Risk Factors Associated With Pressure Injury in Critically Ill Children With Congenital Heart Disease.

BACKGROUND: Children with congenital heart disease have unique risk factors associated with the pathophysiology of an abnormal heart; hence, this population is most likely at increased risk of acquiring a pressure injury during hospitalization. Few studies have included patients with congenital heart disease or examined the factors unique to these patients. OBJECTIVE: To identify risk factors associated with pressure injury development in children with congenital heart disease. METHODS: This retrospective study used a convenience sample from hospital-acquired data at an urban, tertiary, free-standing children's hospital. Patients were admitted to the intensive care unit between 2011 and 2018 with a diagnosis of congenital heart disease. Chi-square analysis was done to compare risk factors between patients, and logistic regression analysis was used to predict the probability that a patient would acquire a pressure injury. RESULTS: Eighty-two (30.5%) of the 269 patients in this study acquired pressure injuries. Sixty-six patients with pressure injuries met the inclusion criteria for analysis; 82% of those patients had had corticosteroids prescribed, and 71% were receiving anticoagulants. The overall predictive model for acquiring a pressure injury indicated an odds ratio of 3.25 (95% CI, 1.58-6.65) with an anticoagulant and an odds ratio of 9.98 (95% CI, 4.68-21.3) with a prescribed corticosteroid (P < .001 for both factors). Inpatient mortality was significantly associated with pressure injuries. CONCLUSIONS: Corticosteroid and anticoagulant use were contributing factors in the development of pressure injuries in children with congenital heart disease.

Measuring Acuity and Pediatric Critical Care Nursing Workload by Using ICU CAMEO III.

BACKGROUND: The Intensive Care Unit Complexity Assessment and Monitoring to Ensure Optimal Outcomes (ICU CAMEO III) acuity tool measures patient acuity in terms of the complexity of nursing cognitive workload. OBJECTIVE: To validate the ICU CAMEO III acuity tool in US children's hospitals. METHODS: Using a convenience sample, 9 sites enrolled children admitted to pediatric intensive care units (ICUs). Descriptive statistics were used to summarize patient, nursing, and unit characteristics. Concurrent validity was evaluated by correlating the ICU CAMEO III with the Therapeutic Intervention Scoring System-Children (TISS-C) and the Pediatric Risk of Mortality III (PRISM III). RESULTS: Patients (N = 840) were enrolled from 15 units (7 cardiac and 8 mixed pediatric ICUs). The mean number of ICU beds was 23 (range, 12-34). Among the patients, 512 (61%) were diagnosed with cardiac and 328 (39%) with noncardiac conditions; 463 patients (55.1%) were admitted for medical reasons, and 377 patients (44.9%) were surgical. The ICU CAMEO III median score was 99 (range, 59-163). The ICU CAMEO complexity classification was determined for all 840 patients: 60 (7.1%) with level I complexity; 183 (21.8%) with level II; 201 (23.9%), level III; 267 (31.8%), level IV; and 129 (15.4%), level V. Strong correlation was found between ICU CAMEO III and both TISS-C (ρ = .822, P < .001) and PRISM III (ρ = .607, P < .001) scores, and between the CAMEO complexity classifications and the PRISM III categories (ρ = .575, P = .001). CONCLUSION: The ICU CAMEO III acuity tool and CAMEO complexity classifications are valid measures of patient acuity and nursing cognitive workload compared with PRISM III and TISS-C in academic children's hospitals.

Prevalence of ICU Delirium in Postoperative Pediatric Cardiac Surgery Patients.

OBJECTIVES: The objective of this study was to determine the prevalence of ICU delirium in children less than 18 years old that underwent cardiac surgery within the last 30 days. The secondary aim of the study was to identify risk factors associated with ICU delirium in postoperative pediatric cardiac surgical patients. DESIGN: A 1-day, multicenter point-prevalence study of delirium in pediatric postoperative cardiac surgery patients. SETTING: Twenty-seven pediatric cardiac and general critical care units caring for postoperative pediatric cardiac surgery patients in North America. PATIENTS: All children less than 18 years old hospitalized in the cardiac critical care units at 06:00 on a randomly selected, study day. INTERVENTIONS: Eligible children were screened for delirium using the Cornell Assessment of Pediatric Delirium by the study team in collaboration with the bedside nurse. MEASUREMENT AND MAIN RESULTS: Overall, 181 patients were enrolled and 40% (n = 73) screened positive for delirium. There were no statistically significant differences in patient demographic information, severity of defect or surgical procedure, past medical history, or postoperative day between patients screening positive or negative for delirium. Our bivariate analysis found those patients screening positive had a longer duration of mechanical ventilation (12.8 vs 5.1 d; p = 0.02); required more vasoactive support (55% vs 26%; p = 0.0009); and had a higher number of invasive catheters (4 vs 3 catheters; p = 0.001). Delirium-positive patients received more total opioid exposure (1.80 vs 0.36 mg/kg/d of morphine equivalents; p < 0.001), did not have an ambulation or physical therapy schedule (p = 0.02), had not been out of bed in the previous 24 hours (p < 0.0002), and parents were not at the bedside at time of data collection (p = 0.008). In the mixed-effects logistic regression analysis of modifiable risk factors, the following variables were associated with a positive delirium screen: 1) pain score, per point increase (odds ratio, 1.3; 1.06-1.60); 2) total opioid exposure, per mg/kg/d increase (odds ratio, 1.35; 1.06-1.73); 3) SBS less than 0 (odds ratio, 4.01; 1.21-13.27); 4) pain medication or sedative administered in the previous 4 hours (odds ratio, 3.49; 1.32-9.28); 5) no progressive physical therapy or ambulation schedule in their medical record (odds ratio, 4.40; 1.41-13.68); and 6) parents not at bedside at time of data collection (odds ratio, 2.31; 1.01-5.31). CONCLUSIONS: We found delirium to be a common problem after cardiac surgery with several important modifiable risk factors.

Early prediction of critical events for infants with single-ventricle physiology in critical care using routinely collected data.

OBJECTIVE: Critical events are common and difficult to predict among infants with congenital heart disease and are associated with mortality and long-term sequelae. We aimed to achieve early prediction of critical events, that is, cardiopulmonary resuscitation, emergency endotracheal intubation, and extracorporeal membrane oxygenation in infants with single-ventricle physiology before second-stage surgery. We hypothesized that naïve Bayesian models learned from expert knowledge and clinical data can predict critical events early and accurately. METHODS: We collected 93 patients with single-ventricle physiology admitted to intensive care units in a single tertiary pediatric hospital between 2014 and 2017. Using knowledge elicited from experienced cardiac-intensive-care-unit providers and machine-learning techniques, we developed and evaluated the Cardiac-intensive-care Warning INdex (C-WIN) system, consisting of a set of naïve Bayesian models that leverage routinely collected data. We evaluated predictive performance using the area under the receiver operating characteristic curve, sensitivity, and specificity. We performed the evaluation at 5 different prediction horizons: 1, 2, 4, 6, and 8 hours before the onset of critical events. RESULTS: The area under the receiver operating characteristic curves of the C-WIN models ranged between 0.73 and 0.88 at different prediction horizons. At 1 hour before critical events, C-WIN was able to detect events with an area under the receiver operating characteristic curve of 0.88 (95% confidence interval, 0.84-0.92) and a sensitivity of 84% at the 81% specificity level. CONCLUSIONS: Predictive models may enhance clinicians' ability to identify infants with single-ventricle physiology at high risk of critical events. Early prediction of critical events may indicate the need to perform timely interventions, potentially reducing morbidity, mortality, and health care costs.