def fremantle(age, male, cvd, hba1c, acr, hdl_mmol, seurope, aboriginal):
"""
Calculate the risk for cardiovascular disease
using the coefficients from the Fremantle Cohort
Parameters
----------
age : numeric
Age of subject
isMale : boolean or int
Subject is male (True or False)
cvd: boolean or int
Previous history of CVD (True or False)
hba1c : numeric
Hba1c (%) of subject
acr : numeric
Urinary albumin : creatinine ratio in mg/mmol
hdl : numeric
High density lipid cholestrol in mmol/L
seurope : bool or int
Subject is Southern European (True or False)
aboriginal : bool or int
Subject is Indigenous Australian (True or False)
"""
xFeat = np.array([
clean_age(age), male, cvd,
np.log(clean_hba1c(hba1c)),
np.log(clean_acr(acr)),
np.log(clean_hdl(hdl_mmol, meas="mmol")), seurope, aboriginal
])
return cox_surv(xFeat, FREMANTLE_COEF, FREMANTLE_SM, FREMANTLE_CONST)
def recode(age,
female,
ethnicity,
smoking,
sbp,
cvdHist,
bpld,
statin,
anticoag,
hba1c,
tchol,
hdl,
creat,
acr,
target="CHF"):
coefInfo = CHD_INFO
if target == "MI":
coefInfo = MI_INFO
if target == "STROKE":
coefInfo = STROKE_INFO
"""
Calculate the survival value
"""
xFeat = np.array([
clean_age(age), female, ethnicity, smoking,
clean_bp(sbp), cvdHist, bpld, statin, anticoag,
clean_hba1c(hba1c),
clean_tot_chol(tchol),
clean_hdl(hdl), creat,
clean_acr(acr)
])
return cox_surv(xFeat, coefInfo["coef"], coefInfo["s0"], 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 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 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 hkdr_stroke(age, hba1c, acr, chd):
xFeat = np.array(
[clean_age(age),
clean_hba1c(hba1c),
np.log10(clean_acr(acr)), chd])
return cox_surv(xFeat, HKDR_STROKE["coef"], HKDR_STROKE["sm"],
HKDR_STROKE["const"])
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_mi_female(ac,
diab_dur,
diab_age,
egfr,
hba1c,
ldl,
mmalb,
pvd,
sbp,
cur_smoke,
wbc,
chf_hist,
chd_hist,
tYear=1):
"""
Calculate the number of years to forecast the risk.
"""
ldl = clean_ldl(ldl)
egfr = clean_egfr(egfr)
xFeat = np.array([
ac, diab_age, egfr / 10 if egfr < 60 else 0,
clean_hba1c(hba1c), ldl * 10 if ldl > 35 else 0, mmalb >= 50, pvd,
clean_bp(sbp) / 10, cur_smoke, wbc, chf_hist, chd_hist
])
return weibull_surv(xFeat, MI_FEMALE_PARAMS["beta"],
MI_FEMALE_PARAMS["lambda"], diab_dur, diab_dur + tYear,
MI_MALE_PARAMS["rho"])
def ukpdsom2_mi_male(ac,
diab_dur,
diab_age,
easian,
hba1c,
hdl,
ldl,
mmalb,
pvd,
sbp,
cur_smoke,
wbc,
amp_hist,
chf_hist,
chd_hist,
stroke_hist,
tYear=1):
"""
Calculate the number of years to forecast the risk.
"""
xFeat = np.array([
ac, diab_age, easian,
clean_hba1c(hba1c),
clean_hdl(hdl) * 10,
clean_ldl(ldl) * 10, mmalb >= 50, pvd,
clean_bp(sbp) / 10, cur_smoke, wbc, amp_hist, chf_hist, chd_hist,
stroke_hist
])
return weibull_surv(xFeat, MI_MALE_PARAMS["beta"],
MI_MALE_PARAMS["lambda"], diab_dur, diab_dur + tYear,
MI_MALE_PARAMS["rho"])
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
def ukpds(ageDiab, age, female, ac, smoking, hba1c, sbp, tchdl, tYear=10):
"""
Calculate the number of years to forecast the risk.
"""
xFeat = np.array([clean_age(age)-55,
female,
ac,
bool(smoking),
clean_hba1c(hba1c)-6.72,
(clean_bp(sbp) - 135.7)/10,
np.log(clean_tchdl(tchdl))-1.59])
q = Q_0 * np.prod(np.power(BETA, xFeat))
uscore = 1 - np.exp(-q * D**(age-ageDiab)* (1-D**tYear)/ (1 - D))
return max(uscore, 0.0)
def ukpdsom2_stroke(diab_dur,
diab_age,
female,
afib,
egfr,
hba1c,
ldl,
mmalb,
sbp,
cur_smoke,
wbc,
amp_hist,
chd_hist,
tYear=1):
"""
Calculate the number of years to forecast the risk.
"""
xFeat = np.array([
diab_age, female, afib, egfr / 10 if egfr < 60 else 0,
clean_hba1c(hba1c), ldl * 10, mmalb >= 50,
clean_bp(sbp) / 10, cur_smoke, wbc, amp_hist, chd_hist
])
return weibull_surv(xFeat, STROKE_PARAMS["beta"], STROKE_PARAMS["lambda"],
diab_dur, diab_dur + tYear, STROKE_PARAMS["rho"])
