Work

Predicting Parental Post-Traumatic Stress Symptoms Following their Child's Stay in a Pediatric Intensive Care Unit, Prior to Discharge

Year: 2024

Type: article

Abstract: Objective: Develop an inpatient predictive model of parental post-traumatic stress (PTS) following their child's care in the Pediatric Intensive Care Unit (PICU). Design: Prospective observational cohort. Setting: Two tertiary care children's hospitals with mixed medical/surgical/cardiac PICUs. Subjects: Parents of patients admitted to the PICU. Interventions: None. Measurements and Main Results: Preadmission and admission data from 169 parents of 129 children who completed follow up screening for parental post-traumatic stress symptoms at 3-9 months post PICU discharge were utilized to develop a predictive model estimating the risk of parental PTS 3-9 months after hospital discharge. The parent cohort was predominantly female (63%), partnered (75%), and working (70%). Child median age was 3 years (IQR 0.36-9.04), and more than half had chronic illnesses (56%) or previous ICU admissions (64%). Thirty-five percent (60/169) of parents met criteria for PTS (>9 on the Post-traumatic Stress Disorder Symptom Scale-Interview). The machine learning model (XGBoost) predicted subjects with parental PTS with 76.7% accuracy, had a sensitivity of 0.83 (95% CI 0.586, 0.964), a specificity of 0.72 (95% CI 0.506, 0.879), a precision of 0.682 (95% CI 0.451, 0.861) and number needed to evaluate of 1.47 (95% CI 1.16, 1.98). The area under the receiver operating curve was 0.78 (95% CI 0.64, 0.92). The most important predictive pre-admission and admission variables were determined using the Local Interpretable Model-Agnostic Explanation, which identified seven variables used 100% of the time. Composite variables of parental history of mental illness and traumatic experiences were most important. Conclusion: A machine learning model using parent risk factors predicted subsequent PTS at 3-9 months following their child's PICU discharge with an accuracy of 76.7% and number needed to evaluate of 1.47. This performance is sufficient to identify parents who are at risk during hospitalization, making inpatient and acute post admission mitigation initiatives possible.

Source: Journal of Intensive Care Medicine

DOI: 10.1177/08850666241287442

Authors: Mekela M. Whyte-Nesfield, Eduardo A. Trujillo Rivera, Daniel Kaplan, Simon Li, Pamela S. Hinds, Murray M. Pollack

Institutions: Children's National, George Washington University, Rutgers, The State University of New Jersey

Language: English

Topic: Family and Patient Care in Intensive Care Units

Subfield: Radiological and Ultrasound Technology

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: University of Pennsylvania, Pennsylvania State University, Children's National Hospital