Intensive care units (ICUs) provide care for critically-ill patients who require constant monitoring and the availability of specialized equipment and personnel. In this environment, a high volume of information and a high degree of uncertainty present a burden to clinicians. In specialized cohorts, such as pediatric patients with congenital heart defects (CHDs), this burden is exacerbated by increased complexity, the inadequacy of existing decision support aids, and the limited and decreasing availability of highly-specialized clinicians. Among CHD patients, infants with single ventricle (SV) physiology are one of the most complex and severely-ill sub-populations. While SV mortality rates have dropped, patient deterioration may happen unexpectedly in the period before patients undergo stage-2 palliative surgery. Even in expert hands, critical and potentially catastrophic events (CEs), such as cardiopulmonary resuscitation (CPR), emergent endotracheal intubation (EEI), or extracorporeal membrane oxygenation (ECMO) are common in SV patients, and may negatively impact morbidity, mortality, and hospital length of stay. There is a clinical need of predictive tools that help intensivists assess and forecast the advent of CEs in SV infants. Although ubiquitous, widely adopted ICU severity-of-illness scores or early warning systems (EWS), e.g., PRISM and PIM, have not met this need. They are often v developed for general ICU use and do not generalize well to specialized populations. Furthermore, most EWS are developed for prediction of patient mortality. Among SV patients, however, death is semi-elective. On the other hand, prediction of CEs may help clinicians improve patient care by anticipating the advent of patient deterioration. In this dissertation, we aimed to develop and validate predictive models that achieve early and accurate prediction of CEs in infants with SV physiology. Such models may provide early and actionable information to clinicians and may be used to perform clinical interventions aimed at preventing CEs, and to reducing morbidity, mortality, and healthcare costs. We assert that our work is significant in that it addresses an unmet clinical need by achieving state-of-the-art, early prediction of patient deterioration in a challenging and vulnerable population. vi TABLE OF CONTENTS

These predictors can form the basis for targeted public health initiatives with a potential reduction in the number of burn injuries.

Triggers embedded into the electronic medical record can identify young children with who need to be evaluated for physical abuse with high sensitivity and specificity.

OBJECTIVE:Headaches represent 0.9% to 2.6% of visits to a pediatric emergency department (PED). We noted a trend of increasing visits for headache in our tertiary care PED and sought to further characterize this trend. METHODS:We identified PED visits with International Classification of Disease, Ninth Revision, Clinical Modification diagnoses for headache at 25 hospitals in Pediatric Health Information System between 2003 and 2013. To further characterize demographics and treatment trends over time we used the electronic health record in our emergency department to identify children ages four to 18 between January 2007 and December 2014 with International Classification of Disease, Ninth Revision codes for headache: a random sample of 50 visits per year were chosen for chart review. RESULTS:Pediatric Health Information System visits for headache increased by 166% (18,041 in 2003 and 48,020 in 2013); by comparison, total PED visits increased by 57.6%. The percent admission increased by 300% (2020 admissions in 2003 and 8087 admissions in 2013). At our hospital, headache visits increased 111% from 896 visits in 2007 to 1887 visits in 2014; total PED visits increased 30.2%. The admission percentage for headache increased 187% with 156 admissions in 2007 and 448 in 2014. Management over time differed in the frequency of head computed tomography which decreased 3.7% per year (r = −0.93, 95% CI −0.99, −0.64) from 34% in 2007 to 18% in 2014. CONCLUSION:Pediatric emergency department visits for headache are increasing and a growing proportion of these patients are admitted. This finding identifies a potential patient population to target for interventions to improve outpatient management and reduce pediatric emergency department utilization.

ABSTRACT

A child abuse clinical decision support system comprised of a trigger system, alerts and a physical abuse order set was quickly accepted into clinical practice. Use of the physical abuse order set always resulted in full compliance with clinical guidelines. Given the high baseline compliance at our site, evaluation of this alert system in hospitals with lower baseline compliance rates will be more valuable in assessing the efficacy in adherence to clinical guidelines for the evaluation of suspected child abuse.

Objectives: Develop and test the performance of electronic version of the Children’s Hospital of Pittsburgh Pediatric Risk of Mortality-IV and electronic version of the Children’s Hospital of Pittsburgh Pediatric Logistic Organ Dysfunction-2 scores. Design: Retrospective, single-center cohort derived from structured electronic health record data. Setting: Large, quaternary PICU at a freestanding, university-affiliated children’s hospital. Patients: All encounters with a PICU admission between January 1, 2009, and December 31, 2017, identified using electronic definitions of inpatient encounter. Interventions: None. Measurements and Main Results: The main outcome was predictive validity of each score for hospital mortality, assessed as model discrimination and calibration. Discrimination was examined with the area under the receiver operating characteristics curve and the area under the precision-recall curve. Calibration was assessed with the Hosmer-Lemeshow goodness of fit test and calculation of a standardized mortality ratio. Models were recalibrated with new regression coefficients in a training subset of 75% of encounters selected randomly from all years of the cohort and the calibrated models were tested in the remaining 25% of the cohort. Content validity was assessed by examining correlation between electronic versions of the scores and prospectively calculated data (electronic version of the Children’s Hospital of Pittsburgh Pediatric Risk of Mortality-IV) and an alternative informatics approach (Children’s Hospital of Pittsburgh Pediatric Logistic Organ Dysfunction-2 score). The cohort included 21,335 encounters. Correlation coefficients indicated strong agreement between different methods of score calculation. Uncalibrated area under the receiver operating characteristics curves were 0.96 (95% CI, 0.95–0.97) for electronic version of the Children’s Hospital of Pittsburgh Pediatric Logistic Organ Dysfunction-2 score and 0.87 (95% CI, 0.85–0.89) for electronic version of the Children’s Hospital of Pittsburgh Pediatric Risk of Mortality-IV for inpatient mortality. The uncalibrated electronic version of the Children’s Hospital of Pittsburgh Pediatric Risk of Mortality-IV standardized mortality ratio was 0.63 (0.59–0.66), demonstrating strong agreement with previous, prospective evaluation at the study center. The uncalibrated electronic version of the Children’s Hospital of Pittsburgh Pediatric Logistic Organ Dysfunction-2 score standardized mortality ratio was 0.20 (0.18–0.21). All models required recalibrating (all Hosmer–Lemeshow goodness-of-fit, p < 0.001) and subsequently demonstrated acceptable goodness-of-fit when examined in a test subset (n = 5,334) of the cohort. Conclusions: Electronically derived intensive care acuity scores demonstrate very good to excellent discrimination and can be calibrated to institutional outcomes. This approach can facilitate both performance improvement and research initiatives and may offer a scalable strategy for comparison of interinstitutional PICU outcomes.

Despite small numbers of participants, this evaluation suggests that knowledge in international health can be expanded through a training program.