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CARDIOVASCULAR JOURNAL OF AFRICA • Volume 31, No 5, September/October 2020
AFRICA
247
visit could be utilised, as long as the statin dose had been stable
for four weeks prior to the blood draw.
Statistical analysis
All statistical analyses were performed using STATA version
15.1, (STATA Corporation, College Station, TX USA). Body
mass index [BMI (kg/m
2
)] was used to classify patients as
underweight (
<
18.5 kg/m
2
), normal weight (18.5–24.9 kg/m
2
),
overweight (25.0–29.9 kg/m
2
) and obese (
>
30.0 kg/m
2
). Waist
circumference (cm) was used to classify patients using a cut-off
value of
>
102 cm for men and
>
88 cm for women. The same
criteria were used for all patients irrespective of ethnicity.
Descriptive statistics were used to characterise the study
population and continuous data were summarised by mean
and standard deviation (SD) or median and interquartile range
(IQR), as appropriate. Categorical data were summarised as
number and proportion.
For the purpose of comparing dose equivalence across
different statins, the following algorithm was used: rosuvastatin
dose amount was unchanged, atorvastatin dose amount was
divided by two, simvastatin dose amount was divided by four,
and pravastatin dose amount was divided by 16. Patients who
were on ezetimibe had their calculated dose equivalent multiplied
by a factor of eight (two to the power of three) as adding
ezetimibe to a statin usually lowers LDL-C level by a further
18%, or three dose doublings, with each doubling lowering
LDL-C level by an additional 6%. For instance, doubling a
10-mg dose of rosuvastatin three times (10
→
20
→
40
→
80) is
equivalent to rosuvastatin 80 mg. Rosuvastatin 10 mg daily plus
ezetimibe 10 mg daily is approximately equivalent to rosuvastatin
80 mg daily.
12
Results
Most investigators were specialists (56.3%), with an equal
distribution of cardiologists and specialist physicians, each
speciality accounting for approximately 25% of investigators.
The practice setting was predominantly private office based
(62.5%), followed by private hospital based (31.2%) (Table 1).
The study was conducted between 15 November 2016 and
13 April 2017. Of the 517 patients screened, 492 were eligible
to participate. One patient was subsequently excluded from the
analysis as the patient had erroneously been enrolled despite
having no documented LDL-C value.
Patient demographics and characteristics, cardiovascular
risk factors and baseline laboratory values are displayed in
Tables 2 and 3. Family history of premature atherosclerotic
cardiovascular events is shown in Table 4. The mean (SD)
age of the patient cohort was 61.6 (11.0) years with a mean
(SD) BMI of 30.1 (6.0) kg/m
2
. A total of 229 patients (46.0%)
were obese. The most common cardiovascular disease reported
was previous acute coronary syndrome (38.9%). A significant
Table 1. Physician characteristics
Characteristics
Number
(%)
Duration of practice
(years), median (IQR)
Patient total
(n
=
492), n (%)
General practitioner 7 (43.8)
29 (18–36)
229 (46.5)
Cardiologist
4 (25.0)
11 (7–19)
111 (22.6)
Specialist physician 4 (25.0)
23 (20–29)
122 (24.8)
Endocrinologist
1 (6.2)
16
30 (6.1)
Practice setting
Private office
10 (62.5)
330 (67.1)
Private hospital
5 (31.2)
99 (20.1)
Public hospital
1 (6.3)
63 (12.8)
Table 2. Baseline characteristics
Variables
All patients
(n
=
491)
Females
(n
=
162; 33%)
Males
(n
=
329; 67%)
Age (years), mean
±
SD
61.6
±
11.0
n
=
491
63.0
±
10.9
n
=
162
60.9
±
11.1
n
=
329
Weight (kg), mean
±
SD
85.8
±
18.2
n
=
485
*
79.2
±
17.9
n
=
162
89.2
±
17.5
n
=
323
*
Height (cm), mean
±
SD
168.8
±
10.5
n
=
486
*
159.4
±
7.9
n
=
162
173.6
±
8.2
n
=
324
*
BMI (kg/m
2
), mean
±
SD 30.1
±
6.0
n
=
485
*
31.3
±
7.5
n
=
162
29.5
±
5.0
n
=
323
*
Waist (cm), mean
±
SD
102.8
±
13.9
n
=
478
*
99.4
±
14.6
n
=
160*
104.5
±
13.3
n
=
318*
Hip (cm), mean
±
SD 105.1
±
12.1
n
=
459*
108.2
±
13.5
n
=
158*
103.4
±
11.0
n
=
301
*
Systolic blood pressure
(mmHg, mean
±
SD)
131
±
15.1
n
=
484*
132
±
15.1
n
=
161*
131
±
15.1
n
=
323*
Diastolic blood pressure
(mmHg), mean
±
SD
77
±
9.4
n
=
484
*
76
±
9.7
n
=
161*
78
±
9.1
n
=
323*
Caucasian,
n
(%)
190 (38.7)
47 (29.0)
143 (43.5)
Asian,
n
(%)
139 (28.3)
36 (22.2)
103 (31.3)
Black,
n
(%)
92 (18.7)
39 (24.1)
53 (16.1)
Mixed,
n
(%)
70 (14.3)
40 (24.7)
30 (9.1)
Underweight,
n
(%)
1
1 (0.2)
1 (0.6)
0 (0)
Normal,
n
(%)
2
85 (17.5)
28 (17.3)
57 (17.7)
Overweight,
n
(%)
3
176 (36.3)
47 (29.0)
129 (39.9)
Obese,
n
(%)
4
223 (46.0)
86 (53.1)
137 (42.4)
Above cut-off for waist
circumference,
n
(%)
316 (64.4)
133 (82.1)
183 (55.6)
1
<
18.5 kg/m
2
,
2
18.5–24.9 kg/m
2
,
3
25.0–29.9 kg/m
2
,
4
>
30.0 kg/m
2
, *missing data
for some patients.
Table 3. Cardiovascular risk factors and baseline laboratory values
Variables
Values
Cardiovascular risk factors (
n
= 491)
Hypertension,
n
(%)
381 (77.6)
Diabetes mellitus,
n
(%)
316 (64.4)
Proteinuria,
n
(%)
71 (14.5)
Total cholesterol
>
8 mmol/l,
n
(%)
67 (13.7)
Diabetic retinopathy,
n
(%)
23 (4.7)
Smoking,
n
(%)
Current
80 (16.3)
Past
127 (25.9)
Non-smoker
284 (57.8)
Severe chronic kidney disease stage IV/V,
n
(%)
6 (1.2)
Alcohol abuse,
n
(%)
#
11 (2.2)
FH,
n
(%)
101 (20.6)
Laboratory values
Total cholesterol (mmol/l), median (IQR)
4.1 (3.4–5.1)
LDL-C (mmol/l), median (IQR)
2.2 (1.6–3.1)
Triglycerides (mmol/l) (473*), median (IQR)
1.6 (1.1–2.4)
HDL-C (mmol/l) (465*), median (IQR)
1.1 (0.9–1.3)
Serum glucose (mmol/l) (304*), median (IQR)
7.1 (5.7–9.8)
HbA
1c
(%) (316*), median (IQR)
7.3 (6.3–8.7)
Serum creatinine (
μ
mol/l) (396*), median (IQR)
82 (70–980
GFR (ml/min) (356*), median (IQR)
77 (60–89)
*Missing data for some patients,
#
based on clinician evaluation.
FH, familial hypercholesterolaemia; LDL-C, low-density lipoprotein choles-
terol; HbA
1c
, glycated haemoglobin; GFR, glomerular filtration rate; HDL-C,
high-density lipoprotein cholesterol.