Dataset Information

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

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.

SUBMITTER: Russell HV

PROVIDER: S-EPMC4197316 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Publications

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

Russell Heidi V HV Okcu M Fatih MF Kamdar Kala K Shah Mona D MD Kim Eugene E Swint J Michael JM Chan Wenyaw W Du Xianglin L XL Franzini Luisa L Ho Vivian V

BMC medical informatics and decision making 20141001

<h4>Background</h4>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 becau ...[more]

PMID: 25274165

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Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

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