Ventilator Weaning and Terminal Extubation: Withdrawal of Life-Sustaining Therapy in Children. Secondary Analysis of the Death One Hour After Terminal Extubation Study.

OBJECTIVE: Terminal extubation (TE) and terminal weaning (TW) during withdrawal of life-sustaining therapies (WLSTs) have been described and defined in adults. The recent Death One Hour After Terminal Extubation study aimed to validate a model developed to predict whether a child would die within 1 hour after discontinuation of mechanical ventilation for WLST. Although TW has not been described in children, pre-extubation weaning has been known to occur before WLST, though to what extent is unknown. In this preplanned secondary analysis, we aim to describe/define TE and pre-extubation weaning (PW) in children and compare characteristics of patients who had ventilatory support decreased before WLST with those who did not. DESIGN: Secondary analysis of multicenter retrospective cohort study. SETTING: Ten PICUs in the United States between 2009 and 2021. PATIENTS: Nine hundred thirteen patients 0-21 years old who died after WLST. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: 71.4% ( n = 652) had TE without decrease in ventilatory support in the 6 hours prior. TE without decrease in ventilatory support in the 6 hours prior = 71.4% ( n = 652) of our sample. Clinically relevant decrease in ventilatory support before WLST = 11% ( n = 100), and 17.6% ( n = 161) had likely incidental decrease in ventilatory support before WLST. Relevant ventilator parameters decreased were F io2 and/or ventilator set rates. There were no significant differences in any of the other evaluated patient characteristics between groups (weight, body mass index, unit type, primary diagnostic category, presence of coma, time to death after WLST, analgosedative requirements, postextubation respiratory support modality). CONCLUSIONS: Decreasing ventilatory support before WLST with extubation in children does occur. This practice was not associated with significant differences in palliative analgosedation doses or time to death after extubation.

Prehospitalization Trauma and Physiologic Factors Associated with the Presence of Post-traumatic Stress 3 Months After PICU Discharge.

BACKGROUND: Children admitted to the pediatric intensive care unit (PICU) have post-traumatic stress (PTS) rates up to 64%, and up to 28% of them meet criteria for PTS disorder (PTSD). We aim to examine whether a prior trauma history and increased physiologic parameters due to a heightened sympathetic response are associated with later PTS. Our hypothesis was children with history of prehospitalization trauma, higher heart rates, blood pressures, cortisol, and extrinsic catecholamine administration during PICU admission are more likely to have PTS after discharge. METHODS: This is a prospective, observational study of children admitted to the PICU at an urban, quaternary, academic children's hospital. Children aged 8 to 17 years old without developmental delay, severe psychiatric disorder, or traumatic brain injury were included. Children's prehospitalization trauma history was assessed with a semistructured interview. All in-hospital variables were from the electronic medical record. PTS was present if children had 4 of the Diagnostic and Statistical Manual of Mental Disorders IV criteria for PTSD. Student's t- and chi-squared tests were used to compare the presence or absence of prior trauma and all of the PICU-associated variables. RESULTS: Of the 110 children at baseline, 67 had 3-month follow-up. In the latter group, 46% met the criteria for PTS, mean age of 13 years (SD 3), 57% male, a mean PRISM III score of 4.9 (SD 4.3), and intensive care unit length of stay 6.5 days (SD 7.8). There were no statistically significant differences in the demographics of the children with and without PTS. The only variable to show significance was trauma history; children with prehospitalization trauma were more likely to have PTS at 3-month follow-up (P = .02). CONCLUSIONS: Prehospitalization trauma history was associated with the presence of PTS after admission to the PICU. This study suggests future studies should shift to the potential predictive benefit of screening children for trauma history upon PICU admission.

A Novel Application of Spectrograms with Machine Learning Can Detect Patient Ventilator Dyssynchrony.

Patients in intensive care units are frequently supported by mechanical ventilation. There is increasing awareness of patient-ventilator dyssynchrony (PVD), a mismatch between patient respiratory effort and assistance provided by the ventilator, as a risk factor for infection, narcotic exposure, lung injury, and adverse neurocognitive effects. One of the most injurious consequences of PVD are double cycled (DC) breaths when two breaths are delivered by the ventilator instead of one. Prior efforts to identify PVD have limited efficacy. An automated method to identify PVD, independent of clinician expertise, acumen, or time, would potentially permit early, targeted treatment to avoid further harm. We performed secondary analyses of data from a clinical trial of children with acute respiratory distress syndrome. Waveforms of ventilator flow, airway pressure and esophageal manometry were annotated to identify DC breaths and underlying PVD subtypes. Spectrograms were generated from those waveforms to train Convolutional Neural Network (CNN) models in detecting DC and underlying PVD subtypes: Reverse Trigger (RT) and Inadequate Support (IS). The DC breath detection model yielded AUROC of 0.980, while the multi-target detection model for underlying dyssynchrony yielded AUROC of 0.980 (RT) and 0.976 (IS). When operating at 75% sensitivity, DC breath detection had a number needed to alert (NNA) 1.3 (99% specificity), while underlying PVD had a NNA 1.6 (98.5% specificity) for RT and NNA 4.0 (98.2% specificity) for IS. CNNs using spectrograms of ventilator waveforms can identify DC breaths and detect the underlying PVD for targeted clinical interventions.

Narrowing the gap: expected versus deployment performance.

OBJECTIVES: Successful model development requires both an accurate a priori understanding of future performance and high performance on deployment. Optimistic estimations of model performance that are unrealized in real-world clinical settings can contribute to nonuse of predictive models. This study used 2 tasks, predicting ICU mortality and Bi-Level Positive Airway Pressure failure, to quantify: (1) how well internal test performances derived from different methods of partitioning data into development and test sets estimate future deployment performance of Recurrent Neural Network models and (2) the effects of including older data in the training set on models' performance. MATERIALS AND METHODS: The cohort consisted of patients admitted between 2010 and 2020 to the Pediatric Intensive Care Unit of a large quaternary children's hospital. 2010-2018 data were partitioned into different development and test sets to measure internal test performance. Deployable models were trained on 2010-2018 data and assessed on 2019-2020 data, which was conceptualized to represent a real-world deployment scenario. Optimism, defined as the overestimation of the deployed performance by internal test performance, was measured. Performances of deployable models were also compared with each other to quantify the effect of including older data during training. RESULTS, DISCUSSION, AND CONCLUSION: Longitudinal partitioning methods, where models are tested on newer data than the development set, yielded the least optimism. Including older years in the training dataset did not degrade deployable model performance. Using all available data for model development fully leveraged longitudinal partitioning by measuring year-to-year performance.

Analgesia and Sedation at Terminal Extubation: A Secondary Analysis From Death One Hour After Terminal Extubation Study Data.

OBJECTIVES: To describe the doses of opioids and benzodiazepines administered around the time of terminal extubation (TE) to children who died within 1 hour of TE and to identify their association with the time to death (TTD). DESIGN: Secondary analysis of data collected for the Death One Hour After Terminal Extubation study. SETTING: Nine U.S. hospitals. PATIENTS: Six hundred eighty patients between 0 and 21 years who died within 1 hour after TE (2010-2021). MEASUREMENTS AND MAIN RESULTS: Medications included total doses of opioids and benzodiazepines 24 hours before and 1 hour after TE. Correlations between drug doses and TTD in minutes were calculated, and multivariable linear regression performed to determine their association with TTD after adjusting for age, sex, last recorded oxygen saturation/F io2 ratio and Glasgow Coma Scale score, inotrope requirement in the last 24 hours, and use of muscle relaxants within 1 hour of TE. Median age of the study population was 2.1 years (interquartile range [IQR], 0.4-11.0 yr). The median TTD was 15 minutes (IQR, 8-23 min). Forty percent patients (278/680) received either opioids or benzodiazepines within 1 hour after TE, with the largest proportion receiving opioids only (23%, 159/680). Among patients who received medications, the median IV morphine equivalent within 1 hour after TE was 0.75 mg/kg/hr (IQR, 0.3-1.8 mg/kg/hr) ( n = 263), and median lorazepam equivalent was 0.22 mg/kg/hr (IQR, 0.11-0.44 mg/kg/hr) ( n = 118). The median morphine equivalent and lorazepam equivalent rates after TE were 7.5-fold and 22-fold greater than the median pre-extubation rates, respectively. No significant direct correlation was observed between either opioid or benzodiazepine doses before or after TE and TTD. After adjusting for confounding variables, regression analysis also failed to show any association between drug dose and TTD. CONCLUSIONS: Children after TE are often prescribed opioids and benzodiazepines. For patients dying within 1 hour of TE, TTD is not associated with the dose of medication administered as part of comfort care.

Development of a deep learning model that predicts Bi-level positive airway pressure failure.

Delaying intubation for patients failing Bi-Level Positive Airway Pressure (BIPAP) may be associated with harm. The objective of this study was to develop a deep learning model capable of aiding clinical decision making by predicting Bi-Level Positive Airway Pressure (BIPAP) failure. This was a retrospective cohort study in a tertiary pediatric intensive care unit (PICU) between 2010 and 2020. Three machine learning models were developed to predict BIPAP failure: two logistic regression models and one deep learning model, a recurrent neural network with a Long Short-Term Memory (LSTM-RNN) architecture. Model performance was evaluated in a holdout test set. 175 (27.7%) of 630 total BIPAP sessions were BIPAP failures. Patients in the BIPAP failure group were on BIPAP for a median of 32.8 (9.2-91.3) hours prior to intubation. Late BIPAP failure (intubation after using BIPAP > 24 h) patients had fewer 28-day Ventilator Free Days (13.40 [0.68-20.96]), longer ICU length of stay and more post-extubation BIPAP days compared to those who were intubated ≤ 24 h from BIPAP initiation. An AUROC above 0.5 indicates that a model has extracted new information, potentially valuable to the clinical team, about BIPAP failure. Within 6 h of BIPAP initiation, the LSTM-RNN model predicted which patients were likely to fail BIPAP with an AUROC of 0.81 (0.80, 0.82), superior to all other models. Within 6 h of BIPAP initiation, the LSTM-RNN model would identify nearly 80% of BIPAP failures with a 50% false alarm rate, equal to an NNA of 2. In conclusion, a deep learning method using readily available data from the electronic health record can identify which patients on BIPAP are likely to fail with good discrimination, oftentimes days before they are intubated in usual practice.

Continuous Prediction of Mortality in the PICU: A Recurrent Neural Network Model in a Single-Center Dataset.

OBJECTIVES: Develop, as a proof of concept, a recurrent neural network model using electronic medical records data capable of continuously assessing an individual child's risk of mortality throughout their ICU stay as a proxy measure of severity of illness. DESIGN: Retrospective cohort study. SETTING: PICU in a tertiary care academic children's hospital. PATIENTS/SUBJECTS: Twelve thousand five hundred sixteen episodes (9,070 children) admitted to the PICU between January 2010 and February 2019, partitioned into training (50%), validation (25%), and test (25%) sets. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: On 2,475 test set episodes lasting greater than or equal to 24 hours in the PICU, the area under the receiver operating characteristic curve of the recurrent neural network's 12th hour predictions was 0.94 (CI, 0.93-0.95), higher than those of Pediatric Index of Mortality 2 (0.88; CI, [0.85-0.91]; p < 0.02), Pediatric Risk of Mortality III (12th hr) (0.89; CI, [0.86-0.92]; p < 0.05), and Pediatric Logistic Organ Dysfunction day 1 (0.85; [0.81-0.89]; p < 0.002). The recurrent neural network's discrimination increased with more acquired data and smaller lead time, achieving a 0.99 area under the receiver operating characteristic curve 24 hours prior to discharge. Despite not having diagnostic information, the recurrent neural network performed well across different primary diagnostic categories, generally achieving higher area under the receiver operating characteristic curve for these groups than the other three scores. On 692 test set episodes lasting greater than or equal to 5 days in the PICU, the recurrent neural network area under the receiver operating characteristic curves significantly outperformed their daily Pediatric Logistic Organ Dysfunction counterparts (p < 0.005). CONCLUSIONS: The recurrent neural network model can process hundreds of input variables contained in a patient's electronic medical record and integrate them dynamically as measurements become available. Its high discrimination suggests the recurrent neural network's potential to provide an accurate, continuous, and real-time assessment of a child in the ICU.

The impact of extraneous features on the performance of recurrent neural network models in clinical tasks.

Electronic Medical Records (EMR) are a rich source of patient information, including measurements reflecting physiologic signs and administered therapies. Identifying which variables or features are useful in predicting clinical outcomes can be challenging. Advanced algorithms, such as deep neural networks, were designed to process high-dimensional inputs containing variables in their measured form, thus bypass separate feature selection or engineering steps. We investigated the effect of extraneous input features on the predictive performance of Recurrent Neural Networks (RNN) by including in the input vector extraneous features that were randomly drawn from theoretical and empirical distributions. RNN models using different input vectors (EMR features only; EMR and extraneous features; extraneous features only) were trained to predict three clinical outcomes: in-ICU mortality, 72-h ICU re-admission, and 30-day ICU-free days. The measured degradations of the RNN's predictive performance with the inclusion of extraneous features to EMR variables were negligible.