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dc.contributor.authorRussell, Heidi V.
Okcu, M. Fatih
Kamdar, Kala
Shah, Mona D.
Kim, Eugene
Swint, J. Michael
Chan, Wenyaw
Du, Xianglin L.
Franzini, Luisa
Ho, Vivian
dc.date.accessioned 2016-07-13T17:49:21Z
dc.date.available 2016-07-13T17:49:21Z
dc.date.issued 2014
dc.identifier.citation Russell, Heidi V., Okcu, M. Fatih, Kamdar, Kala, et al.. "Algorithm for analysis of administrative pediatric cancer hospitalization data according to indication for admission." BMC Medical Informatics and Decision Making, 14, (2014) BioMed Central Ltd: http://dx.doi.org/10.1186/1472-6947-14-88.
dc.identifier.urihttps://hdl.handle.net/1911/90873
dc.description.abstract Background: Childhood cancer relies heavily on inpatient hospital services to deliver tumor-directed therapy and manage toxicities. Hospitalizations have increased over the past decade, though not uniformly across childhood cancer diagnoses. Analysis of the reasons for admission of children with cancer could enhance comparison of resource use between cancers, and allow clinical practice data to be interpreted more readily. Such comparisons using nationwide data sources are difficult because of numerous subdivisions in the International Classification of Diseases Clinical Modification (ICD-9) system and inherent complexities of treatments. This study aimed to develop a systematic approach to classifying cancer-related admissions in administrative data into categories that reflected clinical practice and predicted resource use. Methods: We developed a multistep algorithm to stratify indications for childhood cancer admissions in the Kids Inpatient Databases from 2003, 2006 and 2009 into clinically meaningful categories. This algorithm assumed that primary discharge diagnoses of cancer or cytopenia were insufficient, and relied on procedure codes and secondary diagnoses in these scenarios. Clinical Classification Software developed by the Healthcare Cost and Utilization Project was first used to sort thousands of ICD-9 codes into 5 mutually exclusive diagnosis categories and 3 mutually exclusive procedure categories, and validation was performed by comparison with the ICD-9 codes in the final admission indication. Mean cost, length of stay, and costs per day were compared between categories of indication for admission. Results: A cohort of 202,995 cancer-related admissions was grouped into four categories of indication for admission: chemotherapy (N=77,791, 38%), to undergo a procedure (N=30,858, 15%), treatment for infection (N=30,380, 15%), or treatment for other toxicities (N=43,408, 21.4%). The positive predictive value for the algorithm was >95% for each category. Admissions for procedures had higher mean hospital costs, longer hospital stays, and higher costs per day compared with other admission reasons (p<0.001). Conclusions: This is the first description of a method for grouping indications for childhood cancer admission within an administrative dataset into clinically relevant categories. This algorithm provides a framework for more detailed analyses of pediatric hospitalization data by cancer type.
dc.language.iso eng
dc.publisher BioMed Central Ltd
dc.relation.urihttp://bakerinstitute.org/research/algorithm-analysis-administrative-pediatric-cancer-hospitalization-data-according-indication-admissi/
dc.subjectcancer
health administrative data
healthcare utilization
child
dc.title Algorithm for analysis of administrative pediatric cancer hospitalization data according to indication for admission
dc.type Journal article
dc.citation.journalTitle BMC Medical Informatics and Decision Making
dc.citation.volumeNumber 14
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1186/1472-6947-14-88
dc.type.publication publisher version
dc.citation.articleNumber 88


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