Accepted for/Published in: JMIR Diabetes
Date Submitted: Mar 25, 2023
Open Peer Review Period: Mar 25, 2023 - May 20, 2023
Date Accepted: May 13, 2023
Date Submitted to PubMed: May 24, 2023
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Development of an "all-data-on-hand" deep learning model to predict hospitalization for diabetic ketoacidosis (DKA) in youth with type 1 diabetes (T1D).
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. 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 validity in multiple populations and settings. Rank-ordering youth by probability of DKA-related hospitalization will allow clinics to identify the most at-risk youth and to evaluate novel preventive interventions.
Citation
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Copyright
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