Research
Original Investigation
Cardiovascular Risk Prediction Models for People With Severe Mental Illness Results From the Prediction and Management of Cardiovascular Risk in People With Severe Mental Illnesses (PRIMROSE) Research Program David P. J. Osborn, PhD; Sarah Hardoon, PhD; Rumana Z. Omar, PhD; Richard I. G. Holt, PhD; Michael King, PhD; John Larsen, PhD; Louise Marston, PhD; Richard W. Morris, PhD; Irwin Nazareth, PhD; Kate Walters, PhD; Irene Petersen, PhD
IMPORTANCE People with severe mental illness (SMI), including schizophrenia and bipolar
Supplemental content at jamapsychiatry.com
disorder, have excess rates of cardiovascular disease (CVD). Risk prediction models validated for the general population may not accurately estimate cardiovascular risk in this group. OBJECTIVE To develop and validate a risk model exclusive to predicting CVD events in people with SMI incorporating established cardiovascular risk factors and additional variables. DESIGN, SETTING, AND PARTICIPANTS We used anonymous/deidentified data collected between January 1, 1995, and December 31, 2010, from the Health Improvement Network (THIN) to conduct a primary care, prospective cohort and risk score development study in the United Kingdom. Participants included 38 824 people with a diagnosis of SMI (schizophrenia, bipolar disorder, or other nonorganic psychosis) aged 30 to 90 years. During a median follow-up of 5.6 years, 2324 CVD events (6.0%) occurred. MAIN OUTCOMES AND MEASURES Ten-year risk of the first cardiovascular event (myocardial infarction, angina pectoris, cerebrovascular accidents, or major coronary surgery). Predictors included age, sex, height, weight, systolic blood pressure, diabetes mellitus, smoking, body mass index (BMI), lipid profile, social deprivation, SMI diagnosis, prescriptions for antidepressants and antipsychotics, and reports of heavy alcohol use.
CONCLUSIONS AND RELEVANCE The PRIMROSE BMI and lipid CVD risk prediction models performed better in SMI compared with models that include only established CVD risk factors. Further work on the clinical effectiveness and cost-effectiveness of the PRIMROSE models is needed to ascertain the best thresholds for offering CVD interventions.
Author Affiliations: Division of Psychiatry, UCL (University College London), London, United Kingdom (Osborn, Hardoon, King); Camden and Islington National Health Service Foundation Trust, London, United Kingdom (Osborn, King); Research Department of Primary Care and Population Health, UCL, London, United Kingdom (Hardoon, Marston, Morris, Nazareth, Walters, Petersen); Department of Statistical Science, UCL, London, United Kingdom (Omar); Human Development and Health Academic Unit, Faculty of Medicine, University of Southampton, Southampton, United Kingdom (Holt); Rethink Mental Illness, London, United Kingdom (Larsen).
JAMA Psychiatry. 2015;72(2):143-151. doi:10.1001/jamapsychiatry.2014.2133 Published online December 23, 2014.
Corresponding Author: David P. J. Osborn, PhD, Division of Psychiatry, University College London, Charles Bell House, Riding House Street, London W1W 7EJ, United Kingdom ([email protected]).
RESULTS We developed 2 CVD risk prediction models for people with SMI: the PRIMROSE
BMI model and the PRIMROSE lipid model. These models mutually excluded lipids and BMI. In terms of discrimination, from cross-validations for men, the PRIMROSE lipid model D statistic was 1.92 (95% CI, 1.80-2.03) and C statistic was 0.80 (95% CI, 0.76-0.83) compared with 1.74 (95% CI, 1.63-1.86) and 0.78 (95% CI, 0.75-0.82) for published Cox Framingham risk scores. The corresponding results in women were 1.87 (95% CI, 1.76-1.98) and 0.79 (95% CI, 0.76-0.82) for the PRIMROSE lipid model and 1.58 (95% CI, 1.48-1.68) and 0.77 (95% CI, 0.73-0.81) for the Cox Framingham model. Discrimination statistics for the PRIMROSE BMI model were comparable to those for the PRIMROSE lipid model. Calibration plots suggested that both PRIMROSE models were superior to the Cox Framingham models.
(Reprinted) 143
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Research Original Investigation
Cardiovascular Risk Prediction Models
I
t is well established that people with severe mental illnesses (SMIs), such as schizophrenia and bipolar disorder, have excess rates of cardiovascular disease (CVD), including myocardial infarctions and strokes.1 The risk of dying from CVD is 3-fold higher in people with SMI younger than 50 years and 2-fold in those aged 50 to 75 years.2 There has been an increase in clinical and research efforts addressing this problem, but we lack knowledge regarding the most effective ways to predict and manage cardiovascular risk in people with SMI. We know that the conventional cardiovascular risk factors, including smoking, dyslipidemia (with higher levels of total cholesterol and triglycerides and lower levels of high-density lipoprotein cholesterol [HDL-C]), diabetes mellitus, and obesity and possibly a higher prevalence of hypertension, are more common in people with SMI,3,4 especially those with wellestablished mental disorders.5 There may even be a shared genetic predisposition to comorbidities in patients with SMI. People with SMI are less likely to exercise, have unhealthy diets, and may receive inferior physical health care.6 Antipsychotic medications may contribute to cardiovascular risk through weight gain and its effect on glucose and lipid metabolism, although mortality studies2,7,8 that explore the role of antipsychotic medication in premature deaths show inconsistent findings. For example, an 11-year follow up study of people with schizophrenia7 reported reduced cardiovascular mortality in people treated with olanzapine and clozapine, but methodological issues in the study, particularly unmeasured confounding, have been critiqued in detail.9 For the general population, cardiovascular risk is managed by using CVD risk scores to determine the absolute risk for an individual patient and therefore the likely benefit of prescribing lipid-lowering medications and/or other interventions. The most established scores are the Framingham Heart Study risk scores,10 currently available as both a body mass index (BMI) model (including BMI but not laboratory results for blood lipid levels) and a lipid model (including total cholesterol and HDL-C but not BMI). Clinical guidelines, such as the UK National Institute for Health and Clinical Excellence guidelines on schizophrenia, recommend more intensive screening for cardiovascular risk in people with SMI.1,11 However, we do not know how well the modifiable cardiovascular risk factors, included in models such as Cox Framingham, predict CVD risk in people with SMI. There is reason to believe that existing models may not accurately determine the high level of risk conferred by having a longterm SMI. The established scores, such as the Cox Framingham, were developed by excluding people with SMI and since then have not been tested in this population; in addition, the established scores do not consider SMI-specific exposures, such as antipsychotic medication. To our knowledge, no previous studies have assessed the performance of CVD risk prediction models in people with SMI. Using data from a large UK primary care database, The Health Improvement Network (THIN),12 we aimed to develop and validate cardiovascular risk prediction models specific to people with SMI: Prediction and Management of Cardiovascular Risk in People With Severe Mental Illnesses (PRIMROSE) models. These new models developed from the PRIMROSE 144
study included traditional cardiovascular risk factors and additional SMI-specific variables. We compared the performance of these new risk prediction models against existing published Cox Framingham scores in people with SMI since the Cox Framingham scores are widely used internationally and the coefficients of the scores are readily available to allow comparison. This work formed part of a program of research, PRIMROSE, which is funded by the UK National Institute for Health Research (http://www.ucl.ac.uk/primrose).
Methods Study Design A prospective study of anonymous data collected between January 1, 1995, and December 31, 2010, was conducted to develop and validate a 10-year risk score for predicting newly recorded cardiovascular events in people with SMI. The scheme for THIN to obtain and provide anonymous patient data to researchers was approved by the National Health Service SouthEast Multicentre Research Ethics Committee in 2002, and scientific approval for this study was obtained from CMD Medical Research’s Scientific Review Committee in March 2012.
Setting We used THIN,12 a UK primary care database that includes information obtained from routine clinical practice. Primary care physicians and staff use a hierarchical system of Read codes to enter information in THIN, such as symptoms and diagnoses, during clinical appointments and administration13 that creates a longitudinal record for each patient. At the time we developed the PRIMROSE risk models, THIN included almost 10 million patients with geographic coverage broadly representative of the UK population.14 Approximately 98% of the population is registered with a general practitioner in the United Kingdom.15 THIN data are subject to a range of quality assurance procedures16 and have been successfully used in wideranging studies on CVDs,17,18 including cardiovascular risk score validation work in the general population.19 Primary care data are a particularly suitable source for assessing cardiovascular risk in people with SMI in the United Kingdom since most people with SMI are registered with a general practitioner whom they see frequently and most of the required data for risk scores (eg, laboratory and blood pressure measurements) are available owing to policy initiatives that provide incentives for annual cardiovascular screening.20 The SMI diagnoses have been validated in UK general practice.21
Participants We included individuals aged 30 to 90 years with a diagnostic entry in their primary care electronic health record for an SMI at any time during their follow-up period. We defined SMI as (1) schizophrenia and schizoaffective disorder, (2) bipolar affective disorder, and (3) other nonorganic psychoses. We created lists of the diagnostic codes used by general practitioners or administrators. The codes are usually based on assessments by mental health specialists. We extracted anonymous/deidentified data between January 1, 1995, and December 31, 2010.
JAMA Psychiatry February 2015 Volume 72, Number 2 (Reprinted)
Copyright 2015 American Medical Association. All rights reserved.
Downloaded From: http://archpsyc.jamanetwork.com/ by a BYU Harold B Lee Library User on 05/18/2015
jamapsychiatry.com
Cardiovascular Risk Prediction Models
Original Investigation Research
Table 1. Characteristics of Eligible Patients With SMI Data Before Imputation Men (n = 18 417) Characteristic
Women (n = 20 407)
Total (N = 38 824)
No. Observed
Data
No. Observed
Data
No. Observed
Data
18 417
46.2 (14.0)
20 407
52.4 (16.4)
38 824
49.5 (15.6)
Systolic blood pressure, mean (SD), mm Hg
5586
132.5 (17.6)
8977
131.2 (20.3)
14 563
131.7 (19.3)
Weight, mean (SD), kg
3478
84.8 (18.0)
5105
73.5 (18.2)
8583
78.1 (19.0)
11 927
1.76 (0.08)
13 515
1.61 (0.07)
25 442
1.68 (0.10)
2921
27.74 (5.48)
4200
28.37 (6.75)
7121
28.11 (6.27)
Age, mean (SD), y
Height, mean (SD), m BMI, mean (SD) Total cholesterol level, mean (SD), mmol/L
1737
5.37 (1.13)
1971
5.62 (1.12)
3708
5.51 (1.13)
HDL-C level, mean (SD), mmol/L
1127
1.22 (0.37)
1199
1.46 (0.44)
2326
1.34 (0.42)
Predominant smoking history, No. (%)
17 573
19 592
37 165
Never
5030 (28.6)
9219 (47.0)
Former
2317 (13.2)
2376 (12.1)
14 249 (38.3) 4693 (12.6)
Current
10 226 (58.2)
7997 (40.8)
18 223 (49.0)
History of very heavy drinking/alcohol problems at baseline, No. (%)
18 417
2513 (13.6)
20 407
1111 (5.4)
38 824
3624 (9.3)
History of diabetes mellitus at baseline, No. (%)
18 417
597 (3.2)
20 407
759 (3.7)
38 824
1356 (3.5)
Townsend score of social deprivation (quintiles), No. (%)
18 417
20 407
38 824
1 (Least deprived)
2433 (13.2)
3588 (17.6)
6021 (15.5)
2
2849 (15.5)
3750 (18.4)
6599 (17.0)
3
3688 (20.0)
4279 (21.0)
7967 (20.6)
4
4512 (24.5)
4740 (23.2)
9252 (23.8)
5 (Most deprived)
4935 (26.8)
4050 (19.8)
SMI diagnosis, No. (%)
18 417
Schizophrenia
20 407 7606 (41.3)
8985 (23.1) 38 824
5626 (27.6)
13 232 (34.1)
Bipolar disorder
4005 (21.7)
6093 (29.8)
10 098 (26.0)
Other
4967 (27.0)
6238 (30.6)
11 205 (28.9)
Unknown (on severe mental illness register)
1839 (10.0)
2450 (12.0)
4289 (11.0)
Medications at baseline, No. (%) Antidepressants
18 417
5679 (30.8)
20 407
8439 (41.4)
38 824
14 118 (36.4)
Second-generation antipsychotics
18 417
4184 (22.7)
20 407
3760 (18.4)
38 824
7944 (20.5)
First-generation antipsychotics
18 417
3864 (21.0)
20 407
4769 (23.4)
38 824
8633 (22.2)
Lithium
18 417
1626 (8.8)
20 407
299 (1.5)
38 824
4125 (10.6)
Antihypertensive drugs
18 417
1543 (8.4)
20 407
2505 (12.3)
38 824
4048 (10.4)
Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); HDL-C, high-density lipoprotein cholesterol; SMI, severe mental illness.
SI conversion factor: To convert total cholesterol and HDL-C to milligrams per deciliter, divide by 0.0259.
Main Outcome
In summary, we performed Cox proportional hazards regression with backward elimination to derive the PRIMROSE models. The variables considered in the models are listed in Tables 1, 2, and 3. We compared the performance of different models by calculating the C index22 and D statistic23 for discrimination, by constructing calibration plots, and by assessing the numbers of people classified as having high risk of CVD over the course of 10 years (score >20%) who went on to have a CVD event. After comparing the PRIMROSE risk models against published Cox Framingham models, we performed 2 supplementary comparisons. First, we wanted to ascertain whether our results could be explained by differences between North American and UK source populations. Thus, we reestimated the Cox Framingham model to the UK gen-
Newly recorded fatal and nonfatal cardiovascular events were defined as a diagnostic record for myocardial infarction, angina pectoris, coronary heart disease, major coronary surgery and revascularization, cerebrovascular accident, and transient ischemic attack.
Statistical Analysis We developed 2 PRIMROSE risk models: the BMI model and lipid model. Detailed descriptions of the follow-up period, the variables considered in our analysis, the development of the PRIMROSE risk models and their 10-fold internal crossvalidation, as well as the imputation of missing data and our sample size calculation, are provided in the eAppendix in the Supplement. jamapsychiatry.com
(Reprinted) JAMA Psychiatry February 2015 Volume 72, Number 2
Copyright 2015 American Medical Association. All rights reserved.
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145
Research Original Investigation
Cardiovascular Risk Prediction Models
Table 2. Associations of Predictors With New-Onset CVD After Imputation HR (95% CI)a
Characteristic Sex (female vs male) Age (per log-year increase)
P Value
0.69 (0.64-0.75)
Original Investigation
Cardiovascular Risk Prediction Models for People With Severe Mental Illness Results From the Prediction and Management of Cardiovascular Risk in People With Severe Mental Illnesses (PRIMROSE) Research Program David P. J. Osborn, PhD; Sarah Hardoon, PhD; Rumana Z. Omar, PhD; Richard I. G. Holt, PhD; Michael King, PhD; John Larsen, PhD; Louise Marston, PhD; Richard W. Morris, PhD; Irwin Nazareth, PhD; Kate Walters, PhD; Irene Petersen, PhD
IMPORTANCE People with severe mental illness (SMI), including schizophrenia and bipolar
Supplemental content at jamapsychiatry.com
disorder, have excess rates of cardiovascular disease (CVD). Risk prediction models validated for the general population may not accurately estimate cardiovascular risk in this group. OBJECTIVE To develop and validate a risk model exclusive to predicting CVD events in people with SMI incorporating established cardiovascular risk factors and additional variables. DESIGN, SETTING, AND PARTICIPANTS We used anonymous/deidentified data collected between January 1, 1995, and December 31, 2010, from the Health Improvement Network (THIN) to conduct a primary care, prospective cohort and risk score development study in the United Kingdom. Participants included 38 824 people with a diagnosis of SMI (schizophrenia, bipolar disorder, or other nonorganic psychosis) aged 30 to 90 years. During a median follow-up of 5.6 years, 2324 CVD events (6.0%) occurred. MAIN OUTCOMES AND MEASURES Ten-year risk of the first cardiovascular event (myocardial infarction, angina pectoris, cerebrovascular accidents, or major coronary surgery). Predictors included age, sex, height, weight, systolic blood pressure, diabetes mellitus, smoking, body mass index (BMI), lipid profile, social deprivation, SMI diagnosis, prescriptions for antidepressants and antipsychotics, and reports of heavy alcohol use.
CONCLUSIONS AND RELEVANCE The PRIMROSE BMI and lipid CVD risk prediction models performed better in SMI compared with models that include only established CVD risk factors. Further work on the clinical effectiveness and cost-effectiveness of the PRIMROSE models is needed to ascertain the best thresholds for offering CVD interventions.
Author Affiliations: Division of Psychiatry, UCL (University College London), London, United Kingdom (Osborn, Hardoon, King); Camden and Islington National Health Service Foundation Trust, London, United Kingdom (Osborn, King); Research Department of Primary Care and Population Health, UCL, London, United Kingdom (Hardoon, Marston, Morris, Nazareth, Walters, Petersen); Department of Statistical Science, UCL, London, United Kingdom (Omar); Human Development and Health Academic Unit, Faculty of Medicine, University of Southampton, Southampton, United Kingdom (Holt); Rethink Mental Illness, London, United Kingdom (Larsen).
JAMA Psychiatry. 2015;72(2):143-151. doi:10.1001/jamapsychiatry.2014.2133 Published online December 23, 2014.
Corresponding Author: David P. J. Osborn, PhD, Division of Psychiatry, University College London, Charles Bell House, Riding House Street, London W1W 7EJ, United Kingdom ([email protected]).
RESULTS We developed 2 CVD risk prediction models for people with SMI: the PRIMROSE
BMI model and the PRIMROSE lipid model. These models mutually excluded lipids and BMI. In terms of discrimination, from cross-validations for men, the PRIMROSE lipid model D statistic was 1.92 (95% CI, 1.80-2.03) and C statistic was 0.80 (95% CI, 0.76-0.83) compared with 1.74 (95% CI, 1.63-1.86) and 0.78 (95% CI, 0.75-0.82) for published Cox Framingham risk scores. The corresponding results in women were 1.87 (95% CI, 1.76-1.98) and 0.79 (95% CI, 0.76-0.82) for the PRIMROSE lipid model and 1.58 (95% CI, 1.48-1.68) and 0.77 (95% CI, 0.73-0.81) for the Cox Framingham model. Discrimination statistics for the PRIMROSE BMI model were comparable to those for the PRIMROSE lipid model. Calibration plots suggested that both PRIMROSE models were superior to the Cox Framingham models.
(Reprinted) 143
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Research Original Investigation
Cardiovascular Risk Prediction Models
I
t is well established that people with severe mental illnesses (SMIs), such as schizophrenia and bipolar disorder, have excess rates of cardiovascular disease (CVD), including myocardial infarctions and strokes.1 The risk of dying from CVD is 3-fold higher in people with SMI younger than 50 years and 2-fold in those aged 50 to 75 years.2 There has been an increase in clinical and research efforts addressing this problem, but we lack knowledge regarding the most effective ways to predict and manage cardiovascular risk in people with SMI. We know that the conventional cardiovascular risk factors, including smoking, dyslipidemia (with higher levels of total cholesterol and triglycerides and lower levels of high-density lipoprotein cholesterol [HDL-C]), diabetes mellitus, and obesity and possibly a higher prevalence of hypertension, are more common in people with SMI,3,4 especially those with wellestablished mental disorders.5 There may even be a shared genetic predisposition to comorbidities in patients with SMI. People with SMI are less likely to exercise, have unhealthy diets, and may receive inferior physical health care.6 Antipsychotic medications may contribute to cardiovascular risk through weight gain and its effect on glucose and lipid metabolism, although mortality studies2,7,8 that explore the role of antipsychotic medication in premature deaths show inconsistent findings. For example, an 11-year follow up study of people with schizophrenia7 reported reduced cardiovascular mortality in people treated with olanzapine and clozapine, but methodological issues in the study, particularly unmeasured confounding, have been critiqued in detail.9 For the general population, cardiovascular risk is managed by using CVD risk scores to determine the absolute risk for an individual patient and therefore the likely benefit of prescribing lipid-lowering medications and/or other interventions. The most established scores are the Framingham Heart Study risk scores,10 currently available as both a body mass index (BMI) model (including BMI but not laboratory results for blood lipid levels) and a lipid model (including total cholesterol and HDL-C but not BMI). Clinical guidelines, such as the UK National Institute for Health and Clinical Excellence guidelines on schizophrenia, recommend more intensive screening for cardiovascular risk in people with SMI.1,11 However, we do not know how well the modifiable cardiovascular risk factors, included in models such as Cox Framingham, predict CVD risk in people with SMI. There is reason to believe that existing models may not accurately determine the high level of risk conferred by having a longterm SMI. The established scores, such as the Cox Framingham, were developed by excluding people with SMI and since then have not been tested in this population; in addition, the established scores do not consider SMI-specific exposures, such as antipsychotic medication. To our knowledge, no previous studies have assessed the performance of CVD risk prediction models in people with SMI. Using data from a large UK primary care database, The Health Improvement Network (THIN),12 we aimed to develop and validate cardiovascular risk prediction models specific to people with SMI: Prediction and Management of Cardiovascular Risk in People With Severe Mental Illnesses (PRIMROSE) models. These new models developed from the PRIMROSE 144
study included traditional cardiovascular risk factors and additional SMI-specific variables. We compared the performance of these new risk prediction models against existing published Cox Framingham scores in people with SMI since the Cox Framingham scores are widely used internationally and the coefficients of the scores are readily available to allow comparison. This work formed part of a program of research, PRIMROSE, which is funded by the UK National Institute for Health Research (http://www.ucl.ac.uk/primrose).
Methods Study Design A prospective study of anonymous data collected between January 1, 1995, and December 31, 2010, was conducted to develop and validate a 10-year risk score for predicting newly recorded cardiovascular events in people with SMI. The scheme for THIN to obtain and provide anonymous patient data to researchers was approved by the National Health Service SouthEast Multicentre Research Ethics Committee in 2002, and scientific approval for this study was obtained from CMD Medical Research’s Scientific Review Committee in March 2012.
Setting We used THIN,12 a UK primary care database that includes information obtained from routine clinical practice. Primary care physicians and staff use a hierarchical system of Read codes to enter information in THIN, such as symptoms and diagnoses, during clinical appointments and administration13 that creates a longitudinal record for each patient. At the time we developed the PRIMROSE risk models, THIN included almost 10 million patients with geographic coverage broadly representative of the UK population.14 Approximately 98% of the population is registered with a general practitioner in the United Kingdom.15 THIN data are subject to a range of quality assurance procedures16 and have been successfully used in wideranging studies on CVDs,17,18 including cardiovascular risk score validation work in the general population.19 Primary care data are a particularly suitable source for assessing cardiovascular risk in people with SMI in the United Kingdom since most people with SMI are registered with a general practitioner whom they see frequently and most of the required data for risk scores (eg, laboratory and blood pressure measurements) are available owing to policy initiatives that provide incentives for annual cardiovascular screening.20 The SMI diagnoses have been validated in UK general practice.21
Participants We included individuals aged 30 to 90 years with a diagnostic entry in their primary care electronic health record for an SMI at any time during their follow-up period. We defined SMI as (1) schizophrenia and schizoaffective disorder, (2) bipolar affective disorder, and (3) other nonorganic psychoses. We created lists of the diagnostic codes used by general practitioners or administrators. The codes are usually based on assessments by mental health specialists. We extracted anonymous/deidentified data between January 1, 1995, and December 31, 2010.
JAMA Psychiatry February 2015 Volume 72, Number 2 (Reprinted)
Copyright 2015 American Medical Association. All rights reserved.
Downloaded From: http://archpsyc.jamanetwork.com/ by a BYU Harold B Lee Library User on 05/18/2015
jamapsychiatry.com
Cardiovascular Risk Prediction Models
Original Investigation Research
Table 1. Characteristics of Eligible Patients With SMI Data Before Imputation Men (n = 18 417) Characteristic
Women (n = 20 407)
Total (N = 38 824)
No. Observed
Data
No. Observed
Data
No. Observed
Data
18 417
46.2 (14.0)
20 407
52.4 (16.4)
38 824
49.5 (15.6)
Systolic blood pressure, mean (SD), mm Hg
5586
132.5 (17.6)
8977
131.2 (20.3)
14 563
131.7 (19.3)
Weight, mean (SD), kg
3478
84.8 (18.0)
5105
73.5 (18.2)
8583
78.1 (19.0)
11 927
1.76 (0.08)
13 515
1.61 (0.07)
25 442
1.68 (0.10)
2921
27.74 (5.48)
4200
28.37 (6.75)
7121
28.11 (6.27)
Age, mean (SD), y
Height, mean (SD), m BMI, mean (SD) Total cholesterol level, mean (SD), mmol/L
1737
5.37 (1.13)
1971
5.62 (1.12)
3708
5.51 (1.13)
HDL-C level, mean (SD), mmol/L
1127
1.22 (0.37)
1199
1.46 (0.44)
2326
1.34 (0.42)
Predominant smoking history, No. (%)
17 573
19 592
37 165
Never
5030 (28.6)
9219 (47.0)
Former
2317 (13.2)
2376 (12.1)
14 249 (38.3) 4693 (12.6)
Current
10 226 (58.2)
7997 (40.8)
18 223 (49.0)
History of very heavy drinking/alcohol problems at baseline, No. (%)
18 417
2513 (13.6)
20 407
1111 (5.4)
38 824
3624 (9.3)
History of diabetes mellitus at baseline, No. (%)
18 417
597 (3.2)
20 407
759 (3.7)
38 824
1356 (3.5)
Townsend score of social deprivation (quintiles), No. (%)
18 417
20 407
38 824
1 (Least deprived)
2433 (13.2)
3588 (17.6)
6021 (15.5)
2
2849 (15.5)
3750 (18.4)
6599 (17.0)
3
3688 (20.0)
4279 (21.0)
7967 (20.6)
4
4512 (24.5)
4740 (23.2)
9252 (23.8)
5 (Most deprived)
4935 (26.8)
4050 (19.8)
SMI diagnosis, No. (%)
18 417
Schizophrenia
20 407 7606 (41.3)
8985 (23.1) 38 824
5626 (27.6)
13 232 (34.1)
Bipolar disorder
4005 (21.7)
6093 (29.8)
10 098 (26.0)
Other
4967 (27.0)
6238 (30.6)
11 205 (28.9)
Unknown (on severe mental illness register)
1839 (10.0)
2450 (12.0)
4289 (11.0)
Medications at baseline, No. (%) Antidepressants
18 417
5679 (30.8)
20 407
8439 (41.4)
38 824
14 118 (36.4)
Second-generation antipsychotics
18 417
4184 (22.7)
20 407
3760 (18.4)
38 824
7944 (20.5)
First-generation antipsychotics
18 417
3864 (21.0)
20 407
4769 (23.4)
38 824
8633 (22.2)
Lithium
18 417
1626 (8.8)
20 407
299 (1.5)
38 824
4125 (10.6)
Antihypertensive drugs
18 417
1543 (8.4)
20 407
2505 (12.3)
38 824
4048 (10.4)
Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); HDL-C, high-density lipoprotein cholesterol; SMI, severe mental illness.
SI conversion factor: To convert total cholesterol and HDL-C to milligrams per deciliter, divide by 0.0259.
Main Outcome
In summary, we performed Cox proportional hazards regression with backward elimination to derive the PRIMROSE models. The variables considered in the models are listed in Tables 1, 2, and 3. We compared the performance of different models by calculating the C index22 and D statistic23 for discrimination, by constructing calibration plots, and by assessing the numbers of people classified as having high risk of CVD over the course of 10 years (score >20%) who went on to have a CVD event. After comparing the PRIMROSE risk models against published Cox Framingham models, we performed 2 supplementary comparisons. First, we wanted to ascertain whether our results could be explained by differences between North American and UK source populations. Thus, we reestimated the Cox Framingham model to the UK gen-
Newly recorded fatal and nonfatal cardiovascular events were defined as a diagnostic record for myocardial infarction, angina pectoris, coronary heart disease, major coronary surgery and revascularization, cerebrovascular accident, and transient ischemic attack.
Statistical Analysis We developed 2 PRIMROSE risk models: the BMI model and lipid model. Detailed descriptions of the follow-up period, the variables considered in our analysis, the development of the PRIMROSE risk models and their 10-fold internal crossvalidation, as well as the imputation of missing data and our sample size calculation, are provided in the eAppendix in the Supplement. jamapsychiatry.com
(Reprinted) JAMA Psychiatry February 2015 Volume 72, Number 2
Copyright 2015 American Medical Association. All rights reserved.
Downloaded From: http://archpsyc.jamanetwork.com/ by a BYU Harold B Lee Library User on 05/18/2015
145
Research Original Investigation
Cardiovascular Risk Prediction Models
Table 2. Associations of Predictors With New-Onset CVD After Imputation HR (95% CI)a
Characteristic Sex (female vs male) Age (per log-year increase)
P Value
0.69 (0.64-0.75)
