Cargando…

Algorithm for analysis of administrative pediatric cancer hospitalization data according to indication for admission

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 can...

Descripción completa

Detalles Bibliográficos
Autores principales: Russell, Heidi V, Okcu, M Fatih, Kamdar, Kala, Shah, Mona D, Kim, Eugene, Swint, J Michael, Chan, Wenyaw, Du, Xianglin L, Franzini, Luisa, Ho, Vivian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4197316/
https://www.ncbi.nlm.nih.gov/pubmed/25274165
http://dx.doi.org/10.1186/1472-6947-14-88
_version_ 1782339602786287616
author Russell, Heidi V
Okcu, M Fatih
Kamdar, Kala
Shah, Mona D
Kim, Eugene
Swint, J Michael
Chan, Wenyaw
Du, Xianglin L
Franzini, Luisa
Ho, Vivian
author_facet Russell, Heidi V
Okcu, M Fatih
Kamdar, Kala
Shah, Mona D
Kim, Eugene
Swint, J Michael
Chan, Wenyaw
Du, Xianglin L
Franzini, Luisa
Ho, Vivian
author_sort Russell, Heidi V
collection PubMed
description 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1472-6947-14-88) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-4197316
institution National Center for Biotechnology Information
language English
publishDate 2014
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-41973162014-10-16 Algorithm for analysis of administrative pediatric cancer hospitalization data according to indication for admission Russell, Heidi V Okcu, M Fatih Kamdar, Kala Shah, Mona D Kim, Eugene Swint, J Michael Chan, Wenyaw Du, Xianglin L Franzini, Luisa Ho, Vivian BMC Med Inform Decis Mak Research Article 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1472-6947-14-88) contains supplementary material, which is available to authorized users. BioMed Central 2014-10-01 /pmc/articles/PMC4197316/ /pubmed/25274165 http://dx.doi.org/10.1186/1472-6947-14-88 Text en © Russell et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Russell, Heidi V
Okcu, M Fatih
Kamdar, Kala
Shah, Mona D
Kim, Eugene
Swint, J Michael
Chan, Wenyaw
Du, Xianglin L
Franzini, Luisa
Ho, Vivian
Algorithm for analysis of administrative pediatric cancer hospitalization data according to indication for admission
title Algorithm for analysis of administrative pediatric cancer hospitalization data according to indication for admission
title_full Algorithm for analysis of administrative pediatric cancer hospitalization data according to indication for admission
title_fullStr Algorithm for analysis of administrative pediatric cancer hospitalization data according to indication for admission
title_full_unstemmed Algorithm for analysis of administrative pediatric cancer hospitalization data according to indication for admission
title_short Algorithm for analysis of administrative pediatric cancer hospitalization data according to indication for admission
title_sort algorithm for analysis of administrative pediatric cancer hospitalization data according to indication for admission
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4197316/
https://www.ncbi.nlm.nih.gov/pubmed/25274165
http://dx.doi.org/10.1186/1472-6947-14-88
work_keys_str_mv AT russellheidiv algorithmforanalysisofadministrativepediatriccancerhospitalizationdataaccordingtoindicationforadmission
AT okcumfatih algorithmforanalysisofadministrativepediatriccancerhospitalizationdataaccordingtoindicationforadmission
AT kamdarkala algorithmforanalysisofadministrativepediatriccancerhospitalizationdataaccordingtoindicationforadmission
AT shahmonad algorithmforanalysisofadministrativepediatriccancerhospitalizationdataaccordingtoindicationforadmission
AT kimeugene algorithmforanalysisofadministrativepediatriccancerhospitalizationdataaccordingtoindicationforadmission
AT swintjmichael algorithmforanalysisofadministrativepediatriccancerhospitalizationdataaccordingtoindicationforadmission
AT chanwenyaw algorithmforanalysisofadministrativepediatriccancerhospitalizationdataaccordingtoindicationforadmission
AT duxianglinl algorithmforanalysisofadministrativepediatriccancerhospitalizationdataaccordingtoindicationforadmission
AT franziniluisa algorithmforanalysisofadministrativepediatriccancerhospitalizationdataaccordingtoindicationforadmission
AT hovivian algorithmforanalysisofadministrativepediatriccancerhospitalizationdataaccordingtoindicationforadmission