diff --git a/.bumpversion.cfg b/.bumpversion.cfg index a953884..8186bcc 100644 --- a/.bumpversion.cfg +++ b/.bumpversion.cfg @@ -1,5 +1,5 @@ [bumpversion] -current_version = 4.0.2 +current_version = 4.1.0 tag = False commit = True diff --git a/.gitignore b/.gitignore index a659708..bb378ce 100644 --- a/.gitignore +++ b/.gitignore @@ -133,3 +133,6 @@ dmypy.json # MacOS DS_Store files .DS_Store + +# Fenton data is licenced and not to be shared +rcpchgrowth/data_tables/fenton/*.* diff --git a/rcpchgrowth/__init__.py b/rcpchgrowth/__init__.py index 572eea9..8fbb076 100644 --- a/rcpchgrowth/__init__.py +++ b/rcpchgrowth/__init__.py @@ -1,6 +1,7 @@ from .date_calculations import chronological_decimal_age, corrected_decimal_age, chronological_calendar_age, estimated_date_delivery, corrected_gestational_age -from .global_functions import centile, sds_for_measurement, measurement_from_sds, percentage_median_bmi, mid_parental_height, measurement_for_z +from .global_functions import centile, sds_for_measurement, measurement_from_sds, percentage_median_bmi, mid_parental_height, measurement_for_z, cubic_interpolation, linear_interpolation from .centile_bands import centile_band_for_centile +from .cdc import select_reference_data_for_cdc_chart from .bmi_functions import bmi_from_height_weight, weight_for_bmi_height from .age_advice_strings import comment_prematurity_correction from .fictional_child import generate_fictional_child_data diff --git a/rcpchgrowth/age_advice_strings.py b/rcpchgrowth/age_advice_strings.py index 174f5c5..d049610 100644 --- a/rcpchgrowth/age_advice_strings.py +++ b/rcpchgrowth/age_advice_strings.py @@ -15,7 +15,7 @@ def comment_prematurity_correction( clinician_chronological_decimal_age_comment = "Born Term. No correction has been made for gestation." elif chronological_decimal_age > corrected_decimal_age or chronological_decimal_age < corrected_decimal_age: ## adjustment for gestational age has been made - even if >=37 weeks - lay_corrected_decimal_age_comment = f"Because your child was born at {gestation_weeks}+{gestation_days} weeks gestation, an adjustment has been made to take this into account." + lay_corrected_decimal_age_comment = f"Because your child was born at {gestation_weeks}+{gestation_days} weeks gestation an adjustment has been made to take this into account." clinician_corrected_decimal_age_comment = "Correction for gestational age has been made." lay_chronological_decimal_age_comment = "This is your child's age without taking into account their gestation at birth." clinician_chronological_decimal_age_comment = "No correction has been made for gestational age." diff --git a/rcpchgrowth/cdc.py b/rcpchgrowth/cdc.py new file mode 100644 index 0000000..97a4438 --- /dev/null +++ b/rcpchgrowth/cdc.py @@ -0,0 +1,173 @@ +""" +Handles CDC-specific reference data selection +""" + +# standard imports +import json +from importlib import resources +from pathlib import Path + +# rcpch imports +from .constants import * + +""" +birth_date: date of birth +observation_date: date of observation +sex: sex (string, MALE or FEMALE) +decimal_age: chronological, decimal +corrected_age: corrected for prematurity, decimal +measurement_method: height, weight, bmi, ofc (decimal) +observation: value (float) +gestation_weeks: gestational age(weeks), integer +gestation_days: supplementary days of gestation +lms: L, M or S +reference: reference data +""" + +# load the reference data +data_directory = resources.files("rcpchgrowth.data_tables") + +# data_path = Path(data_directory,"fenton", "fenton.json") # 23 weeks to 50 weeks - currently not in the code base +# with open(data_path) as json_file: +# FENTON_DATA = json.load(json_file) +# json_file.close() +FENTON_DATA = [] + +data_path = Path(data_directory, "cdc_infants.json") # CDC interpretation of WHO 0-2y +with open(data_path) as json_file: + CDC_INFANT_DATA = json.load(json_file) + json_file.close() + +data_path = Path(data_directory, "cdc2-20.json") # 2 years to 20 years +with open(data_path) as json_file: + CDC_CHILD_DATA = json.load(json_file) + json_file.close() +# public functions + + +def reference_data_absent(age: float, measurement_method: str, sex: str): + """ + Helper function. + Returns boolean + Tests presence of valid reference data for a given measurement request + + Reference data is not complete for all ages/sexes/measurements. + - WHO data is used from 0-2 years + - CDC data is used from 2-20 years + """ + + # if age < 0: + # return True, "CDC data does not exist below 40 weeks." + if age > TWENTY_YEARS: # upper threshold of UK90 data + return True, "CDC data does not exist above 20 years." + if measurement_method == HEAD_CIRCUMFERENCE and age > THREE_YEARS: + return True, "CDC data does not exist for head circumference above 3 years." + if measurement_method == BMI and age < TWO_YEARS: + return True, "CDC data does not exist for BMI below 2 years." + else: + return False, None + + +def cdc_reference(age: float, measurement_method, default_youngest_reference: bool = False) -> json: + """ + The purpose of this function is to choose the correct reference for calculation. + The CDC standard is an unusual case because it combines two different reference sources: the 1977 National Center for Health Statistics (NCHS) and the WHO2006. + - CDC reference runs from 2.04 y to 20 y + - WHO 2006 runs from 0 to 2 years + - Preterm data is handled by Fenton (which is Canadian data) + The function return the appropriate reference file as json + """ + + # These conditionals are to select the correct reference + if age < FENTON_LOWER_THRESHOLD: + # Below the range for which we have reference data, we can't provide a calculation. + raise ValueError( + "There is no reference data for ages below 22 weeks gestation." + ) + elif age < 0: + # Below 40 weeks, Fenton data is always used + return FENTON_DATA + + elif age < 2 or (age == 2 and default_youngest_reference) or (measurement_method == HEAD_CIRCUMFERENCE and age <= 3): + # Below 2 years, CDC interpretation of WHO is used + return CDC_INFANT_DATA + + elif age <= CDC_UPPER_THRESHOLD: + # CDC data is used for all children 2-20 years + return CDC_CHILD_DATA + + else: + return ValueError("There is no CDC reference data above the age of 20 years.") + + +def cdc_lms_array_for_measurement_and_sex( + age: float, + measurement_method: str, + sex: str, + default_youngest_reference: bool = False, +) -> list: + + # selects the correct lms data array from the patchwork of references that make up UK-WHO + + try: + selected_reference = cdc_reference( + age=age, measurement_method=measurement_method, default_youngest_reference=default_youngest_reference + ) + except: #  there is no reference for the age supplied + return LookupError("There is no CDC reference for the age supplied.") + + # Check that the measurement requested has reference data at that age + + invalid_data, data_error = reference_data_absent( + age=age, measurement_method=measurement_method, sex=sex + ) + + if invalid_data: + raise LookupError(data_error) + else: + return selected_reference["measurement"][measurement_method][sex] + + +def select_reference_data_for_cdc_chart( + cdc_reference_name: str, + measurement_method: str, + sex: str, + default_youngest_reference: bool = False, +): + + # takes a cdc reference name (see parameter constants), measurement_method and sex to return + # reference data - NOTE for the moment Fenton is not implemented as license not yet secured and reference data therefore not committed to the repo + + if cdc_reference_name == FENTON: + try: + fenton_preterm_reference = cdc_lms_array_for_measurement_and_sex( + age=-0.01, # an arbritrary age to select the preterm data + measurement_method=measurement_method, + sex=sex, + default_youngest_reference=False # should never need younger reference in this calculation + ) + except: + fenton_preterm_reference = [] + return fenton_preterm_reference + elif cdc_reference_name == CDC_INFANT: + try: + cdc_infant_reference = cdc_lms_array_for_measurement_and_sex( + age=0.04, # an arbritrary age to select the infant data + measurement_method=measurement_method, + sex=sex, + default_youngest_reference=default_youngest_reference + ) + except: + cdc_infant_reference = [] + return cdc_infant_reference + else: + try: + cdc_children_reference = cdc_lms_array_for_measurement_and_sex( + age=3, # an arbritrary age to select the child data + measurement_method=measurement_method, + sex=sex, + default_youngest_reference=False # There is no younger reference data for CDC + ) + except: + cdc_children_reference = [] + return cdc_children_reference diff --git a/rcpchgrowth/chart_functions.py b/rcpchgrowth/chart_functions.py index 6ea6148..e75071c 100644 --- a/rcpchgrowth/chart_functions.py +++ b/rcpchgrowth/chart_functions.py @@ -2,8 +2,26 @@ from .global_functions import centile, lms_value_array_for_measurement_for_reference, sds_for_centile, rounded_sds_for_centile, generate_centile from .uk_who import select_reference_data_for_uk_who_chart from .trisomy_21 import select_reference_data_for_trisomy_21 +from .cdc import select_reference_data_for_cdc_chart from .turner import select_reference_data_for_turner -from .constants.reference_constants import FEMALE, HEIGHT, UK_WHO, TURNERS, TRISOMY_21, COLE_TWO_THIRDS_SDS_NINE_CENTILES, COLE_TWO_THIRDS_SDS_NINE_CENTILE_COLLECTION, THREE_PERCENT_CENTILE_COLLECTION, UK_WHO_REFERENCES +from .constants.reference_constants import ( + FEMALE, + HEIGHT, + UK_WHO, + TURNERS, + TRISOMY_21, + COLE_TWO_THIRDS_SDS_NINE_CENTILES, + COLE_TWO_THIRDS_SDS_NINE_CENTILE_COLLECTION, + THREE_PERCENT_CENTILES, + THREE_PERCENT_CENTILE_COLLECTION, + FIVE_PERCENT_CENTILES, + FIVE_PERCENT_CENTILE_COLLECTION, + EIGHTY_FIVE_PERCENT_CENTILES, + EIGHTY_FIVE_PERCENT_CENTILE_COLLECTION, + UK_WHO_REFERENCES, + CDC_REFERENCES, + CDC +) """ Public chart functions @@ -36,6 +54,12 @@ def create_chart( sex=sex, centile_format=centile_format, is_sds=is_sds) + elif reference == CDC: + return create_cdc_chart( + measurement_method=measurement_method, + sex=sex, + centile_format=centile_format, + is_sds=is_sds) else: print("No reference data returned. Is there a spelling mistake in your reference?") @@ -67,6 +91,7 @@ def generate_custom_sds_line( centile_data=[] try: centile_data= build_centile_object( + reference=UK_WHO, measurement_method=measurement_method, sex=sex, lms_array_for_measurement=lms_array_for_measurement, @@ -78,8 +103,30 @@ def generate_custom_sds_line( centile_data=[] # all data can now be tagged by reference_name and added to reference_data reference_data.append({reference: centile_data}) + elif reference == CDC: + for reference_index, reference in enumerate(CDC_REFERENCES): + # the centile reference data + lms_array_for_measurement=select_reference_data_for_cdc_chart( + cdc_reference_name=reference, + measurement_method=measurement_method, + sex=sex) + centile_data=[] + try: + centile_data= build_centile_object( + reference=CDC, + measurement_method=measurement_method, + sex=sex, + lms_array_for_measurement=lms_array_for_measurement, + z=custom_sds, + centile=custom_centile + ) + except: + print(f"Could not generate SDS centile data.") + centile_data=[] + # all data can now be tagged by reference_name and added to reference_data + reference_data.append({reference: centile_data}) else: - # get the reference data + # get the reference data (Trisomy 21, Turner both hav a single reference) lms_array_for_measurement=[] try: lms_array_for_measurement=select_reference_lms_data( @@ -94,6 +141,7 @@ def generate_custom_sds_line( try: centile_data=[] centile_data= build_centile_object( + reference=reference, measurement_method=measurement_method, sex=sex, lms_array_for_measurement=lms_array_for_measurement, @@ -159,6 +207,8 @@ def select_reference_lms_data(reference: str, measurement_method: str, sex: str) lms_array_for_measurement=select_reference_data_for_turner(measurement_method=measurement_method, sex=sex) elif reference == TRISOMY_21: lms_array_for_measurement=select_reference_data_for_trisomy_21(measurement_method=measurement_method, sex=sex) + elif reference == CDC: + lms_array_for_measurement=select_reference_data_for_cdc_chart(measurement_method=measurement_method, sex=sex) else: raise Exception("No data has been selected!") @@ -166,7 +216,7 @@ def select_reference_lms_data(reference: str, measurement_method: str, sex: str) -def build_centile_object(measurement_method: str, sex: str, lms_array_for_measurement: list, z: float, centile: float): +def build_centile_object(reference, measurement_method: str, sex: str, lms_array_for_measurement: list, z: float, centile: float): sex_list: dict = {} # all the data for a given sex are stored here measurements: dict = {} # all the data for a given measurement_method are stored here centiles = [] # all generated centiles for a selected centile collection are stored here @@ -181,7 +231,7 @@ def build_centile_object(measurement_method: str, sex: str, lms_array_for_measur measurement_method=measurement_method, sex=sex, lms_array_for_measurement=lms_array_for_measurement, - reference=UK_WHO + reference=reference ) except: centile_data=None @@ -220,14 +270,13 @@ def create_uk_who_chart( cole_method = False if (type(centile_format) is list): + # a custom list of centiles was provided centile_sds_collection = centile_format - elif centile_format == COLE_TWO_THIRDS_SDS_NINE_CENTILES: - centile_sds_collection = COLE_TWO_THIRDS_SDS_NINE_CENTILE_COLLECTION - cole_method = True - is_sds=False else: - centile_sds_collection = THREE_PERCENT_CENTILE_COLLECTION + # a standard centile collection was selected + centile_sds_collection = select_centile_format(centile_format) is_sds=False + ## # iterate through the 4 references that make up UK-WHO # There will be a list for each one @@ -350,13 +399,11 @@ def create_turner_chart(centile_format: Union[str, list], is_sds=False): cole_method = False if (type(centile_format) is list): + # a custom list of centiles was provided centile_sds_collection = centile_format - elif centile_format == COLE_TWO_THIRDS_SDS_NINE_CENTILES: - centile_sds_collection = COLE_TWO_THIRDS_SDS_NINE_CENTILE_COLLECTION - cole_method = True - is_sds=False else: - centile_sds_collection = THREE_PERCENT_CENTILE_COLLECTION + # a standard centile collection was selected + centile_sds_collection = select_centile_format(centile_format) is_sds=False # all data for a the reference are stored here: this is returned to the user @@ -456,13 +503,11 @@ def create_trisomy_21_chart(measurement_method: str, sex: str, centile_format: U cole_method = False if (type(centile_format) is list): + # a custom list of centiles was provided centile_sds_collection = centile_format - elif centile_format == COLE_TWO_THIRDS_SDS_NINE_CENTILES: - centile_sds_collection = COLE_TWO_THIRDS_SDS_NINE_CENTILE_COLLECTION - cole_method = True - is_sds=False else: - centile_sds_collection = THREE_PERCENT_CENTILE_COLLECTION + # a standard centile collection was selected + centile_sds_collection = select_centile_format(centile_format) is_sds=False # all data for a the reference are stored here: this is returned to the user @@ -546,3 +591,170 @@ def create_trisomy_21_chart(measurement_method: str, sex: str, centile_format: U female {...} }] """ + +def create_cdc_chart( + measurement_method: str, + sex: str, + centile_format: Union[str, list] = COLE_TWO_THIRDS_SDS_NINE_CENTILES, + is_sds = False + ): + + # user selects which centile collection they want, for sex and measurement_method + # If the Cole method is selected, conversion between centile and SDS + # is different as SDS is rounded to the nearest 2/3 + # Cole method selection is stored in the cole_method flag. + # If no parameter is passed, default is the Cole method + # Alternatively it is possible to pass a custom list of values - if the is_sds flag is False (default) these are centiles + + centile_sds_collection = [] + cole_method = False + + if (type(centile_format) is list): + # a custom list of centiles was provided + centile_sds_collection = centile_format + else: + # a standard centile collection was selected + centile_sds_collection = select_centile_format(centile_format) + is_sds=False + + ## + # iterate through the 4 references that make up UK-WHO + # There will be a list for each one + ## + + # all data for a given reference are stored here: this is returned to the user + reference_data = [] + + for reference_index, reference in enumerate(CDC_REFERENCES): + sex_list: dict = {} # all the data for a given sex are stored here + # For each reference we have 2 sexes + # for sex_index, sex in enumerate(SEXES): + # For each sex we have 4 measurement_methods + + measurements: dict = {} # all the data for a given measurement_method are stored here + + # for measurement_index, measurement_method in enumerate(MEASUREMENT_METHODS): + # for every measurement method we have as many centiles + # as have been requested + + centiles = [] # all generated centiles for a selected centile collection are stored here + + # the centile reference data + try: + lms_array_for_measurement=select_reference_data_for_cdc_chart( + cdc_reference_name=reference, + measurement_method=measurement_method, + sex=sex) + except: + lms_array_for_measurement = [] + + for centile_index, centile_sds in enumerate(centile_sds_collection): + # we must create a z for each requested centile + # if the Cole 9 centiles were selected, these are rounded, + # so conversion to SDS is different + # Otherwise standard conversation of centile to z is used + + z=0.0 #initialise + centile_value=0.0 #initialise + + if cole_method: + z = rounded_sds_for_centile(centile_sds) # a centile was provided, so convert to z + centile_value=centile_sds # store the original centile value + else: + if (is_sds): + z=centile_sds # an sds was supplied + centile_value=centile(centile_sds) # convert the z to a centile and store + else: + z = sds_for_centile(centile_sds) # a centile was provided, so convert to z + centile_value=centile_sds # store the original centile value + centile_data = [] + + try: + # Generate a centile. there will be nine of these if Cole method selected. + # Some data does not exist at all ages, so any error reflects missing data. + # If this happens, an empty list is returned. + centile_data = generate_centile( + z=z, + centile=centile_value, + measurement_method=measurement_method, + sex=sex, + lms_array_for_measurement=lms_array_for_measurement, + reference=CDC, + is_sds=is_sds + ) + except: + print(f"Not possible to generate centile data for CDC {measurement_method} in {sex}s.") + centile_data=None + # Store this centile for a given measurement + + centiles.append({"sds": round(z * 100) / 100, + "centile": centile_value, "data": centile_data}) + + # this is the end of the centile_collection for loop + # All the centiles for this measurement, sex and reference are added to the measurements list + measurements.update({measurement_method: centiles}) + + # this is the end of the measurement_methods loop + # All data for all measurement_methods for this sex are added to the sex_list list + + sex_list.update({sex: measurements}) + + # all data can now be tagged by reference_name and added to reference_data + reference_data.append({reference: sex_list}) + + # returns a list of 4 references, each containing 2 lists for each sex, + # each sex in turn containing 4 datasets for each measurement_method + return reference_data + + """ + # return object structure + [ cdc_infant: { + male: { + height: [ + { + sds: -2.667, + centile: 0.4 + data: [{l: , x: , y: }, ....] + } + ], + weight: [...], + bmi: [...], + ofc: [...] + }, + female {...} + }, + cdc_child: { + male: { + height: [ + { + sds: -2.667, + centile: 0.4 + data: [{l: , x: , y: }, ....] + } + ], + weight: [...], + bmi: [...], + ofc: [...] + }, + female {...} + } + ] + """ + +# Private functions +def select_centile_format(centile_format: str): + """ + Select the centile format + Helper function to select the correct centile collection + the pre-defined collections are in the constants file and have string names: 'cole-nine-centiles', 'three-percent-centiles', 'five-percent-centiles', 'eighty-five-percent_centiles' + """ + if centile_format == COLE_TWO_THIRDS_SDS_NINE_CENTILES: + return COLE_TWO_THIRDS_SDS_NINE_CENTILE_COLLECTION + elif centile_format == THREE_PERCENT_CENTILES: + return THREE_PERCENT_CENTILE_COLLECTION + elif centile_format == FIVE_PERCENT_CENTILES: + return FIVE_PERCENT_CENTILE_COLLECTION + elif centile_format == EIGHTY_FIVE_PERCENT_CENTILES: + return EIGHTY_FIVE_PERCENT_CENTILE_COLLECTION + else: + return COLE_TWO_THIRDS_SDS_NINE_CENTILE_COLLECTION \ No newline at end of file diff --git a/rcpchgrowth/constants/age_constants.py b/rcpchgrowth/constants/age_constants.py index 5535a6f..0095471 100644 --- a/rcpchgrowth/constants/age_constants.py +++ b/rcpchgrowth/constants/age_constants.py @@ -1,10 +1,14 @@ TERM_PREGNANCY_LENGTH_DAYS = 40 * 7 TERM_LOWER_THRESHOLD_LENGTH_DAYS = 37 * 7 EXTREME_PREMATURITY_THRESHOLD_LENGTH_DAYS = 32 * 7 -TWENTY_FIVE_WEEKS_GESTATION = ((25 * 7) - (40*7)) / 365.25 # 25 weeks as decimal age -THIRTY_SEVEN_WEEKS_GESTATION = ((37 * 7) - (40*7)) / 365.25 # 37 weeks as decimal age -FORTY_TWO_WEEKS_GESTATION = ((42 * 7) - (40*7)) / 365.25 # 42 weeks as decimal age -TWENTY_THREE_WEEKS_GESTATION = ((23 * 7) - (40*7)) / 365.25 # 23 weeks as decimal age +TWENTY_TWO_WEEKS_GESTATION = ((22 * 7) - (40 * 7)) / 365.25 # 22 weeks as decimal age +TWENTY_FIVE_WEEKS_GESTATION = ((25 * 7) - (40 * 7)) / 365.25 # 25 weeks as decimal age +THIRTY_SEVEN_WEEKS_GESTATION = ((37 * 7) - (40 * 7)) / 365.25 # 37 weeks as decimal age +FORTY_TWO_WEEKS_GESTATION = ((42 * 7) - (40 * 7)) / 365.25 # 42 weeks as decimal age +TWENTY_THREE_WEEKS_GESTATION = ((23 * 7) - (40 * 7)) / 365.25 # 23 weeks as decimal age +FIFTY_WEEKS_GESTATION = ((50 * 7) - (40 * 7)) / 365.25 # 50 weeks as decimal age +TWO_YEARS = 2.0 +THREE_YEARS = 3.0 SEVENTEEN_YEARS = 17.0 EIGHTEEN_YEARS = 18.0 TWENTY_YEARS = 20.0 diff --git a/rcpchgrowth/constants/reference_constants.py b/rcpchgrowth/constants/reference_constants.py index d2d5e03..f5ad130 100644 --- a/rcpchgrowth/constants/reference_constants.py +++ b/rcpchgrowth/constants/reference_constants.py @@ -16,9 +16,17 @@ UK90_CHILD = "uk90_child" UK_WHO_REFERENCES = [UK90_PRETERM, UK_WHO_INFANT, UK_WHO_CHILD, UK90_CHILD] +# CDC constants +CDC = "cdc" # CDC is the overarching reference +CDC_INFANT = "cdc_infant" # CDC infant is the reference name for children 0-2 years +CDC_CHILD = "cdc_child" # CDC child is the reference name for children 2-20 years +FENTON = "fenton" # Fenton is the reference name for preterm infants +CDC_REFERENCES = [FENTON, CDC_INFANT, CDC_CHILD] # CDC references + # 23 weeks is the lowest decimal age available on the UK90 charts UK90_REFERENCE_LOWER_THRESHOLD = ( - (23 * 7) - (40 * 7)) / 365.25 # 23 weeks as decimal age + (23 * 7) - (40 * 7) +) / 365.25 # 23 weeks as decimal age # The WHO references change from measuring infants in the lying position to measuring children in the standing position at 2.0 years. WHO_CHILD_LOWER_THRESHOLD = 2.0 # 2 years as decimal age @@ -33,31 +41,54 @@ WHO_CHILDREN_UPPER_THRESHOLD = 4.0 UK_WHO_INFANT_LOWER_THRESHOLD = ( - (42 * 7) - (40 * 7)) / 365.25 # 42 weeks as decimal age + (42 * 7) - (40 * 7) +) / 365.25 # 42 weeks as decimal age UK90_UPPER_THRESHOLD = 20 +WHO_NEWBORN_LOWER_THRESHOLD = 0.0 +CDC_LOWER_THRESHOLD = 2.0 +CDC_UPPER_THRESHOLD = 20.0 + +FENTON_LOWER_THRESHOLD = ((22 * 7) - (40 * 7)) / 365.25 # 22 weeks as decimal age +FENTON_UPPER_THRESHOLD = ((50 * 7) - (40 * 7)) / 365.25 # 50 weeks as decimal age + # Generic constants -MALE="male" -FEMALE="female" -HEIGHT="height" -WEIGHT="weight" -HEAD_CIRCUMFERENCE="ofc" -BMI="bmi" +MALE = "male" +FEMALE = "female" +HEIGHT = "height" +WEIGHT = "weight" +HEAD_CIRCUMFERENCE = "ofc" +BMI = "bmi" REFERENCES = [UK_WHO, TRISOMY_21, TURNERS] SEXES = [MALE, FEMALE] MEASUREMENT_METHODS = [HEIGHT, WEIGHT, HEAD_CIRCUMFERENCE, BMI] # Nomenclature # Centile formats describe the collection - the two most common used are the cole-nine-centiles format used in UK-WHO -# and the three-percent-centiles used by the CDC. They use 5/10/25/50/75/90/95 and suggest 3rd centile -# is unlikely necessary. +# Each centile in the cole format is 2/3 of a standard deviation apart. They map to 0.04/2/9/25/50/75/91/98/99.6 + +# and the three-percent-centiles and five-percent-centiles are used by the CDC. They use 5/10/25/50/75/90/95 but also replace the 5th centile with a 3rd/97th centile.. +# The eight-five-percent-centiles format is also used by the CDC and adds an extra 85th centile as well as extended centiles for BMI [5.0, 10.0, 25.0, 50.0, 75.0, 85, 90.0, 98.0, 99.0, 99.9, 99.99 centiles] for use in BMI calculations - this is not used in the UK-WHO references. +# They were introduced in December 2022 -THREE_PERCENT_CENTILES = 'three-percent-centiles' -COLE_TWO_THIRDS_SDS_NINE_CENTILES = 'cole-nine-centiles' -CENTILE_FORMATS=[THREE_PERCENT_CENTILES, COLE_TWO_THIRDS_SDS_NINE_CENTILES] +THREE_PERCENT_CENTILES = "three-percent-centiles" +FIVE_PERCENT_CENTILES = "five-percent-centiles" +EIGHTY_FIVE_PERCENT_CENTILES = "eighty-five-percent-centiles" +COLE_TWO_THIRDS_SDS_NINE_CENTILES = "cole-nine-centiles" +CENTILE_FORMATS = [THREE_PERCENT_CENTILES, COLE_TWO_THIRDS_SDS_NINE_CENTILES] -THREE_PERCENT_CENTILE_COLLECTION = [ - 3.0, 5.0, 10.0, 25.0, 50.0, 75.0, 90.0, 95.0, 97.0] +THREE_PERCENT_CENTILE_COLLECTION = [3.0, 10.0, 25.0, 50.0, 75.0, 90.0, 97.0] +EIGHTY_FIVE_PERCENT_CENTILE_COLLECTION = [3.0, 5.0, 10.0, 25.0, 50.0, 75.0, 85, 90.0, 95, 98.0, 99.0, 99.9, 99.99] # use for CDC Extended BMI centiles 2022 +FIVE_PERCENT_CENTILE_COLLECTION = [5.0, 10.0, 25.0, 50.0, 75.0, 90.0, 95.0] COLE_TWO_THIRDS_SDS_NINE_CENTILE_COLLECTION = [ - 0.4, 2.0, 9.0, 25.0, 50.0, 75.0, 91.0, 98.0, 99.6] + 0.4, + 2.0, + 9.0, + 25.0, + 50.0, + 75.0, + 91.0, + 98.0, + 99.6, +] diff --git a/rcpchgrowth/data_tables/cdc.json b/rcpchgrowth/data_tables/cdc.json new file mode 100644 index 0000000..cfc19a3 --- /dev/null +++ b/rcpchgrowth/data_tables/cdc.json @@ -0,0 +1,9393 @@ +{ + "acknowledgement_text": "CDC_2000.", + "notes": "", + "filename": "USCDC2000", + "measurement": { + "height": { + "male": + [ + { + "decimal_age": 0, + "L": 1.267004226, + "M": 49.98888408, + "S": 0.053112191 + }, + { + "decimal_age": 0.041666667, + "L": 0.511237696, + "M": 52.6959753, + "S": 0.048692684 + }, + 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"L": 2.350847202, + "M": 48.63342328, + "S": 0.031948378 + } + ] + } + } +} \ No newline at end of file diff --git a/rcpchgrowth/data_tables/cdc_resources/GrowthchartLMSmethod07.pdf b/rcpchgrowth/data_tables/cdc_resources/GrowthchartLMSmethod07.pdf new file mode 100644 index 0000000..fcc3a0a Binary files /dev/null and b/rcpchgrowth/data_tables/cdc_resources/GrowthchartLMSmethod07.pdf differ diff --git a/rcpchgrowth/data_tables/who_infants.json b/rcpchgrowth/data_tables/who_infants.json index 279230f..c9f110c 100644 --- a/rcpchgrowth/data_tables/who_infants.json +++ b/rcpchgrowth/data_tables/who_infants.json @@ -6,7 +6,19 @@ "height": { "male": - [ + [ + { + "decimal_age": 0, + "L": 1, + "M": 49.8842, + "S": 0.03795 + }, + { + "decimal_age": 0.019164956, + "L": 1, + "M": 51.1152, + "S": 0.03723 + }, {"decimal_age": 0.038329911019849415, "interval": "weeks", "M": 52.3461, "L": 1, "value": 2, "S": 0.03652}, {"decimal_age": 0.057494866529774126, "interval": "weeks", "M": 53.3905, "L": 1, "value": 3, "S": 0.03609}, {"decimal_age": 0.07665982203969883, "interval": "weeks", "M": 54.3881, "L": 1, "value": 4, "S": 0.0357}, @@ -45,6 +57,18 @@ {"decimal_age": 2.0, "interval": "months", "M": 87.8161, "L": 1, "value": 24, "S": 0.03479} ], "female": [ + { + "decimal_age": 0, + "L": 1, + "M": 49.1477, + "S": 0.0379 + }, + { + "decimal_age": 0.019164956, + "L": 1, + "M": 50.3298, + "S": 0.03742 + }, {"decimal_age": 0.038329911019849415, "interval": "weeks", "M": 51.512, "L": 1, "value": 2, "S": 0.03694}, {"decimal_age": 0.057494866529774126, "interval": "weeks", "M": 52.4695, "L": 1, "value": 3, "S": 0.03669}, {"decimal_age": 0.07665982203969883, "interval": "weeks", "M": 53.3809, "L": 1, "value": 4, "S": 0.03647}, @@ -85,6 +109,18 @@ }, "weight": { "male":[ + { + "decimal_age": 0, + "L": 0.3487, + "M": 3.3464, + "S": 0.14602 + }, + { + "decimal_age": 0.019164956, + "L": 0.2776, + "M": 3.4879, + "S": 0.14483 + }, {"interval": "weeks", "M": 3.7529, "L": 0.2581, "value": 2, "S": 0.14142, "decimal_age": 0.038329911019849415}, {"interval": "weeks", "M": 4.0603, "L": 0.2442, "value": 3, "S": 0.13807, "decimal_age": 0.057494866529774126}, {"interval": "weeks", "M": 4.3671, "L": 0.2331, "value": 4, "S": 0.13497, "decimal_age": 0.07665982203969883}, @@ -123,6 +159,18 @@ {"interval": "months", "M": 12.1515, "L": -0.0137, "value": 24, "S": 0.11426, "decimal_age": 2.0} ], "female": [ + { + "decimal_age": 0, + "L": 0.3809, + "M": 3.2322, + "S": 0.14171 + }, + { + "decimal_age": 0.019164956, + "L": 0.2671, + "M": 3.3388, + "S": 0.146 + }, {"interval": "weeks", "M": 3.5693, "L": 0.2304, "value": 2, "S": 0.14339, "decimal_age": 0.038329911019849415}, {"interval": "weeks", "M": 3.8352, "L": 0.2024, "value": 3, "S": 0.1406, "decimal_age": 0.057494866529774126}, {"interval": "weeks", "M": 4.0987, "L": 0.1789, "value": 4, "S": 0.13805, "decimal_age": 0.07665982203969883}, @@ -163,6 +211,18 @@ }, "bmi": { "male":[ + { + "decimal_age": 0, + "L": -0.3053, + "M": 13.4069, + "S": 0.0956 + }, + { + "decimal_age": 0.019164956, + "L": 0.5247, + "M": 13.3421, + "S": 0.09821 + }, {"interval": "weeks", "M": 13.6377, "L": 0.4177, "value": 2, "S": 0.09454, "decimal_age": 0.038329911019849415}, {"interval": "weeks", "M": 14.2241, "L": 0.3449, "value": 3, "S": 0.0923, "decimal_age": 0.057494866529774126}, {"interval": "weeks", "M": 14.7714, "L": 0.2881, "value": 4, "S": 0.09072, "decimal_age": 0.07665982203969883}, @@ -201,6 +261,18 @@ {"interval": "months", "M": 15.7356, "L": -0.6473, "value": 24, "S": 0.07771, "decimal_age": 2.0} ], "female": [ + { + "decimal_age": 0, + "L": -0.0631, + "M": 13.3363, + "S": 0.09272 + }, + { + "decimal_age": 0.019164956, + "L": 0.6319, + "M": 13.2113, + "S": 0.09887 + }, {"interval": "weeks", "M": 13.4501, "L": 0.5082, "value": 2, "S": 0.09741, "decimal_age": 0.038329911019849415}, {"interval": "weeks", "M": 13.9505, "L": 0.4263, "value": 3, "S": 0.09647, "decimal_age": 0.057494866529774126}, {"interval": "weeks", "M": 14.4208, "L": 0.3637, "value": 4, "S": 0.09577, "decimal_age": 0.07665982203969883}, @@ -241,6 +313,18 @@ }, "ofc": { "male":[ + { + "decimal_age": 0, + "L": 1, + "M": 34.4618, + "S": 0.0369 + }, + { + "decimal_age": 0.019164956, + "L": 1, + "M": 35.1634, + "S": 0.0347 + }, {"interval": "weeks", "M": 35.8649, "L": 1, "value": 2, "S": 0.03258, "decimal_age": 0.038329911019849415}, {"interval": "weeks", "M": 36.5216, "L": 1, "value": 3, "S": 0.03197, "decimal_age": 0.057494866529774126}, {"interval": "weeks", "M": 37.0926, "L": 1, "value": 4, "S": 0.03148, "decimal_age": 0.07665982203969883}, @@ -279,6 +363,18 @@ {"interval": "months", "M": 48.2515, "L": 1, "value": 24, "S": 0.02821, "decimal_age": 2.0} ], "female": [ + { + "decimal_age": 0, + "L": 1, + "M": 33.8787, + "S": 0.035 + }, + { + "decimal_age": 0.019164956, + "L": 1, + "M": 34.5529, + "S": 0.0337 + }, {"interval": "weeks", "M": 35.2272, "L": 1, "value": 2, "S": 0.03251, "decimal_age": 0.038329911019849415}, {"interval": "weeks", "M": 35.843, "L": 1, "value": 3, "S": 0.03231, "decimal_age": 0.057494866529774126}, {"interval": "weeks", "M": 36.3761, "L": 1, "value": 4, "S": 0.03215, "decimal_age": 0.07665982203969883}, diff --git a/rcpchgrowth/global_functions.py b/rcpchgrowth/global_functions.py index 2768db3..7a85c6a 100644 --- a/rcpchgrowth/global_functions.py +++ b/rcpchgrowth/global_functions.py @@ -4,9 +4,11 @@ from .uk_who import uk_who_lms_array_for_measurement_and_sex from .turner import turner_lms_array_for_measurement_and_sex from .trisomy_21 import trisomy_21_lms_array_for_measurement_and_sex +from .cdc import cdc_lms_array_for_measurement_and_sex + # from scipy import interpolate #see below, comment back in if swapping interpolation method # from scipy.interpolate import CubicSpline #see below, comment back in if swapping interpolation method -from .constants.reference_constants import MALE, UK_WHO, TURNERS, TRISOMY_21, SEXES, BMI +from .constants.reference_constants import MALE, UK_WHO, TURNERS, TRISOMY_21, SEXES, BMI, CDC """Public functions""" @@ -18,68 +20,101 @@ def measurement_from_sds( measurement_method: str, sex: str, age: float, - default_youngest_reference: bool = False + default_youngest_reference: bool = False, ) -> float: try: lms_value_array_for_measurement = lms_value_array_for_measurement_for_reference( - reference=reference, - age=age, - measurement_method=measurement_method, + reference=reference, + age=age, + measurement_method=measurement_method, sex=sex, - default_youngest_reference=default_youngest_reference + default_youngest_reference=default_youngest_reference, ) except LookupError as err: raise LookupError(err) # get LMS values from the reference: check for age match, interpolate if none lms = fetch_lms( - age=age, lms_value_array_for_measurement=lms_value_array_for_measurement) + age=age, lms_value_array_for_measurement=lms_value_array_for_measurement + ) l = lms["l"] m = lms["m"] s = lms["s"] observation_value = None - try: - observation_value = measurement_for_z(z=requested_sds, l=l, m=m, s=s) - except Exception as e: - print(e) - return None + + if reference == CDC and measurement_method == BMI: + # CDC BMI references require a different method to calculate the centile + # This is because the centile is calculated from the z-score using the cumulative distribution function + # if the centile is below 95% (or the inverse if the centile is above 95%) + # It takes the sigma value from the reference data and applies the cdf to the z-score + + sigma = lms["sigma"] + if requested_sds <= 1.645: # 95th centile + observation_value = m * (1 + l * s * requested_sds)**(1/l) + else: + # inverse of the cdf applied to the bmi percentile - 90 / 10, + # then multiplied by the sigma value and added to the 95th centile + p95 = m * (1 + l * s * 1.645)**(1/l) # 95th centile measurement + centile = stats.norm.cdf(requested_sds) * 100 # convert z-score to centile + observation_value = stats.norm.ppf((centile - 90)/10) * sigma + p95 + else: + # all other references use the standard method + try: + observation_value = measurement_for_z(z=requested_sds, l=l, m=m, s=s) + except Exception as e: + print(e) + return None + return observation_value + def sds_for_measurement( reference: str, age: float, measurement_method: str, observation_value: float, - sex: str + sex: str, ) -> float: try: lms_value_array_for_measurement = lms_value_array_for_measurement_for_reference( - reference=reference, - age=age, - measurement_method=measurement_method, + reference=reference, + age=age, + measurement_method=measurement_method, sex=sex, - default_youngest_reference=False # The oldest child reference should always be selected for SDS calculation + default_youngest_reference=False, # The oldest child reference should always be selected for SDS calculation ) except LookupError as err: raise LookupError(err) # get LMS values from the reference: check for age match, interpolate if none lms = fetch_lms( - age=age, lms_value_array_for_measurement=lms_value_array_for_measurement) + age=age, lms_value_array_for_measurement=lms_value_array_for_measurement + ) l = lms["l"] m = lms["m"] s = lms["s"] + # this calculation is different for CDC BMI references and uses the + # cumulative distribution function to calculate the z-score + # (if the centile is below 95% (or the inverse if the centile is above 95%) + if reference == CDC and measurement_method == BMI: + sigma = lms["sigma"] + if observation_value > m * (1 + l * s * 1.645)**(1/l): + # above 95th centile + p95 = m * (1 + l * s * 1.645)**(1/l) + centile = stats.norm.cdf((observation_value - p95) / sigma)*10 + 90 + z = stats.norm.ppf(centile/100) + return z + return z_score(l=l, m=m, s=s, observation=observation_value) + def percentage_median_bmi( - reference: str, - age: float, - actual_bmi: float, - sex: str) -> float: + reference: str, age: float, actual_bmi: float, sex: str +) -> float: """ public method This returns a child"s BMI expressed as a percentage of the median value for age and sex. @@ -90,17 +125,20 @@ def percentage_median_bmi( # fetch the LMS values for the requested measurement try: lms_value_array_for_measurement = lms_value_array_for_measurement_for_reference( - reference=reference, - measurement_method=BMI, sex=sex, + reference=reference, + measurement_method=BMI, + sex=sex, age=age, - default_youngest_reference=False) # The oldest reference should always be chosen for this calculation + default_youngest_reference=False, + ) # The oldest reference should always be chosen for this calculation except LookupError as err: raise LookupError(err) # get LMS values from the reference: check for age match, interpolate if none try: lms = fetch_lms( - age=age, lms_value_array_for_measurement=lms_value_array_for_measurement) + age=age, lms_value_array_for_measurement=lms_value_array_for_measurement + ) except LookupError as err: print(err) return None @@ -110,59 +148,60 @@ def percentage_median_bmi( percent_median_bmi = (actual_bmi / m) * 100.0 return percent_median_bmi + def generate_centile( - z: float, - centile: float, - measurement_method: str, - sex: str, - lms_array_for_measurement: list, - reference: str, - is_sds: bool=False) -> list: + z: float, + centile: float, + measurement_method: str, + sex: str, + lms_array_for_measurement: list, + reference: str, + is_sds: bool = False, +) -> list: """ - Generates a centile curve for a given reference. + Generates a centile curve for a given reference. Takes the z-score equivalent of the centile, the centile to be used as a label, the sex and measurement method. """ - if (len(lms_array_for_measurement)==0): - raise Exception(f"No reference data available for {measurement_method} in {sex} in {reference}") - + if len(lms_array_for_measurement) == 0: + raise Exception( + f"No reference data available for {measurement_method} in {sex} in {reference}" + ) + min_age = lms_array_for_measurement[0]["decimal_age"] max_age = lms_array_for_measurement[-1]["decimal_age"] # if this is an sds line, the label reflects the sds value. The default is to reflect the centile label_value = centile if is_sds: - label_value=round(z, 3) + label_value = round(z, 3) centile_measurements = [] age = min_age while age < max_age: # loop through the reference in steps of 0.1y - try: measurement = measurement_from_sds( - reference=reference, - measurement_method=measurement_method, - requested_sds=z, - sex=sex, + reference=reference, + measurement_method=measurement_method, + requested_sds=z, + sex=sex, age=age, - default_youngest_reference=False + default_youngest_reference=False, ) except Exception as err: print(err) value = create_data_point( - age=age, - measurement=measurement, - label_value=label_value + age=age, measurement=measurement, label_value=label_value ) centile_measurements.append(value) # weekly intervals until 4 y, then monthly if age <= 2: - age += (7 / 365.25) # weekly intervals + age += 7 / 365.25 # weekly intervals else: age += 1 / 12 # monthly intervals @@ -170,48 +209,47 @@ def generate_centile( # even after minifying, which are not practical. Weekly values makes plotting easier. # Here we have used weekly points from preterm to 2 y, monthly values after. # age += (7/365.25) # weekly intervals - + # add the final value in the data set so the lines overlap cleanly try: measurement = measurement_from_sds( - reference=reference, - measurement_method=measurement_method, - requested_sds=z, - sex=sex, + reference=reference, + measurement_method=measurement_method, + requested_sds=z, + sex=sex, age=max_age, - default_youngest_reference=True + default_youngest_reference=True, ) except Exception as err: print(err) - + value = create_data_point( - age=max_age, - measurement=measurement, - label_value=label_value - ) + age=max_age, measurement=measurement, label_value=label_value + ) centile_measurements.append(value) return centile_measurements + def mid_parental_height( - height_paternal: float, - height_maternal: float, - sex: str + height_paternal: float, height_maternal: float, sex: str ) -> float: """ Calculates mid-parental height for the child. All units are in cm """ if sex == MALE: - return (height_paternal + height_maternal + 13)/2 + return (height_paternal + height_maternal + 13) / 2 else: - return (height_paternal + height_maternal - 13)/2 + return (height_paternal + height_maternal - 13) / 2 + """ *** PUBLIC FUNCTIONS THAT CONVERT BETWEEN CENTILE AND SDS """ + def sds_for_centile(centile: float) -> float: """ converts a centile (supplied as a percentage) using the scipy package to an SDS. @@ -219,6 +257,7 @@ def sds_for_centile(centile: float) -> float: sds = stats.norm.ppf(centile / 100) return sds + def rounded_sds_for_centile(centile: float) -> float: """ converts a centile (supplied as a percentage) using the scipy package to the nearest 2/3 SDS. @@ -230,12 +269,13 @@ def rounded_sds_for_centile(centile: float) -> float: rounded_to_nearest_two_thirds = round(sds / (2 / 3)) return rounded_to_nearest_two_thirds * (2 / 3) + def centile(z_score: float): """ Converts a Z Score to a p value (2-tailed) using the SciPy library, which it returns as a percentage """ try: - centile = (stats.norm.cdf(z_score) * 100) + centile = stats.norm.cdf(z_score) * 100 return centile except TypeError as err: raise TypeError(err) @@ -246,28 +286,37 @@ def centile(z_score: float): These are essential to the public functions but are not needed outside this file """ + def create_data_point(age: float, measurement: float, label_value: str): # creates a data point if measurement is not None: try: rounded = round(measurement, 4) except Exception as e: - print(f"{e} z:{z} age:{age} measurement: {measurement}") + print(f"{e} age:{age} measurement: {measurement}") return else: rounded = None - value = { - "l": label_value, - "x": round(age, 4), - "y": rounded - } + value = {"l": label_value, "x": round(age, 4), "y": rounded} return value + """ ***** INTERPOLATION FUNCTIONS ***** """ -def cubic_interpolation(age: float, age_one_below: float, age_two_below: float, age_one_above: float, age_two_above: float, parameter_two_below: float, parameter_one_below: float, parameter_one_above: float, parameter_two_above: float) -> float: + +def cubic_interpolation( + age: float, + age_one_below: float, + age_two_below: float, + age_one_above: float, + age_two_above: float, + parameter_two_below: float, + parameter_one_below: float, + parameter_one_above: float, + parameter_two_above: float, +) -> float: """ See sds function. This method tests if the age of the child (either corrected for prematurity or chronological) is at a threshold of the reference data This method is specific to the UK-WHO data set. @@ -303,9 +352,12 @@ def cubic_interpolation(age: float, age_one_below: float, age_two_below: float, t13 = age_one_below - age_two_above t23 = age_one_above - age_two_above - cubic_interpolated_value = parameter_two_below * tt1 * tt2 * tt3 / t01 / t02 / t03 - parameter_one_below * tt0 * tt2 * tt3 / t01 / \ - t12 / t13 + parameter_one_above * tt0 * tt1 * tt3 / t02 / t12 / \ - t23 - parameter_two_above * tt0 * tt1 * tt2 / t03 / t13 / t23 + cubic_interpolated_value = ( + parameter_two_below * tt1 * tt2 * tt3 / t01 / t02 / t03 + - parameter_one_below * tt0 * tt2 * tt3 / t01 / t12 / t13 + + parameter_one_above * tt0 * tt1 * tt3 / t02 / t12 / t23 + - parameter_two_above * tt0 * tt1 * tt2 / t03 / t13 / t23 + ) # prerequisite arrays for either of below functions # xpoints = [age_two_below, age_one_below, age_one_above, age_two_above] @@ -322,7 +374,13 @@ def cubic_interpolation(age: float, age_one_below: float, age_two_below: float, return cubic_interpolated_value -def linear_interpolation(age: float, age_one_below: float, age_one_above: float, parameter_one_below: float, parameter_one_above: float) -> float: +def linear_interpolation( + age: float, + age_one_below: float, + age_one_above: float, + parameter_one_below: float, + parameter_one_above: float, +) -> float: """ See sds function. This method is to do linear interpolation of L, M and S values for children whose ages are at the threshold of the reference data, making cubic interpolation impossible """ @@ -336,26 +394,28 @@ def linear_interpolation(age: float, age_one_below: float, age_one_above: float, linear_interpolated_value = intermediate(age) return linear_interpolated_value + """ ***** DO THE CALCULATIONS ***** """ + def measurement_for_z(z: float, l: float, m: float, s: float) -> float: """ Returns a measurement for a z score, L, M and S x = M (1 + L S z)^(1/L) - Note, in some circumstances, 1 + l * s * z will be negative, and + Note, in some circumstances, 1 + l * s * z will be negative, and it will not be possible to calculate a power. In these circumstances, None is returned """ - measurement_value=0.0 + measurement_value = 0.0 if l != 0.0: - first_step= (1 + l * s * z) - exponent= 1 / l + first_step = 1 + l * s * z + exponent = 1 / l if first_step < 0: return None try: - measurement_value= (first_step ** exponent) * m + measurement_value = (first_step**exponent) * m except Exception as e: print(e) return @@ -363,15 +423,16 @@ def measurement_for_z(z: float, l: float, m: float, s: float) -> float: measurement_value = math.exp(s * z) * m return measurement_value + def z_score(l: float, m: float, s: float, observation: float): """ Converts the (age-specific) L, M and S parameters into a z-score """ sds = 0.0 if l != 0.0: - sds = (((observation / m) ** l) - 1) / (l*s) + sds = (((observation / m) ** l) - 1) / (l * s) else: - sds = (math.log(observation / m) / s) + sds = math.log(observation / m) / s return sds @@ -379,12 +440,10 @@ def z_score(l: float, m: float, s: float, observation: float): ***** LOOKUP FUNCTIONS ***** """ -def nearest_lowest_index( - lms_array: list, - age: float -) -> int: + +def nearest_lowest_index(lms_array: list, age: float) -> int: """ - loops through the array of LMS values and returns either + loops through the array of LMS values and returns either the index of an exact match or the lowest nearest decimal age """ lowest_index = 0 @@ -401,67 +460,151 @@ def nearest_lowest_index( def fetch_lms(age: float, lms_value_array_for_measurement: list): """ - Retuns the LMS for a given age. If there is no exact match in the reference - an interpolated LMS is returned. Cubic interpolation is used except at the fringes of the + Retuns the LMS for a given age, and sigma if present (CDC BMI references). If there is no exact match in the reference + an interpolated LMS is returned. Cubic interpolation is used except at the fringes of the reference where linear interpolation is used. It accepts the age and a python list of the LMS values for that measurement_method and sex. """ age_matched_index = nearest_lowest_index( - lms_value_array_for_measurement, age) # returns nearest LMS for age - if round(lms_value_array_for_measurement[age_matched_index]["decimal_age"], 16) == round(age, 16): + lms_value_array_for_measurement, age + ) # returns nearest LMS for age + if round( + lms_value_array_for_measurement[age_matched_index]["decimal_age"], 16 + ) == round(age, 16): # there is an exact match in the data with the requested age l = lms_value_array_for_measurement[age_matched_index]["L"] m = lms_value_array_for_measurement[age_matched_index]["M"] s = lms_value_array_for_measurement[age_matched_index]["S"] + + if "sigma" in lms_value_array_for_measurement[age_matched_index]: + # CDC BMI references have an additional sigma value + sigma = lms_value_array_for_measurement[age_matched_index]["sigma"] + return {"l": l, "m": m, "s": s, "sigma": sigma} else: # there has not been an exact match in the reference data # Interpolation will be required. # The age_matched_index is one below the age supplied. There # needs to be a value below that, and two values above the supplied age, # for cubic interpolation to be possible. - age_one_below = lms_value_array_for_measurement[age_matched_index]["decimal_age"] - age_one_above = lms_value_array_for_measurement[age_matched_index + 1]["decimal_age"] + age_one_below = lms_value_array_for_measurement[age_matched_index][ + "decimal_age" + ] + age_one_above = lms_value_array_for_measurement[age_matched_index + 1][ + "decimal_age" + ] parameter_one_below = lms_value_array_for_measurement[age_matched_index] parameter_one_above = lms_value_array_for_measurement[age_matched_index + 1] - if age_matched_index >= 1 and age_matched_index < len(lms_value_array_for_measurement) - 2: + if ( + age_matched_index >= 1 + and age_matched_index < len(lms_value_array_for_measurement) - 2 + and "sigma" not in lms_value_array_for_measurement[age_matched_index] # CDC BMI references have an additional sigma value + # and CDC only use linear interpolation + ): # cubic interpolation is possible - age_two_below = lms_value_array_for_measurement[age_matched_index - 1]["decimal_age"] - age_two_above = lms_value_array_for_measurement[age_matched_index + 2]["decimal_age"] + age_two_below = lms_value_array_for_measurement[age_matched_index - 1][ + "decimal_age" + ] + age_two_above = lms_value_array_for_measurement[age_matched_index + 2][ + "decimal_age" + ] parameter_two_below = lms_value_array_for_measurement[age_matched_index - 1] parameter_two_above = lms_value_array_for_measurement[age_matched_index + 2] - l = cubic_interpolation(age=age, age_one_below=age_one_below, age_two_below=age_two_below, age_one_above=age_one_above, age_two_above=age_two_above, - parameter_two_below=parameter_two_below["L"], parameter_one_below=parameter_one_below["L"], parameter_one_above=parameter_one_above["L"], parameter_two_above=parameter_two_above["L"]) - m = cubic_interpolation(age=age, age_one_below=age_one_below, age_two_below=age_two_below, age_one_above=age_one_above, age_two_above=age_two_above, - parameter_two_below=parameter_two_below["M"], parameter_one_below=parameter_one_below["M"], parameter_one_above=parameter_one_above["M"], parameter_two_above=parameter_two_above["M"]) - s = cubic_interpolation(age=age, age_one_below=age_one_below, age_two_below=age_two_below, age_one_above=age_one_above, age_two_above=age_two_above, - parameter_two_below=parameter_two_below["S"], parameter_one_below=parameter_one_below["S"], parameter_one_above=parameter_one_above["S"], parameter_two_above=parameter_two_above["S"]) + l = cubic_interpolation( + age=age, + age_one_below=age_one_below, + age_two_below=age_two_below, + age_one_above=age_one_above, + age_two_above=age_two_above, + parameter_two_below=parameter_two_below["L"], + parameter_one_below=parameter_one_below["L"], + parameter_one_above=parameter_one_above["L"], + parameter_two_above=parameter_two_above["L"], + ) + m = cubic_interpolation( + age=age, + age_one_below=age_one_below, + age_two_below=age_two_below, + age_one_above=age_one_above, + age_two_above=age_two_above, + parameter_two_below=parameter_two_below["M"], + parameter_one_below=parameter_one_below["M"], + parameter_one_above=parameter_one_above["M"], + parameter_two_above=parameter_two_above["M"], + ) + s = cubic_interpolation( + age=age, + age_one_below=age_one_below, + age_two_below=age_two_below, + age_one_above=age_one_above, + age_two_above=age_two_above, + parameter_two_below=parameter_two_below["S"], + parameter_one_below=parameter_one_below["S"], + parameter_one_above=parameter_one_above["S"], + parameter_two_above=parameter_two_above["S"], + ) + if "sigma" in lms_value_array_for_measurement[age_matched_index]: + # CDC BMI references have an additional sigma value + sigma = cubic_interpolation( + age=age, + age_one_below=age_one_below, + age_two_below=age_two_below, + age_one_above=age_one_above, + age_two_above=age_two_above, + parameter_two_below=parameter_two_below["sigma"], + parameter_one_below=parameter_one_below["sigma"], + parameter_one_above=parameter_one_above["sigma"], + parameter_two_above=parameter_two_above["sigma"], + ) + return {"l": l, "m": m, "s": s, "sigma": sigma} else: - # we are at the thresholds of this reference. Only linear interpolation is possible - l = linear_interpolation(age=age, age_one_below=age_one_below, age_one_above=age_one_above, - parameter_one_below=parameter_one_below["L"], parameter_one_above=parameter_one_above["L"]) - m = linear_interpolation(age=age, age_one_below=age_one_below, age_one_above=age_one_above, - parameter_one_below=parameter_one_below["M"], parameter_one_above=parameter_one_above["M"]) - s = linear_interpolation(age=age, age_one_below=age_one_below, age_one_above=age_one_above, - parameter_one_below=parameter_one_below["S"], parameter_one_above=parameter_one_above["S"]) - - return { - "l": l, - "m": m, - "s": s - } + # we are at the thresholds of this reference or are using CDC. Only linear interpolation is possible + l = linear_interpolation( + age=age, + age_one_below=age_one_below, + age_one_above=age_one_above, + parameter_one_below=parameter_one_below["L"], + parameter_one_above=parameter_one_above["L"], + ) + m = linear_interpolation( + age=age, + age_one_below=age_one_below, + age_one_above=age_one_above, + parameter_one_below=parameter_one_below["M"], + parameter_one_above=parameter_one_above["M"], + ) + s = linear_interpolation( + age=age, + age_one_below=age_one_below, + age_one_above=age_one_above, + parameter_one_below=parameter_one_below["S"], + parameter_one_above=parameter_one_above["S"], + ) + if "sigma" in lms_value_array_for_measurement[age_matched_index]: + # CDC BMI references have an additional sigma value + sigma = linear_interpolation( + age=age, + age_one_below=age_one_below, + age_one_above=age_one_above, + parameter_one_below=parameter_one_below["sigma"], + parameter_one_above=parameter_one_above["sigma"], + ) + return {"l": l, "m": m, "s": s, "sigma": sigma} + + return {"l": l, "m": m, "s": s} + def lms_value_array_for_measurement_for_reference( reference: str, age: float, measurement_method: str, sex: str, - default_youngest_reference: bool = False + default_youngest_reference: bool = False, ) -> list: """ This is a private function which returns the LMS array for measurement_method and sex and reference - It accepts the reference ('uk-who', 'turners-syndrome' or 'trisomy-21') + It accepts the reference ('uk-who', 'turners-syndrome', 'trisomy-21', 'cdc') If the UK-WHO reference is requested, it is possible to be select the younger reference for overlap values, using the default_youngest_reference flag. """ @@ -469,25 +612,39 @@ def lms_value_array_for_measurement_for_reference( if reference == UK_WHO: try: lms_value_array_for_measurement = uk_who_lms_array_for_measurement_and_sex( - age=age, - measurement_method=measurement_method, + age=age, + measurement_method=measurement_method, sex=sex, - default_youngest_reference=default_youngest_reference + default_youngest_reference=default_youngest_reference, ) except LookupError as error: raise LookupError(error) elif reference == TURNERS: try: lms_value_array_for_measurement = turner_lms_array_for_measurement_and_sex( - measurement_method=measurement_method, sex=sex, age=age) + measurement_method=measurement_method, sex=sex, age=age + ) except LookupError as error: raise LookupError(error) elif reference == TRISOMY_21: try: - lms_value_array_for_measurement = trisomy_21_lms_array_for_measurement_and_sex( - measurement_method=measurement_method, sex=sex, age=age) + lms_value_array_for_measurement = ( + trisomy_21_lms_array_for_measurement_and_sex( + measurement_method=measurement_method, sex=sex, age=age + ) + ) + except LookupError as error: + raise LookupError(error) + elif reference == CDC: + try: + lms_value_array_for_measurement = cdc_lms_array_for_measurement_and_sex( + age=age, + measurement_method=measurement_method, + sex=sex, + default_youngest_reference=default_youngest_reference + ) except LookupError as error: raise LookupError(error) else: raise ValueError("Incorrect reference supplied") - return lms_value_array_for_measurement \ No newline at end of file + return lms_value_array_for_measurement diff --git a/rcpchgrowth/measurement.py b/rcpchgrowth/measurement.py index 5e75ce9..88718e0 100644 --- a/rcpchgrowth/measurement.py +++ b/rcpchgrowth/measurement.py @@ -1,6 +1,5 @@ # standard imports from datetime import date -from pprint import pprint # rcpch imports from .centile_bands import centile_band_for_centile @@ -72,6 +71,7 @@ def __init__( # self.height_prediction_reference = height_prediction_reference self.events_text = events_text + # validate the measurement method to ensure that the observation value is within the expected range - TODO this will be changed to SDS-based cutoffs try: self.__validate_measurement_method( measurement_method=measurement_method, observation_value=observation_value) @@ -86,7 +86,8 @@ def __init__( observation_date=self.observation_date, gestation_weeks=self.gestation_weeks, gestation_days=self.gestation_days) - + + # the calculate_measurements_object receives the child_observation_value and measurement_calculated_values objects self.calculated_measurements_object = self.sds_and_centile_for_measurement_method( sex=self.sex, @@ -236,7 +237,8 @@ def sds_and_centile_for_measurement_method( # calculate sds based on reference, age, measurement, sex and prematurity if corrected_age is None or chronological_age is None: - # there has been an age calculation error. Further calculation impossible + # there has been an age calculation error. Further calculation impossible - this may be due to a date error or because CDC reference data is not available in preterm infants + self.return_measurement_object = self.__create_measurement_object( measurement_method=measurement_method, observation_value=observation_value, @@ -253,13 +255,18 @@ def sds_and_centile_for_measurement_method( chronological_percentage_median_bmi=None ) return self.return_measurement_object - - try: - corrected_measurement_sds = sds_for_measurement(reference=reference, age=corrected_age, measurement_method=measurement_method, - observation_value=observation_value, sex=sex) - except Exception as err: - corrected_measurement_error = f"{err}" + + # CDC data cannot be used for preterm infants who are not yet term. We will not be able to calculate SDS scores and centiles so will return none, but signpost to the user that this is what we are doing. + if corrected_age < 0 and reference == "cdc": + corrected_measurement_error = "This baby is born premature. CDC data is not available for preterm infants." corrected_measurement_sds = None + else: + try: + corrected_measurement_sds = sds_for_measurement(reference=reference, age=corrected_age, measurement_method=measurement_method, + observation_value=observation_value, sex=sex) + except Exception as err: + corrected_measurement_error = f"{err}" + corrected_measurement_sds = None try: chronological_measurement_sds = sds_for_measurement(reference=reference, age=chronological_age, measurement_method=measurement_method, @@ -310,7 +317,8 @@ def sds_and_centile_for_measurement_method( except TypeError as err: corrected_measurement_error = "Not possible to calculate centile" corrected_centile_band = None - + + # calculate BMI centiles and percentage median BMI corrected_percentage_median_bmi = None chronological_percentage_median_bmi = None if measurement_method == BMI and corrected_age is not None and chronological_age is not None: @@ -466,17 +474,14 @@ def __calculate_ages( self.estimated_date_delivery_string = None chronological_decimal_age_error = "Estimated date of delivery calculation error." - # ISSUE: #155 observation date COULD be before estimated date delivery in a preterm try: self.corrected_calendar_age = chronological_calendar_age( self.estimated_date_delivery, observation_date) - except: + except Exception as err: + # The EDD is still in the future as this preterm baby is not yet term. The error returned is not useful as the function is expecting a birth date in the past but is being passed an EDD in the future. + # It is not really an error, but a limitation of the function. The calendar age really is the same as the corrected gestational age here. self.corrected_calendar_age = None - if self.estimated_date_delivery > observation_date: - # ISSUE: #157 if observation date is BEFORE birth date then an error should be raised - chronological_decimal_age_error = "The due date is after the observation date - a calendar age cannot be calculated." - else: - chronological_decimal_age_error = "A calendar age cannot be calculated." + chronological_decimal_age_error = None try: self.estimated_date_delivery_string = self.estimated_date_delivery.strftime( diff --git a/rcpchgrowth/tests/cdc/bmi_height_weight_validation.json b/rcpchgrowth/tests/cdc/bmi_height_weight_validation.json new file mode 100644 index 0000000..baba242 --- /dev/null +++ b/rcpchgrowth/tests/cdc/bmi_height_weight_validation.json @@ -0,0 +1,69163 @@ +[ + { + "birth_date": "2013-01-02", + "observation_date": "2022-04-01", + "gestation_weeks": 35, + "gestation_days": 3, + "sex": "female", + "measurement_method": "weight", + "chronological_age": 9.243, + "corrected_age": 9.1554, + "observation_value": 25.58, + "corrected_sds": -0.8431, + "chronological_sds": -0.9022, + "age": 110.9158, + "weight": 25.58, + "waz": -0.894, + "wap": 18.5652, + "p50": 16.4128, + "p95": 22.0502, + "mod_waz": -1.0496, + "z_score": -0.894 + }, + { + "birth_date": "2013-01-02", + "observation_date": "2022-04-01", + "gestation_weeks": 35, + "gestation_days": 3, + "sex": "female", + "measurement_method": "height", + "chronological_age": 9.243, + "corrected_age": 9.1554, + "observation_value": 128.7, + "corrected_sds": -0.8398, + "chronological_sds": -0.9154, + "age": 110.9158, + "height": 128.7, + "haz": -0.8755, + "hap": 19.0639, + "p50": 16.4128, + "p95": 22.0502, + "mod_haz": -0.8962, + "z_score": -0.8755 + }, + { + "birth_date": "2013-01-02", + "observation_date": "2022-04-01", + "gestation_weeks": 35, + "gestation_days": 3, + "sex": "female", + "measurement_method": "bmi", + "chronological_age": 9.243, + "corrected_age": 9.1554, + "observation_value": 15.4434, + "corrected_sds": -0.5501, + "chronological_sds": -0.5709, + "age": 110.9158, + "bmi": 15.4434, + "height": 98.5539, + "weight": 15, + "checksum": 15.4434, + "bmiz": -0.503, + "bmip": 30.7497, + "waz": -5.1741, + "wap": 0, + "haz": -6.4189, + "hap": 6.865e-09, + "p50": 16.4128, + "p95": 22.0502, + "bmip95": 70.0376, + "original_bmip": 30.7497, + "original_bmiz": -0.503, + "perc_median": -5.9063, + "mod_bmiz": -0.6416, + "mod_waz": -3.6253, + "mod_haz": -5.8625, + "z_score": -0.503 + }, + { + "birth_date": "2017-05-15", + "observation_date": "2020-12-09", + "gestation_weeks": 37, + "gestation_days": 5, + "sex": "male", + "measurement_method": "weight", + "chronological_age": 3.5702, + "corrected_age": 3.5264, + "observation_value": 15.76, + "corrected_sds": 0.1848, + "chronological_sds": 0.1388, + "age": 42.8419, + "weight": 15.76, + "waz": 0.2119, + "wap": 58.3906, + "p50": 15.7837, + "p95": 17.9551, + "mod_waz": 0.1716, + "z_score": 0.2119 + }, + { + "birth_date": "2017-05-15", + "observation_date": "2020-12-09", + "gestation_weeks": 37, + "gestation_days": 5, + "sex": "male", + "measurement_method": "height", + "chronological_age": 3.5702, + "corrected_age": 3.5264, + "observation_value": 100.9, + "corrected_sds": 0.2159, + "chronological_sds": 0.1364, + "age": 42.8419, + "height": 100.9, + "haz": 0.4153, + "hap": 66.1045, + "p50": 15.7837, + "p95": 17.9551, + "mod_haz": 0.4069, + "z_score": 0.4153 + }, + { + "birth_date": "2017-05-15", + "observation_date": "2020-12-09", + "gestation_weeks": 37, + "gestation_days": 5, + "sex": "male", + "measurement_method": "bmi", + "chronological_age": 3.5702, + "corrected_age": 3.5264, + "observation_value": 15.4801, + "corrected_sds": 0.036, + "chronological_sds": 0.0447, + "age": 42.8419, + "bmi": 15.4801, + "height": 98.4371, + "weight": 15, + "checksum": 15.4801, + "bmiz": -0.2755, + "bmip": 39.1459, + "waz": -0.2152, + "wap": 41.4813, + "haz": -0.1903, + "hap": 42.4535, + "p50": 15.7837, + "p95": 17.9551, + "bmip95": 86.2159, + "original_bmip": 39.1459, + "original_bmiz": -0.2755, + "perc_median": -1.9232, + "mod_bmiz": -0.3157, + "mod_waz": -0.2549, + "mod_haz": -0.1949, + "z_score": -0.2755 + }, + { + "birth_date": "2018-09-25", + "observation_date": "2027-10-31", + "gestation_weeks": 32, + "gestation_days": 6, + "sex": "male", + "measurement_method": "weight", + "chronological_age": 9.0979, + "corrected_age": 8.961, + "observation_value": 32.01, + "corrected_sds": 0.7484, + "chronological_sds": 0.665, + "age": 109.1745, + "weight": 32.01, + "waz": 0.5783, + "wap": 71.8478, + "p50": 16.1918, + "p95": 21.1482, + "mod_waz": 0.3913, + "z_score": 0.5783 + }, + { + "birth_date": "2018-09-25", + "observation_date": "2027-10-31", + "gestation_weeks": 32, + "gestation_days": 6, + "sex": "male", + "measurement_method": "height", + "chronological_age": 9.0979, + "corrected_age": 8.961, + "observation_value": 137.4, 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diff --git a/rcpchgrowth/tests/test_cdc.py b/rcpchgrowth/tests/test_cdc.py new file mode 100644 index 0000000..82f9dcd --- /dev/null +++ b/rcpchgrowth/tests/test_cdc.py @@ -0,0 +1,70 @@ +# standard imports +from datetime import datetime +import json +import os +# from pprint import pprint + +# third-party imports +import pytest + +# rcpch imports +from rcpchgrowth import Measurement + +# the ACCURACY constant defines the accuracy of the test comparisons +# owing to variations in statistical calculations it's impossible to get exact +# agreement between R and Python, so our statistician feels we can set a tolerance +# within which we will accept a result as correct. + +ACCURACY = 1e-3 + + +def load_valid_data_set(): + """ + Loads in the testing data from JSON file + """ + with open(os.path.abspath(os.path.dirname(__file__)) + "/cdc/bmi_height_weight_validation.json") as f: + return json.load(f) + + +@pytest.mark.parametrize("line", load_valid_data_set()) +def test_measurement_class_cdc_data(line): + """ + Test which iterates through a JSON file of 4000 fictional children and known calculated (TC via R) + correct SDS values. Compares the output of the Measurement.measurement method with the known + correct value. + """ + + measurement_object = Measurement( + sex=str(line["sex"]), + birth_date=datetime.strptime(line["birth_date"], "%Y-%m-%d"), + observation_date=datetime.strptime( + line["observation_date"], "%Y-%m-%d"), + measurement_method=str(line["measurement_method"]), + observation_value=float(line["observation_value"]), + gestation_weeks=40, + gestation_days=0, + reference="cdc" + ) + + # pprint(vars(measurement_object)) + # pprint(line) + + # all comparisons using absolute tolerance (not relative) + + # this conditional guards against failure of pytest.approx with NoneTypes + # if line["corrected_sds"] is None: + # assert measurement_object.measurement[ + # "measurement_calculated_values"]['corrected_sds'] is None + # else: + # assert measurement_object.measurement[ + # "measurement_calculated_values"]['corrected_sds'] == pytest.approx( + # line["corrected_sds"], abs=ACCURACY) + + # this conditional guards against failure of pytest.approx with NoneTypes + if line["z_score"] is None: + assert measurement_object.measurement[ + "measurement_calculated_values"]['chronological_sds'] is None + else: + assert measurement_object.measurement[ + "measurement_calculated_values"]['chronological_sds'] == pytest.approx( + line["z_score"], abs=ACCURACY) diff --git a/setup.py b/setup.py index ae467c6..dc2b076 100644 --- a/setup.py +++ b/setup.py @@ -8,7 +8,7 @@ setup( name="rcpchgrowth", - version="4.0.2", + version="4.1.0", description="SDS and Centile calculations for UK Growth Data", long_description=long_description, url="https://github.com/rcpch/digital-growth-charts/blob/master/README.md", @@ -21,7 +21,7 @@ "Programming Language :: Python :: 3 :: Only", "Topic :: Scientific/Engineering :: Medical Science Apps.", ], - keywords="growth charts, anthropometry, SDS, centile, UK-WHO, UK90, Trisomy 21, Turner", + keywords="growth charts, anthropometry, SDS, centile, UK-WHO, UK90, Trisomy 21, Turner, CDC", packages=find_packages(), python_requires=">3.8", install_requires=["python-dateutil", "scipy"],