Development and Validation of a Model for Endotracheal Intubation and Mechanical Ventilation Prediction in PICU Patients

Association of the child opportunity index with in-hospital mortality and persistence of organ dysfunction at one week after onset of Phoenix Sepsis among children admitted to the pediatric intensive care unit with suspected infection

The social determinants of health (SDoH) are fundamental factors that contribute to overall health and health-related outcomes. Children living in lower socioeconomic areas have a higher risk of critical illness and worse outcomes compared to children living in more socioeconomically advantaged areas. In this work, we determine whether the Child Opportunity Index (COI 3.0), a multi-dimensional child-specific indicator of neighborhood environment, is associated with in-hospital mortality or persistence of a Phoenix Sepsis Score  ≥  2 at one week following Phoenix Sepsis onset in children admitted to pediatric intensive care units (PICUs) with suspected infection. We performed a retrospective cohort analysis of 63,824 patients with suspected or confirmed infection admission diagnosis in two PICUs in Atlanta, Georgia with a Georgia residential address that could be geocoded and linked to a census tract. The primary outcome was the composite of in-hospital mortality or persistence of a Phoenix Sepsis Score  ≥  2 at one week following Phoenix Sepsis onset. Model performance measures of interest were the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). Models developed with electronic medical record (EMR) data using Egleston (EG) or Scottish Rite (SR) as the training site achieved AUROCs of 0.81-0.84 (95% CI range: 0.8-0.85) and 0.82-0.82 (95% CI range: 0.81-0.83) and AUPRCs of 0.59-0.68 (95% CI range: 0.58-0.69) and 0.62-0.64 (95% CI range: 0.61-0.65) respectively. Despite significant differences in COI 3.0 characteristics and overall in-hospital mortality of children with Phoenix suspected infection between the EG and SR PICUs, the addition of COI 3.0 did not improve the overall model performance metrics. While children admitted to both PICUs were more often from COI 3.0 neighborhoods in the lowest two quintiles, these neighborhood features had less of an impact on the model's predictive performance compared to patient physiologic and biologic features available in the EMR.

PROGNOSTIC ACCURACY OF MACHINE LEARNING MODELS FOR IN-HOSPITAL MORTALITY AMONG CHILDREN WITH PHOENIX SEPSIS ADMITTED TO THE PEDIATRIC INTENSIVE CARE UNIT

ABSTRACT

Objective: The Phoenix sepsis criteria define sepsis in children with suspected or confirmed infection who have ≥2 in the Phoenix Sepsis Score. The adoption of the Phoenix sepsis criteria eliminated the Systemic Inflammatory Response Syndrome criteria from the definition of pediatric sepsis. The objective of this study is to derive and validate machine learning models predicting in-hospital mortality for children with suspected or confirmed infection or who met the Phoenix sepsis criteria for sepsis and septic shock. Materials and Methods: Retrospective cohort analysis of 63,824 patients with suspected or confirmed infection admission diagnosis in two pediatric intensive care units (PICUs) in Atlanta, Georgia, from January 1, 2010, through May 10, 2022. The Phoenix Sepsis Score criteria were applied to data collected within 24 h of PICU admission. The primary outcome was in-hospital mortality. The composite secondary outcome was in-hospital mortality or PICU length of stay (LOS) ≥ 72 h. Model-based score performance measures were the area under the precision-recall curve (AUPRC) and the area under the receiver operating characteristic curve (AUROC). Results: Among 18,389/63,824 (29%) children with suspected infection (median age [25th - 75th interquartile range [IQR]): 3.9 [1.1,10.9]; female, 45%, a total of 5,355 met Phoenix sepsis criteria within 24 h of PICU admission. Of the children with Phoenix sepsis, a total of 514 (9.6%) died in the hospital, and 2,848 (53.2%) died or had a PICU stay of ≥72 h. Children with Phoenix septic shock had an in-hospital mortality of 386 (16.4%) and 1,294 (54.9%) had in-hospital mortality or PICU stay of ≥72 h. For children with Phoenix sepsis and Phoenix septic shock, the multivariable logistic regression, light gradient boosting machine, random forest, eXtreme Gradient Boosting, support vector machine, multilayer perceptron, and decision tree models predicting in-hospital mortality had AUPRCs of 0.48-0.65 (95% CI range: 0.42-0.66), 0.50-0.70 (95% CI range: 0.44-0.70), 0.52-0.70 (95% CI range: 0.47-0.71), 0.50-0.70 (95% CI range: 0.44-0.70), 0.49-0.67 (95% CI range: 0.43-0.68), 0.49-0.66 (95% CI range: 0.45-0.67), and 0.30-0.38 (95% CI range: 0.28-0.40) and AUROCs of 0.82-0.88 (95% CI range: 0.82-0.90), 0.84-0.88 (95% CI range: 0.84-0.90), 0.81-0.88 (95% CI range: 0.81-0.90), 0.84-0.88 (95% CI range: 0.83-0.90), 0.82-0.87 (95% CI range: 0.82-0.90), 0.80-0.86 (95% CI range: 0.79-0.89), and 0.76-0.82 (95% CI range: 0.75-0.85), respectively. Conclusion: Among children with Phoenix sepsis admitted to a PICU, the random forest model had the best AUPRC for in-hospital mortality compared to the light gradient boosting machine, eXtreme Gradient Boosting, logistic regression, multilayer perceptron, support vector machine, and decision tree models or a Phoenix Sepsis Score ≥ 2. These findings suggest that machine learning methods to predict in-hospital mortality in children with suspected infection predict mortality in a PICU setting with more accuracy than application of the Phoenix sepsis criteria.

A risk prediction model for efficient intubation in the emergency department: A 4-year single-center retrospective analysis

Objective

To analyze the risk factors associated with intubated critically ill patients in the emergency department (ED) and develop a prediction model by machine learning algorithms.

Methods

This study was conducted in an academic tertiary hospital in Hangzhou, China. Critically ill patients admitted to the ED were retrospectively analyzed from May 2018 to July 2022. The demographic characteristics, distribution of organ dysfunction, parameters for different organs' examination, and status of mechanical ventilation were recorded. These patients were assigned to the intubation and non-intubation groups according to ventilation support. We used the eXtreme Gradient Boosting (XGBoost) algorithm to develop the prediction model and compared it with other algorithms, such as logistic regression, artificial neural network, and random forest. SHapley Additive exPlanations was used to analyze the risk factors of intubated critically ill patients in the ED.

Results

Of 14,589 critically ill patients, 10,212 comprised the training group and 4377 comprised the test group; 2289 intubated patients were obtained from the electronic medical records. The mean age, mean scores of vital signs, parameters of different organs, and blood oxygen examination results differed significantly between the two groups (p < 0.05). The white blood cell count, international normalized ratio, respiratory rate, and pH are the top four risk factors for intubation in critically ill patients. Based on the risk factors in different predictive models, the XGBoost model showed the highest area under the receiver operating characteristic curve (0.84) for predicting ED intubation.

Conclusions

For critically ill patients in the ED, the proposed model can predict potential intubation based on the risk factors in the clinically predictive model.