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 frs_simple(female, age, bmi, sbp, htn, smk, diab):
"""
10-year risk calculated using the Simple Non-Laboratory
Framingham Risk Score (FRS) Calculation.
Parameters
----------
female : boolean
age : numeric
Age of subject
bmi : numeric
BMI of subject
sbp : numeric
Systolic blood pressure of subject
ht_treat : bool or int
Treatment for hypertension (True or False)
smk : bool or int
Subject is smoker (True or False)
diab : bool or int
Subject has diabetes (True or False)
"""
xFeat = np.array([np.log(clean_age(age)),
np.log(clean_bmi(bmi)),
np.log(clean_bp(sbp))*(1-htn),
np.log(clean_bp(sbp))*htn,
smk,
diab])
genderInfo = NONLAB_MEN
if female:
genderInfo = NONLAB_WOMEN
return cox_surv(xFeat, genderInfo["coef"],
genderInfo["s0"], genderInfo["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_hf(female, age, bmi, hba1c, acr, hb, chdHist):
"""
Calculate the risk for heart failure
using the coefficients from the HKDR Cohort
Parameters
----------
age : numeric
Age of subject
bmi : numeric
BMI of the subject (in kg/m^2)
hba1c: numeric
HBA1C (%)
acr : numeric
Urinary albumin : creatinine ratio in mg/mmol
hb : numeric
Blood Hemoglobin (g/dl)
chdHist : boolean or int
Subject had CHD (true or False)
"""
baseSurv = HKDR_HF["male_sm"]
if female:
baseSurv = HKDR_HF["female_sm"]
xFeat = np.array([
clean_age(age),
clean_bmi(bmi),
clean_hba1c(hba1c),
np.log10(1 + clean_acr(acr)),
clean_hb(hb), chdHist
])
return cox_surv(xFeat, HKDR_HF["coef"], baseSurv, HKDR_HF["const"],
HKDR_HF["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 ukpdsom2_chf(diabDur,
diabAge,
afib,
bmi,
egfr,
ldl,
mmalb,
pvd,
ampHist,
ulcHist,
tYear=1):
"""
Calculate the number of years to forecast the risk.
"""
egfr = clean_egfr(egfr)
xFeat = np.array([
diabAge, afib,
clean_bmi(bmi), egfr / 10 if egfr < 60 else 0,
clean_ldl(ldl) * 10, mmalb >= 50, pvd, ampHist, ulcHist
])
return weibull_surv(xFeat, CHF_PARAMS["beta"], CHF_PARAMS["lambda"],
diabDur, diabDur + tYear, CHF_PARAMS["rho"])