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Predicting cardiac arrests in pediatric intensive care units.

ABSTRACT: BACKGROUND:Early identification of children at risk for cardiac arrest would allow for skill training associated with improved outcomes and provides a prevention opportunity. OBJECTIVE:Develop and assess a predictive model for cardiopulmonary arrest using data available in the first 4?h. METHODS:Data from PICU patients from 8 institutions included descriptive, severity of illness, cardiac arrest, and outcomes. RESULTS:Of the 10074 patients, 120 satisfying inclusion criteria sustained a cardiac arrest and 67 (55.9%) died. In univariate analysis, patients with cardiac arrest prior to admission were over 6 times and those with cardiac arrests during the first 4?h were over 50 times more likely to have a subsequent arrest. The multivariate logistic regression model performance was excellent (area under the ROC curve?=?0.85 and Hosmer-Lemeshow statistic, p?=?0.35). The variables with the highest odds ratio's for sustaining a cardiac arrest in the multivariable model were admission from an inpatient unit (8.23 (CI: 4.35-15.54)), and cardiac arrest in the first 4?h (6.48 (CI: 2.07-20.36). The average risk predicted by the model was highest (11.6%) among children sustaining an arrest during hours >4-12 and continued to be high even for days after the risk assessment period; the average predicted risk was 9.5% for arrests that occurred after 8 PICU days. CONCLUSIONS:Patients at high risk of cardiac arrest can be identified with routinely available data after 4?h. The cardiac arrest may occur relatively close to the risk assessment period or days later.

SUBMITTER: Pollack MM

PROVIDER: S-EPMC6258339 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Predicting cardiac arrests in pediatric intensive care units.

Pollack Murray M MM Holubkov Richard R Berg Robert A RA Newth Christopher J L CJL Meert Kathleen L KL Harrison Rick E RE Carcillo Joseph J Dalton Heidi H Wessel David L DL Dean J Michael JM

Resuscitation 20180925

<h4>Background</h4>Early identification of children at risk for cardiac arrest would allow for skill training associated with improved outcomes and provides a prevention opportunity.<h4>Objective</h4>Develop and assess a predictive model for cardiopulmonary arrest using data available in the first 4 h.<h4>Methods</h4>Data from PICU patients from 8 institutions included descriptive, severity of illness, cardiac arrest, and outcomes.<h4>Results</h4>Of the 10074 patients, 120 satisfying inclusion c ...[more]

PMID: 30261219

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Project description:BACKGROUND:Healthcare-associated infections (HAIs) represent serious complications for patients within pediatric cardiac intensive care units (CICU). HAIs are associated with increased morbidity, mortality and resource utilization. There are few studies describing the epidemiology of HAIs across the entire spectrum of patients (surgical and nonsurgical) receiving care in dedicated pediatric CICUs. METHODS:Retrospective analyses of 22,839 CICU encounters from October 2013 to September 2016 across 22 North American CICUs contributing data to the Pediatric Cardiac Critical Care Consortium clinical registry. RESULTS:HAIs occurred in 2.4% of CICU encounters at a rate of 3.3 HAIs/1000 CICU days, with 73% of HAIs occurring in children <1 year. Eighty encounters (14%) had ≥2 HAIs. Aggregate rates for the 4 primary HAIs are as follows: central line-associated blood stream infection, 1.1/1000 line days; catheter-associated urinary tract infections, 1.5/1000 catheter days; ventilator-associated pneumonia, 1.9/1000 ventilator days; surgical site infections, 0.81/100 operations. Surgical and nonsurgical patients had similar HAIs rates/1000 CICU days. Incidence was twice as high in surgical encounters and increased with surgical complexity; postoperative infection occurred in 2.8% of encounters. Prematurity, younger age, presence of congenital anomaly, Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery Congenital Heart Surgery Mortality Categories (STAT) 4-5 surgery, admission with an active medical condition, open sternum and extracorporeal membrane oxygenation were independently associated with HAIs. In univariable analysis, HAI was associated with longer hospital length of stay and durations of urinary catheter, central venous catheter and ventilation. Mortality was 24.4% in patients with HAIs versus 3.4% in those without, P < 0.0001. CONCLUSIONS:We provide comprehensive multicenter benchmark data regarding rates of HAIs within dedicated pediatric CICUs. We confirm that although rare, HAIs of all types are associated with significant resource utilization and mortality.

Project description:Hypothermia is a life-threatening condition where the temperature of the body drops below 35°C and is a key source of concern in Intensive Care Units (ICUs). Early identification can help to nudge clinical management to initiate early interventions. Despite its importance, very few studies have focused on the early prediction of hypothermia. In this study, we aim to monitor and predict Hypothermia (30 min-4 h) ahead of its onset using machine learning (ML) models developed on physiological vitals and to prospectively validate the best performing model in the pediatric ICU. We developed and evaluated ML algorithms for the early prediction of hypothermia in a pediatric ICU. Sepsis advanced forecasting engine ICU Database (SafeICU) data resource is an in-house ICU source of data built in the Pediatric ICU at the All-India Institute of Medical Science (AIIMS), New Delhi. Each time-stamp at 1-min resolution was labeled for the presence of hypothermia to construct a retrospective cohort of pediatric patients in the SafeICU data resource. The training set consisted of windows of the length of 4.2 h with a lead time of 30 min-4 h from the onset of hypothermia. A set of 3,835 hand-engineered time-series features were calculated to capture physiological features from the time series. Features selection using the Boruta algorithm was performed to select the most important predictors of hypothermia. A battery of models such as gradient boosting machine, random forest, AdaBoost, and support vector machine (SVM) was evaluated utilizing five-fold test sets. The best-performing model was prospectively validated. A total of 148 patients with 193 ICU stays were eligible for the model development cohort. Of 3,939 features, 726 were statistically significant in the Boruta analysis for the prediction of Hypothermia. The gradient boosting model performed best with an Area Under the Receiver Operating Characteristic curve (AUROC) of 85% (SD = 1.6) and a precision of 59.2% (SD = 8.8) for a 30-min lead time before the onset of Hypothermia onset. As expected, the model showed a decline in model performance at higher lead times, such as AUROC of 77.2% (SD = 2.3) and precision of 41.34% (SD = 4.8) for 4 h ahead of Hypothermia onset. Our GBM(gradient boosting machine) model produced equal and superior results for the prospective validation, where an AUROC of 79.8% and a precision of 53% for a 30-min lead time before the onset of Hypothermia whereas an AUROC of 69.6% and a precision of 38.52% for a (30 min-4 h) lead time prospective validation of Hypothermia. Therefore, this work establishes a pipeline termed ThermoGnose for predicting hypothermia, a major complication in pediatric ICUs.

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Predicting cardiac arrests in pediatric intensive care units.

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Predicting cardiac arrests in pediatric intensive care units.

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