This document discusses reducing stroke risk in males through improved cholesterol and blood pressure management. It presents the case of a 59-year-old male with a history of stroke, hypertension, sleep apnea and obesity. His ASCVD 10-year risk is calculated at 12.9% and lifetime risk at 50%. The document then reviews evidence that education programs can improve medication compliance and lifestyle changes to lower stroke risk. It proposes a project targeting high-risk males over 40 through automated ASCVD risk scoring in medical records and patient education classes to enhance long-term outcomes monitoring and management of cholesterol and blood pressure.
2. Case
S 59 yo White Male
S PMH
S Previous Stroke 2015
S Hypertension
S Sleep Apnea
S Obesity
S SH: No Smoking, EtOH, Illicits
S FH: No history of CVD or Stoke
3. Case Cont.
S PE: BP 128/75, HR 60, RR 25, BMI 42.7
S CV: NRRR no MRG
S Lungs: CTAB
S Abdomen: nontender, NABS
Labs:
S Total cholesterol 222; HDL 35
S Medications:
S Amlodipine, Carvedilol, Lisinopril, Atorvastatin, Meloxicam
4. ASCVD Risk Calculation
S Age 59
S HDL 35
S Total Cholesterol 222
S SBP 128
S Being treated for Hypertension, No Diabetes or Smoking
ASCVD RISK: 10-years = 12.9%; Lifetime = 50%
5.
6.
7. Community-Based Secondary
Stroke Prevention Program
S Conducted in China
S Patients with previous stroke
S Education on stoke disease process, self-health
monitoring, and lifestyle changes
S Improvement in knowledge of warning signs, seeking
treatment in case of stroke, medication compliance,
home BP monitoring, and lifestyle modifications
8. ASCVD Risk calculation in
Males
Sex
(M/F)
Age
(years) Race
HDL cholesterol
(mg/DL)
Total
Cholesterol
(mg/dL)
Triglycerides
(mg/dL) BMI (kg/m2)
Diabetes
(Y/N)
Treatment
for
Hypertension
(y/n)
Systolic Blood
Pressure
Active
Tobacco
Use (Y/N)
ASCVD 10
year risk
score
ASCVD
Lifetime
Risk Score
M 64 H 38 103 99 30.4 Y Y 142 N 29.1 N/A
M 50 W 42 161 131 36.2 N Y 128 Y 7.9 69
M 45 W 31 214 657 26.5 N N 110 Y 8.4 50
M 60 H 33 147 45 31.6 N Y 134 N 7.7 N/A
M 40 W 42 175 120 27.4 N N 125 Y 3.5 50
M 58 W 26 142 127 23.1 N Y 132 N 9.6 50
M 50 H 28 191 355 29.1 N N 140 N 6.9 46
M 65 W 42 223 123 21.1 N Y 99 N 11.4 N/A
M 59 W 35 222 126 42.7 N Y 128 N 12.9 50
11. Target of Risk Reduction:
Cholesterol and Systolic Blood
Pressure Management
S Why:
S High Cholesterol and systolic blood pressures have been
shown to increase the risk of developing or recurring CVD
and Stroke
12. Planned Intervention:
Bioinformatics and Patient
Education to improve long term
outcomes
S Design a module within StarPanel utilizing the most recent
available data within the EMR to calculate ASCVD risk scores
and have them displayed in the Patient Summary
S StarPanel already tracks BP, BMI, and smoking status upfront in the
patient summary, though Cholesterol levels and diagnoses of
Hypertension and Diabetes are not displayed
S More up to date and regular Lipid lab procurement by Primary Care
providers
S Educational “class” for those individuals at higher risk based on
ASCVD to improve long term outcomes
13. Measuring Efficacy
S Select 3-4 Primary Care clinics to partner with
S Implement goal of most recent lipid panel within 1 year
S Change in Patient Cholesterol levels
S Identify if interventions lead to improved cholesterol
management in participating patient populations
S Compare Cholesterol levels to non-participating clinics
(controls)
14. Time Course
S 1-2 years
S To attain desired lipid testing prevalence
S >2 years
S To look at changes in cholesterol, SPB and ASCVD risk
15. Defense
S CVD and Stroke are very important and prevalent
disease processes that affect a huge proportion of the
population.
S Could significantly reduce the incidence of poor outcomes
with earlier interventions/better management
S Addressing these diseases would have great impact on
overall public health and have effects on decreasing
healthcare costs due to secondary complications.
Editor's Notes
Looking at the prevalence of Stroke by age and sex, we can see in this graph that the prevalence of stroke for our patient, a 59 year old male is 1.9%, which is relatively low. We can see that there is a spike in the population for prevalence of stroke starting in the next age range of 60-79.
Here we can see a graph of the probability of death within 1 year after a stroke for various age ranges, genders, and ethnicities. For our patient, it places his risk of death within this first year of his stroke at 11%.
So, what are some things we can do to improve outcomes for stroke survivors? One such program identified is the Community-based secondary stroke prevention program. This program was conducted in China involving a population of patients with prior stroke. It endeavored to increase education of stroke disease process, self-health monitoring, and lifestyle changes and achieved improvement in patient knowledge of warning signs, seeking treatment in case of subsequent stroke, medication compliance, home BP monitoring, and lifestyle modifications.
Here we can see a chart outlining some of the details of the patient Panel. I chose to narrow the scope to males as they are at higher risk for stoke compared to females. You can find the data for the patient presented in the case in the last row.
Here we see a chart stratifying the ASCVD risk scores for these patients. I grouped the patients into three risk groups, a low risk group of 1-5%, a moderate risk group of 5-10 percent, and a high risk group of greater than 10%. Out of the 9 male patients, you can see that the majority fell within the moderate risk group of 5-10% for 10 year ASCVD risk. 1/3 of these patients were found to have a high risk of greater than 10% 10 year ASCVD risk, the patient from our case was one of these, at 12.9%
There are several mechanisms to measure the efficacy of this project. First, I would like to select 3-4 primary care clinics to partner with, and implement a goal of having a recent lipid panel for each patient in the clinic that was performed within one year. Having this up to date information, we could then investigate if having more reliable data points for these patients translates to healthier cholesterol levels over time. Finally, I would like to compare the data from the patients in these participating clinics to those patient populations in clinics not implementing these measures.
The time course for this project would be 1-2 years to attain the desired lipid testing prevalence of having all patients in participating clinics with recent lipid panels within one year. And then the time 2 years and onwards to look at changes in cholesterol, systolic blood pressure and ASCVD risk between the study populations