- Email: [email protected]
Prediction of five-year all-cause mortality in Chinese patients with type 2 diabetes mellitus – A population-based retrospective cohort study
Prediction of five-year all-cause mortality in Chinese Patients with Type 2 Diabetes Mellitus – A Population-based Retrospective Cohort Study Eric Yuk Fai Wan, Daniel Yee Tak Fong, Colman Siu Cheung Fung, Esther Yee Tak Yu, Weng Yee Chin, Anca Ka Chun Chan, Cindy Lo Kuen Lam PII: DOI: Reference:
S1056-8727(16)30969-2 doi: 10.1016/j.jdiacomp.2017.01.017 JDC 6948
To appear in:
Journal of Diabetes and Its Complications
Received date: Revised date: Accepted date:
7 December 2016 25 January 2017 29 January 2017
Please cite this article as: Wan, E.Y.F., Fong, D.Y.T., Fung, C.S.C., Yu, E.Y.T., Chin, W.Y., Chan, A.K.C. & Lam, C.L.K., Prediction of five-year all-cause mortality in Chinese Patients with Type 2 Diabetes Mellitus – A Population-based Retrospective Cohort Study, Journal of Diabetes and Its Complications (2017), doi: 10.1016/j.jdiacomp.2017.01.017
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Prediction of five-year all-cause mortality in Chinese Patients with Type 2 Diabetes
RI P
T
Mellitus – A Population-based Retrospective Cohort Study
SC
Authors: Eric Yuk Fai Wana*, Daniel Yee Tak Fongb, Colman Siu Cheung Funga, Esther Yee
a
MA NU
Tak Yua, Weng Yee China, Anca Ka Chun Chana, Cindy Lo Kuen Lama
Department of Family Medicine and Primary Care, the University of Hong Kong, 3/F Ap
Lei Chau Clinic, 161 Main Street, Ap Lei Chau, Hong Kong
ED
School of Nursing, the University of Hong Kong, Hong Kong
PT
b
CE
Mr. Eric Yuk Fai Wan1*
AC
* Corresponding author Tel. (852) 2552 4690
Fax. (852) 2814 7475
Email: [email protected]
Page 1 of 26
ACCEPTED MANUSCRIPT Abstract
T
Aims:
RI P
This study aimed to develop and validate an all-cause mortality risk prediction model for
SC
Chinese primary care patients with Type 2 Diabetes Mellitus(T2DM) in Hong Kong.
MA NU
Methods:
A population-based retrospective cohort study was conducted on 132,462 Chinese patients who had received public primary care services during 2010. Each gender sample was
ED
randomly split on a 2:1 basis into derivation and validation cohorts and was follow-up for a
PT
median period of 5 years. Gender-specific mortality risk prediction models showing the
CE
interaction effect between predictors and age were derived using Cox proportional hazards
AC
regression with forward stepwise approach. Developed models were compared with pre-existing models by Harrell's C-statistic and calibration plot using validation cohort.
Results: Common predictors of increased mortality risk in both genders included: age; smoking habit; diabetes duration; use of anti-hypertensive agents, insulin and lipid-lowering drugs; body mass index; haemoglobin A1c; systolic blood pressure(BP); total cholesterol to high-density lipoprotein-cholesterol ratio; urine albumin to creatinine ratio(urine ACR); and estimated
Page 2 of 26
ACCEPTED MANUSCRIPT glomerular filtration rate(eGFR). Prediction models showed better discrimination with
T
Harrell's C-statistics of 0.768(males) and 0.782(females) and calibration power from the plots
SC
RI P
than previously established models.
Conclusions:
MA NU
Our newly developed gender-specific models provide a more accurate predicted 5-year mortality risk for Chinese diabetic patients than other established models.
AC
CE
PT
ED
Keywords: Type 2 Diabetes mellitus; Prediction; Risk; Mortality; Primary care
Page 3 of 26
ACCEPTED MANUSCRIPT
T
Manuscript Text
RI P
1 Introduction
SC
Diabetes Mellitus (DM) is a well-known public health issue, affecting 415 million people, causing around 5 million deaths and accounting for 14.5% of all-cause mortality worldwide
MA NU
in 2015 (1). The World Health Organization has projected that diabetes will be the seventh leading cause of mortality by 2030 (2). Compared to patients without DM, the risk of overall mortality is almost double in patients with DM (3). Diabetic patients at age 50 years have a
ED
6-year shorter life expectancy than their non-diabetic counterparts (3). Previous studies have
PT
suggested that many of the causes of mortality in diabetic patients may be preventable
CE
through better control of DM and other cardiovascular risk factors during earlier stages of the
AC
disease (1, 4). Early identification of high-risk diabetic patients is therefore important so that appropriate and timely interventions can be provided to help reduce the risk of premature mortality.
Many studies have previously evaluated the risk factors for all-cause mortality among diabetic patients, but only a few have established prediction models (5-9). Amongst these, almost all have been developed based on non-Chinese populations. One model, the Joint Asia Diabetes Evaluation (JADE), was developed using a Chinese diabetic population,
Page 4 of 26
ACCEPTED MANUSCRIPT however it was limited by a small number of mortalities and was developed using
T
hospital-based patients (5). Models from these studies may not accurately reflect the risk
RI P
profile of Chinese patients with DM managed in primary care settings where the bulk of
SC
patients with DM are treated.
MA NU
According to the International Diabetes Federation (IDF) the Chinese currently constitute approximately 25% of the global DM population (1). In 2015, the Chinese had the highest number of deaths (1.3 million) due to diabetes, and had nearly double the prevalence of
ED
DM-related mortality for people under the age of 60 when compared to Europeans and
PT
Australians (1). Given that different DM populations can differ in disease profile and other
CE
determinants such as genetics, health care policy and culture (10-14), risk prediction models
AC
developed in Western settings may not accurately apply to Chinese populations. As there have been no population-based studies performed to establish a mortality risk prediction model for Chinese diabetic patients, this study aimed to develop a 5-year mortality risk prediction model among Chinese primary care patients in Hong Kong with Type 2 DM (T2DM).
2 Subjects, Materials and Methods 2.1 Study Design
Page 5 of 26
ACCEPTED MANUSCRIPT This was a population-based retrospective cohort study. Subject inclusion criteria included: (1)
T
Chinese, (2) aged between 18 and 79 years, (3) clinically diagnosed with T2DM as identified
RI P
using the International Classification of Primary Care-2 (ICPC-2) code of ‘T90’, (4) no prior
SC
history of cardiovascular disease (CVD), cancer or chronic lung disease, and (5) received medical care for T2DM from one of the 74 general out-patient clinics of the Hong Kong
MA NU
Hospital Authority (HA). The HA is the largest governmental organisation coordinating all public-sector hospitals and primary care clinics in Hong Kong. The HA manages over 50% of all DM primary care patients in Hong Kong. Clinical data between 1 January 2010 and 31
ED
December 2010 was retrieved through the central management system of the HA. The data
PT
was made available as part of a territory-wide study for the evaluation of quality of care of
CE
the HA primary care diabetic programs (15). Mortality data was retrieved from the Hong
AC
Kong Death Registry, which is compulsory for all residents in Hong Kong. All subjects were followed-up until death or to their last follow-up as of the censoring date of 30 November 2015, whichever occurred first.
Informed consent has been obtained from patients and ethics approval was received from all the regional Institutional Review Boards (IRB) of the Hong Kong Hospital Authority. The reported investigations have been carried out in accordance with the principles of the Declaration of Helsinki as revised in 2008.
Page 6 of 26
ACCEPTED MANUSCRIPT
T
2.2 Potential Predictors
RI P
The potential predictors included patient’s socio-demographics, disease characteristics,
SC
treatment modalities and clinical parameters at baseline. Socio-demographics consisted of: gender, age and smoking status. Disease characteristics consisted of: self-reported duration of
MA NU
T2DM, diagnosed hypertension and presence of sight threatening diabetic retinopathy (STDR), including pre-proliferative, proliferative diabetic retinopathy or maculopathy. Treatment modalities consisted of: the usages of anti-hypertensive drugs, oral anti-diabetic
ED
drugs, insulin and lipid-lowering agents. Clinical parameters consisted of: body mass index
PT
(BMI), waist circumference, haemoglobin A1c (HbA1c), systolic blood pressure (SBP),
CE
diastolic blood pressure (DBP), lipid profile (Low-density lipoprotein-cholesterol (LDL-C)
AC
and total cholesterol to high-density lipoprotein-cholesterol ratio (TC/ HDL-C ratio)), triglyceride, urine albumin to creatinine ratio (ACR) and eGFR. All laboratory assays were performed in accredited laboratories.
2.3 Data Analysis Missing data were handled by multiple imputation (16). Specifically, each missing value was imputed five times using the chained equation method, which resulted in a relative efficiency
Page 7 of 26
ACCEPTED MANUSCRIPT of 95% (17, 18). The analysis was repeated for each imputed dataset, and the five sets of
RI P
T
results were combined by the Rubin’s rule (17).
SC
A separate risk prediction model for each gender was developed as two previous studies had found evidence for gender differences (19, 20). All samples were randomly divided into two
MA NU
sub-datasets: derivation and validation cohorts. Two-thirds of the samples formed the derivation cohort for developing the model; the remaining one-third formed the validation cohort for validating the model. Differences in potential predictors between the two cohorts
ED
were assessed by chi-square tests for categorical variables or independent t-tests for
CE
PT
continuous variables.
AC
Using the derivation cohorts, a forward stepwise Cox regression was performed to obtain the set of significant predictors affecting risk of mortality. F-test was used to calculate p-values; variables with p-values < 0.05 were retained and variables with p-values > 0.10 were removed. As several earlier studies had identified revealed curvilinear relationships between the risk of mortality and parameters like HbA1c, SBP, DBP and BMI (21-25), the significance of the quadratic terms of these parameters was also assessed. As one previous study had found that the effects of some predictors, such as lipid profile, may vary vastly with age, their interactions with age were also assessed in the analyses (26). The final model was
Page 8 of 26
ACCEPTED MANUSCRIPT obtained after removing all insignificant interactions and quadratic effects. The proportional
T
hazards assumption of the Cox model was examined using plots of the scaled Schoenfeld
RI P
residuals against time for the covariates. A violation of the assumption was indicated by a
SC
non-random pattern of the plot and thereafter transformation of covariates would be necessary. After checking, all models in the study did not show any signs of violation of the assumption.
MA NU
The formula for the estimation of 5-year risk of mortality for male and female were developed based on the baseline survival at 5-year follow-up.
ED
Using the validation cohorts, the performance of the developed models, were compared
PT
against the JADE and New Zealand risk scores for 5-year all-cause mortality (5, 6). The
CE
predictive power of each model was compared using Harrell's C statistic, D statistic and R2
AC
statistic. For Harrell's C statistics, values < 0.7 indicates limited discriminating power, 0.7 to 0.9 is acceptable, and >0.9 indicates strong discrimination (27). Similarly, for D statistic, higher values indicate better discrimination. R2 statistic measures the variation explained by a model with higher values indicating better performance. The corresponding 95% confidence intervals (CIs) were obtained by bootstrapping of size 2,000. Calibration plots were displayed to compare the mean of predicted risk at 5 years with the observed risk of mortality, which was obtained by 5-year Kaplan-Meier estimate, by deciles of predicted risk. Two sensitivity analyses were conducted. Firstly, patients with missing data on waist circumference, urine
Page 9 of 26
ACCEPTED MANUSCRIPT ACR and STDR were excluded. Secondly, a 10-fold cross validation was used to calculate
RI P
T
the Harrell’s C statistic.
SC
All significance tests were two-tailed with p-values< 0.05 considered statistically significant.
MA NU
The statistical analysis was performed in STATA Version 13.0.
3 Results
There were 149,333 T2DM patients who were Chinese, aged between 18 and 79 and
ED
received primary care in HA clinics in 2010. After excluding 16,178 patients with prior
PT
history of CVD, cancer, chronic lung disease or end stage renal disease at baseline and 609
CE
patients with no follow-up recorded, 132,462 T2DM patients (70,799 females and 61,663
AC
males) were included in the analysis. The three potential predictors with the lowest data completion rates were waist circumference (79%), urine ACR (71%) and STDR (68%) respectively. All other potential predictors attained a data completion rate greater than 90%. During a median follow-up period of 5.0 years (range: 0.04 to 6.0 years), the overall incidence of mortality was 4.67% (6,189): 5.81% (3,585) in males and 3.68% (2,604) in females. The overall mortality incidence rates per 1,000 person-years were 9.6 (95% CI: 9.3-9.8): 12.0 (95% CI: 11.6-12.4) in males and 7.5 (95% CI: 7.2-7.8) in females. Baseline characteristics by gender of the derivation and validation cohorts (after multiple imputation)
Page 10 of 26
ACCEPTED MANUSCRIPT are shown in Table 1. The mean age for each cohort was 61 years (for males) and 63 years
T
(for females). For both genders, there were no significant differences between the derivation
SC
RI P
cohorts and validation cohorts for all potential predictors.
Table 2 displays the risk prediction models for both gender groups using Cox regression.
MA NU
Predictors common to both genders included older age, smoking, usage of anti-hypertensive drugs and insulin, urine ACR and eGFR. The risk of mortality was also associated with the quadratic terms of BMI, HbA1c and SBP. The interaction term between age and eGFR was
ED
significant, implying that the risk effect of eGFR in younger patients was significantly
PT
greater than that in older patients. For males, the effects for BMI and SBP on the risk of
CE
mortality decreased with age. For females, additional predictors included longer duration of
AC
T2DM and use of oral anti-diabetic drugs and lipid-lowering agents. The quadratic term of DBP was also associated with incidence of mortality. The effects for HbA1c and DBP on the risk of mortality decreased with age increased with aged for lipid-lowering agents. Supplementary Table S1 shows the formula and examples for the estimation of the 5-year risk of mortality in both genders and the estimated risk can be calculated using an online risk engine (http://www.fmpc.hku.hk/DMCx_RiskEngine.php).
Page 11 of 26
ACCEPTED MANUSCRIPT Comparison of the discrimination and prediction powers of the developed and two
T
pre-existing mortality risk models (New Zealand and JADE) using the validation cohort are
RI P
shown in Table 3. In terms of all validation statistics, the new model performed the best
SC
amongst the three models as it possessed the highest Harrell’s C statistic (male: 0.768, female: 0.782), D statistic (male: 1.586; female: 1.737), and R2 value (male: 37.5%, female:
MA NU
41.9%). Figure 1 shows the calibration plot on the 5-year observed and predicted risks among the three risk prediction models. The newly developed model performed the best in terms of calibration. After excluding patients with missing data on waist circumference,
ED
urine ACR and STDR, and using 10-fold cross validation in sensitivity analyses, similar
AC
4 Discussion
CE
PT
results for the Harrell’s C statistic were obtained.
This large population-based cohort study was the first to develop and validate the 5-year all-cause mortality risk prediction model amongst Chinese primary care patients with T2DM. Our models demonstrated good discrimination power and calibration, and outperformed other established prediction models for mortality risk. The findings confirmed our hypothesis that risk prediction models for T2DM patients being treated in primary care should be ethnic-specific and based on data obtained from subjects being treated in primary care settings.
Page 12 of 26
ACCEPTED MANUSCRIPT
T
A number of limited mortality risk prediction models for diabetic patients have been
RI P
reported previously, however the equations used in many of these studies were unavailable
SC
for external validation (5-9). Only two models, one developed based on a primary care cohort from New Zealand, and the other, developed from a Chinese hospital-based cohort
MA NU
(JADE), had sufficient published data to allow comparisons with our model (5, 6). Common predictors among all three models were age, BMI, urine ACR and eGFR. The other predictors identified in this study were similar to those found in the New Zealand model
ED
including smoking habit, duration of T2DM, HbA1c and blood pressure (BP) that are key
PT
predictors for progression for macro-vascular complications (28, 29), which may in turn
CE
predict mortality. Both the New Zealand sample and this current study had large sample
AC
sizes with a high number of deaths and were well powered to evaluate the predictors. On the other hand, subjects in the JADE study were recruited from hospital-based settings, with potential differences in disease profile. The JADE study had relatively smaller numbers of deaths due to a smaller overall sample size and it did not include risk parameters such as smoking habit, duration of T2DM, HbA1c and BP.
In comparison to our model, the New Zealand model had adequate discriminatory power but poor calibration of absolute risk for Chinese patients. Calibration is one of key
Page 13 of 26
ACCEPTED MANUSCRIPT measurements for the evaluation of the agreement between observed and predicted risks and
T
thus the underestimated risk in Chinese diabetic patients by New Zealand model may defeat
RI P
the purpose of earlier identification for patients who are at high risk of death. Although the
SC
New Zealand model included the ethnicity to allow adjustments for the different risks of mortality between populations, their sample only had a small proportion of Asian subjects
MA NU
(746 out of 26,864) and may not be fully representative of a Chinese diabetic population. Aside from ethnic differences in disease profile, other environmental factors such as health care system and cultural behaviours among different diabetic populations may contribute to
PT
ED
the lower calibration power of the New Zealand model (10-14).
CE
This study extends on previous models by including the effect of gender and age. We found
AC
there were gender differences in some of the predictors, and that there were interactions with age. Our models also included the effect of drug treatments and found that use of lipid-lowering agents significantly reduced the mortality risk. Previous studies have demonstrated that treatment with statins has a markedly attenuating effect on cancer risk as well as lowers the risk of incidence of diabetic kidney disease and coronary heart disease by 60% and 40% respectively (30-32). Our findings provide further evidence to support the cancer-, reno- and cardio- protective effects of statins in primary prevention and reduced mortality.
Page 14 of 26
ACCEPTED MANUSCRIPT
T
Our mortality risk prediction models also included urine ACR and eGFR, as a measure of
RI P
the degree of renal impairment (5, 6). Studies have shown that diabetic patients with kidney
SC
disease have elevated mortality rates (33-35). The role of diabetic kidney disease is of particular concern in Chinese populations (36) as multi-ethnic studies have found that
MA NU
Chinese diabetic patients have a higher prevalence of nephropathy than Caucasians (10, 11, 36-38). Furthermore, a global survey found that only 20% of Asian diabetic patients received reno-protective drugs compared to 30% of Caucasians. Given the importance of
ED
diabetic kidney disease in Chinese patients, regular urine ACR and eGFR assessment should
AC
CE
disease progression.
PT
be advocated as part of routine care for early identification and intervention to help delay the
There were several limitations to this study. Firstly, our study design was retrospective rather than prospective, which may introduce some bias to the results. However, our data were extracted systematically from the central management database of the Hospital Authority and thus the findings should be similar to that gained from a prospective cohort design. Secondly, several potential predictors such as diet and exercise were not available and were not considered in our models. Thirdly, our prediction models were developed using data obtained from diabetic patients without any major co-morbidities. In our setting
Page 15 of 26
ACCEPTED MANUSCRIPT patients with CVD and end stage renal disease are usually managed in specialist clinics
T
rather than in primary care. As a result, the model developed is only applicable to diabetic
RI P
patient with no comorbidities. Also, our model was limited to predict 5-year all-cause
SC
mortality. Future studies with longer follow-up periods such as 10 years for diabetic patients with and without co-morbidity are needed to produce models that can predict the
MA NU
longer-term mortality risks for more diverse diabetic populations. Lastly, only internal validation but not external validation was available in the current study. An external validation should be warranted to validate our model by using Chinese population in other
PT
ED
regions.
CE
In conclusion, our newly developed gender-specific models provide a more accurate and
AC
valid 5-year mortality risk predictions for Chinese diabetic primary care patients than other currently existing models. As many of the risk factors in the model (such as like smoking, HbA1c, blood pressure, lipid profile, urine ACR) are modifiable clinicians can use the model as a guide to screen, identify those who are at increased risk of mortality and intervene to reduce the risk of premature death. These risk prediction models can be incorporated into local management guidelines to facilitate a more evidence-based use of health care resources. Future studies with a longer follow-up period of 10 years and external validation should be
Page 16 of 26
ACCEPTED MANUSCRIPT conducted to model the longer-term mortality risks for Chinese diabetic populations and
RI P
T
validate the model.
SC
5 Acknowledgements
The authors wish to acknowledge the contributions of the Risk Assessment Management
MA NU
Program for Diabetes Mellitus (RAMP-DM) program team at the Hospital Authority head office, and the Chiefs of Service and RAMP-DM program coordinators in each cluster, and
ED
the Statistics and Workforce Planning Department at the Hong Kong Hospital Authority.
PT
6 Funding Sources
CE
This work was supported by the Health Services Research Fund, Food and Health Bureau,
AC
HKSAR Commissioned Research on Enhanced Primary Care Study [grant number EPC-HKU-2]. No funding organization had any role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation of the manuscript. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
Page 17 of 26
ACCEPTED MANUSCRIPT 7 References 1.
International Diabetes Federation. IDF Diabetes Atlas, 7 ed. Brussels: 2015.
2.
Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. Collaboration ERF. Diabetes mellitus, fasting glucose, and risk of cause-specific death. N Engl J
RI P
3.
T
Plos med. 2006;3(11):e442. Med. 2011;2011(364):829-41. 4.
Gæde P, Lund-Andersen H, Parving H-H, Pedersen O. Effect of a multifactorial intervention on
5.
SC
mortality in type 2 diabetes. New England Journal of Medicine. 2008;358(6):580-91. Yang X, So WY, Tong PC, Ma RC, Kong AP, Lam CW, et al. Development and validation of an
internal medicine. 2008;168(5):451-7. 6.
MA NU
all-cause mortality risk score in type 2 diabetes: The Hong Kong Diabetes Registry. Archives of Robinson TE, Elley CR, Kenealy T, Drury PL. Development and validation of a predictive risk
model for all-cause mortality in type 2 diabetes. Diabetes research and clinical practice. 2015;108(3):482-8. 7.
Wells BJ, Jain A, Arrigain S, Yu C, Rosenkrans WA, Kattan MW. Predicting 6-year mortality risk in
8.
ED
patients with type 2 diabetes. Diabetes care. 2008;31(12):2301-6. McEwen LN, Karter AJ, Waitzfelder BE, Crosson JC, Marrero DG, Mangione CM, et al. Predictors
of mortality over 8 years in type 2 diabetic patients. Diabetes Care. 2012:DC_112281. De Cosmo S, Copetti M, Lamacchia O, Fontana A, Massa M, Morini E, et al. Development and
PT
9.
validation of a predicting model of all-cause mortality in patients with type 2 diabetes. Diabetes Care.
CE
2013;36(9):2830-5.
10. Group DD. Prevalence of small vessel and large vessel disease in diabetic patients from 14centres: the world health organization multinational study of vascular disease in diabetics.
AC
Diabetologia. 1985;28:615-40.
11. Chi Z, Lee E, Lu M, Keen H, Bennett P, Group WMS. Vascular disease prevalence in diabetic patients in China: standardised comparison with the 14 centres in the WHO Multinational Study of Vascular Disease in Diabetes. Diabetologia. 2001;44(2):S82-S6. 12. Forouhi NG, Sattar N. CVD risk factors and ethnicity—a homogeneous relationship? Atherosclerosis Supplements. 2006;7(1):11-9. 13. Byrne CD, Wild SH. The metabolic syndrome: John Wiley & Sons; 2011. 14. Zhao W, Chen J. Implications from and for food cultures for cardiovascular disease: diet, nutrition and cardiovascular diseases in China. Asia Pacific journal of clinical nutrition. 2001;10(2):146-52. 15. Fung CS, Chin WY, Dai DS, Kwok RL, Tsui EL, Wan YF, et al. Evaluation of the quality of care of a multi-disciplinary risk factor assessment and management programme (RAMP) for diabetic patients. BMC family practice. 2012;13(1):116. 16. Royston P. Multiple imputation of missing values. Stata Journal. 2004;4:227-41.
Page 18 of 26
ACCEPTED MANUSCRIPT 17. Rubin DB. Multiple imputation for nonresponse in surveys: John Wiley & Sons; 2004. 18. Van Buuren S, Boshuizen HC, Knook DL. Multiple imputation of missing blood pressure covariates in survival analysis. Statistics in medicine. 1999;18(6):681-94. 19. JANGHORBANI M, HEDLEY AJ, JONES RB, ZHIANPOUR M, GILMOUR WH. Gender differential in 1993;22(6):1056-63.
RI P
T
all-cause and cardiovascular disease mortality. International journal of epidemiology. 20. Arnetz L, Ekberg NR, Alvarsson M. Sex differences in type 2 diabetes: focus on disease course and outcomes. Diabetes Metab Syndr Obes. 2014;7:409-20.
SC
21. Cederholm J, Gudbjörnsdottir S, Eliasson B, Zethelius B, Eeg-Olofsson K, Nilsson PM. Blood pressure and risk of cardiovascular diseases in type 2 diabetes: further findings from the Swedish
MA NU
National Diabetes Register (NDR-BP II). Journal of hypertension. 2012;30(10):2020-30. 22. Kontopantelis E, Springate DA, Reeves D, Ashcroft DM, Rutter M, Buchan I, et al. Glucose, blood pressure and cholesterol levels and their relationships to clinical outcomes in type 2 diabetes: a retrospective cohort study. Diabetologia. 2015;58(3):505-18.
23. Wan EYF, Fung CSC, Wong CKH, Chin WY, Lam CLK. Association of Hemoglobin A1c Levels With Cardiovascular Disease and Mortality in Chinese Patients With Diabetes. Journal of the American
ED
College of Cardiology. 2016;67(4):456-8.
24. Wan EYF, Fung CSC, Fong DYT, Chan AKC, Lam CLK. A curvilinear association of body mass index with cardiovascular diseases in Chinese patients with type 2 diabetes mellitus–A population-based
PT
retrospective cohort study. Journal of diabetes and its complications. 2016. 25. Wan EYF, Fong DYT, Fung CSC, Lam CLK. Incidence and Predictors for Cardiovascular Disease in
CE
Chinese Patients With Type 2 Diabetes Mellitus – A Population-Based Retrospective Cohort Study. Journal of Diabetes and its Complications. 26. Collaboration PS. Blood cholesterol and vascular mortality by age, sex, and blood pressure: a
AC
meta-analysis of individual data from 61 prospective studies with 55 000 vascular deaths. The Lancet. 2007;370(9602):1829-39. 27. Swets JA. Measuring the accuracy of diagnostic systems. Science. 1988;240(4857):1285-93. 28. Stratton IM, Adler AI, Neil HAW, Matthews DR, Manley SE, Cull CA, et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. Bmj. 2000;321(7258):405-12. 29. Adler AI, Stratton IM, Neil HAW, Yudkin JS, Matthews DR, Cull CA, et al. Association of systolic blood pressure with macrovascular and microvascular complications of type 2 diabetes (UKPDS 36): prospective observational study. Bmj. 2000;321(7258):412-9. 30. Yang X, Ma RC, So WY, Yu LW, Kong AP, Ko GT, et al. Low triglyceride and nonuse of statins is associated with cancer in type 2 diabetes mellitus. Cancer. 2011;117(4):862-71. 31. Luk AO, Yang X, Ma RC, Ng VW, Linda WY, Lau WW, et al. Association of statin use and development of renal dysfunction in type 2 diabetes—The Hong Kong Diabetes Registry. diabetes research and clinical practice. 2010;88(3):227-33.
Page 19 of 26
ACCEPTED MANUSCRIPT 32. Ting RZ, Yang X, Linda W, Luk AO, Kong AP, Tong PC, et al. Lipid control and use of lipid-regulating drugs for prevention of cardiovascular events in Chinese type 2 diabetic patients: a prospective cohort study. Cardiovascular diabetology. 2010;9(1):1. 33. Matsushita K, Coresh J, Sang Y, Chalmers J, Fox C, Guallar E, et al. Estimated glomerular
T
filtration rate and albuminuria for prediction of cardiovascular outcomes: a collaborative
RI P
meta-analysis of individual participant data. The Lancet Diabetes & Endocrinology. 2015;3(7):514-25. 34. Wang Y, Katzmarzyk PT, Horswell R, Zhao W, Johnson J, Hu G. Kidney function and the risk of cardiovascular disease in patients with type 2 diabetes. Kidney international. 2014;85(5):1192-9.
SC
35. Soriano LC, Johansson S, Stefansson B, Rodríguez LAG. Cardiovascular events and all-cause mortality in a cohort of 57,946 patients with type 2 diabetes: associations with renal function and
MA NU
cardiovascular risk factors. Cardiovascular diabetology. 2015;14(1):1. 36. Kong AP, Xu G, Brown N, So W-Y, Ma RC, Chan JC. Diabetes and its comorbidities—where East meets West. Nature Reviews Endocrinology. 2013;9(9):537-47. 37. Ma RC, Chan JC. Type 2 diabetes in East Asians: similarities and differences with populations in Europe and the United States. Annals of the New York Academy of Sciences. 2013;1281(1):64-91. 38. Chan JC, So W, Ma RC, Tong PC, Wong R, Yang X. The complexity of vascular and non-vascular
ED
complications of diabetes: the Hong Kong Diabetes Registry. Current cardiovascular risk reports.
AC
CE
PT
2011;5(3):230-9.
Page 20 of 26
ACCEPTED MANUSCRIPT 8 Figure Legends
AC
CE
PT
ED
MA NU
SC
RI P
T
Figure 1. Calibration plots for observed and predicted 5-year risks of mortality
Page 21 of 26
ACCEPTED MANUSCRIPT 9 Tables Table 1. Baseline characteristics of derivation and validation cohorts in male and female groups
60.9±10.0 4,458 (21.7%)
6.7±6.3 28,092 (68.3%) 2,495 (6.1%)
6.6±6.0 13,990 (68.1%) 1,206 (5.9%)
MA NU
62.7±9.9 495 (2.1%)
7.2±6.6 35,218 (74.6%) 2,346 (5.0%)
7.2±7.0 17,587 (74.5%) 1,177 (5.0%)
15,246 (74.2%) 18,106 (88.1%) 440 (2.1%) 5,098 (24.8%)
36,257 (76.8%) 40,394 (85.6%) 939 (2.0%) 13,832 (29.3%)
18,056 (76.5%) 20,186 (85.5%) 474 (2.0%) 6,870 (29.1%)
25.5±3.8 90.8±22.6 7.2±1.3 55.2±14.2 134.4±16.0 77.3±9.7 2.9±0.8 4.3±1.2 1.5±1.0 7.2±48.9
25.5±3.8 90.8±17.9 7.2±1.4 55.2±15.3 134.4±16.0 77.5±9.7 2.9±0.8 4.3±1.2 1.5±1.0 7.0±31.0
25.6±4.2 87.6±24.7 7.2±1.2 55.2±13.1 135.1±16.5 74.0±9.7 3.0±0.8 4.0±1.1 1.6±0.9 7.1±35.8
25.6±4.3 87.4±20.3 7.2±1.2 55.2±13.1 135.1±16.5 74.0±9.7 3.0±0.8 4.0±1.1 1.6±1.0 7.1±45.7
30,434 (74.0%) 9,379 (22.8%) 1,233 (3.0%) 63 (0.2%)
15,145 (73.7%) 4,809 (23.4%) 568 (2.8%) 32 (0.2%)
34,443 (73.0%) 10,745 (22.8%) 1,905 (4.0%) 106 (0.2%)
17,328 (73.4%) 5,250 (22.2%) 970 (4.1%) 52 (0.2%)
ED
30,457 (74.1%) 36,124 (87.9%) 842 (2.0%) 10,015 (24.4%)
CE
AC
62.6±9.9 1,006 (2.1%)
SC
61.0±10.0 8,791 (21.4%)
PT
Socio-demographics Age, years Smoker Disease characteristics Duration of T2DM, years Diagnosed Hypertension STDR Treatment modalities Anti-hypertensive drugs usage Oral anti-diabetic drugs usage Insulin usage Lipid-lowering agents usage Clinical parameters BMI, kg/m2 Waist circumference, cm HbA1c, % HbA1c, mmol/mol SBP, mmHg DBP, mmHg LDL-C, mmol/L TC/HDL-C ratio Triglyceride, mmol/L Urine ACR, mg/mmol eGFR, ml/min/1.73m2 ≥ 90 60-89 30-59 <30
RI P
T
Male Female Derivation cohort Validation cohort Derivation cohort Validation cohort (n=41,109) (n=20,554) (n=47,199) (n=23,600)
Characteristics
T2DM = Type 2 Diabetes Mellitus; STDR = Sight Threatening Diabetic Retinopathy; BMI = Body Mass Index; HbA1c = Hemoglobin A1c; SBP = Systolic Blood Pressure; DBP = Diastolic Blood Pressure; LDL-C = Low Density Lipoprotein-Cholesterol; TC = Total Cholesterol; HDL-C = High-density Lipoprotein-Cholesterol; ACR = Albumin/Creatinine Ratio; eGFR = estimated Glomerular Filtration Rate
Page 22 of 26
ACCEPTED MANUSCRIPT Table 2 Cox regression models for predictors of developing 5-year mortality in derivation cohort
P-value
HR†
1.13 1.71
(1.11,1.16) (1.56,1.88)
<0.001* <0.001*
1.25 0.79
CE
AC
(1.01,1.55) (0.72,0.88)
(0.71,0.84) (1.00,1.01) (0.75,1.00) (1.00,1.02) (0.93,0.98) (1.0001,1.0004)
ED
0.77 1.01 0.86 1.01 0.96 1.0002
(1.23,1.59)
1.21
P-value
1.14 2.04
(1.11,1.17) (1.58,2.64)
<0.001* <0.001*
1.01
(1.00,1.02)
0.045*
1.27
(1.08,1.50)
0.003*
1.26 1.97
(1.07,1.48) (1.57,2.46)
0.006* <0.001*
2.09
(0.81,5.38)
0.128
<0.001* 0.86 <0.001* 1.003 0.046* 0.63 0.002* 1.06 <0.001* 0.96 <0.001* 1.0001 0.93 1.00 <0.001* 1.15
(0.79,0.93) (1.001,1.004) (0.51,0.78) (1.04,1.07) (0.94,0.99) (1.0000,1.0002) (0.89,0.97) (1.00,1.00) (1.08,1.23)
0.001* 0.001* <0.001* <0.001* 0.005* 0.003* 0.002* <0.001* <0.001*
RI P
SC
1.39
Female 95%CI
(1.17,1.25)
T
Male 95%CI
PT
Socio-demographics Age, years Smoker (Non-smoker) Disease characteristics Duration of T2DM, years Treatment modalities Anti-hypertensive drugs usage (No) Oral anti-diabetic drugs (No) Insulin usage (No) Lipid-lowering agents usage (No) Clinical parameters BMI, kg/m2 BMI2, kg/m2 HbA1c, % HbA1c2, % SBP, mmHg SBP2, mmHg DBP, mmHg DBP2, mmHg ln (Urine ACR +1), mg/mmol eGFR (>90ml/min/1.73m2) 60-89ml/min/1.73m2 30-59ml/min/1.73m2 <30ml/min/1.73m2 Age interaction term Age*eGFR (>90ml/min/1.73m2) 60-89ml/min/1.73m2 30-59ml/min/1.73m2 <30ml/min/1.73m2 Age*(BMI+BMI2) Age*(HbA1c+HbA1c2) Age*(SBP+SBP2) Age*(DBP+DBP2) Age*Lipid-lowering agents used
HR†
<0.001* 0.043
*
MA NU
Predictors
<0.001*
1.31 40.35 23.93
(0.54,3.15) (10.55,154.34) (1.08,532.01)
0.546 <0.001* 0.045*
1.33 17.28 47.12
(0.43,4.09) (3.78,79.03) (1.45,1,531.15)
0.624 <0.001* 0.030*
0.997 0.96 0.98 0.999
(0.985,1.010) (0.94,0.98) (0.94,1.03) (0.999,1.000)
0.657 <0.001* 0.396 0.024*
0.998 0.97 0.97
(0.982,1.013) (0.95,0.99) (0.93,1.02)
0.767 0.006* 0.261
0.999
(0.999,0.999)
<0.001*
0.999
(0.999,0.999)
0.020*
0.99
(0.97,1.00)
0.044*
0.999
(0.999,1.000)
0.013
*
T2DM = Type 2 Diabetes Mellitus; BMI = Body Mass Index; HbA1c = Hemoglobin A1c; SBP = Systolic Blood Pressure; DBP = Diastolic Blood Pressure; TC = Total Cholesterol; HDL-C = High-density Lipoprotein-Cholesterol; ACR = Albumin/Creatinine Ratio; eGFR = estimated Glomerular Filtration Rate; T2DM = Type 2 Diabetes Mellitus; HR = Hazard Ratio Notes: * Significant difference (P < 0.05) † HR > 1 indicates greater risk of event occurrence
Page 23 of 26
ACCEPTED MANUSCRIPT Table 3. Performance of new and existing risk models for mortality in validation cohort for predicting 5-year risk of mortality Validation statistics
New model
New Zealand model
0.768 (0.754,0.782)
0.762 (0.748,0.776)
0.748 (0.734,0.763)
-
0.006*
0.019*
1.586 (1.484,1.673)
1.537 (1.449,1.626)
1.388 (1.291,1.478)
37.5 (34.6,40.0)
36.1 (33.4,38.8)
31.5 (28.5,34.3)
Harrell's C statistic test in
RI P
Harrell's C statistic
T
Male
2
R
Harrell's C statistic
MA NU
Female 0.782 (0.766,0.799)
0.780 (0.764,0.797)
0.754 (0.736,0.771)
-
0.002
0.029*
1.737 (1.626,1.848)
1.703 (1.591,1.809)
1.504 (1.391,1.606)
41.9 (38.5,45.2)
40.9 (37.9,43.8)
35.1 (31.3,38.4)
Harrell's C statistic test in comparison with new model D statistic R2
SC
comparison with new model D statistic
JADE model
ED
The brackets represented 95% confidence interval of corresponding validation statistic
AC
CE
PT
* Significant difference in Harrell's C statistic (P-value < 0.05)
Page 24 of 26
ACCEPTED MANUSCRIPT Figure 1. Calibration plots for observed and predicted 5-year risks of mortality Male
Female
SC
RI P
T
5-year risk of mortality (%)
Model 1
ED
5-year risk of mortality (%)
MA NU
New Zealand risk score
PT CE AC
5-year risk of mortality (%)
JADE risk score
Deciles of estimated risk
Deciles of estimated risk
Page 25 of 26
ACCEPTED MANUSCRIPT Title: Prediction of five-year all-cause mortality in Chinese Patients with Type 2 Diabetes
T
Mellitus – A Population-based Retrospective Cohort Study
Current existing prediction model for all-cause mortality either overestimated or
SC
RI P
Highlights
underestimated the risk for Chinese primary care patients with Type 2 Diabetes Mellitus Our newly developed gender-specific models provide a more accurate and valid 5-year
MA NU
mortality risk predictions for Chinese diabetic primary care patients than other currently existing models.
As many of the risk factors in the model (such as like smoking, HbA1c, blood pressure,
ED
PT
lipid profile, urine ACR) are modifiable clinicians can use the model as a guide to screen,
CE
identify those who are at increased risk of mortality and intervene to reduce the risk of
AC
premature death.
These risk prediction models can be incorporated into local management guidelines to facilitate a more evidence-based use of health care resources.
Page 26 of 26
S1056-8727(16)30969-2 doi: 10.1016/j.jdiacomp.2017.01.017 JDC 6948
To appear in:
Journal of Diabetes and Its Complications
Received date: Revised date: Accepted date:
7 December 2016 25 January 2017 29 January 2017
Please cite this article as: Wan, E.Y.F., Fong, D.Y.T., Fung, C.S.C., Yu, E.Y.T., Chin, W.Y., Chan, A.K.C. & Lam, C.L.K., Prediction of five-year all-cause mortality in Chinese Patients with Type 2 Diabetes Mellitus – A Population-based Retrospective Cohort Study, Journal of Diabetes and Its Complications (2017), doi: 10.1016/j.jdiacomp.2017.01.017
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Prediction of five-year all-cause mortality in Chinese Patients with Type 2 Diabetes
RI P
T
Mellitus – A Population-based Retrospective Cohort Study
SC
Authors: Eric Yuk Fai Wana*, Daniel Yee Tak Fongb, Colman Siu Cheung Funga, Esther Yee
a
MA NU
Tak Yua, Weng Yee China, Anca Ka Chun Chana, Cindy Lo Kuen Lama
Department of Family Medicine and Primary Care, the University of Hong Kong, 3/F Ap
Lei Chau Clinic, 161 Main Street, Ap Lei Chau, Hong Kong
ED
School of Nursing, the University of Hong Kong, Hong Kong
PT
b
CE
Mr. Eric Yuk Fai Wan1*
AC
* Corresponding author Tel. (852) 2552 4690
Fax. (852) 2814 7475
Email: [email protected]
Page 1 of 26
ACCEPTED MANUSCRIPT Abstract
T
Aims:
RI P
This study aimed to develop and validate an all-cause mortality risk prediction model for
SC
Chinese primary care patients with Type 2 Diabetes Mellitus(T2DM) in Hong Kong.
MA NU
Methods:
A population-based retrospective cohort study was conducted on 132,462 Chinese patients who had received public primary care services during 2010. Each gender sample was
ED
randomly split on a 2:1 basis into derivation and validation cohorts and was follow-up for a
PT
median period of 5 years. Gender-specific mortality risk prediction models showing the
CE
interaction effect between predictors and age were derived using Cox proportional hazards
AC
regression with forward stepwise approach. Developed models were compared with pre-existing models by Harrell's C-statistic and calibration plot using validation cohort.
Results: Common predictors of increased mortality risk in both genders included: age; smoking habit; diabetes duration; use of anti-hypertensive agents, insulin and lipid-lowering drugs; body mass index; haemoglobin A1c; systolic blood pressure(BP); total cholesterol to high-density lipoprotein-cholesterol ratio; urine albumin to creatinine ratio(urine ACR); and estimated
Page 2 of 26
ACCEPTED MANUSCRIPT glomerular filtration rate(eGFR). Prediction models showed better discrimination with
T
Harrell's C-statistics of 0.768(males) and 0.782(females) and calibration power from the plots
SC
RI P
than previously established models.
Conclusions:
MA NU
Our newly developed gender-specific models provide a more accurate predicted 5-year mortality risk for Chinese diabetic patients than other established models.
AC
CE
PT
ED
Keywords: Type 2 Diabetes mellitus; Prediction; Risk; Mortality; Primary care
Page 3 of 26
ACCEPTED MANUSCRIPT
T
Manuscript Text
RI P
1 Introduction
SC
Diabetes Mellitus (DM) is a well-known public health issue, affecting 415 million people, causing around 5 million deaths and accounting for 14.5% of all-cause mortality worldwide
MA NU
in 2015 (1). The World Health Organization has projected that diabetes will be the seventh leading cause of mortality by 2030 (2). Compared to patients without DM, the risk of overall mortality is almost double in patients with DM (3). Diabetic patients at age 50 years have a
ED
6-year shorter life expectancy than their non-diabetic counterparts (3). Previous studies have
PT
suggested that many of the causes of mortality in diabetic patients may be preventable
CE
through better control of DM and other cardiovascular risk factors during earlier stages of the
AC
disease (1, 4). Early identification of high-risk diabetic patients is therefore important so that appropriate and timely interventions can be provided to help reduce the risk of premature mortality.
Many studies have previously evaluated the risk factors for all-cause mortality among diabetic patients, but only a few have established prediction models (5-9). Amongst these, almost all have been developed based on non-Chinese populations. One model, the Joint Asia Diabetes Evaluation (JADE), was developed using a Chinese diabetic population,
Page 4 of 26
ACCEPTED MANUSCRIPT however it was limited by a small number of mortalities and was developed using
T
hospital-based patients (5). Models from these studies may not accurately reflect the risk
RI P
profile of Chinese patients with DM managed in primary care settings where the bulk of
SC
patients with DM are treated.
MA NU
According to the International Diabetes Federation (IDF) the Chinese currently constitute approximately 25% of the global DM population (1). In 2015, the Chinese had the highest number of deaths (1.3 million) due to diabetes, and had nearly double the prevalence of
ED
DM-related mortality for people under the age of 60 when compared to Europeans and
PT
Australians (1). Given that different DM populations can differ in disease profile and other
CE
determinants such as genetics, health care policy and culture (10-14), risk prediction models
AC
developed in Western settings may not accurately apply to Chinese populations. As there have been no population-based studies performed to establish a mortality risk prediction model for Chinese diabetic patients, this study aimed to develop a 5-year mortality risk prediction model among Chinese primary care patients in Hong Kong with Type 2 DM (T2DM).
2 Subjects, Materials and Methods 2.1 Study Design
Page 5 of 26
ACCEPTED MANUSCRIPT This was a population-based retrospective cohort study. Subject inclusion criteria included: (1)
T
Chinese, (2) aged between 18 and 79 years, (3) clinically diagnosed with T2DM as identified
RI P
using the International Classification of Primary Care-2 (ICPC-2) code of ‘T90’, (4) no prior
SC
history of cardiovascular disease (CVD), cancer or chronic lung disease, and (5) received medical care for T2DM from one of the 74 general out-patient clinics of the Hong Kong
MA NU
Hospital Authority (HA). The HA is the largest governmental organisation coordinating all public-sector hospitals and primary care clinics in Hong Kong. The HA manages over 50% of all DM primary care patients in Hong Kong. Clinical data between 1 January 2010 and 31
ED
December 2010 was retrieved through the central management system of the HA. The data
PT
was made available as part of a territory-wide study for the evaluation of quality of care of
CE
the HA primary care diabetic programs (15). Mortality data was retrieved from the Hong
AC
Kong Death Registry, which is compulsory for all residents in Hong Kong. All subjects were followed-up until death or to their last follow-up as of the censoring date of 30 November 2015, whichever occurred first.
Informed consent has been obtained from patients and ethics approval was received from all the regional Institutional Review Boards (IRB) of the Hong Kong Hospital Authority. The reported investigations have been carried out in accordance with the principles of the Declaration of Helsinki as revised in 2008.
Page 6 of 26
ACCEPTED MANUSCRIPT
T
2.2 Potential Predictors
RI P
The potential predictors included patient’s socio-demographics, disease characteristics,
SC
treatment modalities and clinical parameters at baseline. Socio-demographics consisted of: gender, age and smoking status. Disease characteristics consisted of: self-reported duration of
MA NU
T2DM, diagnosed hypertension and presence of sight threatening diabetic retinopathy (STDR), including pre-proliferative, proliferative diabetic retinopathy or maculopathy. Treatment modalities consisted of: the usages of anti-hypertensive drugs, oral anti-diabetic
ED
drugs, insulin and lipid-lowering agents. Clinical parameters consisted of: body mass index
PT
(BMI), waist circumference, haemoglobin A1c (HbA1c), systolic blood pressure (SBP),
CE
diastolic blood pressure (DBP), lipid profile (Low-density lipoprotein-cholesterol (LDL-C)
AC
and total cholesterol to high-density lipoprotein-cholesterol ratio (TC/ HDL-C ratio)), triglyceride, urine albumin to creatinine ratio (ACR) and eGFR. All laboratory assays were performed in accredited laboratories.
2.3 Data Analysis Missing data were handled by multiple imputation (16). Specifically, each missing value was imputed five times using the chained equation method, which resulted in a relative efficiency
Page 7 of 26
ACCEPTED MANUSCRIPT of 95% (17, 18). The analysis was repeated for each imputed dataset, and the five sets of
RI P
T
results were combined by the Rubin’s rule (17).
SC
A separate risk prediction model for each gender was developed as two previous studies had found evidence for gender differences (19, 20). All samples were randomly divided into two
MA NU
sub-datasets: derivation and validation cohorts. Two-thirds of the samples formed the derivation cohort for developing the model; the remaining one-third formed the validation cohort for validating the model. Differences in potential predictors between the two cohorts
ED
were assessed by chi-square tests for categorical variables or independent t-tests for
CE
PT
continuous variables.
AC
Using the derivation cohorts, a forward stepwise Cox regression was performed to obtain the set of significant predictors affecting risk of mortality. F-test was used to calculate p-values; variables with p-values < 0.05 were retained and variables with p-values > 0.10 were removed. As several earlier studies had identified revealed curvilinear relationships between the risk of mortality and parameters like HbA1c, SBP, DBP and BMI (21-25), the significance of the quadratic terms of these parameters was also assessed. As one previous study had found that the effects of some predictors, such as lipid profile, may vary vastly with age, their interactions with age were also assessed in the analyses (26). The final model was
Page 8 of 26
ACCEPTED MANUSCRIPT obtained after removing all insignificant interactions and quadratic effects. The proportional
T
hazards assumption of the Cox model was examined using plots of the scaled Schoenfeld
RI P
residuals against time for the covariates. A violation of the assumption was indicated by a
SC
non-random pattern of the plot and thereafter transformation of covariates would be necessary. After checking, all models in the study did not show any signs of violation of the assumption.
MA NU
The formula for the estimation of 5-year risk of mortality for male and female were developed based on the baseline survival at 5-year follow-up.
ED
Using the validation cohorts, the performance of the developed models, were compared
PT
against the JADE and New Zealand risk scores for 5-year all-cause mortality (5, 6). The
CE
predictive power of each model was compared using Harrell's C statistic, D statistic and R2
AC
statistic. For Harrell's C statistics, values < 0.7 indicates limited discriminating power, 0.7 to 0.9 is acceptable, and >0.9 indicates strong discrimination (27). Similarly, for D statistic, higher values indicate better discrimination. R2 statistic measures the variation explained by a model with higher values indicating better performance. The corresponding 95% confidence intervals (CIs) were obtained by bootstrapping of size 2,000. Calibration plots were displayed to compare the mean of predicted risk at 5 years with the observed risk of mortality, which was obtained by 5-year Kaplan-Meier estimate, by deciles of predicted risk. Two sensitivity analyses were conducted. Firstly, patients with missing data on waist circumference, urine
Page 9 of 26
ACCEPTED MANUSCRIPT ACR and STDR were excluded. Secondly, a 10-fold cross validation was used to calculate
RI P
T
the Harrell’s C statistic.
SC
All significance tests were two-tailed with p-values< 0.05 considered statistically significant.
MA NU
The statistical analysis was performed in STATA Version 13.0.
3 Results
There were 149,333 T2DM patients who were Chinese, aged between 18 and 79 and
ED
received primary care in HA clinics in 2010. After excluding 16,178 patients with prior
PT
history of CVD, cancer, chronic lung disease or end stage renal disease at baseline and 609
CE
patients with no follow-up recorded, 132,462 T2DM patients (70,799 females and 61,663
AC
males) were included in the analysis. The three potential predictors with the lowest data completion rates were waist circumference (79%), urine ACR (71%) and STDR (68%) respectively. All other potential predictors attained a data completion rate greater than 90%. During a median follow-up period of 5.0 years (range: 0.04 to 6.0 years), the overall incidence of mortality was 4.67% (6,189): 5.81% (3,585) in males and 3.68% (2,604) in females. The overall mortality incidence rates per 1,000 person-years were 9.6 (95% CI: 9.3-9.8): 12.0 (95% CI: 11.6-12.4) in males and 7.5 (95% CI: 7.2-7.8) in females. Baseline characteristics by gender of the derivation and validation cohorts (after multiple imputation)
Page 10 of 26
ACCEPTED MANUSCRIPT are shown in Table 1. The mean age for each cohort was 61 years (for males) and 63 years
T
(for females). For both genders, there were no significant differences between the derivation
SC
RI P
cohorts and validation cohorts for all potential predictors.
Table 2 displays the risk prediction models for both gender groups using Cox regression.
MA NU
Predictors common to both genders included older age, smoking, usage of anti-hypertensive drugs and insulin, urine ACR and eGFR. The risk of mortality was also associated with the quadratic terms of BMI, HbA1c and SBP. The interaction term between age and eGFR was
ED
significant, implying that the risk effect of eGFR in younger patients was significantly
PT
greater than that in older patients. For males, the effects for BMI and SBP on the risk of
CE
mortality decreased with age. For females, additional predictors included longer duration of
AC
T2DM and use of oral anti-diabetic drugs and lipid-lowering agents. The quadratic term of DBP was also associated with incidence of mortality. The effects for HbA1c and DBP on the risk of mortality decreased with age increased with aged for lipid-lowering agents. Supplementary Table S1 shows the formula and examples for the estimation of the 5-year risk of mortality in both genders and the estimated risk can be calculated using an online risk engine (http://www.fmpc.hku.hk/DMCx_RiskEngine.php).
Page 11 of 26
ACCEPTED MANUSCRIPT Comparison of the discrimination and prediction powers of the developed and two
T
pre-existing mortality risk models (New Zealand and JADE) using the validation cohort are
RI P
shown in Table 3. In terms of all validation statistics, the new model performed the best
SC
amongst the three models as it possessed the highest Harrell’s C statistic (male: 0.768, female: 0.782), D statistic (male: 1.586; female: 1.737), and R2 value (male: 37.5%, female:
MA NU
41.9%). Figure 1 shows the calibration plot on the 5-year observed and predicted risks among the three risk prediction models. The newly developed model performed the best in terms of calibration. After excluding patients with missing data on waist circumference,
ED
urine ACR and STDR, and using 10-fold cross validation in sensitivity analyses, similar
AC
4 Discussion
CE
PT
results for the Harrell’s C statistic were obtained.
This large population-based cohort study was the first to develop and validate the 5-year all-cause mortality risk prediction model amongst Chinese primary care patients with T2DM. Our models demonstrated good discrimination power and calibration, and outperformed other established prediction models for mortality risk. The findings confirmed our hypothesis that risk prediction models for T2DM patients being treated in primary care should be ethnic-specific and based on data obtained from subjects being treated in primary care settings.
Page 12 of 26
ACCEPTED MANUSCRIPT
T
A number of limited mortality risk prediction models for diabetic patients have been
RI P
reported previously, however the equations used in many of these studies were unavailable
SC
for external validation (5-9). Only two models, one developed based on a primary care cohort from New Zealand, and the other, developed from a Chinese hospital-based cohort
MA NU
(JADE), had sufficient published data to allow comparisons with our model (5, 6). Common predictors among all three models were age, BMI, urine ACR and eGFR. The other predictors identified in this study were similar to those found in the New Zealand model
ED
including smoking habit, duration of T2DM, HbA1c and blood pressure (BP) that are key
PT
predictors for progression for macro-vascular complications (28, 29), which may in turn
CE
predict mortality. Both the New Zealand sample and this current study had large sample
AC
sizes with a high number of deaths and were well powered to evaluate the predictors. On the other hand, subjects in the JADE study were recruited from hospital-based settings, with potential differences in disease profile. The JADE study had relatively smaller numbers of deaths due to a smaller overall sample size and it did not include risk parameters such as smoking habit, duration of T2DM, HbA1c and BP.
In comparison to our model, the New Zealand model had adequate discriminatory power but poor calibration of absolute risk for Chinese patients. Calibration is one of key
Page 13 of 26
ACCEPTED MANUSCRIPT measurements for the evaluation of the agreement between observed and predicted risks and
T
thus the underestimated risk in Chinese diabetic patients by New Zealand model may defeat
RI P
the purpose of earlier identification for patients who are at high risk of death. Although the
SC
New Zealand model included the ethnicity to allow adjustments for the different risks of mortality between populations, their sample only had a small proportion of Asian subjects
MA NU
(746 out of 26,864) and may not be fully representative of a Chinese diabetic population. Aside from ethnic differences in disease profile, other environmental factors such as health care system and cultural behaviours among different diabetic populations may contribute to
PT
ED
the lower calibration power of the New Zealand model (10-14).
CE
This study extends on previous models by including the effect of gender and age. We found
AC
there were gender differences in some of the predictors, and that there were interactions with age. Our models also included the effect of drug treatments and found that use of lipid-lowering agents significantly reduced the mortality risk. Previous studies have demonstrated that treatment with statins has a markedly attenuating effect on cancer risk as well as lowers the risk of incidence of diabetic kidney disease and coronary heart disease by 60% and 40% respectively (30-32). Our findings provide further evidence to support the cancer-, reno- and cardio- protective effects of statins in primary prevention and reduced mortality.
Page 14 of 26
ACCEPTED MANUSCRIPT
T
Our mortality risk prediction models also included urine ACR and eGFR, as a measure of
RI P
the degree of renal impairment (5, 6). Studies have shown that diabetic patients with kidney
SC
disease have elevated mortality rates (33-35). The role of diabetic kidney disease is of particular concern in Chinese populations (36) as multi-ethnic studies have found that
MA NU
Chinese diabetic patients have a higher prevalence of nephropathy than Caucasians (10, 11, 36-38). Furthermore, a global survey found that only 20% of Asian diabetic patients received reno-protective drugs compared to 30% of Caucasians. Given the importance of
ED
diabetic kidney disease in Chinese patients, regular urine ACR and eGFR assessment should
AC
CE
disease progression.
PT
be advocated as part of routine care for early identification and intervention to help delay the
There were several limitations to this study. Firstly, our study design was retrospective rather than prospective, which may introduce some bias to the results. However, our data were extracted systematically from the central management database of the Hospital Authority and thus the findings should be similar to that gained from a prospective cohort design. Secondly, several potential predictors such as diet and exercise were not available and were not considered in our models. Thirdly, our prediction models were developed using data obtained from diabetic patients without any major co-morbidities. In our setting
Page 15 of 26
ACCEPTED MANUSCRIPT patients with CVD and end stage renal disease are usually managed in specialist clinics
T
rather than in primary care. As a result, the model developed is only applicable to diabetic
RI P
patient with no comorbidities. Also, our model was limited to predict 5-year all-cause
SC
mortality. Future studies with longer follow-up periods such as 10 years for diabetic patients with and without co-morbidity are needed to produce models that can predict the
MA NU
longer-term mortality risks for more diverse diabetic populations. Lastly, only internal validation but not external validation was available in the current study. An external validation should be warranted to validate our model by using Chinese population in other
PT
ED
regions.
CE
In conclusion, our newly developed gender-specific models provide a more accurate and
AC
valid 5-year mortality risk predictions for Chinese diabetic primary care patients than other currently existing models. As many of the risk factors in the model (such as like smoking, HbA1c, blood pressure, lipid profile, urine ACR) are modifiable clinicians can use the model as a guide to screen, identify those who are at increased risk of mortality and intervene to reduce the risk of premature death. These risk prediction models can be incorporated into local management guidelines to facilitate a more evidence-based use of health care resources. Future studies with a longer follow-up period of 10 years and external validation should be
Page 16 of 26
ACCEPTED MANUSCRIPT conducted to model the longer-term mortality risks for Chinese diabetic populations and
RI P
T
validate the model.
SC
5 Acknowledgements
The authors wish to acknowledge the contributions of the Risk Assessment Management
MA NU
Program for Diabetes Mellitus (RAMP-DM) program team at the Hospital Authority head office, and the Chiefs of Service and RAMP-DM program coordinators in each cluster, and
ED
the Statistics and Workforce Planning Department at the Hong Kong Hospital Authority.
PT
6 Funding Sources
CE
This work was supported by the Health Services Research Fund, Food and Health Bureau,
AC
HKSAR Commissioned Research on Enhanced Primary Care Study [grant number EPC-HKU-2]. No funding organization had any role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation of the manuscript. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
Page 17 of 26
ACCEPTED MANUSCRIPT 7 References 1.
International Diabetes Federation. IDF Diabetes Atlas, 7 ed. Brussels: 2015.
2.
Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. Collaboration ERF. Diabetes mellitus, fasting glucose, and risk of cause-specific death. N Engl J
RI P
3.
T
Plos med. 2006;3(11):e442. Med. 2011;2011(364):829-41. 4.
Gæde P, Lund-Andersen H, Parving H-H, Pedersen O. Effect of a multifactorial intervention on
5.
SC
mortality in type 2 diabetes. New England Journal of Medicine. 2008;358(6):580-91. Yang X, So WY, Tong PC, Ma RC, Kong AP, Lam CW, et al. Development and validation of an
internal medicine. 2008;168(5):451-7. 6.
MA NU
all-cause mortality risk score in type 2 diabetes: The Hong Kong Diabetes Registry. Archives of Robinson TE, Elley CR, Kenealy T, Drury PL. Development and validation of a predictive risk
model for all-cause mortality in type 2 diabetes. Diabetes research and clinical practice. 2015;108(3):482-8. 7.
Wells BJ, Jain A, Arrigain S, Yu C, Rosenkrans WA, Kattan MW. Predicting 6-year mortality risk in
8.
ED
patients with type 2 diabetes. Diabetes care. 2008;31(12):2301-6. McEwen LN, Karter AJ, Waitzfelder BE, Crosson JC, Marrero DG, Mangione CM, et al. Predictors
of mortality over 8 years in type 2 diabetic patients. Diabetes Care. 2012:DC_112281. De Cosmo S, Copetti M, Lamacchia O, Fontana A, Massa M, Morini E, et al. Development and
PT
9.
validation of a predicting model of all-cause mortality in patients with type 2 diabetes. Diabetes Care.
CE
2013;36(9):2830-5.
10. Group DD. Prevalence of small vessel and large vessel disease in diabetic patients from 14centres: the world health organization multinational study of vascular disease in diabetics.
AC
Diabetologia. 1985;28:615-40.
11. Chi Z, Lee E, Lu M, Keen H, Bennett P, Group WMS. Vascular disease prevalence in diabetic patients in China: standardised comparison with the 14 centres in the WHO Multinational Study of Vascular Disease in Diabetes. Diabetologia. 2001;44(2):S82-S6. 12. Forouhi NG, Sattar N. CVD risk factors and ethnicity—a homogeneous relationship? Atherosclerosis Supplements. 2006;7(1):11-9. 13. Byrne CD, Wild SH. The metabolic syndrome: John Wiley & Sons; 2011. 14. Zhao W, Chen J. Implications from and for food cultures for cardiovascular disease: diet, nutrition and cardiovascular diseases in China. Asia Pacific journal of clinical nutrition. 2001;10(2):146-52. 15. Fung CS, Chin WY, Dai DS, Kwok RL, Tsui EL, Wan YF, et al. Evaluation of the quality of care of a multi-disciplinary risk factor assessment and management programme (RAMP) for diabetic patients. BMC family practice. 2012;13(1):116. 16. Royston P. Multiple imputation of missing values. Stata Journal. 2004;4:227-41.
Page 18 of 26
ACCEPTED MANUSCRIPT 17. Rubin DB. Multiple imputation for nonresponse in surveys: John Wiley & Sons; 2004. 18. Van Buuren S, Boshuizen HC, Knook DL. Multiple imputation of missing blood pressure covariates in survival analysis. Statistics in medicine. 1999;18(6):681-94. 19. JANGHORBANI M, HEDLEY AJ, JONES RB, ZHIANPOUR M, GILMOUR WH. Gender differential in 1993;22(6):1056-63.
RI P
T
all-cause and cardiovascular disease mortality. International journal of epidemiology. 20. Arnetz L, Ekberg NR, Alvarsson M. Sex differences in type 2 diabetes: focus on disease course and outcomes. Diabetes Metab Syndr Obes. 2014;7:409-20.
SC
21. Cederholm J, Gudbjörnsdottir S, Eliasson B, Zethelius B, Eeg-Olofsson K, Nilsson PM. Blood pressure and risk of cardiovascular diseases in type 2 diabetes: further findings from the Swedish
MA NU
National Diabetes Register (NDR-BP II). Journal of hypertension. 2012;30(10):2020-30. 22. Kontopantelis E, Springate DA, Reeves D, Ashcroft DM, Rutter M, Buchan I, et al. Glucose, blood pressure and cholesterol levels and their relationships to clinical outcomes in type 2 diabetes: a retrospective cohort study. Diabetologia. 2015;58(3):505-18.
23. Wan EYF, Fung CSC, Wong CKH, Chin WY, Lam CLK. Association of Hemoglobin A1c Levels With Cardiovascular Disease and Mortality in Chinese Patients With Diabetes. Journal of the American
ED
College of Cardiology. 2016;67(4):456-8.
24. Wan EYF, Fung CSC, Fong DYT, Chan AKC, Lam CLK. A curvilinear association of body mass index with cardiovascular diseases in Chinese patients with type 2 diabetes mellitus–A population-based
PT
retrospective cohort study. Journal of diabetes and its complications. 2016. 25. Wan EYF, Fong DYT, Fung CSC, Lam CLK. Incidence and Predictors for Cardiovascular Disease in
CE
Chinese Patients With Type 2 Diabetes Mellitus – A Population-Based Retrospective Cohort Study. Journal of Diabetes and its Complications. 26. Collaboration PS. Blood cholesterol and vascular mortality by age, sex, and blood pressure: a
AC
meta-analysis of individual data from 61 prospective studies with 55 000 vascular deaths. The Lancet. 2007;370(9602):1829-39. 27. Swets JA. Measuring the accuracy of diagnostic systems. Science. 1988;240(4857):1285-93. 28. Stratton IM, Adler AI, Neil HAW, Matthews DR, Manley SE, Cull CA, et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. Bmj. 2000;321(7258):405-12. 29. Adler AI, Stratton IM, Neil HAW, Yudkin JS, Matthews DR, Cull CA, et al. Association of systolic blood pressure with macrovascular and microvascular complications of type 2 diabetes (UKPDS 36): prospective observational study. Bmj. 2000;321(7258):412-9. 30. Yang X, Ma RC, So WY, Yu LW, Kong AP, Ko GT, et al. Low triglyceride and nonuse of statins is associated with cancer in type 2 diabetes mellitus. Cancer. 2011;117(4):862-71. 31. Luk AO, Yang X, Ma RC, Ng VW, Linda WY, Lau WW, et al. Association of statin use and development of renal dysfunction in type 2 diabetes—The Hong Kong Diabetes Registry. diabetes research and clinical practice. 2010;88(3):227-33.
Page 19 of 26
ACCEPTED MANUSCRIPT 32. Ting RZ, Yang X, Linda W, Luk AO, Kong AP, Tong PC, et al. Lipid control and use of lipid-regulating drugs for prevention of cardiovascular events in Chinese type 2 diabetic patients: a prospective cohort study. Cardiovascular diabetology. 2010;9(1):1. 33. Matsushita K, Coresh J, Sang Y, Chalmers J, Fox C, Guallar E, et al. Estimated glomerular
T
filtration rate and albuminuria for prediction of cardiovascular outcomes: a collaborative
RI P
meta-analysis of individual participant data. The Lancet Diabetes & Endocrinology. 2015;3(7):514-25. 34. Wang Y, Katzmarzyk PT, Horswell R, Zhao W, Johnson J, Hu G. Kidney function and the risk of cardiovascular disease in patients with type 2 diabetes. Kidney international. 2014;85(5):1192-9.
SC
35. Soriano LC, Johansson S, Stefansson B, Rodríguez LAG. Cardiovascular events and all-cause mortality in a cohort of 57,946 patients with type 2 diabetes: associations with renal function and
MA NU
cardiovascular risk factors. Cardiovascular diabetology. 2015;14(1):1. 36. Kong AP, Xu G, Brown N, So W-Y, Ma RC, Chan JC. Diabetes and its comorbidities—where East meets West. Nature Reviews Endocrinology. 2013;9(9):537-47. 37. Ma RC, Chan JC. Type 2 diabetes in East Asians: similarities and differences with populations in Europe and the United States. Annals of the New York Academy of Sciences. 2013;1281(1):64-91. 38. Chan JC, So W, Ma RC, Tong PC, Wong R, Yang X. The complexity of vascular and non-vascular
ED
complications of diabetes: the Hong Kong Diabetes Registry. Current cardiovascular risk reports.
AC
CE
PT
2011;5(3):230-9.
Page 20 of 26
ACCEPTED MANUSCRIPT 8 Figure Legends
AC
CE
PT
ED
MA NU
SC
RI P
T
Figure 1. Calibration plots for observed and predicted 5-year risks of mortality
Page 21 of 26
ACCEPTED MANUSCRIPT 9 Tables Table 1. Baseline characteristics of derivation and validation cohorts in male and female groups
60.9±10.0 4,458 (21.7%)
6.7±6.3 28,092 (68.3%) 2,495 (6.1%)
6.6±6.0 13,990 (68.1%) 1,206 (5.9%)
MA NU
62.7±9.9 495 (2.1%)
7.2±6.6 35,218 (74.6%) 2,346 (5.0%)
7.2±7.0 17,587 (74.5%) 1,177 (5.0%)
15,246 (74.2%) 18,106 (88.1%) 440 (2.1%) 5,098 (24.8%)
36,257 (76.8%) 40,394 (85.6%) 939 (2.0%) 13,832 (29.3%)
18,056 (76.5%) 20,186 (85.5%) 474 (2.0%) 6,870 (29.1%)
25.5±3.8 90.8±22.6 7.2±1.3 55.2±14.2 134.4±16.0 77.3±9.7 2.9±0.8 4.3±1.2 1.5±1.0 7.2±48.9
25.5±3.8 90.8±17.9 7.2±1.4 55.2±15.3 134.4±16.0 77.5±9.7 2.9±0.8 4.3±1.2 1.5±1.0 7.0±31.0
25.6±4.2 87.6±24.7 7.2±1.2 55.2±13.1 135.1±16.5 74.0±9.7 3.0±0.8 4.0±1.1 1.6±0.9 7.1±35.8
25.6±4.3 87.4±20.3 7.2±1.2 55.2±13.1 135.1±16.5 74.0±9.7 3.0±0.8 4.0±1.1 1.6±1.0 7.1±45.7
30,434 (74.0%) 9,379 (22.8%) 1,233 (3.0%) 63 (0.2%)
15,145 (73.7%) 4,809 (23.4%) 568 (2.8%) 32 (0.2%)
34,443 (73.0%) 10,745 (22.8%) 1,905 (4.0%) 106 (0.2%)
17,328 (73.4%) 5,250 (22.2%) 970 (4.1%) 52 (0.2%)
ED
30,457 (74.1%) 36,124 (87.9%) 842 (2.0%) 10,015 (24.4%)
CE
AC
62.6±9.9 1,006 (2.1%)
SC
61.0±10.0 8,791 (21.4%)
PT
Socio-demographics Age, years Smoker Disease characteristics Duration of T2DM, years Diagnosed Hypertension STDR Treatment modalities Anti-hypertensive drugs usage Oral anti-diabetic drugs usage Insulin usage Lipid-lowering agents usage Clinical parameters BMI, kg/m2 Waist circumference, cm HbA1c, % HbA1c, mmol/mol SBP, mmHg DBP, mmHg LDL-C, mmol/L TC/HDL-C ratio Triglyceride, mmol/L Urine ACR, mg/mmol eGFR, ml/min/1.73m2 ≥ 90 60-89 30-59 <30
RI P
T
Male Female Derivation cohort Validation cohort Derivation cohort Validation cohort (n=41,109) (n=20,554) (n=47,199) (n=23,600)
Characteristics
T2DM = Type 2 Diabetes Mellitus; STDR = Sight Threatening Diabetic Retinopathy; BMI = Body Mass Index; HbA1c = Hemoglobin A1c; SBP = Systolic Blood Pressure; DBP = Diastolic Blood Pressure; LDL-C = Low Density Lipoprotein-Cholesterol; TC = Total Cholesterol; HDL-C = High-density Lipoprotein-Cholesterol; ACR = Albumin/Creatinine Ratio; eGFR = estimated Glomerular Filtration Rate
Page 22 of 26
ACCEPTED MANUSCRIPT Table 2 Cox regression models for predictors of developing 5-year mortality in derivation cohort
P-value
HR†
1.13 1.71
(1.11,1.16) (1.56,1.88)
<0.001* <0.001*
1.25 0.79
CE
AC
(1.01,1.55) (0.72,0.88)
(0.71,0.84) (1.00,1.01) (0.75,1.00) (1.00,1.02) (0.93,0.98) (1.0001,1.0004)
ED
0.77 1.01 0.86 1.01 0.96 1.0002
(1.23,1.59)
1.21
P-value
1.14 2.04
(1.11,1.17) (1.58,2.64)
<0.001* <0.001*
1.01
(1.00,1.02)
0.045*
1.27
(1.08,1.50)
0.003*
1.26 1.97
(1.07,1.48) (1.57,2.46)
0.006* <0.001*
2.09
(0.81,5.38)
0.128
<0.001* 0.86 <0.001* 1.003 0.046* 0.63 0.002* 1.06 <0.001* 0.96 <0.001* 1.0001 0.93 1.00 <0.001* 1.15
(0.79,0.93) (1.001,1.004) (0.51,0.78) (1.04,1.07) (0.94,0.99) (1.0000,1.0002) (0.89,0.97) (1.00,1.00) (1.08,1.23)
0.001* 0.001* <0.001* <0.001* 0.005* 0.003* 0.002* <0.001* <0.001*
RI P
SC
1.39
Female 95%CI
(1.17,1.25)
T
Male 95%CI
PT
Socio-demographics Age, years Smoker (Non-smoker) Disease characteristics Duration of T2DM, years Treatment modalities Anti-hypertensive drugs usage (No) Oral anti-diabetic drugs (No) Insulin usage (No) Lipid-lowering agents usage (No) Clinical parameters BMI, kg/m2 BMI2, kg/m2 HbA1c, % HbA1c2, % SBP, mmHg SBP2, mmHg DBP, mmHg DBP2, mmHg ln (Urine ACR +1), mg/mmol eGFR (>90ml/min/1.73m2) 60-89ml/min/1.73m2 30-59ml/min/1.73m2 <30ml/min/1.73m2 Age interaction term Age*eGFR (>90ml/min/1.73m2) 60-89ml/min/1.73m2 30-59ml/min/1.73m2 <30ml/min/1.73m2 Age*(BMI+BMI2) Age*(HbA1c+HbA1c2) Age*(SBP+SBP2) Age*(DBP+DBP2) Age*Lipid-lowering agents used
HR†
<0.001* 0.043
*
MA NU
Predictors
<0.001*
1.31 40.35 23.93
(0.54,3.15) (10.55,154.34) (1.08,532.01)
0.546 <0.001* 0.045*
1.33 17.28 47.12
(0.43,4.09) (3.78,79.03) (1.45,1,531.15)
0.624 <0.001* 0.030*
0.997 0.96 0.98 0.999
(0.985,1.010) (0.94,0.98) (0.94,1.03) (0.999,1.000)
0.657 <0.001* 0.396 0.024*
0.998 0.97 0.97
(0.982,1.013) (0.95,0.99) (0.93,1.02)
0.767 0.006* 0.261
0.999
(0.999,0.999)
<0.001*
0.999
(0.999,0.999)
0.020*
0.99
(0.97,1.00)
0.044*
0.999
(0.999,1.000)
0.013
*
T2DM = Type 2 Diabetes Mellitus; BMI = Body Mass Index; HbA1c = Hemoglobin A1c; SBP = Systolic Blood Pressure; DBP = Diastolic Blood Pressure; TC = Total Cholesterol; HDL-C = High-density Lipoprotein-Cholesterol; ACR = Albumin/Creatinine Ratio; eGFR = estimated Glomerular Filtration Rate; T2DM = Type 2 Diabetes Mellitus; HR = Hazard Ratio Notes: * Significant difference (P < 0.05) † HR > 1 indicates greater risk of event occurrence
Page 23 of 26
ACCEPTED MANUSCRIPT Table 3. Performance of new and existing risk models for mortality in validation cohort for predicting 5-year risk of mortality Validation statistics
New model
New Zealand model
0.768 (0.754,0.782)
0.762 (0.748,0.776)
0.748 (0.734,0.763)
-
0.006*
0.019*
1.586 (1.484,1.673)
1.537 (1.449,1.626)
1.388 (1.291,1.478)
37.5 (34.6,40.0)
36.1 (33.4,38.8)
31.5 (28.5,34.3)
Harrell's C statistic test in
RI P
Harrell's C statistic
T
Male
2
R
Harrell's C statistic
MA NU
Female 0.782 (0.766,0.799)
0.780 (0.764,0.797)
0.754 (0.736,0.771)
-
0.002
0.029*
1.737 (1.626,1.848)
1.703 (1.591,1.809)
1.504 (1.391,1.606)
41.9 (38.5,45.2)
40.9 (37.9,43.8)
35.1 (31.3,38.4)
Harrell's C statistic test in comparison with new model D statistic R2
SC
comparison with new model D statistic
JADE model
ED
The brackets represented 95% confidence interval of corresponding validation statistic
AC
CE
PT
* Significant difference in Harrell's C statistic (P-value < 0.05)
Page 24 of 26
ACCEPTED MANUSCRIPT Figure 1. Calibration plots for observed and predicted 5-year risks of mortality Male
Female
SC
RI P
T
5-year risk of mortality (%)
Model 1
ED
5-year risk of mortality (%)
MA NU
New Zealand risk score
PT CE AC
5-year risk of mortality (%)
JADE risk score
Deciles of estimated risk
Deciles of estimated risk
Page 25 of 26
ACCEPTED MANUSCRIPT Title: Prediction of five-year all-cause mortality in Chinese Patients with Type 2 Diabetes
T
Mellitus – A Population-based Retrospective Cohort Study
Current existing prediction model for all-cause mortality either overestimated or
SC
RI P
Highlights
underestimated the risk for Chinese primary care patients with Type 2 Diabetes Mellitus Our newly developed gender-specific models provide a more accurate and valid 5-year
MA NU
mortality risk predictions for Chinese diabetic primary care patients than other currently existing models.
As many of the risk factors in the model (such as like smoking, HbA1c, blood pressure,
ED
PT
lipid profile, urine ACR) are modifiable clinicians can use the model as a guide to screen,
CE
identify those who are at increased risk of mortality and intervene to reduce the risk of
AC
premature death.
These risk prediction models can be incorporated into local management guidelines to facilitate a more evidence-based use of health care resources.
Page 26 of 26