Abstract
Objectives: To validate the conceptual framework of "criticality," a new pediatric inpatient severity measure based on physiology, therapy, and therapeutic intensity calibrated to care intensity, operationalized as ICU care. Design: Deep neural network analysis of a pediatric cohort from the Health Facts (Cerner Corporation, Kansas City, MO) national database. Setting: Hospitals with pediatric routine inpatient and ICU care. Patients: Children cared for in the ICU (n = 20,014) and in routine care units without an ICU admission (n = 20,130) from 2009 to 2016. All patients had laboratory, vital sign, and medication data. Interventions: None. Measurements and Main Results: A calibrated, deep neural network used physiology (laboratory tests and vital signs), therapy (medications), and therapeutic intensity (number of physiology tests and medications) to model care intensity, operationalized as ICU (versus routine) care every 6 hours of a patient's hospital course. The probability of ICU care is termed the Criticality Index. First, the model demonstrated excellent separation of criticality distributions from a severity hierarchy of five patient groups: routine care, routine care for those who also received ICU care, transition from routine to ICU care, ICU care, and high-intensity ICU care. Second, model performance assessed with statistical metrics was excellent with an area under the curve for the receiver operating characteristic of 0.95 for 327,189 6-hour time periods, excellent calibration, sensitivity of 0.817, specificity of 0.892, accuracy of 0.866, and precision of 0.799. Third, the performance in individual patients with greater than one care designation indicated as 88.03% (95% CI, 87.72-88.34) of the Criticality Indices in the more intensive locations was higher than the less intense locations. Conclusions: The Criticality Index is a quantification of severity of illness for hospitalized children using physiology, therapy, and care intensity. This new conceptual model is applicable to clinical investigations and predicting future care needs.
| Original language | English (US) |
|---|---|
| Pages (from-to) | E33-E43 |
| Journal | Pediatric Critical Care Medicine |
| DOIs | |
| State | Accepted/In press - 2021 |
| Externally published | Yes |
Bibliographical note
Funding Information:Supported, in part, by the philanthropic support from Mallinckrodt LLC, and, in part, by Award Numbers UL1TR001876 from the National Institutes of Health (NIH) National Center for Advancing Translational Sciences (NCATS) and KL2TR001877 from the NIH NCATS (to Dr. Patel).
Funding Information:
Drs. Rivera’s, Patel’s, Chamberlain’s, Morizono’s, Kim’s, Bost’s, and Pollack’s institutions received funding from Mallinckrodt LLC. Drs. Patel, Workman, Morizono, and Pollack received support for article research from the National Institutes of Health (NIH). Dr. Patel’s institution received funding from Awards Ul1TR001876 and KL2TR001877 from the NIH National Center for Advancing Translational Sciences (NCATS). Drs. Workman’s and Pollack’s institutions received funding from the NIH. Dr. Workman received funding from Institute of Electrical and Electronics Engineers. Dr. Morizono’s institution received funding from the NIH NCATS. Dr. Morizono received funding from Cogthera LLC and received support for article research from Mallinckrodt. The remaining authors have disclosed that they do not have any potential conflicts of interest.
Publisher Copyright:
© 2021 Lippincott Williams and Wilkins. All rights reserved.
Keywords
- dynamic modeling
- intensive care
- machine learning
- pediatric intensive care unit
- pediatrics
- severity of illness