PROPER: Development of an Early Pediatric Intensive Care Unit Readmission Risk Prediction Tool

2016 ◽  
Vol 33 (1) ◽  
pp. 29-36 ◽  
Author(s):  
Harsheen Kaur ◽  
James M. Naessens ◽  
Andrew C. Hanson ◽  
Karen Fryer ◽  
Michael E. Nemergut ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Prediction Model ◽  
Pediatric Intensive Care Unit ◽  
Risk Prediction ◽  
Pediatric Population ◽  
Cerebral Performance Category ◽  
Pediatric Intensive Care ◽  
Risk Prediction Model ◽  
Retrospective Case

Objective: No risk prediction model is currently available to measure patient’s probability for readmission to the pediatric intensive care unit (PICU). This retrospective case–control study was designed to assess the applicability of an adult risk prediction score (Stability and Workload Index for Transfer [SWIFT]) and to create a pediatric version (PRediction Of PICU Early Readmissions [PROPER]). Design: Eighty-six unplanned early (<48 hours) PICU readmissions from January 07, 2007, to June 30, 2014, were compared with 170 random controls. Patient- and disease-specific data and PICU workload factors were compared across the 2 groups. Factors statistically significant on multivariate analysis were included in the creation of the risk prediction model. The SWIFT scores were calculated for cases and controls and compared for validation. Results: Readmitted patients were younger, weighed less, and were more likely to be admitted from the emergency department. There were no differences in gender, race, or admission Pediatric Index of Mortality scores. A higher proportion of patients in the readmission group had a Pediatric Cerebral Performance Category in the moderate to severe disability category. Cases and controls did not differ with respect to staff workload at discharge or discharge day of the week; there was a much higher proportion of patients on supplemental oxygen in the readmission group. Only 2 of 5 categories in the SWIFT model were significantly different, and although the median SWIFT score was significantly higher in the readmissions group, the model discriminated poorly between cases and controls (area under the curve: 0.613). A 7-category PROPER score was created based on a multiple logistic regression model. Sensitivity of this model (score ≥12) for the detection of readmission was 81% with a positive predictive value of 0.50. Conclusion: We have created a preliminary model for predicting patients at risk of early readmissions to the PICU from the hospital floor. The SWIFT score is not applicable for predicting the risk for pediatric population.

Assessment and Management of Delirium in the Pediatric Intensive Care Unit: A Review

2021 ◽  
Author(s):  
Caren Liviskie ◽  
Christopher McPherson ◽  
Caitlyn Luecke
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Adverse Effects ◽  
Pediatric Intensive Care Unit ◽  
Positive Impact ◽  
Pediatric Population ◽  
Pediatric Intensive Care ◽  
Signs And Symptoms ◽  
Screening Tools ◽  
Oral Medications

AbstractMany critically ill patients suffer from delirium which is associated with significant morbidity and mortality. There is a paucity of data about the incidence, symptoms, or treatment of delirium in the pediatric intensive care unit (PICU). Risk factors for delirium are common in the PICU including central nervous system immaturity, developmental delay, mechanical ventilation, and use of anticholinergic agents, corticosteroids, vasopressors, opioids, or benzodiazepines. Hypoactive delirium is the most common subtype in pediatric patients; however, hyperactive delirium has also been reported. Various screening tools are validated in the pediatric population, with the Cornell Assessment of Pediatric Delirium (CAPD) applicable to the largest age range and able to detect signs and symptoms consistent with both hypo- and hyperactive delirium. Treatment of delirium should always include identification and reversal of the underlying etiology, reserving pharmacologic management for those patients without symptom resolution, or with significant impact to medical care. Atypical antipsychotics (olanzapine, quetiapine, and risperidone) should be used first-line in patients requiring pharmacologic treatment owing to their apparent efficacy and low incidence of reported adverse effects. The choice of atypical antipsychotic should be based on adverse effect profile, available dosage forms, and consideration of medication interactions. Intravenous haloperidol may be a potential treatment option in patients unable to tolerate oral medications and with significant symptoms. However, given the high incidence of serious adverse effects with intravenous haloperidol, routine use should be avoided. Dexmedetomidine should be used when sedation is needed and when clinically appropriate, given the positive impact on delirium. Additional well-designed trials assessing screening and treatment of PICU delirium are needed.

Overcoming Barriers to Delirium Screening in the Pediatric Intensive Care Unit

2018 ◽  
Vol 38 (4) ◽  
pp. 57-67 ◽  
Author(s):  
Gina M. Rohlik ◽  
Karen R. Fryer ◽  
Sandeep Tripathi ◽  
Julie M. Duncan ◽  
Heather L. Coon ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Quality Improvement ◽  
Pediatric Intensive Care Unit ◽  
Failure Modes ◽  
Cerebral Performance Category ◽  
Pediatric Intensive Care ◽  
Severe Illness ◽  
Successful Implementation ◽  
Delirium Assessment

BACKGROUNDDelirium is associated with poor outcomes in adults but is less extensively studied in children.OBJECTIVESTo describe a quality improvement initiative to implement delirium assessment in a pediatric intensive care unit and to identify barriers to delirium screening completion.METHODSA survey identified perceived barriers to delirium assessment. Failure modes and effects analysis characterized factors likely to impede assessment. A randomized case-control study evaluated factors affecting assessment by comparing patients always assessed with patients never assessed.RESULTSDelirium assessment was completed in 57% of opportunities over 1 year, with 2% positive screen results. Education improved screening completion by 20%. Barriers to assessment identified by survey (n = 25) included remembering to complete assessments, documentation outside workflow, and “busy patient.” Factors with high risk prediction numbers were lack of time and paper charting. Patients always assessed had more severe illness (median Pediatric Index of Mortality 2 score, 0.90 vs 0.36; P &lt; .001), more developmental disabilities (moderate to severe pediatric cerebral performance category score, 54% vs 32%; P = .007), and admission during lower pediatric intensive care unit census (median [interquartile range], 10 [9–12] vs 12 [10–13]; P &lt; .001) than did those never assessed (each group, n = 80). Patients receiving mechanical ventilation were less likely to be assessed (41.0% vs 51.2%, P &lt; .001).CONCLUSIONSSuccessful implementation of pediatric delirium screening may be associated with early use of quality improvement tools to identify assessment barriers, comprehensive education, monitoring system with feedback, multidisciplinary team involvement, and incorporation into nursing workflow models.

scholarly journals Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation

10.2196/23128 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e23128
Author(s):  
Pan Pan ◽  
Yichao Li ◽  
Yongjiu Xiao ◽  
Bingchao Han ◽  
Longxiang Su ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Data Set ◽  
Icu Patients ◽  
Potential Risk Factors

Background Patients with COVID-19 in the intensive care unit (ICU) have a high mortality rate, and methods to assess patients’ prognosis early and administer precise treatment are of great significance. Objective The aim of this study was to use machine learning to construct a model for the analysis of risk factors and prediction of mortality among ICU patients with COVID-19. Methods In this study, 123 patients with COVID-19 in the ICU of Vulcan Hill Hospital were retrospectively selected from the database, and the data were randomly divided into a training data set (n=98) and test data set (n=25) with a 4:1 ratio. Significance tests, correlation analysis, and factor analysis were used to screen 100 potential risk factors individually. Conventional logistic regression methods and four machine learning algorithms were used to construct the risk prediction model for the prognosis of patients with COVID-19 in the ICU. The performance of these machine learning models was measured by the area under the receiver operating characteristic curve (AUC). Interpretation and evaluation of the risk prediction model were performed using calibration curves, SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), etc, to ensure its stability and reliability. The outcome was based on the ICU deaths recorded from the database. Results Layer-by-layer screening of 100 potential risk factors finally revealed 8 important risk factors that were included in the risk prediction model: lymphocyte percentage, prothrombin time, lactate dehydrogenase, total bilirubin, eosinophil percentage, creatinine, neutrophil percentage, and albumin level. Finally, an eXtreme Gradient Boosting (XGBoost) model established with the 8 important risk factors showed the best recognition ability in the training set of 5-fold cross validation (AUC=0.86) and the verification queue (AUC=0.92). The calibration curve showed that the risk predicted by the model was in good agreement with the actual risk. In addition, using the SHAP and LIME algorithms, feature interpretation and sample prediction interpretation algorithms of the XGBoost black box model were implemented. Additionally, the model was translated into a web-based risk calculator that is freely available for public usage. Conclusions The 8-factor XGBoost model predicts risk of death in ICU patients with COVID-19 well; it initially demonstrates stability and can be used effectively to predict COVID-19 prognosis in ICU patients.

scholarly journals Dexmedetomidine-Associated Fever in a Critically Ill Obese Child

2018 ◽  
Vol 23 (6) ◽  
pp. 486-489
Author(s):  
R. Zachary Thompson ◽  
Lori McDonald ◽  
Keegan Ziemba ◽  
Joseph D. Tobias ◽  
Claire A. Stewart
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Critically Ill ◽  
Pediatric Intensive Care Unit ◽  
Pediatric Patient ◽  
Obese Child ◽  
Pediatric Population ◽  
Pediatric Intensive Care ◽  
Persistent Fever ◽  
Drug Fever

Dexmedetomidine use in the pediatric intensive care unit has increased in recent years. Reports of dexmedetomidine-associated drug fever have been described in adult patients; however, this has not been reported in the pediatric population. We report a case of persistent fever that resolved with the discontinuation of dexmedetomidine and successful transition to clonidine. This is the first report of dexmedetomidine drug fever in a pediatric patient.

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