Validation of the new diagnosis grouping system for pediatric emergency department visits using the International Classification of Diseases, 10th Revision

Pilot study using machine learning to improve estimation of physical abuse prevalence

BACKGROUND

International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes have been shown to underestimate physical abuse prevalence. Machine learning models are capable of efficiently processing a wide variety of data and may provide better estimates of abuse.

OBJECTIVE

To achieve proof of concept applying machine learning to identify codes associated with abuse.

PARTICIPANTS AND SETTING

Children <5 years, presenting to the emergency department with an injury or abuse-specific ICD-10-CM code and evaluated by the child protection team (CPT) from 2016 to 2020 at a large Midwestern children's hospital.

METHODS

The Pediatric Health Information System (PHIS) and the CPT administrative databases were used to identify the study sample and injury and abuse-specific ICD-10-CM codes. Subjects were divided into abused and non-abused groups based on the CPT's evaluation. A LASSO logistic regression model was constructed using ICD-10-CM codes and patient age to identify children likely to be diagnosed by the CPT as abused. Performance was evaluated using repeated cross-validation (CV) and Reciever Operator Characteristic curve.

RESULTS

We identified 2028 patients evaluated by the CPT with 512 diagnosed as abused. Using diagnosis codes and patient age, our model was able to accurately identify patients with confirmed PA (mean CV AUC = 0.87). Performance was still weaker for patients without existing ICD codes for abuse (mean CV AUC = 0.81).

CONCLUSIONS

We built a model that employs injury ICD-10-CM codes and age to improve accuracy of distinguishing abusive from non-abusive injuries. This pilot modelling endeavor is a steppingstone towards improving population-level estimates of abuse.

Diagnosis codes dramatically underestimate the burden of abuse

BACKGROUND

International Classification of Diseases (ICD) billing codes are not well-suited to estimate physical abuse prevalence among hospitalized patients and may be even less accurate in emergency departments (EDs). The Centers for Disease Control and Prevention (CDC) has recently published a child abuse and neglect syndromic surveillance definition to more accurately examine national abuse trends among ED visits.

OBJECTIVE

To retrospectively apply the CDC syndromic definition to a population of physically abused children and determine its sensitivity for abuse in an ED and at hospital discharge.

PARTICIPANTS AND SETTING

All physically abused children <5 years seen in the ED and evaluated by the child protection team from 2016 to 2020 at a large Midwestern children's hospital.

METHODS

Retrospective cross-sectional study utilizing the hospital's child protection team administrative database, the Pediatric Health Information System and the electronic health record to identify the study sample, chief complaint, and abuse-specific codes assigned in the ED and at hospital discharge. Abuse-specific codes were defined as all ICD-10-CM and Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) codes included in the CDC syndromic definition, which was applied to the sample and its sensitivity determined.

RESULTS

Among the 550 abused patients identified, most were male (58.4 %), white (65.1 %), <2 years old (80.4 %), and had public insurance (81.6 %). When applying the CDC syndromic definition, only 11.6 % were identified as abused in the ED and 65.3 % were identified at hospital discharge.

CONCLUSIONS

The CDC syndrome surveillance definition lacks sensitivity in identifying abuse in the ED or at hospital discharge.

[An update of the diagnostic coding system by the Spanish Society of Pediatric Emergencies]

The Quality Working Group of the Spanish Society of Pediatric Emergencies (SEUP) presents an update of the diagnostic coding list. The original list was prepared and published in Anales de Pediatría in 2000, being based on the International Coding system ICD-9-CM current at that time. Following the same methodology used at that time and based on the 2014 edition of the ICD-9-CM, 35 new codes have been added to the list, 15 have been updated, and a list of the most frequent references to trauma diagnoses in pediatrics have been provided. In the current list of diagnoses, SEUP reflects the significant changes that have taken place in Pediatric Emergency Services in the last decade.