def dmcx(age, egfr, tchdl, acr, smoker, diab_dur, female, sbp, dbp, hba1c,
htn_med, bmi, insulin, aGlucose):
"""
Calculate the risk for cardiovascular disease
using the coefficients from the DMCX Cohort
Parameters
----------
age : numeric
Age of subject
"""
# do some preprocessing
age = clean_age(age)
egfr = clean_egfr(egfr)
sbp = clean_bp(sbp)
dbp = clean_bp(dbp)
bmi = clean_bmi(bmi)
hba1c = clean_hba1c(hba1c)
tchdl = clean_tchdl(tchdl)
xFeat = np.array([
age, egfr >= 60 and egfr < 90, egfr >= 30 and egfr < 60, egfr < 30,
tchdl,
np.log(clean_acr(acr) + 1), smoker,
clean_diab_dur(diab_dur), sbp, hba1c, htn_med, dbp, bmi, insulin,
dbp**2, bmi**2, sbp**2, hba1c**2, age * tchdl, age * hba1c,
age * smoker, aGlucose
])
coefInfo = MALE_DCMX
if female:
coefInfo = FEMALE_DCMX
return cox_surv(xFeat, coefInfo["coef"], coefInfo["sm"], coefInfo["const"])
def ndr(diab_age,
diab_dur,
tchdl,
hba1c,
sbp,
bmi,
male,
smoker,
microalbum,
macroalbum,
afib,
cvd,
risk=5):
if risk not in [4, 5]:
raise NotImplementedError("Does not support risk that is not 4 or 5")
baseSurv = S0_4
if risk == 5:
baseSurv = S0_5
xFeat = np.array([
diab_age - 53.858,
clean_diab_dur(diab_dur) - 7.7360,
np.log(clean_tchdl(tchdl)) - 1.3948,
np.log(clean_hba1c(hba1c)) - 1.9736,
np.log(clean_bp(sbp)) - 4.9441,
np.log(clean_bmi(bmi)) - 3.3718, male - 0.6005, smoker - 0.1778,
microalbum - 0.1604, macroalbum - 0.0638, afib - 0.0319, cvd - 0.1525
])
s = cox_surv(xFeat, BETA, baseSurv)
return s
def hkdr_chd(age, female, cur_smoker, diab_dur, egfr, acr, nonhdl_mmol):
"""
Calculate the risk for coronary heart disease
using the coefficients from the HKDR CHD Cohort
Parameters
----------
age : numeric
Age of subject
isFemale : boolean or int
Subject is female (True or False)
curSmoke: boolean or int
Previous history of CVD (True or False)
diabDur : numeric
Nubmer of years of diabetes
egfr : numeric
Estimated Glomerular Filteration Rate
acr : numeric
Urinary albumin : creatinine ratio in mg/mmol
nonHDL : numeric
Non-HDL cholesterol (mmol/L)
"""
xFeat = np.array([
clean_age(age), female, cur_smoker,
clean_diab_dur(diab_dur),
np.log10(clean_egfr(egfr)),
np.log10(1 + clean_acr(acr)),
clean_nonhdl(nonhdl_mmol, meas="mmol")
])
return cox_surv(xFeat, HKDR_CHD["coef"], HKDR_CHD["sm"], HKDR_CHD["const"],
HKDR_CHD["shrink"])
def dial(is_male,
age,
bmi,
cur_smoke,
sbp,
non_hdl,
hba1c,
egfr,
microalbumin,
macroalbumin,
diab_dur,
cvd_hist,
insulin,
hz_treat=0,
high_risk_county=False):
# fix the age to be within 34-94
age = np.clip(clean_age(age), 34, 94)
diab_dur = int(round(clean_diab_dur(diab_dur)))
bmi = clean_bmi(bmi)
sbp = clean_bp(sbp)
non_hdl = clean_nonhdl(non_hdl, meas="mmol")
egfr = clean_egfr(egfr)
hba1c = clean_hba1c(hba1c, meas="mmol")
xFeat = np.array([
is_male, age * is_male, bmi - 30, bmi**2 - 30**2, cur_smoke,
age * cur_smoke, sbp - 140, sbp**2 - 140**2, non_hdl - 3.8,
non_hdl**2 - 3.8**2, hba1c - 50, hba1c**2 - 50**2, egfr - 80,
egfr**2 - 80**2, microalbumin, macroalbumin, diab_dur, cvd_hist,
age * cvd_hist, insulin, age * insulin, hz_treat, high_risk_county
])
# look-up the age-specific value
s0 = AGE_S0[age]
return cox_surv(xFeat, DIAL_COEF, s0)
def qdiabetes(age,
male,
bmi,
diab_dur,
ac,
easian,
hba1c,
tchdl,
sbp,
heavy_smoke,
moderate_smoke,
light_smoke,
prev_smoke,
afib,
cvd,
renal,
tYear=5,
dmt1=False):
genderInfo = FEMALE_CCF
fractalFunc = _frac_poly_female
if male:
genderInfo = MALE_CCF
fractalFunc = _frac_poly_male
return _survival(clean_age(age), clean_bmi(bmi),
clean_diab_dur(diab_dur, 0), ac, easian,
clean_hba1c(hba1c, meas="mmol"), clean_tchdl(tchdl),
clean_bp(sbp), heavy_smoke, moderate_smoke, light_smoke,
prev_smoke, afib, cvd, renal, dmt1, genderInfo, tYear,
fractalFunc)
def advance(diab_age, female, diab_dur, pp, retin, afib, hba1c, acr, non_hdl,
htn_treat):
# add ability to ensure specific values are not negative
xFeat = np.array([
diab_age, female,
clean_diab_dur(diab_dur),
clean_pp(pp), retin, afib,
clean_hba1c(hba1c),
np.log(clean_acr(acr)),
clean_nonhdl(non_hdl), htn_treat
])
s = cox_surv(xFeat, BETA, S_0, CONST)
return s
def darts(diab_age, diab_dur, chol_tot, prev_smoker, cur_smoker, male, hba1c,
follow5, sbp, htn, height, t):
hba1c = clean_hba1c(hba1c)
sbp = clean_bp(sbp)
xFeat = np.array([
np.log(clean_diab_dur(diab_dur)), diab_age,
clean_tot_chol(chol_tot), prev_smoker, cur_smoker, male,
np.log(hba1c),
np.log(hba1c) * follow5, sbp, htn, sbp * htn,
clean_height(height)
])
s = weibull_atf_surv(xFeat, BETA, INTERCEPT, SIGMA, t)
return s