This package has various functions for cleaning and processing biomarkers in EHR, especially in CPRD Aurum. All functions can be used on local data (loaded into R) or data stored in MySQL (by using the dbplyr package or another package which uses dbplyr e.g. aurum).
Two functions for cleaning biomarker values are included in this package:
clean_biomarker_values removes values outside of plausible limits (run biomarkerAcceptableLimits to see limits, also our CPRD-Codelists repository). Run ?biomarkerAcceptableLimits for details of how these were ascertained and further explanation of variables. NB: biomarkers with more than one-commonly used unit must be converted to standard units (haematocrit: proportion between 0 and 1, haemoglobin: g/L, HbA1c: mmol/L).
clean_biomarker_units retains only values with appropriate unit codes (numunitid) or missing unit code in CPRD Aurum (run biomarkerAcceptableLimits to see appropriate unit codes, also our CPRD-Codelists repository). Run ?biomarkerAcceptableUnits for details of how these were ascertained and further explanation of variables.
These functions can be applied to the following biomarkers:
- Albumin-creatinine ratio (
acr), output is in mg/mmol / g/mol - (Blood) albumin (
albumin_blood), output in g/L - Alanine aminotransferase (
alt), output in U/L - Aspartate aminotransferase(
ast), output in U/L - Bilirubin (
bilirubin), output in umol/L - BMI (adults only;
bmi), output in kg/m2 - Serum/plasma creatinine (
creatinine_blood), output in umol/L - Diastolic blood pressure (
dbp), output in mmHg - Fasting glucose (
fastingglucose), output in mmol/L - Haematocrit (
haematocrit), output as a proportion out of 1 - Haemoglobin (
haemoglobin), output in g/L - HbA1c (
hba1c), output in mmol/mol - HDL (
hdl), output in mmol/L - Height (adults only;
height), output in cm - LDL (
ldl), output in mmol/L - Protein-creatinine ratio (
pcr), output in mg/mmol - Systolic blood pressure (
sbp), output in mmHg - Total cholesterol (
totalcholesterol), output in mmol/L - Triglycerides (
triglyceride), output in mmol/L - Weight (adults only;
weight), output in kg
Example:
clean_sbp <- raw_sbp %>%
clean_biomarker_values(biomrkr_col=testvalue, biomrkr="sbp") %>%
clean_biomarker_units(numunitid_col=numunitid, biomrkr="sbp")Further info on how we implement these functions as part of a CPRD Aurum processing pipeline can be found here: CPRD-Codelists.
This function implements the CKD-EPI Creatinine Equation (2021) equation to calculate eGFR from creatinine, age and sex. See help (?ckd_epi_2021_egfr) for further explanation of variables.
Example:
clean_egfr_medcodes <- clean_creatinine_blood_medcodes %>%
ckd_epi_2021_egfr(creatinine=testvalue, sex=sex, age_at_creatinine=age_at_creat)
Functions for calculating QRISK2 (2017) and QDiabetes-Heart Failure (2015) are included in this package. NB: both functions will calculate scores for individuals with values (e.g. age, BMI) outside of the range for which the model is valid without warning; these individuals need to be removed prior to using the functions. See help files (?calculate_qrisk2 and ?calculate_qdiabeteshf) for further explanation of variables.
Example:
results <- dataframe %>%
calculate_qrisk2(age = age_var,
sex = sex_var,
ethrisk = ethrisk_var,
town = town_var,
smoking = smoking_var,
fh_cvd = fh_cvd_var,
renal = renal_var,
af = af_var,
rheumatoid_arth=rheumatoid_arth_var,
bp_med = bp_med_var,
cholhdl = cholhdl_var,
sbp = sbp_var,
bmi = bmi_var,
type1 = type1_var,
type2 = type2_var,
surv = surv_var)
Example:
results <- dataframe %>%
calculate_qdiabeteshf(age = age_var
sex = sex_var,
ethrisk = ethrisk_var,
town = town_var,
smoking = smoking_var,
duration = diabetes_duration_var,
renal = renal_var,
af = af_var,
cvd = cvd_var,
hba1c = hba1c_var,
cholhdl = cholhdl_var,
sbp = sbp_var,
bmi = bmi_var,
type1 = type1_var,
surv = surv_var)
Functions for calculating two Chronic Kidney Disease Prognosis Consortium (CKD-PC) risk scores are included in this package: 5-year risk of eGFR <60 mL/min/1.73m2 (https://ckdpcrisk.org/ckdrisk/; total and confirmed events) and 3-years risk of 40% decline in eGFR (https://ckdpcrisk.org/gfrdecline40/). The former includes versions for missing ACR, and where missing ACR is substituted with 10mg/g as per the model development paper (Nelson RG, Grams ME, Ballew SH. Development of Risk Prediction Equations for Incident Chronic Kidney Disease. JAMA. doi:10.1001/jama.2019.17379 (https://jamanetwork.com/journals/jama/fullarticle/2755299)). NB: both functions will calculate scores for individuals with values (e.g. age, BMI) outside of the range for which the model is valid without warning; these individuals need to be removed prior to using the functions. See help files (?calculate_ckdpc_egfr60_risk and ?calculate_ckdpc_40egfr_risk) for further explanation of variables.
Example:
results <- dataframe %>%
calculate_ckdpc_egfr60_risk(age = age_var,
sex = sex_var,
black_eth = black_eth_var,
egfr = egfr_var,
cvd = cvd_var,
hba1c = hba1c_var,
insulin = insulin_var,
oha = oha_var,
ever_smoker = ever_smoker_var,
hypertension = hypertension_var,
bmi = bmi_var,
acr = acr_var,
complete_acr = FALSE,
remote = TRUE)
Example:
results <- dataframe %>%
calculate_ckdpc_40egfr_risk(age = age_var
sex = sex_var,
egfr = egfr_var,
acr = acr_var,
sbp = sbp_var,
bp_meds = bp_meds_var,
hf = hf_var,
chd = chd_var,
af = af_var,
current_smoker = current_smoker_var,
ex_smoker = ex_smoker_var,
bmi = bmi_var,
hba1c = hba1c_var,
oha = oha_var,
insulin = insulin_var,
remote=TRUE)