Development and evaluation of a computable phenotype to identify pediatric patients with leukemia and lymphoma treated with chemotherapy using electronic health record data

Abstract

Background Widespread implementation of electronic health records (EHR) has created new opportunities for pediatric oncology observational research. Little attention has been given to using EHR data to identify patients with pediatric hematologic malignancies. Methods This study used EHR‐derived data in a pediatric clinical data research network, PEDSnet, to develop and evaluate a computable phenotype algorithm to identify pediatric patients with leukemia and lymphoma who received treatment with chemotherapy. To guide early development, multiple computable phenotype‐defined cohorts were compared to one institution's tumor registry. The most promising algorithm was chosen for formal evaluation and consisted of at least two leukemia/lymphoma diagnoses (Systematized Nomenclature of Medicine codes) within a 90‐day period, two chemotherapy exposures, and three hematology‐oncology provider encounters. During evaluation, the computable phenotype was executed against EHR data from 2011 to 2016 at three large institutions. Classification accuracy was assessed by masked medical record review with phenotype‐identified patients compared to a control group with at least three hematology‐oncology encounters. Results The computable phenotype had sensitivity of 100% (confidence interval [CI] 99%, 100%), specificity of 99% (CI 99%, 100%), positive predictive value (PPV) and negative predictive value (NPV) of 100%, and C‐statistic of 1 at the development institution. The computable phenotype performance was similar at the two test institutions with sensitivity of 100% (CI 99%, 100%), specificity of 99% (CI 99%, 100%), PPV of 96%, NPV of 100%, and C‐statistic of 0.99. Conclusion The EHR‐based computable phenotype is an accurate cohort identification tool for pediatric patients with leukemia and lymphoma who have been treated with chemotherapy and is ready for use in clinical studies.

Implementation science in pediatric oncology: A narrative review and future directions

“…PHIS has been utilized to conduct pediatric oncology research outside the clinical trials infrastructure to address topics such as racial inequities 29 and off‐study immunotherapy use 30 . Patients with leukemia and lymphoma have accurately been identified within PEDSnet 31 . However, the hospitals participating in both PHIS and PEDSnet tend to be large academic centers and do not include many community hospitals.…”

Clinical data for paediatric research: the Swiss approach

“…PEDSnet estimates were more stable because the dataset was much larger [ 28 ]. Computable phenotyping : PEDSnet data is used to develop and evaluate computable phenotypes for the identification of patients with rare diseases, such as cancer and glomerular disorders [ 29 , 30 ]. Longitudinal observational studies: PEDSnet data provided quantitative evidence of the effect of obesity on incident asthma (30% increase in risk [ 31 ]), of an association between antibiotics use before 2 years of age and body mass index at age 5 [ 28 , 32 , 33 ] and of an association between oral thrush by age 1 year and development of childhood caries at the age of 2 to 5 years [ 34 ].…”

“…Computable phenotyping : PEDSnet data is used to develop and evaluate computable phenotypes for the identification of patients with rare diseases, such as cancer and glomerular disorders [ 29 , 30 ].…”

Positive predictive value and sensitivity of ICD‐9‐CM codes for identifying pediatric leukemia

“…Also, nonspecific ICD codes for blood-related disorders are sometimes used for coding leukemia (e.g., neoplasm of uncertain behavior of other lymphatic and hematopoietic tissues) but may identify many nonleukemia diagnoses. The few publications examining sensitivity and positive predictive value (PPV) of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes to identify pediatric leukemia cases in electronic databases reported good results but were restricted to tertiary medical center settings [2][3][4] where patient referral and visit patterns may be different from community health care centers and where medical records are often restricted to the hospital setting. Epidemiologic studies of some risk factors for childhood leukemia are best done at the primary care level because that is where clinical data from the child's regular medical care can be found in the EMR.…”