Elsevier

Academic Pediatrics

Volume 22, Issue 1, January–February 2022, Pages 55-61
Academic Pediatrics

Original Article
Comparing Artificial Intelligence and Traditional Methods to Identify Factors Associated With Pediatric Asthma Readmission

https://doi.org/10.1016/j.acap.2021.07.015Get rights and content

Abstract

Objective

To identify and contrast risk factors for six-month pediatric asthma readmissions using traditional models (Cox proportional-hazards and logistic regression) and artificial neural-network modeling.

Methods

This retrospective cohort study of the 2013 Nationwide Readmissions Database included children 5 to 18 years old with a primary diagnosis of asthma. The primary outcome was time to asthma readmission in the Cox model, and readmission within 180 days in logistic regression. A basic neural network construction with 2 hidden layers and multiple replications considered all dataset variables and potential variable interactions to predict 180-day readmissions. Logistic regression and neural-network models were compared on area-under-the receiver-operating curve.

Results

Of 18,489 pediatric asthma hospitalizations, 1858 were readmitted within 180 days. In Cox and logistic models, longer index length of stay, public insurance, and nonwinter index admission seasons were associated with readmission risk, whereas micropolitan county was protective. In neural-network modeling, 9 factors were significantly associated with readmissions. Four overlapped with the Cox model (nonwinter-month admission, long length of stay, public insurance, and micropolitan hospitals), whereas 5 were unique (age, hospital bed number, teaching-hospital status, weekend index admission, and complex chronic conditions). The area under the curve was 0.592 for logistic regression and 0.637 for the neural network.

Conclusions

Different methods can produce different readmission models. Relying on traditional modeling alone overlooks key readmission risk factors and complex factor interactions identified by neural networks.

Section snippets

Study Design and Setting

This was a 1-year retrospective cohort analysis of the 2013 Agency for Healthcare Research and Quality Nationwide Readmission Database (NRD), an all-payer claims database with linkages across hospitalizations.9 The NRD consists of 49% of US inpatient and observation hospitalizations, and is drawn from 22 state inpatient databases. The unit of analysis for the NRD is discharge records. De-identified patient-level discharge records, with verified individual identifiers, track patients across

Results

The database yielded 18,489 pediatric asthma index admissions and 1044 asthma readmissions, for a readmission rate of 5.7% at 180 days (Table 1). A total of 521 discharges were excluded from analyses, due to missing data (n = 341), transfer to another acute hospital (n = 81), leaving against medical advice (n = 43), or death during hospitalization (n = 6). The median age at index admission was 8 years old (interquartile range, 6–12 years old); 41% of index admissions were female patients, two

Discussion

In a nationally representative dataset, an ANN predicted 180-day readmissions marginally better than traditional statistical models. Although model prediction was poor, both traditional and ANN models performed similarly to models in other studies using retrospective datasets for asthma readmission prediction.8

The Cox and logistic models showed that nonwinter season, long LOS, public insurance, and micropolitan hospitals were associated with 180-day asthma readmissions. ANN also retained these

Conclusions

Different methods can produce different readmission models. Relying on traditional modeling alone may overlook key readmission risk factors and complex factor interactions identified by neural networks. Four risk factors—nonwinter-month admission, long LOS, public insurance, and micropolitan hospitals—were shared by all 3 models, and could prove particularly powerful for predicting readmissions. Five risk factors were uniquely identified by ANN: age, hospital bed number, teaching-hospital

Acknowledgments

Financial statement: Dr Hogan was supported by the Connecticut Institute for Clinical and Translational Science at the University of Connecticut, and an Academic Pediatric Association's Young Investigator Award. The study sponsors had no role in study design, the collection, analysis, interpretation of data, the writing of the report, or the decision to submit the paper for publication. The content is solely the responsibility of the authors, and does not necessarily represent the official

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    The authors have no conflicts of interest to disclose.

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