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 frs_primary(female, age, tot_chol, hdl, sbp, htn, smk, diab):
"""
"""
xFeat = np.array([np.log(clean_age(age)),
np.log(clean_tot_chol(tot_chol)),
np.log(clean_hdl(hdl)),
np.log(clean_bp(sbp))*(1-htn),
np.log(clean_bp(sbp))*htn,
smk,
diab])
genderInfo = LAB_MEN
if female:
genderInfo = LAB_WOMEN
return cox_surv(xFeat, genderInfo["coef"],
genderInfo["s0"], genderInfo["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 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 pce(female, ac, age, tot_chol, hdl, sbp, smoker, htn, diab, risk=5):
if risk not in [5, 10]:
raise NotImplementedError("Does not support risk that is not 5 or 10")
baseSurv = "s10"
if risk == 5:
baseSurv = "s5"
# figure out what the betas are
cohortInfo = WHITE_MALE
if female and ac:
cohortInfo = BLACK_FEMALE
elif female:
cohortInfo = WHITE_FEMALE
elif ac:
cohortInfo = BLACK_MALE
age = clean_age(age)
tot_chol = clean_tot_chol(tot_chol)
hdl = clean_hdl(hdl)
sbp = clean_bp(sbp)
xFeat = np.array([
np.log(age),
np.log(age)**2,
np.log(tot_chol),
np.log(tot_chol) * np.log(age),
np.log(hdl),
np.log(hdl) * np.log(age),
np.log(sbp) * (1 - htn),
np.log(age) * np.log(sbp) * (1 - htn),
np.log(sbp) * htn,
np.log(age) * np.log(sbp) * htn, smoker, smoker * np.log(age), diab
])
s = cox_surv(xFeat, cohortInfo["coef"], cohortInfo[baseSurv],
cohortInfo["const"])
return s
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 score(female, age, chol_mmol, sbp, smoking, low_risk):
sc = None
beta = HIGH_RISK_MEN
if female and low_risk:
beta = LOW_RISK_WOMEN
elif female:
beta = HIGH_RISK_WOMEN
elif low_risk:
beta = LOW_RISK_MEN
sc = _calculate_score(beta, max(20, age), clean_chol(chol_mmol),
clean_bp(sbp), smoking)
return max(0, min(sc, 1))
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 aric(age, male, cauc, tc, hdl, sbp, htn, smoke):
# set the coeff based on male or female
genderInfo = FEMALE_INFO
if male:
genderInfo = MALE_INFO
age = clean_age(age)
tc = clean_tot_chol(tc)
hdl = clean_hdl(hdl)
xFeat = np.array([
age, age**2, cauc, tc >= 200 and tc <= 279, tc >= 280, hdl < 45,
hdl >= 45 and hdl <= 49,
clean_bp(sbp), htn, smoke
])
return cox_surv(xFeat, genderInfo["coef"], genderInfo["sm"],
genderInfo["xBetaMed"])
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"])
