JMIR Publications

ABSTRACT

Background:

While prior research has identified multiple risk factors for diabetic ketoacidosis (DKA), clinicians continue to lack clinic-ready models to predict dangerous and costly episodes of DKA. We asked whether we could apply deep learning, specifically use of a long short-term (LSTM) model, to accurately predict 180-day risk of DKA-related hospitalization for youth with type 1 diabetes (T1D).

Objective:

To describe the development of a LSTM model to predict 180-day risk of DKA-related hospitalization for youth with T1D.

Methods:

We used 17 consecutive calendar quarters of clinical data (01/10/2016-03/18/2020) for 1745 youth 8 to 18-years with T1D from a pediatric diabetes clinic network in the Midwestern US. Input data included demographics, discrete clinical observations (lab results, vital signs, anthropometric measures, diagnosis and procedure codes), medications, visit counts by type of encounter, number of historic DKA episodes, number of days since last DKA admission, patient-reported outcomes (answers to clinic intake questions), and data features derived from diabetes- and non-diabetes-related clinical notes via natural language processing (NLP). We trained the model using input data from quarters 1-7 (n=1377), validated using input from quarters 3-9 in a partial out-of-sample cohort (OOS-P; n=1505), and further validated in a full out-of-sample cohort (OOS-F; n=354) with input from quarters 10-15.

Results:

DKA admissions occurred at a rate of 5% per 180-days in both OOS cohorts. For the OOS-P and OOS-F cohorts, respectively: median age was 13.7 years (IQR=11.3,15.8) and 13.1 years (10.7,15.5); and HbA1c at enrollment was 8.6% (7.6,9.8) [70 (60,84) mmol/mol] and 8.1% (6.9,9.5) [65 (52,80) mmol/mol]; 14% and 13% had prior DKA admissions (post-T1D-diagnosis); and recall was 0.33 and 0.50 for the top-ranked 5% of youth with T1D. For lists rank-ordered by probability of hospitalization, precision increased from 0.33 to 0.56 to 1.0 for positions 1-80, 1-25, and 1-10 in the OOS-P cohort and from 0.50 to 0.60 to 0.80 for positions 1-18, 1-10, and 1-5 in the OOS-F cohort.

Conclusions:

The proposed LSTM model for predicting 180-day DKA-related hospitalization is valid in the present sample. Future work should evaluate model validity in multiple populations and settings to account for health inequities that may be present in different segments of the population (e.g., racially and/or socioeconomically diverse cohorts). Rank-ordering youth by probability of DKA-related hospitalization will allow clinics to identify the most at-risk youth. The clinical implication of this is that clinics may then create and evaluate novel preventive interventions based on available resources.