ 30

  56. Prediction of lifecourse of patients ◮ Only possible if we model the entire lifecourse. ◮ Only events (ESRD, CVD, Death) are modelled ◮ Changes in clinical parameters are ignored — all is conditional on baseline only . ◮ Possible to model rates as a function of current clinical parameters (time-updated variables) ◮ no model for the clinical parameters (HbA 1c , cholesterol, . . . ) ◮ so we lose the ability to predict the lifecourse ◮ This was not done in the Danish kidney-complications study. ◮ . . . but it is possible with the SDC EPR. 22/ 30

  57. Prediction of lifecourse of patients ◮ Only possible if we model the entire lifecourse. ◮ Only events (ESRD, CVD, Death) are modelled ◮ Changes in clinical parameters are ignored — all is conditional on baseline only . ◮ Possible to model rates as a function of current clinical parameters (time-updated variables) ◮ no model for the clinical parameters (HbA 1c , cholesterol, . . . ) ◮ so we lose the ability to predict the lifecourse ◮ This was not done in the Danish kidney-complications study. ◮ . . . but it is possible with the SDC EPR. 22/ 30

  58. Prediction of lifecourse of patients ◮ Only possible if we model the entire lifecourse. ◮ Only events (ESRD, CVD, Death) are modelled ◮ Changes in clinical parameters are ignored — all is conditional on baseline only . ◮ Possible to model rates as a function of current clinical parameters (time-updated variables) ◮ no model for the clinical parameters (HbA 1c , cholesterol, . . . ) ◮ so we lose the ability to predict the lifecourse ◮ This was not done in the Danish kidney-complications study. ◮ . . . but it is possible with the SDC EPR. 22/ 30

  59. Prediction of lifecourse of patients ◮ Only possible if we model the entire lifecourse. ◮ Only events (ESRD, CVD, Death) are modelled ◮ Changes in clinical parameters are ignored — all is conditional on baseline only . ◮ Possible to model rates as a function of current clinical parameters (time-updated variables) ◮ no model for the clinical parameters (HbA 1c , cholesterol, . . . ) ◮ so we lose the ability to predict the lifecourse ◮ This was not done in the Danish kidney-complications study. ◮ . . . but it is possible with the SDC EPR. 22/ 30

  60. Prediction of lifecourse of patients ◮ Only possible if we model the entire lifecourse. ◮ Only events (ESRD, CVD, Death) are modelled ◮ Changes in clinical parameters are ignored — all is conditional on baseline only . ◮ Possible to model rates as a function of current clinical parameters (time-updated variables) ◮ no model for the clinical parameters (HbA 1c , cholesterol, . . . ) ◮ so we lose the ability to predict the lifecourse ◮ This was not done in the Danish kidney-complications study. ◮ . . . but it is possible with the SDC EPR. 22/ 30

  61. Modelling rates with current parameters ◮ But we gain the possibility to compare populations (e.g. HK & DK) with respect to ◮ occurrence rates ◮ conditional on clinical parameters: ◮ are there differences that cannot be explained in terms of the clinical status of patients? ◮ i.e. are there factors that influence rates that are not mediated through the measured clinical variables? 23/ 30

  62. Modelling rates with current parameters ◮ But we gain the possibility to compare populations (e.g. HK & DK) with respect to ◮ occurrence rates ◮ conditional on clinical parameters: ◮ are there differences that cannot be explained in terms of the clinical status of patients? ◮ i.e. are there factors that influence rates that are not mediated through the measured clinical variables? 23/ 30

  63. Modelling rates with current parameters ◮ But we gain the possibility to compare populations (e.g. HK & DK) with respect to ◮ occurrence rates ◮ conditional on clinical parameters: ◮ are there differences that cannot be explained in terms of the clinical status of patients? ◮ i.e. are there factors that influence rates that are not mediated through the measured clinical variables? 23/ 30

  64. Modelling rates with current parameters ◮ But we gain the possibility to compare populations (e.g. HK & DK) with respect to ◮ occurrence rates ◮ conditional on clinical parameters: ◮ are there differences that cannot be explained in terms of the clinical status of patients? ◮ i.e. are there factors that influence rates that are not mediated through the measured clinical variables? 23/ 30

  65. Modelling rates with current parameters ◮ But we gain the possibility to compare populations (e.g. HK & DK) with respect to ◮ occurrence rates ◮ conditional on clinical parameters: ◮ are there differences that cannot be explained in terms of the clinical status of patients? ◮ i.e. are there factors that influence rates that are not mediated through the measured clinical variables? 23/ 30

  66. Modelling rates with current parameters ◮ Also gain the possibility to evaluate time-trends in mortality: ◮ If trend in mortality by calendar time is negative, overall patient prognosis is improving ◮ But trend may be less negative or even positive when controlling for updated clinical variables, conditional on current (updated) clinical parameters: ◮ improvement in overall patient prognosis mediated through improvement in clinical variables? 24/ 30

  67. Modelling rates with current parameters ◮ Also gain the possibility to evaluate time-trends in mortality: ◮ If trend in mortality by calendar time is negative, overall patient prognosis is improving ◮ But trend may be less negative or even positive when controlling for updated clinical variables, conditional on current (updated) clinical parameters: ◮ improvement in overall patient prognosis mediated through improvement in clinical variables? 24/ 30

  68. Modelling rates with current parameters ◮ Also gain the possibility to evaluate time-trends in mortality: ◮ If trend in mortality by calendar time is negative, overall patient prognosis is improving ◮ But trend may be less negative or even positive when controlling for updated clinical variables, conditional on current (updated) clinical parameters: ◮ improvement in overall patient prognosis mediated through improvement in clinical variables? 24/ 30

  69. Modelling rates with current parameters ◮ Also gain the possibility to evaluate time-trends in mortality: ◮ If trend in mortality by calendar time is negative, overall patient prognosis is improving ◮ But trend may be less negative or even positive when controlling for updated clinical variables, conditional on current (updated) clinical parameters: ◮ improvement in overall patient prognosis mediated through improvement in clinical variables? 24/ 30

  70. Population level prediction ◮ Demographers compute the life expectancy in a population ◮ as the expected length of life ◮ under the assumption that rates are as seen in the population ◮ at a certain point in time: Alive Alive Alive Dead Dead Dead 25/ 30

  71. Population level prediction ◮ Demographers compute the life expectancy in a population ◮ as the expected length of life ◮ under the assumption that rates are as seen in the population ◮ at a certain point in time: Alive Alive Alive Dead Dead Dead 25/ 30

  72. Population level prediction ◮ Demographers compute the life expectancy in a population ◮ as the expected length of life ◮ under the assumption that rates are as seen in the population ◮ at a certain point in time: Alive Alive Alive Dead Dead Dead 25/ 30

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