Abstract The aim of this study was to describe the performance of a novel Situational Awareness Scoring System (SASS) in discriminating between patients who had cardiac arrest (CA), and those who did not, in a pediatric cardiac intensive care unit (PCICU). This is a retrospective, observational-cohort study in a quaternary-care PCICU. Patients who had CA in the PCICU between January 2014 and December 2018, and patients admitted to the PCICU in 2018 who did not have CA were included. Patients with do not resuscitate or do not intubate orders, extracorporeal membrane oxygenation, ventricular assist device, and PCICU stay < 2 hours were excluded. SASS score statistics were calculated within 2-, 4-, 6-, and 8-hour time intervals counting backward from the time of CA, or end of PCICU stay in patients who did not have CA. Cross-validated discrete time logistic regression models were used to calculate area under the receiver operating characteristic (AUC) curves. Odds ratios (ORs) for CA were calculated per unit increase of the SASS score. Twenty-eight CA events were analyzed in 462 PCICU admissions from 267 patients. Maximum SASS score within 4-hour time interval before CA achieved the highest AUC of 0.91 (95% confidence interval [CI]: 0.86–0.96) compared with maximum SASS score within 2-, 6-, and 8-hour time intervals before CA of 0.88 (0.79–96), 0.90 (0.85–0.95), and 0.89 (0.83–0.95), respectively. A cutoff value of 60 for maximum SASS score within 4-hour time interval before CA resulted in 82.1 and 83.2% of sensitivity and specificity, respectively. OR for CA was 1.32 (95% CI: 1.26–1.39) for every 10 units increase in the maximum SASS score within each 4-hour time interval before CA. The maximum SASS score within various time intervals before CA achieved promising performance in discriminating patients regarding occurrence of CA.
Aims & Objectives: Length of stay (LOS) is an important driver of healthcare costs in the Pediatric Intensive Care Unit (PICU). Historically, long-stay patients (LSPs) have been defined as patients whose length of stay in PICU is longer than the 95th percentile. Though small in number, these patients contribute significantly to cost. The aim of this study is to assess the impact of readmissions on the 95th centile of length of stay in the PICU. Methods: Retrospective cohort study of all patients admitted to PICUs in the Virtual Pediatric Intensive Care Unit Systems (VPS) database from January 1st, 2009 to January 1st, 2012. Intercept only quantile regressions were used to estimate 95% level quantiles and their 95% confidence intervals. The quantile estimation was performed separately for each PICU admission which preserved independent individual contributions to quantile estimation. Results: 224, 960 unique patient encounters were identified. The 95th percentile of LOS for all encounters was 16 days. However, as the number of previous PICU admissions increases, the 95th percentile of subsequent PICU admissions increases up to 26 days. In comparison, the median length of stay increased by approximately one day. Table -1: Length of stay with subsequent pediatric intensive care admissionsConclusions: Readmissions have a disproportionate impact on 95th percentile of LOS and significantly adds to hospital costs.
All the authors whose names are listed above certify that they have no conflict of interest. They have no affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony, or patent-licensing arrangements), or nonfinancial interest (such as personal or professional relationships, affiliations, knowledge, or beliefs) in the subject matter or materials discussed in this manuscript.
Abstract Purpose of review Cardiac output (CO) is a fundamental physiological parameter that measures the volume of blood that is pumped by the heart per unit of time, and helps define how oxygen is delivered to the tissues of the human body. In this paper, we discuss current methods of continuous CO monitoring while defining low CO syndrome (LCOS) and how analytical tools may help improve CO management in the subpopulation of patients with congenital heart disease (CHD). Recent findings Non-invasive methods of measuring CO have become increasingly available in recent years. Advantages of non-invasive over invasive techniques include decreased risk of procedural complications, decreased exposure to sedative and/or anesthetic agents, and increased patient comfort. Pediatric patient populations are particularly sensitive to the risks and complications of invasive techniques given the relative size of current technologies to pediatric vascular and cardiac dimensions. Summary Novel device technologies, combined with emerging analytical techniques, may help improve measurement of CO in children and those with CHD, and allow earlier detection of LCOS.
Abstract Objectives Our objective was to build a proof of concept of the clinical mathematical model estimating postoperative urine output (UOP) utilizing preoperative, intraoperative, and immediate postoperative variables in children who underwent cardiopulmonary bypass (CPB) for congenital heart surgery. Methods This was a single-center, retrospective cohort study in a university-affiliated children's hospital. Patients younger than 21 years old who underwent CPB for congenital heart surgery and were postoperatively admitted to West Virginia University Children's Hospital's pediatric intensive care unit (PICU) between September 1, 2007 and June 31, 2013 were included in the study. Body surface area, CPB duration, first measured hematocrit, serum pH, central venous pressure, and vasoactive-inotropic score in the PICU were used to build the mathematical model. A randomly selected 50% of the dataset was used to calculate model parameters. A cross-validation was used to assess model performance. Results A total of 256 patients met the inclusion criteria. The model was able to achieve mean absolute error of 1.065 mL/kg/h (95% confidence interval (CI): 1.062–1.067 mL/kg/h), root mean squared error of 1.80 mL/kg/h (95% CI: 1.799–1.804 mL/kg/h), and R2 of 0.648 (95% CI: 0.646–0.650) in estimating UOP in the first 32 hours of postoperative period. Conclusions The mathematical model utilizing preoperative, intraoperative, and immediate postoperative variables may be a potentially useful clinical tool in estimating UOP in the first 32 hours postoperative period.
