def get_EDC(self, lmp=None, nullipara=True):
result = cClinicalResult(_('unknown EDC'))
result.formula_name = u'EDC (Mittendorf 1990)'
result.formula_source = u'Mittendorf, R. et al., "The length of uncomplicated human gestation," OB/GYN, Vol. 75, No., 6 June, 1990, pp. 907-932.'
if lmp is None:
result.message = _('EDC: unknown LMP')
return result
result.variables['LMP'] = lmp
result.variables['nullipara'] = nullipara
if nullipara:
result.variables['parity_offset'] = 15 # days
else:
result.variables['parity_offset'] = 10 # days
now = gmDateTime.pydt_now_here()
if lmp > now:
result.warnings.append(_(u'LMP in the future'))
if self.__patient is None:
result.warnings.append(_(u'cannot run sanity checks, no patient'))
else:
if self.__patient['dob'] is None:
result.warnings.append(_(u'cannot run sanity checks, no DOB'))
else:
years, months, days, hours, minutes, seconds = gmDateTime.calculate_apparent_age(
start=self.__patient['dob'])
# 5 years -- Myth ?
# http://www.mirror.co.uk/news/uk-news/top-10-crazy-amazing-and-world-789842
if years < 10:
result.warnings.append(
_(u'patient less than 10 years old'))
if self.__patient['gender'] in [None, u'm']:
result.warnings.append(
_(u'atypical gender for pregnancy: %s') %
self.__patient.gender_string)
if self.__patient['deceased'] is not None:
result.warnings.append(_(u'patient already passed away'))
if lmp.month > 3:
edc_month = lmp.month - 3
edc_year = lmp.year + 1
else:
edc_month = lmp.month + 9
edc_year = lmp.year
result.numeric_value = gmDateTime.pydt_replace(
dt=lmp, year=edc_year, month=edc_month,
strict=False) + pydt.timedelta(
days=result.variables['parity_offset'])
result.message = _('EDC: %s') % gmDateTime.pydt_strftime(
result.numeric_value, format='%Y %b %d')
result.date_valid = now
_log.debug(u'%s' % result)
return result
def _get_egfr(self):
# < 18 ?
Schwartz = self.eGFR_Schwartz
if Schwartz.numeric_value is not None:
return Schwartz
# this logic is based on "KVH aktuell 2/2014 Seite 10-15"
# expect normal GFR
CKD = self.eGFR_CKD_EPI
if CKD.numeric_value is not None:
if CKD.numeric_value > self.d(60):
return CKD
# CKD at or below 60
if self.__patient['dob'] is None:
return CKD # no method will work, so return CKD anyway
CG = self.eGFR_Cockcroft_Gault
MDRD = self.eGFR_MDRD_short
age = None
if age is None:
try:
age = CKD.variables['age@crea']
except KeyError:
_log.warning('CKD-EPI: no age@crea')
if age is None:
try:
age = CG.variables['age@crea']
except KeyError:
_log.warning('CG: no age@crea')
if age is None:
try:
age = MDRD.variables['age@crea']
except KeyError:
_log.warning('MDRD: no age@crea')
if age is None:
age = gmDateTime.calculate_apparent_age(
start=self.__patient['dob'])[0]
# geriatric ?
if age > self.d(65):
if CG.numeric_value is not None:
return CG
# non-geriatric or CG not computable
if MDRD.numeric_value is None:
if (CKD.numeric_value is not None) or (CG.numeric_value is None):
return CKD
return CG
if MDRD.numeric_value > self.d(60):
if CKD.numeric_value is not None:
# probably normal after all (>60) -> use CKD-EPI
return CKD
return MDRD
def _get_egfr(self):
# < 18 ?
Schwartz = self.eGFR_Schwartz
if Schwartz.numeric_value is not None:
return Schwartz
# this logic is based on "KVH aktuell 2/2014 Seite 10-15"
# expect normal GFR
CKD = self.eGFR_CKD_EPI
if CKD.numeric_value is not None:
if CKD.numeric_value > self.d(60):
return CKD
# CKD at or below 60
if self.__patient['dob'] is None:
return CKD # no method will work, so return CKD anyway
CG = self.eGFR_Cockcroft_Gault
MDRD = self.eGFR_MDRD_short
age = None
if age is None:
try:
age = CKD.variables['age@crea']
except KeyError:
_log.warning('CKD-EPI: no age@crea')
if age is None:
try:
age = CG.variables['age@crea']
except KeyError:
_log.warning('CG: no age@crea')
if age is None:
try:
age = MDRD.variables['age@crea']
except KeyError:
_log.warning('MDRD: no age@crea')
if age is None:
age = gmDateTime.calculate_apparent_age(start = self.__patient['dob'])[0]
# geriatric ?
if age > self.d(65):
if CG.numeric_value is not None:
return CG
# non-geriatric or CG not computable
if MDRD.numeric_value is None:
if (CKD.numeric_value is not None) or (CG.numeric_value is None):
return CKD
return CG
if MDRD.numeric_value > self.d(60):
if CKD.numeric_value is not None:
# probably normal after all (>60) -> use CKD-EPI
return CKD
return MDRD
def get_EDC(self, lmp=None, nullipara=True):
result = cClinicalResult(_('unknown EDC'))
result.formula_name = 'EDC (Mittendorf 1990)'
result.formula_source = 'Mittendorf, R. et al., "The length of uncomplicated human gestation," OB/GYN, Vol. 75, No., 6 June, 1990, pp. 907-932.'
if lmp is None:
result.message = _('EDC: unknown LMP')
return result
result.variables['LMP'] = lmp
result.variables['nullipara'] = nullipara
if nullipara:
result.variables['parity_offset'] = 15 # days
else:
result.variables['parity_offset'] = 10 # days
now = gmDateTime.pydt_now_here()
if lmp > now:
result.warnings.append(_('LMP in the future'))
if self.__patient is None:
result.warnings.append(_('cannot run sanity checks, no patient'))
else:
if self.__patient['dob'] is None:
result.warnings.append(_('cannot run sanity checks, no DOB'))
else:
years, months, days, hours, minutes, seconds = gmDateTime.calculate_apparent_age(start = self.__patient['dob'])
# 5 years -- Myth ?
# http://www.mirror.co.uk/news/uk-news/top-10-crazy-amazing-and-world-789842
if years < 10:
result.warnings.append(_('patient less than 10 years old'))
if self.__patient['gender'] in [None, 'm']:
result.warnings.append(_('atypical gender for pregnancy: %s') % self.__patient.gender_string)
if self.__patient['deceased'] is not None:
result.warnings.append(_('patient already passed away'))
if lmp.month > 3:
edc_month = lmp.month - 3
edc_year = lmp.year + 1
else:
edc_month = lmp.month + 9
edc_year = lmp.year
result.numeric_value = gmDateTime.pydt_replace(dt = lmp, year = edc_year, month = edc_month, strict = False) + pydt.timedelta(days = result.variables['parity_offset'])
result.message = _('EDC: %s') % gmDateTime.pydt_strftime (
result.numeric_value,
format = '%Y %b %d'
)
result.date_valid = now
_log.debug('%s' % result)
return result
def _get_gfr_cockcroft_gault(self):
try:
return self.__cache['cockcroft_gault']
except KeyError:
pass
result = cClinicalResult(_('unknown Cockcroft-Gault'))
result.formula_name = u'eGFR from Cockcroft-Gault'
result.formula_source = u'8/2014: http://en.wikipedia.org/Renal_function'
result.hints.append(_('best @ age >65'))
if self.__patient is None:
result.message = _('Cockcroft-Gault: no patient')
return result
if self.__patient['dob'] is None:
result.message = _('Cockcroft-Gault: no DOB (no age)')
return result
# 1) gender
from Gnumed.business.gmPerson import map_gender2mf
result.variables['gender'] = self.__patient['gender']
result.variables['gender_mf'] = map_gender2mf[self.__patient['gender']]
if result.variables['gender_mf'] not in ['m', 'f']:
result.message = _('Cockcroft-Gault: neither male nor female')
return result
# 2) creatinine
result.variables[
'serum_crea'] = self.__patient.emr.get_most_recent_results(
loinc=gmLOINC.LOINC_creatinine_quantity, no_of_results=1)
if result.variables['serum_crea'] is None:
result.message = _(
'Cockcroft-Gault: serum creatinine value not found (LOINC: %s)'
) % gmLOINC.LOINC_creatinine_quantity
return result
if result.variables['serum_crea']['val_num'] is None:
result.message = _('Cockcroft-Gault: creatinine value not numeric')
return result
result.variables['serum_crea_val'] = self.d(
result.variables['serum_crea']['val_num'])
if result.variables['serum_crea']['val_unit'] in [u'mg/dl', u'mg/dL']:
result.variables['unit_multiplier'] = self.d(72)
if result.variables['gender_mf'] == 'm':
result.variables['gender_multiplier'] = self.d('1')
else: #result.variables['gender_mf'] == 'f'
result.variables['gender_multiplier'] = self.d('0.85')
elif result.variables['serum_crea']['val_unit'] in [
u'µmol/L', u'µmol/l'
]:
result.variables['unit_multiplier'] = self.d(1)
if result.variables['gender_mf'] == 'm':
result.variables['gender_multiplier'] = self.d('1.23')
else: #result.variables['gender_mf'] == 'f'
result.variables['gender_multiplier'] = self.d('1.04')
else:
result.message = _(
'Cockcroft-Gault: unknown serum creatinine unit (%s)'
) % result.variables['serum_crea']['val_unit']
return result
# 3) age (at creatinine evaluation)
result.variables['dob'] = self.__patient['dob']
result.variables['age@crea'] = self.d(
gmDateTime.calculate_apparent_age(
start=result.variables['dob'],
end=result.variables['serum_crea']['clin_when'])[0])
if (result.variables['age@crea'] < 18):
result.message = _(
'Cockcroft-Gault: formula does not apply at age [%s] (17 < age)'
) % result.variables['age@crea']
return result
result.variables[
'weight'] = self.__patient.emr.get_most_recent_results(
loinc=gmLOINC.LOINC_weight, no_of_results=1)
if result.variables['weight'] is None:
result.message = _('Cockcroft-Gault: weight not found')
return result
if result.variables['weight']['val_num'] is None:
result.message = _('Cockcroft-Gault: weight not numeric')
return result
if result.variables['weight']['val_unit'] == u'kg':
result.variables['weight_kg'] = self.d(
result.variables['weight']['val_num'])
elif result.variables['weight']['val_unit'] == u'g':
result.variables['weight_kg'] = self.d(
result.variables['weight']['val_num'] / self.d(1000))
else:
result.message = _('Cockcroft-Gault: weight not in kg or g')
return result
# calculate
result.numeric_value = ((
(140 - result.variables['age@crea']) * result.variables['weight_kg'] * result.variables['gender_multiplier']) \
/ \
(result.variables['unit_multiplier'] * result.variables['serum_crea_val'])
)
result.unit = u'ml/min' #/1.73m²
result.message = _('eGFR(CG): %.1f %s (%s)') % (
result.numeric_value, result.unit,
gmDateTime.pydt_strftime(
result.variables['serum_crea']['clin_when'],
format='%Y %b %d'))
result.date_valid = result.variables['serum_crea']['clin_when']
self.__cache['cockroft_gault'] = result
_log.debug(u'%s' % result)
return result
def _get_gfr_ckd_epi(self):
try:
return self.__cache['CKD-EPI']
except KeyError:
pass
result = cClinicalResult(_('unknown CKD-EPI'))
result.formula_name = u'eGFR from CKD-EPI'
result.formula_source = u'8/2014: http://en.wikipedia.org/Renal_function'
result.hints.append(_('best @ GFR > 60 ml/min'))
if self.__patient is None:
result.message = _('CKD-EPI: no patient')
return result
if self.__patient['dob'] is None:
result.message = _('CKD-EPI: no DOB (no age)')
return result
# 1) gender
from Gnumed.business.gmPerson import map_gender2mf
result.variables['gender'] = self.__patient['gender']
result.variables['gender_mf'] = map_gender2mf[self.__patient['gender']]
if result.variables['gender_mf'] == 'm':
result.variables['gender_multiplier'] = self.d(1)
result.variables['k:gender_divisor'] = self.d('0.9')
result.variables['a:gender_power'] = self.d('-0.411')
elif result.variables['gender_mf'] == 'f':
result.variables['gender_multiplier'] = self.d('1.018')
result.variables['k:gender_divisor'] = self.d('0.7')
result.variables['a:gender_power'] = self.d('-0.329')
else:
result.message = _('CKD-EPI: neither male nor female')
return result
# 2) creatinine
result.variables[
'serum_crea'] = self.__patient.emr.get_most_recent_results(
loinc=gmLOINC.LOINC_creatinine_quantity, no_of_results=1)
if result.variables['serum_crea'] is None:
result.message = _(
'CKD-EPI: serum creatinine value not found (LOINC: %s)'
) % gmLOINC.LOINC_creatinine_quantity
return result
if result.variables['serum_crea']['val_num'] is None:
result.message = _('CKD-EPI: creatinine value not numeric')
return result
result.variables['serum_crea_val'] = self.d(
result.variables['serum_crea']['val_num'])
if result.variables['serum_crea']['val_unit'] in [u'mg/dl', u'mg/dL']:
result.variables['serum_crea_val'] = self.d(
result.variables['serum_crea']['val_num'])
elif result.variables['serum_crea']['val_unit'] in [
u'µmol/L', u'µmol/l'
]:
result.variables['serum_crea_val'] = self.d(
result.variables['serum_crea']['val_num']) / self.d('88.4')
else:
result.message = _('CKD-EPI: unknown serum creatinine unit (%s)'
) % result.variables['serum_crea']['val_unit']
return result
# 3) age (at creatinine evaluation)
result.variables['dob'] = self.__patient['dob']
result.variables['age@crea'] = self.d(
gmDateTime.calculate_apparent_age(
start=result.variables['dob'],
end=result.variables['serum_crea']['clin_when'])[0])
# if (result.variables['age@crea'] > 84) or (result.variables['age@crea'] < 18):
# result.message = _('CKD-EPI: formula does not apply at age [%s] (17 < age < 85)') % result.variables['age@crea']
# return result
# 4) ethnicity
result.variables['ethnicity_multiplier'] = self.d(1) # non-black
result.warnings.append(
_('ethnicity: GNUmed does not know patient ethnicity, ignoring correction factor of 1.519 for "black"'
))
# calculate
result.numeric_value = (
self.d(141) * \
pow(min((result.variables['serum_crea_val'] / result.variables['k:gender_divisor']), self.d(1)), result.variables['a:gender_power']) * \
pow(max((result.variables['serum_crea_val'] / result.variables['k:gender_divisor']), self.d(1)), self.d('-1.209')) * \
pow(self.d('0.993'), result.variables['age@crea']) * \
result.variables['gender_multiplier'] * \
result.variables['ethnicity_multiplier']
)
result.unit = u'ml/min/1.73m²'
result.message = _('eGFR(CKD-EPI): %.1f %s (%s)') % (
result.numeric_value, result.unit,
gmDateTime.pydt_strftime(
result.variables['serum_crea']['clin_when'],
format='%Y %b %d'))
result.date_valid = result.variables['serum_crea']['clin_when']
self.__cache['CKD-EPI'] = result
_log.debug(u'%s' % result)
return result
def _get_gfr_mdrd_short(self):
try:
return self.__cache['MDRD_short']
except KeyError:
pass
result = cClinicalResult(_('unknown MDRD (4 vars/IDMS)'))
result.formula_name = u'eGFR from 4-variables IDMS-MDRD'
result.formula_source = u'1/2013: http://en.wikipedia.org/Renal_function / http://www.ganfyd.org/index.php?title=Estimated_glomerular_filtration_rate (NHS)'
result.hints.append(_('best @ 30 < GFR < 60 ml/min'))
if self.__patient is None:
result.message = _('MDRD (4 vars/IDMS): no patient')
return result
if self.__patient['dob'] is None:
result.message = _('MDRD (4 vars/IDMS): no DOB (no age)')
return result
# 1) gender
from Gnumed.business.gmPerson import map_gender2mf
result.variables['gender'] = self.__patient['gender']
result.variables['gender_mf'] = map_gender2mf[self.__patient['gender']]
if result.variables['gender_mf'] == 'm':
result.variables['gender_multiplier'] = self.d(1)
elif result.variables['gender_mf'] == 'f':
result.variables['gender_multiplier'] = self.d('0.742')
else:
result.message = _('MDRD (4 vars/IDMS): neither male nor female')
return result
# 2) creatinine
result.variables[
'serum_crea'] = self.__patient.emr.get_most_recent_results(
loinc=gmLOINC.LOINC_creatinine_quantity, no_of_results=1)
if result.variables['serum_crea'] is None:
result.message = _(
'MDRD (4 vars/IDMS): serum creatinine value not found (LOINC: %s)'
) % gmLOINC.LOINC_creatinine_quantity
return result
if result.variables['serum_crea']['val_num'] is None:
result.message = _(
'MDRD (4 vars/IDMS): creatinine value not numeric')
return result
result.variables['serum_crea_val'] = self.d(
result.variables['serum_crea']['val_num'])
if result.variables['serum_crea']['val_unit'] in [u'mg/dl', u'mg/dL']:
result.variables['unit_multiplier'] = self.d(175) # older: 186
elif result.variables['serum_crea']['val_unit'] in [
u'µmol/L', u'µmol/l'
]:
result.variables['unit_multiplier'] = self.d(30849) # older: 32788
else:
result.message = _(
'MDRD (4 vars/IDMS): unknown serum creatinine unit (%s)'
) % result.variables['serum_crea']['val_unit']
return result
# 3) age (at creatinine evaluation)
result.variables['dob'] = self.__patient['dob']
result.variables['age@crea'] = self.d(
gmDateTime.calculate_apparent_age(
start=result.variables['dob'],
end=result.variables['serum_crea']['clin_when'])[0])
if (result.variables['age@crea'] > 84) or (result.variables['age@crea']
< 18):
result.message = _(
'MDRD (4 vars/IDMS): formula does not apply at age [%s] (17 < age < 85)'
) % result.variables['age@crea']
return result
# 4) ethnicity
result.variables['ethnicity_multiplier'] = self.d(1) # non-black
result.warnings.append(
_('ethnicity: GNUmed does not know patient ethnicity, ignoring correction factor'
))
# calculate
result.numeric_value = result.variables['unit_multiplier'] * \
pow(result.variables['serum_crea_val'], self.d('-1.154')) * \
pow(result.variables['age@crea'], self.d('-0.203')) * \
result.variables['ethnicity_multiplier'] * \
result.variables['gender_multiplier']
result.unit = u'ml/min/1.73m²'
BSA = self.body_surface_area
result.sub_results.append(BSA)
if BSA.numeric_value is None:
result.warnings.append(
_(u'NOT corrected for non-average body surface (average = 1.73m²)'
))
else:
result.variables['BSA'] = BSA.numeric_value
result_numeric_value = result.numeric_value / BSA.numeric_value
result.message = _('eGFR(MDRD): %.1f %s (%s) [4-vars, IDMS]') % (
result.numeric_value, result.unit,
gmDateTime.pydt_strftime(
result.variables['serum_crea']['clin_when'],
format='%Y %b %d'))
result.date_valid = result.variables['serum_crea']['clin_when']
self.__cache['MDRD_short'] = result
_log.debug(u'%s' % result)
return result
def __update_age_label(self):
# no patient
if not self.curr_pat.connected:
self._LBL_age.SetLabel(_('<Age>'))
self._LBL_age.SetToolTip(_('no patient selected'))
return
# gender is always known
tt = _('Gender: %s (%s) - %s\n') % (self.curr_pat.gender_symbol,
gmTools.coalesce(
self.curr_pat['gender'], '?'),
self.curr_pat.gender_string)
# dob is not known
if self.curr_pat['dob'] is None:
age = '%s %s' % (self.curr_pat.gender_symbol,
self.curr_pat.get_formatted_dob())
self._LBL_age.SetLabel(age)
self._LBL_age.SetToolTip(tt)
return
tt += _('Born: %s\n') % self.curr_pat.get_formatted_dob(
format='%d %b %Y')
# patient is dead
if self.curr_pat['deceased'] is not None:
tt += _('Died: %s\n') % gmDateTime.pydt_strftime(
self.curr_pat['deceased'], '%d %b %Y')
tt += _('At age: %s\n') % self.curr_pat['medical_age']
age = '%s %s - %s (%s)' % (
self.curr_pat.gender_symbol,
self.curr_pat.get_formatted_dob(format='%d %b %Y'),
gmDateTime.pydt_strftime(
self.curr_pat['deceased'],
'%d %b %Y'), self.curr_pat['medical_age'])
if self.curr_pat['dob_is_estimated']:
tt += _(' (date of birth and age are estimated)\n')
self._LBL_age.SetLabel(age)
self._LBL_age.SetToolTip(tt)
return
# patient alive
now = gmDateTime.pydt_now_here()
# patient birthday ?
if self.curr_pat.get_formatted_dob(
format='%m-%d',
honor_estimation=False) == now.strftime('%m-%d'):
template = _('%(sex)s %(dob)s (%(age)s today !)')
tt += _("\nToday is the patient's birthday !\n\n")
tt += _('Age: %s\n') % self.curr_pat['medical_age']
else:
tt += _('Age: %s, birthday:\n') % self.curr_pat['medical_age']
if self.curr_pat.current_birthday_passed is True:
template = '%(sex)s %(dob)s%(l_arr)s (%(age)s)'
tt += ' ' + _('last: %s ago (this year)'
) % gmDateTime.format_apparent_age_medically(
age=gmDateTime.calculate_apparent_age(
start=self.curr_pat.birthday_this_year,
end=now)) + '\n'
tt += ' ' + _(
'next: in %s (next year)'
) % gmDateTime.format_apparent_age_medically(
age=gmDateTime.calculate_apparent_age(
start=now,
end=self.curr_pat.birthday_next_year)) + '\n'
elif self.curr_pat.current_birthday_passed is False:
template = '%(sex)s %(r_arr)s%(dob)s (%(age)s)'
tt += ' ' + _(
'next: in %s (this year)'
) % gmDateTime.format_apparent_age_medically(
age=gmDateTime.calculate_apparent_age(
start=now,
end=self.curr_pat.birthday_this_year)) + '\n'
tt += ' ' + _('last: %s ago (last year)'
) % gmDateTime.format_apparent_age_medically(
age=gmDateTime.calculate_apparent_age(
start=self.curr_pat.birthday_last_year,
end=now)) + '\n'
else: # None, unknown
template = '%(sex)s %(dob)s (%(age)s)'
# FIXME: if the age is below, say, 2 hours we should fire
# a timer here that updates the age in increments of 1 minute ... :-)
age = template % {
'sex': self.curr_pat.gender_symbol,
'dob': self.curr_pat.get_formatted_dob(format='%d %b %Y'),
'age': self.curr_pat['medical_age'],
'r_arr': gmTools.u_arrow2right,
'l_arr': gmTools.u_left_arrow
}
# Easter Egg ;-)
if self.curr_pat['lastnames'] == 'Leibner':
if self.curr_pat['firstnames'] == 'Steffi':
if self.curr_pat['preferred'] == 'Wildfang':
age = '%s %s' % (gmTools.u_black_heart, age)
if self.curr_pat['dob_is_estimated']:
tt += _(' (date of birth and age are estimated)\n')
self._LBL_age.SetLabel(age)
self._LBL_age.SetToolTip(tt)
def __update_age_label(self):
# no patient
if not self.curr_pat.connected:
self._LBL_age.SetLabel(_('<Age>'))
self._LBL_age.SetToolTipString(_('no patient selected'))
return
# gender is always known
tt = _(u'Gender: %s (%s) - %s\n') % (
self.curr_pat.gender_symbol,
gmTools.coalesce(self.curr_pat[u'gender'], u'?'),
self.curr_pat.gender_string
)
# dob is not known
if self.curr_pat['dob'] is None:
age = u'%s %s' % (
self.curr_pat.gender_symbol,
self.curr_pat.get_formatted_dob()
)
self._LBL_age.SetLabel(age)
self._LBL_age.SetToolTipString(tt)
return
tt += _('Born: %s\n') % self.curr_pat.get_formatted_dob(format = '%d %b %Y', encoding = gmI18N.get_encoding())
# patient is dead
if self.curr_pat['deceased'] is not None:
tt += _('Died: %s\n') % gmDateTime.pydt_strftime(self.curr_pat['deceased'], '%d %b %Y')
tt += _('At age: %s\n') % self.curr_pat['medical_age']
age = u'%s %s - %s (%s)' % (
self.curr_pat.gender_symbol,
self.curr_pat.get_formatted_dob(format = '%d %b %Y', encoding = gmI18N.get_encoding()),
gmDateTime.pydt_strftime(self.curr_pat['deceased'], '%d %b %Y'),
self.curr_pat['medical_age']
)
if self.curr_pat['dob_is_estimated']:
tt += _(' (date of birth and age are estimated)\n')
self._LBL_age.SetLabel(age)
self._LBL_age.SetToolTipString(tt)
return
# patient alive
now = gmDateTime.pydt_now_here()
# patient birthday ?
if self.curr_pat.get_formatted_dob(format = '%m-%d') == now.strftime('%m-%d'):
template = _('%(sex)s %(dob)s (%(age)s today !)')
tt += _("\nToday is the patient's birthday !\n\n")
else:
if self.curr_pat.current_birthday_passed():
template = u'%(sex)s %(dob)s%(l_arr)s (%(age)s)'
tt += _(u'Birthday: %s ago\n') % gmDateTime.format_apparent_age_medically (
age = gmDateTime.calculate_apparent_age(start = self.curr_pat.birthday_this_year, end = now)
)
else:
template = u'%(sex)s %(r_arr)s%(dob)s (%(age)s)'
tt += _(u'Birthday: in %s\n') % gmDateTime.format_apparent_age_medically (
age = gmDateTime.calculate_apparent_age(start = now, end = self.curr_pat.birthday_this_year)
)
tt += _('Age: %s\n') % self.curr_pat['medical_age']
# FIXME: if the age is below, say, 2 hours we should fire
# a timer here that updates the age in increments of 1 minute ... :-)
age = template % {
u'sex': self.curr_pat.gender_symbol,
u'dob': self.curr_pat.get_formatted_dob(format = '%d %b %Y', encoding = gmI18N.get_encoding()),
u'age': self.curr_pat['medical_age'],
u'r_arr': gmTools.u_right_arrow,
u'l_arr': gmTools.u_left_arrow
}
# Easter Egg ;-)
if self.curr_pat['lastnames'] == u'Leibner':
if self.curr_pat['firstnames'] == u'Steffi':
if self.curr_pat['preferred'] == u'Wildfang':
age = u'%s %s' % (gmTools.u_black_heart, age)
if self.curr_pat['dob_is_estimated']:
tt += _(' (date of birth and age are estimated)\n')
self._LBL_age.SetLabel(age)
self._LBL_age.SetToolTipString(tt)
def _get_gfr_schwartz(self):
try:
return self.__cache['gfr_schwartz']
except KeyError:
pass
result = cClinicalResult(_('unknown eGFR (Schwartz)'))
result.formula_name = 'eGFR from updated Schwartz "bedside" formula (age < 19yrs)'
result.formula_source = '1/2013: http://en.wikipedia.org/Renal_function / http://www.ganfyd.org/index.php?title=Estimated_glomerular_filtration_rate (NHS) / doi 10.1681/ASN.2008030287 / doi: 10.2215/CJN.01640309'
result.hints.append(_('only applies @ age <18'))
if self.__patient is None:
result.message = _('eGFR (Schwartz): no patient')
return result
if self.__patient['dob'] is None:
result.message = _('eGFR (Schwartz): DOB needed for age')
return result
result.variables['dob'] = self.__patient['dob']
# creatinine
result.variables['serum_crea'] = self.__patient.emr.get_most_recent_results_in_loinc_group(loincs = gmLOINC.LOINC_creatinine_quantity, no_of_results = 1)
if result.variables['serum_crea'] is None:
result.message = _('eGFR (Schwartz): serum creatinine value not found (LOINC: %s') % gmLOINC.LOINC_creatinine_quantity
return result
if result.variables['serum_crea']['val_num'] is None:
result.message = _('eGFR (Schwartz): creatinine value not numeric')
return result
result.variables['serum_crea_val'] = self.d(result.variables['serum_crea']['val_num'])
if result.variables['serum_crea']['val_unit'] in ['mg/dl', 'mg/dL']:
result.variables['unit_multiplier'] = self.d(1)
elif result.variables['serum_crea']['val_unit'] in ['µmol/L', 'µmol/l']:
result.variables['unit_multiplier'] = self.d('0.00113')
else:
result.message = _('eGFR (Schwartz): unknown serum creatinine unit (%s)') % result.variables['serum_crea']['val_unit']
return result
# age
result.variables['age@crea'] = self.d (
gmDateTime.calculate_apparent_age (
start = result.variables['dob'],
end = result.variables['serum_crea']['clin_when']
)[0]
)
if result.variables['age@crea'] > 17:
result.message = _('eGFR (Schwartz): formula does not apply at age [%s] (age must be <18)') % result.variables['age@crea']
return result
# age-dependant constant
if result.variables['age@crea'] < 1:
# first year pre-term: k = 0.33
# first year full-term: k = (0.45) 0.41 (updated)
result.variables['constant_for_age'] = self.d('0.41')
result.warnings.append(_('eGFR (Schwartz): not known whether pre-term birth, applying full-term formula'))
else:
result.variables['constant_for_age'] = self.d('0.41')
# height
result.variables['height'] = self.__patient.emr.get_result_at_timestamp (
timestamp = result.variables['serum_crea']['clin_when'],
loinc = gmLOINC.LOINC_height,
tolerance_interval = '7 days'
)
if result.variables['height'] is None:
result.message = _('eGFR (Schwartz): height not found')
return result
if result.variables['height']['val_num'] is None:
result.message = _('eGFR (Schwartz): height not numeric')
return result
if result.variables['height']['val_unit'] == 'cm':
result.variables['height_cm'] = self.d(result.variables['height']['val_num'])
elif result.variables['height']['val_unit'] == 'mm':
result.variables['height_cm'] = self.d(result.variables['height']['val_num'] / self.d(10))
elif result.variables['height']['val_unit'] == 'm':
result.variables['height_cm'] = self.d(result.variables['height']['val_num'] * 100)
else:
result.message = _('eGFR (Schwartz): height not in m, cm, or mm')
return result
# calculate
result.numeric_value = (
result.variables['constant_for_age'] * result.variables['height_cm']
) / (
result.variables['unit_multiplier'] * result.variables['serum_crea_val']
)
result.unit = 'ml/min/1.73m²'
result.message = _('eGFR (Schwartz): %.1f %s (%s)') % (
result.numeric_value,
result.unit,
gmDateTime.pydt_strftime (
result.variables['serum_crea']['clin_when'],
format = '%Y %b %d'
)
)
result.date_valid = result.variables['serum_crea']['clin_when']
self.__cache['gfr_schwartz'] = result
_log.debug('%s' % result)
return result
def _get_gfr_cockcroft_gault(self):
try:
return self.__cache['cockcroft_gault']
except KeyError:
pass
result = cClinicalResult(_('unknown Cockcroft-Gault'))
result.formula_name = 'eGFR from Cockcroft-Gault'
result.formula_source = '8/2014: http://en.wikipedia.org/Renal_function'
result.hints.append(_('best @ age >65'))
if self.__patient is None:
result.message = _('Cockcroft-Gault: no patient')
return result
if self.__patient['dob'] is None:
result.message = _('Cockcroft-Gault: no DOB (no age)')
return result
# 1) gender
from Gnumed.business.gmPerson import map_gender2mf
result.variables['gender'] = self.__patient['gender']
result.variables['gender_mf'] = map_gender2mf[self.__patient['gender']]
if result.variables['gender_mf'] not in ['m', 'f']:
result.message = _('Cockcroft-Gault: neither male nor female')
return result
# 2) creatinine
result.variables['serum_crea'] = self.__patient.emr.get_most_recent_results_in_loinc_group(loincs = gmLOINC.LOINC_creatinine_quantity, no_of_results = 1)
if result.variables['serum_crea'] is None:
result.message = _('Cockcroft-Gault: serum creatinine value not found (LOINC: %s)') % gmLOINC.LOINC_creatinine_quantity
return result
if result.variables['serum_crea']['val_num'] is None:
result.message = _('Cockcroft-Gault: creatinine value not numeric')
return result
result.variables['serum_crea_val'] = self.d(result.variables['serum_crea']['val_num'])
if result.variables['serum_crea']['val_unit'] in ['mg/dl', 'mg/dL']:
result.variables['unit_multiplier'] = self.d(72)
if result.variables['gender_mf'] == 'm':
result.variables['gender_multiplier'] = self.d('1')
else: #result.variables['gender_mf'] == 'f'
result.variables['gender_multiplier'] = self.d('0.85')
elif result.variables['serum_crea']['val_unit'] in ['µmol/L', 'µmol/l']:
result.variables['unit_multiplier'] = self.d(1)
if result.variables['gender_mf'] == 'm':
result.variables['gender_multiplier'] = self.d('1.23')
else: #result.variables['gender_mf'] == 'f'
result.variables['gender_multiplier'] = self.d('1.04')
else:
result.message = _('Cockcroft-Gault: unknown serum creatinine unit (%s)') % result.variables['serum_crea']['val_unit']
return result
# 3) age (at creatinine evaluation)
result.variables['dob'] = self.__patient['dob']
result.variables['age@crea'] = self.d (
gmDateTime.calculate_apparent_age (
start = result.variables['dob'],
end = result.variables['serum_crea']['clin_when']
)[0]
)
if (result.variables['age@crea'] < 18):
result.message = _('Cockcroft-Gault: formula does not apply at age [%s] (17 < age)') % result.variables['age@crea']
return result
result.variables['weight'] = self.__patient.emr.get_most_recent_results_in_loinc_group(loincs = gmLOINC.LOINC_weight, no_of_results = 1)
if result.variables['weight'] is None:
result.message = _('Cockcroft-Gault: weight not found')
return result
if result.variables['weight']['val_num'] is None:
result.message = _('Cockcroft-Gault: weight not numeric')
return result
if result.variables['weight']['val_unit'] == 'kg':
result.variables['weight_kg'] = self.d(result.variables['weight']['val_num'])
elif result.variables['weight']['val_unit'] == 'g':
result.variables['weight_kg'] = self.d(result.variables['weight']['val_num'] / self.d(1000))
else:
result.message = _('Cockcroft-Gault: weight not in kg or g')
return result
# calculate
result.numeric_value = ((
(140 - result.variables['age@crea']) * result.variables['weight_kg'] * result.variables['gender_multiplier']) \
/ \
(result.variables['unit_multiplier'] * result.variables['serum_crea_val'])
)
result.unit = 'ml/min' #/1.73m²
result.message = _('eGFR(CG): %.1f %s (%s)') % (
result.numeric_value,
result.unit,
gmDateTime.pydt_strftime (
result.variables['serum_crea']['clin_when'],
format = '%Y %b %d'
)
)
result.date_valid = result.variables['serum_crea']['clin_when']
self.__cache['cockroft_gault'] = result
_log.debug('%s' % result)
return result
def _get_gfr_ckd_epi(self):
try:
return self.__cache['CKD-EPI']
except KeyError:
pass
result = cClinicalResult(_('unknown CKD-EPI'))
result.formula_name = 'eGFR from CKD-EPI'
result.formula_source = '8/2014: http://en.wikipedia.org/Renal_function'
result.hints.append(_('best @ GFR > 60 ml/min'))
if self.__patient is None:
result.message = _('CKD-EPI: no patient')
return result
if self.__patient['dob'] is None:
result.message = _('CKD-EPI: no DOB (no age)')
return result
# 1) gender
from Gnumed.business.gmPerson import map_gender2mf
result.variables['gender'] = self.__patient['gender']
result.variables['gender_mf'] = map_gender2mf[self.__patient['gender']]
if result.variables['gender_mf'] == 'm':
result.variables['gender_multiplier'] = self.d(1)
result.variables['k:gender_divisor'] = self.d('0.9')
result.variables['a:gender_power'] = self.d('-0.411')
elif result.variables['gender_mf'] == 'f':
result.variables['gender_multiplier'] = self.d('1.018')
result.variables['k:gender_divisor'] = self.d('0.7')
result.variables['a:gender_power'] = self.d('-0.329')
else:
result.message = _('CKD-EPI: neither male nor female')
return result
# 2) creatinine
result.variables['serum_crea'] = self.__patient.emr.get_most_recent_results_in_loinc_group(loincs = gmLOINC.LOINC_creatinine_quantity, no_of_results = 1)
if result.variables['serum_crea'] is None:
result.message = _('CKD-EPI: serum creatinine value not found (LOINC: %s)') % gmLOINC.LOINC_creatinine_quantity
return result
if result.variables['serum_crea']['val_num'] is None:
result.message = _('CKD-EPI: creatinine value not numeric')
return result
result.variables['serum_crea_val'] = self.d(result.variables['serum_crea']['val_num'])
if result.variables['serum_crea']['val_unit'] in ['mg/dl', 'mg/dL']:
result.variables['serum_crea_val'] = self.d(result.variables['serum_crea']['val_num'])
elif result.variables['serum_crea']['val_unit'] in ['µmol/L', 'µmol/l']:
result.variables['serum_crea_val'] = self.d(result.variables['serum_crea']['val_num']) / self.d('88.4')
else:
result.message = _('CKD-EPI: unknown serum creatinine unit (%s)') % result.variables['serum_crea']['val_unit']
return result
# 3) age (at creatinine evaluation)
result.variables['dob'] = self.__patient['dob']
result.variables['age@crea'] = self.d (
gmDateTime.calculate_apparent_age (
start = result.variables['dob'],
end = result.variables['serum_crea']['clin_when']
)[0]
)
# if (result.variables['age@crea'] > 84) or (result.variables['age@crea'] < 18):
# result.message = _('CKD-EPI: formula does not apply at age [%s] (17 < age < 85)') % result.variables['age@crea']
# return result
# 4) ethnicity
result.variables['ethnicity_multiplier'] = self.d(1) # non-black
result.warnings.append(_('ethnicity: GNUmed does not know patient ethnicity, ignoring correction factor of 1.519 for "black"'))
# calculate
result.numeric_value = (
self.d(141) * \
pow(min((result.variables['serum_crea_val'] / result.variables['k:gender_divisor']), self.d(1)), result.variables['a:gender_power']) * \
pow(max((result.variables['serum_crea_val'] / result.variables['k:gender_divisor']), self.d(1)), self.d('-1.209')) * \
pow(self.d('0.993'), result.variables['age@crea']) * \
result.variables['gender_multiplier'] * \
result.variables['ethnicity_multiplier']
)
result.unit = 'ml/min/1.73m²'
result.message = _('eGFR(CKD-EPI): %.1f %s (%s)') % (
result.numeric_value,
result.unit,
gmDateTime.pydt_strftime (
result.variables['serum_crea']['clin_when'],
format = '%Y %b %d'
)
)
result.date_valid = result.variables['serum_crea']['clin_when']
self.__cache['CKD-EPI'] = result
_log.debug('%s' % result)
return result
def _get_gfr_mdrd_short(self):
try:
return self.__cache['MDRD_short']
except KeyError:
pass
result = cClinicalResult(_('unknown MDRD (4 vars/IDMS)'))
result.formula_name = 'eGFR from 4-variables IDMS-MDRD'
result.formula_source = '1/2013: http://en.wikipedia.org/Renal_function / http://www.ganfyd.org/index.php?title=Estimated_glomerular_filtration_rate (NHS)'
result.hints.append(_('best @ 30 < GFR < 60 ml/min'))
if self.__patient is None:
result.message = _('MDRD (4 vars/IDMS): no patient')
return result
if self.__patient['dob'] is None:
result.message = _('MDRD (4 vars/IDMS): no DOB (no age)')
return result
# 1) gender
from Gnumed.business.gmPerson import map_gender2mf
result.variables['gender'] = self.__patient['gender']
result.variables['gender_mf'] = map_gender2mf[self.__patient['gender']]
if result.variables['gender_mf'] == 'm':
result.variables['gender_multiplier'] = self.d(1)
elif result.variables['gender_mf'] == 'f':
result.variables['gender_multiplier'] = self.d('0.742')
else:
result.message = _('MDRD (4 vars/IDMS): neither male nor female')
return result
# 2) creatinine
result.variables['serum_crea'] = self.__patient.emr.get_most_recent_results_in_loinc_group(loincs = gmLOINC.LOINC_creatinine_quantity, no_of_results = 1)
if result.variables['serum_crea'] is None:
result.message = _('MDRD (4 vars/IDMS): serum creatinine value not found (LOINC: %s)') % gmLOINC.LOINC_creatinine_quantity
return result
if result.variables['serum_crea']['val_num'] is None:
result.message = _('MDRD (4 vars/IDMS): creatinine value not numeric')
return result
result.variables['serum_crea_val'] = self.d(result.variables['serum_crea']['val_num'])
if result.variables['serum_crea']['val_unit'] in ['mg/dl', 'mg/dL']:
result.variables['unit_multiplier'] = self.d(175) # older: 186
elif result.variables['serum_crea']['val_unit'] in ['µmol/L', 'µmol/l']:
result.variables['unit_multiplier'] = self.d(30849) # older: 32788
else:
result.message = _('MDRD (4 vars/IDMS): unknown serum creatinine unit (%s)') % result.variables['serum_crea']['val_unit']
return result
# 3) age (at creatinine evaluation)
result.variables['dob'] = self.__patient['dob']
result.variables['age@crea'] = self.d (
gmDateTime.calculate_apparent_age (
start = result.variables['dob'],
end = result.variables['serum_crea']['clin_when']
)[0]
)
if (result.variables['age@crea'] > 84) or (result.variables['age@crea'] < 18):
result.message = _('MDRD (4 vars/IDMS): formula does not apply at age [%s] (17 < age < 85)') % result.variables['age@crea']
return result
# 4) ethnicity
result.variables['ethnicity_multiplier'] = self.d(1) # non-black
result.warnings.append(_('ethnicity: GNUmed does not know patient ethnicity, ignoring correction factor'))
# calculate
result.numeric_value = result.variables['unit_multiplier'] * \
pow(result.variables['serum_crea_val'], self.d('-1.154')) * \
pow(result.variables['age@crea'], self.d('-0.203')) * \
result.variables['ethnicity_multiplier'] * \
result.variables['gender_multiplier']
result.unit = 'ml/min/1.73m²'
BSA = self.body_surface_area
result.sub_results.append(BSA)
if BSA.numeric_value is None:
result.warnings.append(_('NOT corrected for non-average body surface (average = 1.73m²)'))
else:
result.variables['BSA'] = BSA.numeric_value
result_numeric_value = result.numeric_value / BSA.numeric_value
result.message = _('eGFR(MDRD): %.1f %s (%s) [4-vars, IDMS]') % (
result.numeric_value,
result.unit,
gmDateTime.pydt_strftime (
result.variables['serum_crea']['clin_when'],
format = '%Y %b %d'
)
)
result.date_valid = result.variables['serum_crea']['clin_when']
self.__cache['MDRD_short'] = result
_log.debug('%s' % result)
return result
def create_timeline_file(patient=None, filename=None, include_documents=False, include_vaccinations=False, include_encounters=False):
emr = patient.emr
global now
now = gmDateTime.pydt_now_here()
if filename is None:
timeline_fname = gmTools.get_unique_filename(prefix = 'gm-', suffix = '.timeline') # .timeline required ...
else:
timeline_fname = filename
_log.debug('exporting EMR as timeline into [%s]', timeline_fname)
timeline = io.open(timeline_fname, mode = 'wt', encoding = 'utf8', errors = 'xmlcharrefreplace')
if patient['dob'] is None:
lifespan_start = format_pydt(now.replace(year = now.year - 100))
else:
lifespan_start = format_pydt(patient['dob'])
if patient['deceased'] is None:
life_ends2day = 'True'
lifespan_end = format_pydt(now)
else:
life_ends2day = 'False'
lifespan_end = format_pydt(patient['deceased'])
earliest_care_date = emr.earliest_care_date
most_recent_care_date = emr.most_recent_care_date
if most_recent_care_date is None:
most_recent_care_date = lifespan_end
care_ends2day = life_ends2day
else:
most_recent_care_date = format_pydt(most_recent_care_date)
care_ends2day = 'False'
timeline.write(xml_start % (
# era: life span of patient
_('Lifespan'),
lifespan_start,
lifespan_end,
life_ends2day,
ERA_NAME_CARE_PERIOD,
format_pydt(earliest_care_date),
most_recent_care_date,
care_ends2day,
# categories
_('Health issues'),
_('Episodes'),
_('Encounters'),
_('Hospital stays'),
_('Procedures'),
_('Documents'),
_('Vaccinations'),
_('Substances'),
_('Life events')
))
# birth
if patient['dob'] is None:
start = now.replace(year = now.year - 100)
timeline.write(__xml_encounter_template % (
format_pydt(start),
format_pydt(start),
'?',
_('Life events'),
_('Date of birth unknown'),
'True'
))
else:
start = patient['dob']
timeline.write(__xml_encounter_template % (
format_pydt(patient['dob']),
format_pydt(patient['dob']),
'*',
_('Life events'),
_('Birth: %s') % patient.get_formatted_dob(format = '%Y %b %d %H:%M', honor_estimation = True),
'True'
))
# start of care
timeline.write(__xml_encounter_template % (
format_pydt(earliest_care_date),
format_pydt(earliest_care_date),
gmTools.u_heavy_greek_cross,
_('Life events'),
_('Start of Care: %s\n(the earliest recorded event of care in this praxis)') % format_pydt(earliest_care_date, format = '%Y %b %d'),
'True'
))
# containers must be defined before their
# subevents, so put health issues first
timeline.write('\n <!-- ========================================\n Health issues\n======================================== -->')
for issue in emr.health_issues:
timeline.write(__format_health_issue_as_timeline_xml(issue, patient, emr))
timeline.write('\n <!-- ========================================\n Episodes\n======================================== -->')
for epi in emr.get_episodes(order_by = 'pk_health_issue'):
timeline.write(__format_episode_as_timeline_xml(epi, patient))
if include_encounters:
timeline.write(u'\n<!--\n========================================\n Encounters\n======================================== -->')
for enc in emr.get_encounters(skip_empty = True):
timeline.write(__format_encounter_as_timeline_xml(enc, patient))
timeline.write('\n<!--\n========================================\n Hospital stays\n======================================== -->')
for stay in emr.hospital_stays:
timeline.write(__format_hospital_stay_as_timeline_xml(stay))
timeline.write('\n<!--\n========================================\n Procedures\n======================================== -->')
for proc in emr.performed_procedures:
timeline.write(__format_procedure_as_timeline_xml(proc))
if include_vaccinations:
timeline.write(u'\n<!--\n========================================\n Vaccinations\n======================================== -->')
for vacc in emr.vaccinations:
timeline.write(__format_vaccination_as_timeline_xml(vacc))
timeline.write('\n<!--\n========================================\n Substance intakes\n======================================== -->')
for intake in emr.get_current_medications(include_inactive = True, include_unapproved = False):
timeline.write(__format_intake_as_timeline_xml(intake))
if include_documents:
timeline.write(u'\n<!--\n========================================\n Documents\n======================================== -->')
for doc in patient.document_folder.documents:
timeline.write(__format_document_as_timeline_xml(doc))
# allergies ?
# - unclear where and how to place
# test results ?
# - too many events, at most "day sample drawn"
# death
if patient['deceased'] is None:
end = now
else:
end = patient['deceased']
death_ago = gmDateTime.format_apparent_age_medically (
age = gmDateTime.calculate_apparent_age(start = end, end = now)
)
timeline.write(__xml_encounter_template % (
format_pydt(end),
format_pydt(end),
gmTools.u_dagger,
_('Life events'),
_('Death: %s\n(%s ago at age %s)') % (
format_pydt(end, format = '%Y %b %d %H:%M'),
death_ago,
patient.get_medical_age()
),
'True'
))
# display range
if end.month == 2:
if end.day == 29:
# leap years aren't consecutive
end = end.replace(day = 28)
target_year = end.year + 1
end = end.replace(year = target_year)
timeline.write(xml_end % (
format_pydt(start),
format_pydt(end)
))
timeline.close()
return timeline_fname
def _get_gfr_schwartz(self):
try:
return self.__cache['gfr_schwartz']
except KeyError:
pass
result = cClinicalResult(_('unknown eGFR (Schwartz)'))
result.formula_name = u'eGFR from updated Schwartz "bedside" formula (age < 19yrs)'
result.formula_source = u'1/2013: http://en.wikipedia.org/Renal_function / http://www.ganfyd.org/index.php?title=Estimated_glomerular_filtration_rate (NHS) / doi 10.1681/ASN.2008030287 / doi: 10.2215/CJN.01640309'
result.hints.append(_('only applies @ age <18'))
if self.__patient is None:
result.message = _('eGFR (Schwartz): no patient')
return result
if self.__patient['dob'] is None:
result.message = _('eGFR (Schwartz): DOB needed for age')
return result
result.variables['dob'] = self.__patient['dob']
# creatinine
result.variables[
'serum_crea'] = self.__patient.emr.get_most_recent_results(
loinc=gmLOINC.LOINC_creatinine_quantity, no_of_results=1)
if result.variables['serum_crea'] is None:
result.message = _(
'eGFR (Schwartz): serum creatinine value not found (LOINC: %s'
) % gmLOINC.LOINC_creatinine_quantity
return result
if result.variables['serum_crea']['val_num'] is None:
result.message = _('eGFR (Schwartz): creatinine value not numeric')
return result
result.variables['serum_crea_val'] = self.d(
result.variables['serum_crea']['val_num'])
if result.variables['serum_crea']['val_unit'] in [u'mg/dl', u'mg/dL']:
result.variables['unit_multiplier'] = self.d(1)
elif result.variables['serum_crea']['val_unit'] in [
u'µmol/L', u'µmol/l'
]:
result.variables['unit_multiplier'] = self.d('0.00113')
else:
result.message = _(
'eGFR (Schwartz): unknown serum creatinine unit (%s)'
) % result.variables['serum_crea']['val_unit']
return result
# age
result.variables['age@crea'] = self.d(
gmDateTime.calculate_apparent_age(
start=result.variables['dob'],
end=result.variables['serum_crea']['clin_when'])[0])
if result.variables['age@crea'] > 17:
result.message = _(
'eGFR (Schwartz): formula does not apply at age [%s] (age must be <18)'
) % result.variables['age@crea']
return result
# age-dependant constant
if result.variables['age@crea'] < 1:
# first year pre-term: k = 0.33
# first year full-term: k = (0.45) 0.41 (updated)
result.variables['constant_for_age'] = self.d('0.41')
result.warnings.append(
_('eGFR (Schwartz): not known whether pre-term birth, applying full-term formula'
))
else:
result.variables['constant_for_age'] = self.d('0.41')
# height
result.variables[
'height'] = self.__patient.emr.get_result_at_timestamp(
timestamp=result.variables['serum_crea']['clin_when'],
loinc=gmLOINC.LOINC_height,
tolerance_interval='7 days')
if result.variables['height'] is None:
result.message = _('eGFR (Schwartz): height not found')
return result
if result.variables['height']['val_num'] is None:
result.message = _('eGFR (Schwartz): height not numeric')
return result
if result.variables['height']['val_unit'] == u'cm':
result.variables['height_cm'] = self.d(
result.variables['height']['val_num'])
elif result.variables['height']['val_unit'] == u'mm':
result.variables['height_cm'] = self.d(
result.variables['height']['val_num'] / self.d(10))
elif result.variables['height']['val_unit'] == u'm':
result.variables['height_cm'] = self.d(
result.variables['height']['val_num'] * 100)
else:
result.message = _('eGFR (Schwartz): height not in m, cm, or mm')
return result
# calculate
result.numeric_value = (result.variables['constant_for_age'] *
result.variables['height_cm']) / (
result.variables['unit_multiplier'] *
result.variables['serum_crea_val'])
result.unit = u'ml/min/1.73m²'
result.message = _('eGFR (Schwartz): %.1f %s (%s)') % (
result.numeric_value, result.unit,
gmDateTime.pydt_strftime(
result.variables['serum_crea']['clin_when'],
format='%Y %b %d'))
result.date_valid = result.variables['serum_crea']['clin_when']
self.__cache['gfr_schwartz'] = result
_log.debug(u'%s' % result)
return result
def _get_body_mass_index(self):
try:
return self.__cache['body_mass_index']
except KeyError:
pass
result = cClinicalResult(_('unknown BMI'))
result.formula_name = 'BMI/Quetelet Index'
result.formula_source = '08/2014: https://en.wikipedia.org/wiki/Body_mass_index'
if self.__patient is None:
result.message = _('BMI: no patient')
return result
result.variables['height'] = self.__patient.emr.get_most_recent_results_in_loinc_group(loincs = gmLOINC.LOINC_height, no_of_results = 1)
if result.variables['height'] is None:
result.message = _('BMI: height not found')
return result
if result.variables['height']['val_num'] is None:
result.message = _('BMI: height not numeric')
return result
if result.variables['height']['val_unit'] == 'cm':
result.variables['height_m'] = self.d(result.variables['height']['val_num'] / self.d(100))
elif result.variables['height']['val_unit'] == 'mm':
result.variables['height_m'] = self.d(result.variables['height']['val_num'] / self.d(1000))
elif result.variables['height']['val_unit'] == 'm':
result.variables['height_m'] = self.d(result.variables['height']['val_num'])
else:
result.message = _('BMI: height not in m, cm, or mm')
return result
result.variables['weight'] = self.__patient.emr.get_result_at_timestamp (
timestamp = result.variables['height']['clin_when'],
loinc = gmLOINC.LOINC_weight,
tolerance_interval = '10 days'
)
if result.variables['weight'] is None:
result.message = _('BMI: weight not found')
return result
if result.variables['weight']['val_num'] is None:
result.message = _('BMI: weight not numeric')
return result
if result.variables['weight']['val_unit'] == 'kg':
result.variables['weight_kg'] = self.d(result.variables['weight']['val_num'])
elif result.variables['weight']['val_unit'] == 'g':
result.variables['weight_kg'] = self.d(result.variables['weight']['val_num'] / self.d(1000))
else:
result.message = _('BMI: weight not in kg or g')
return result
result.variables['dob'] = self.__patient['dob']
start = result.variables['dob']
end = result.variables['height']['clin_when']
multiplier = 1
if end < start:
start = result.variables['height']['clin_when']
end = result.variables['dob']
multiplier = -1
result.variables['age@height'] = multiplier * self.d(gmDateTime.calculate_apparent_age(start, end)[0])
if (result.variables['age@height'] < 18):
result.message = _('BMI (Quetelet): formula does not apply at age [%s] (0 < age < 18)') % result.variables['age@height']
return result
# bmi = mass kg / height m2
result.numeric_value = result.variables['weight_kg'] / (result.variables['height_m'] * result.variables['height_m'])
result.unit = 'kg/m²'
result.message = _('BMI (Quetelet): %.2f %s') % (
result.numeric_value,
result.unit
)
result.date_valid = gmDateTime.pydt_now_here()
self.__cache['body_mass_index'] = result
_log.debug('%s' % result)
return result
def _get_body_mass_index(self):
try:
return self.__cache['body_mass_index']
except KeyError:
pass
result = cClinicalResult(_('unknown BMI'))
result.formula_name = u'BMI/Quetelet Index'
result.formula_source = u'08/2014: https://en.wikipedia.org/wiki/Body_mass_index'
if self.__patient is None:
result.message = _('BMI: no patient')
return result
result.variables[
'height'] = self.__patient.emr.get_most_recent_results(
loinc=gmLOINC.LOINC_height, no_of_results=1)
if result.variables['height'] is None:
result.message = _('BMI: height not found')
return result
if result.variables['height']['val_num'] is None:
result.message = _('BMI: height not numeric')
return result
if result.variables['height']['val_unit'] == u'cm':
result.variables['height_m'] = self.d(
result.variables['height']['val_num'] / self.d(100))
elif result.variables['height']['val_unit'] == u'mm':
result.variables['height_m'] = self.d(
result.variables['height']['val_num'] / self.d(1000))
elif result.variables['height']['val_unit'] == u'm':
result.variables['height_m'] = self.d(
result.variables['height']['val_num'])
else:
result.message = _('BMI: height not in m, cm, or mm')
return result
result.variables[
'weight'] = self.__patient.emr.get_result_at_timestamp(
timestamp=result.variables['height']['clin_when'],
loinc=gmLOINC.LOINC_weight,
tolerance_interval='10 days')
if result.variables['weight'] is None:
result.message = _('BMI: weight not found')
return result
if result.variables['weight']['val_num'] is None:
result.message = _('BMI: weight not numeric')
return result
if result.variables['weight']['val_unit'] == u'kg':
result.variables['weight_kg'] = self.d(
result.variables['weight']['val_num'])
elif result.variables['weight']['val_unit'] == u'g':
result.variables['weight_kg'] = self.d(
result.variables['weight']['val_num'] / self.d(1000))
else:
result.message = _('BMI: weight not in kg or g')
return result
result.variables['dob'] = self.__patient['dob']
start = result.variables['dob']
end = result.variables['height']['clin_when']
multiplier = 1
if end < start:
start = result.variables['height']['clin_when']
end = result.variables['dob']
multiplier = -1
result.variables['age@height'] = multiplier * self.d(
gmDateTime.calculate_apparent_age(start, end)[0])
if (result.variables['age@height'] < 18):
result.message = _(
'BMI (Quetelet): formula does not apply at age [%s] (0 < age < 18)'
) % result.variables['age@height']
return result
# bmi = mass kg / height m2
result.numeric_value = result.variables['weight_kg'] / (
result.variables['height_m'] * result.variables['height_m'])
result.unit = u'kg/m²'
result.message = _('BMI (Quetelet): %.2f %s') % (result.numeric_value,
result.unit)
result.date_valid = gmDateTime.pydt_now_here()
self.__cache['body_mass_index'] = result
_log.debug(u'%s' % result)
return result
def __update_lab(self):
if not self.curr_pat.connected:
self._LBL_lab.SetLabel('')
self._LBL_lab.SetToolTip(None)
return
tests2show = []
tooltip_lines = []
RRs = self.curr_pat.emr.get_most_recent_results_in_loinc_group(
loincs=gmLOINC.LOINC_rr_quantity, max_no_of_results=1)
if len(RRs) == 0:
tests2show.append(_('BP ?'))
else:
tests2show.append(RRs[0]['unified_val'])
tooltip_lines.append(
_('BP: %s ago') % gmDateTime.format_apparent_age_medically(
age=gmDateTime.calculate_apparent_age(
start=RRs[0]['clin_when'])))
HRs = self.curr_pat.emr.get_most_recent_results_in_loinc_group(
loincs=gmLOINC.LOINC_heart_rate_quantity, max_no_of_results=1)
if len(HRs) > 0:
tests2show.append('@ %s' % HRs[0]['unified_val'])
tooltip_lines.append(
_('%s (@) in bpm: %s ago') %
(HRs[0]['abbrev_tt'],
gmDateTime.format_apparent_age_medically(
age=gmDateTime.calculate_apparent_age(
start=HRs[0]['clin_when']))))
bmi = self.curr_pat.emr.bmi
if bmi.numeric_value is not None:
tests2show.append(
_('BMI %s') %
bmi.numeric_value.quantize(decimal.Decimal('1.')))
tooltip_lines.append(
_('BMI: %s ago') % gmDateTime.format_apparent_age_medically(
age=gmDateTime.calculate_apparent_age(
start=bmi.date_valid)))
else:
weights = self.curr_pat.emr.get_most_recent_results_in_loinc_group(
loincs=gmLOINC.LOINC_weight, max_no_of_results=1)
if len(weights) == 0:
tests2show.append(_('BMI ?'))
else:
tests2show.append(
'%s%s' %
(weights[0]['unified_val'], weights[0]['val_unit']))
tooltip_lines.append(
_('weight: %s ago') %
gmDateTime.format_apparent_age_medically(
age=gmDateTime.calculate_apparent_age(
start=weights[0]['clin_when'])))
gfr_or_crea = self.curr_pat.emr.best_gfr_or_crea
if gfr_or_crea is None:
tests2show.append(_('GFR ?'))
else:
try:
tests2show.append(
_('GFR %s') %
gfr_or_crea.numeric_value.quantize(decimal.Decimal('1.')))
tooltip_lines.append(
_('GFR: %s ago') %
gmDateTime.format_apparent_age_medically(
age=gmDateTime.calculate_apparent_age(
start=gfr_or_crea.date_valid)))
except AttributeError:
tests2show.append(
'%s %s' %
(gfr_or_crea['abbrev_tt'], gfr_or_crea['unified_val']))
tooltip_lines.append(
_('%s: %s ago') %
(gfr_or_crea['abbrev_tt'],
gmDateTime.format_apparent_age_medically(
age=gmDateTime.calculate_apparent_age(
start=gfr_or_crea['clin_when']))))
edc = self.curr_pat.emr.EDC
if edc is not None:
if self.curr_pat.emr.EDC_is_fishy:
tests2show.append(
_('?EDC %s') % gmDateTime.pydt_strftime(
edc, '%Y-%b-%d', accuracy=gmDateTime.acc_days))
else:
tests2show.append(
_('EDC %s') % gmDateTime.pydt_strftime(
edc, '%Y-%b-%d', accuracy=gmDateTime.acc_days))
INRs = self.curr_pat.emr.get_most_recent_results_in_loinc_group(
loincs=gmLOINC.LOINC_inr_quantity, max_no_of_results=1)
if len(INRs) > 0:
tests2show.append('%s %s' %
(INRs[0]['abbrev_tt'], INRs[0]['unified_val']))
tooltip_lines.append(
_('%s: %s ago') % (INRs[0]['abbrev_tt'],
gmDateTime.format_apparent_age_medically(
age=gmDateTime.calculate_apparent_age(
start=INRs[0]['clin_when']))))
# include panel if configured, only show if exist
if self.__lab_panel is not None:
for result in self.__lab_panel.get_most_recent_results(
pk_patient=self.curr_pat.ID,
order_by='unified_abbrev',
group_by_meta_type=True):
tests2show.append('%s %s' %
(result['abbrev_tt'], result['unified_val']))
tooltip_lines.append(
_('%s: %s ago') %
(result['abbrev_tt'],
gmDateTime.format_apparent_age_medically(
age=gmDateTime.calculate_apparent_age(
start=result['clin_when']))))
self._LBL_lab.Label = '; '.join(tests2show)
self._LBL_lab.SetToolTip('\n'.join(tooltip_lines))
