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.

Figure 1

Multi-step algorithm for stratifying childhood-cancer admissions. (A) Identification of a cohort of cancer-related admissions from the full KID dataset. (B) Stratification of admissions into indications for admissions. KID indicates Kids Inpatient Database; CCS indicates Clinical Classification Software. &: Includes admissions with chemotherapy as primary procedure from states with no reported procedure dates. ^: Excludes CCS diagnostic group 237 non-infectious ICD-9 codes and non-cytopenia toxicities.

Figure 2

Comparison of resource utilization measures between admission indication categories for all KID cancer-related admissions from 2003, 2006, and 2009. Mean length of stay (A), total costs per hospitalization (B) and cost per day (C) were significantly different (p < 0.001). Similarly, the proportion of admissions including hematopoietic stem cell transplantation (D), the mean day of the transplant procedure (D, squares), admissions requiring intensive care (E), and death (F) were significantly different (p < 0.001). I:C indicates Intent-Chemotherapy; I:P, Intent-Procedure; I:I, Intent-Infection; I:T, Intent-Toxicity; U, Undefined and other admissions.