Criticality: A New Concept of Severity of Illness for Hospitalized Children

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.

Figure 1.

Criticality Index for Survivors, Deaths, and All Patients for Each of the Patient Categories in the Test Sample. Note that the horizontal axis uses the logit function. The diamond and bars indicate the mean and 95 percent confidence interval. The boxes indicate the median, 25th-75th quantiles. Routine indicates patients in the routine care units, some of whom will be admitted to the ICU (ICU patient) and some were not (Non ICU Patient). ICU indicates patients in the ICU and ICU High Intensity indicates patients receiving positive pressure ventilation. Transition (Trans.) indicates the 6-hour time period when patients transitioned from Routine to ICU and were in both cares during the time period.

Figure 2.

Model calibration. The y axis shows the expected proportion of ICU admission based on the Criticality Index and the x-axis shows the observed proportion. The line of identity is the dashed line. The circles indicate the observed divided by the expected proportions of ICU 6-hour time periods over 2000 ascending Criticality Index intervals. More than 99% of the intervals had at least 106 time periods. All Criticality Index interval lengths had a Critical Index range of less than .0025.