Changes in real-world dispensing of ADHD stimulants in youth from 2019 to 2021 in California.

Introduction: Attention-deficit/hyperactivity disorder (ADHD) is one of the most common pediatric neurobehavioral disorders in the U.S. Stimulants, classified as controlled substances, are commonly used for ADHD management. We conducted an analysis of real-world stimulants dispensing data to evaluate the pandemic's impact on young patients (≤ 26 years) in California. Methods: Annual prevalence of patients on stimulants per capita across various California counties from 2019 and 2021 were analyzed and further compared across different years, sexes, and age groups. New patients initiating simulants therapy were also examined. A case study was conducted to determine the impact of socioeconomic status on patient prevalence within different quintiles in Los Angeles County using patient zip codes. Logistic regression analysis using R Project was employed to determine demographic factors associated with concurrent use of stimulants with other controlled substances. Results: There was a notable reduction in prevalence of patients ≤26 years old on stimulants during and after the pandemic per 100,000 people (777 in 2019; 743 in 2020; 751 in 2021). These decreases were more evident among the elementary and adolescent age groups. The most prevalent age group on stimulants were adolescents (12-17 years) irrespective of the pandemic. A significant rise in the number of female patients using stimulants was observed, increasing from 107,957 (35.2%) in 2019 to 121,241 (41.1%) in 2021. New patients initiating stimulants rose from 102,754 in 2020 to 106,660 in 2021, with 33.2% being young adults. In Los Angeles County, there was an increasing trend in patient prevalence from Q1 to Q5 income quintiles among patients ≥6 years. Consistently each year, the highest average income quintile exhibited the highest per capita prevalence. Age was associated with higher risk of concurrent use of benzodiazepines (OR, 1.198 [95% CI, 1.195-1.201], p < 0.0001) and opioids (OR, 1.132 [95% CI, 1.130-1.134], p < 0.0001) with stimulants. Discussion: Our study provides real-world information on dispensing of ADHD stimulants in California youth from 2019 to 2021. The results underscore the importance of optimizing evidence-based ADHD management in pediatric patients and young adults to mitigate disparities in the use of stimulants.

De Novo Drug Design Using Transformer-Based Machine Translation and Reinforcement Learning of an Adaptive Monte Carlo Tree Search.

The discovery of novel therapeutic compounds through de novo drug design represents a critical challenge in the field of pharmaceutical research. Traditional drug discovery approaches are often resource intensive and time consuming, leading researchers to explore innovative methods that harness the power of deep learning and reinforcement learning techniques. Here, we introduce a novel drug design approach called drugAI that leverages the Encoder-Decoder Transformer architecture in tandem with Reinforcement Learning via a Monte Carlo Tree Search (RL-MCTS) to expedite the process of drug discovery while ensuring the production of valid small molecules with drug-like characteristics and strong binding affinities towards their targets. We successfully integrated the Encoder-Decoder Transformer architecture, which generates molecular structures (drugs) from scratch with the RL-MCTS, serving as a reinforcement learning framework. The RL-MCTS combines the exploitation and exploration capabilities of a Monte Carlo Tree Search with the machine translation of a transformer-based Encoder-Decoder model. This dynamic approach allows the model to iteratively refine its drug candidate generation process, ensuring that the generated molecules adhere to essential physicochemical and biological constraints and effectively bind to their targets. The results from drugAI showcase the effectiveness of the proposed approach across various benchmark datasets, demonstrating a significant improvement in both the validity and drug-likeness of the generated compounds, compared to two existing benchmark methods. Moreover, drugAI ensures that the generated molecules exhibit strong binding affinities to their respective targets. In summary, this research highlights the real-world applications of drugAI in drug discovery pipelines, potentially accelerating the identification of promising drug candidates for a wide range of diseases.

A causal inference study: The impact of the combined administration of Donepezil and Memantine on decreasing hospital and emergency department visits of Alzheimer's disease patients.

Alzheimer's disease is the most common type of dementia that currently affects over 6.5 million people in the U.S. Currently there is no cure and the existing drug therapies attempt to delay the mental decline and improve cognitive abilities. Two of the most commonly prescribed such drugs are Donepezil and Memantine. We formally tested and confirmed the presence of a beneficial drug-drug interaction of Donepezil and Memantine using a causal inference analysis. We applied doubly robust estimators to one of the largest and high-quality medical databases to estimate the effect of two commonly prescribed Alzheimer's disease (AD) medications, Donepezil and Memantine, on the average number of hospital or emergency department visits per year among patients diagnosed with AD. Our results show that, compared to the absence of medication scenario, the Memantine monotherapy, and the Donepezil monotherapy, the combined use of Donepezil and Memantine treatment significantly reduces the average number of hospital or emergency department visits per year by 0.078 (13.8%), 0.144 (25.5%), and 0.132 days (23.4%), respectively. The assessed decline in the average number of hospital or emergency department visits per year is consequently associated with a substantial reduction in medical costs. As of 2022, according to the Alzheimer's Disease Association, there were over 6.5 million individuals aged 65 and older living with AD in the US alone. If patients who are currently on no drug treatment or using either Donepezil or Memantine alone were switched to the combined used of Donepezil and Memantine therapy, the average number of hospital or emergency department visits could decrease by over 613 thousand visits per year. This, in turn, would lead to a remarkable reduction in medical expenses associated with hospitalization of AD patients in the US, totaling over 940 million dollars per year.

Predicting suicidal and self-injurious events in a correctional setting using AI algorithms on unstructured medical notes and structured data.

Suicidal and self-injurious incidents in correctional settings deplete the institutional and healthcare resources, create disorder and stress for staff and other inmates. Traditional statistical analyses provide some guidance, but they can only be applied to structured data that are often difficult to collect and their recommendations are often expensive to act upon. This study aims to extract information from medical and mental health progress notes using AI algorithms to make actionable predictions of suicidal and self-injurious events to improve the efficiency of triage for health care services and prevent suicidal and injurious events from happening at California's Orange County Jails. The results showed that the notes data contain more information with respect to suicidal or injurious behaviors than the structured data available in the EHR database at the Orange County Jails. Using the notes data alone (under-sampled to 50%) in a Transformer Encoder model produced an AUC-ROC of 0.862, a Sensitivity of 0.816, and a Specificity of 0.738. Incorporating the information extracted from the notes data into traditional Machine Learning models as a feature alongside structured data (under-sampled to 50%) yielded better performance in terms of Sensitivity (AUC-ROC: 0.77, Sensitivity: 0.89, Specificity: 0.65). In addition, under-sampling is an effective approach to mitigating the impact of the extremely imbalanced classes.

An artificial intelligence-based noninvasive solution to estimate pulmonary artery pressure.

Aims: Design to develop an artificial intelligence (AI) algorithm to accurately predict the pulmonary artery pressure (PAP) waveform using non-invasive signal inputs. Methods and results: We randomly sampled training, validation, and testing datasets from a waveform database containing 180 patients with pulmonary atrial catheters (PACs) placed for PAP waves collection. The waveform database consisted of six hemodynamic parameters from bedside monitoring machines, including PAP, artery blood pressure (ABP), central venous pressure (CVP), respiration waveform (RESP), photoplethysmogram (PPG), and electrocardiogram (ECG). We trained a Residual Convolutional Network using a training dataset containing 144 (80%) patients, tuned learning parameters using a validation set including 18 (10%) patients, and tested the performance of the method using 18 (10%) patients, respectively. After comparing all multi-stage algorithms on the testing cohort, the combination of the residual neural network model and wavelet scattering transform data preprocessing method attained the highest coefficient of determination R 2 of 90.78% as well as the following other performance metrics and corresponding 95% confidence intervals (CIs): mean square error of 11.55 (10.22-13.5), mean absolute error of 2.42 (2.06-2.85), mean absolute percentage error of 0.91 (0.76-1.13), and explained variance score of 90.87 (85.32-93.31). Conclusion: The proposed analytical approach that combines data preprocessing, sampling method, and AI algorithm can precisely predict PAP waveform using three input signals obtained by noninvasive approaches.

Using Continuous Intraoperative Pressure Mapping to Describe Patterns of Tissue Interface Pressure.

The purpose of this study was to describe patterns of intraoperative tissue interface pressure, identify the amount of time during which the pressure at four anatomical locations exceeded 32 mm Hg, and examine associations between patient- and surgery-related variables and peak tissue interface pressure. We used a pressure mapping system to measure the intraoperative tissue interface pressure of 150 patients. We implemented linear mixed-effects models to assess trends in the mean and peak tissue interface pressure. The percentage of time during which the interface pressure exceeded 32 mm Hg at the scapulae, interscapular area, and sacral area was 70%, 70%, and 90%, respectively. Body mass index, length of surgery, and intraoperative position were major predictors of increased pressure. Understanding patterns of tissue interface pressure of patients during surgery may help perioperative nurses develop strategies to attenuate pressure and protect skin integrity.

A comparative study on deep learning models for text classification of unstructured medical notes with various levels of class imbalance.

BACKGROUND: Discharge medical notes written by physicians contain important information about the health condition of patients. Many deep learning algorithms have been successfully applied to extract important information from unstructured medical notes data that can entail subsequent actionable results in the medical domain. This study aims to explore the model performance of various deep learning algorithms in text classification tasks on medical notes with respect to different disease class imbalance scenarios. METHODS: In this study, we employed seven artificial intelligence models, a CNN (Convolutional Neural Network), a Transformer encoder, a pretrained BERT (Bidirectional Encoder Representations from Transformers), and four typical sequence neural networks models, namely, RNN (Recurrent Neural Network), GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), and Bi-LSTM (Bi-directional Long Short-Term Memory) to classify the presence or absence of 16 disease conditions from patients' discharge summary notes. We analyzed this question as a composition of 16 binary separate classification problems. The model performance of the seven models on each of the 16 datasets with various levels of imbalance between classes were compared in terms of AUC-ROC (Area Under the Curve of the Receiver Operating Characteristic), AUC-PR (Area Under the Curve of Precision and Recall), F1 Score, and Balanced Accuracy as well as the training time. The model performances were also compared in combination with different word embedding approaches (GloVe, BioWordVec, and no pre-trained word embeddings). RESULTS: The analyses of these 16 binary classification problems showed that the Transformer encoder model performs the best in nearly all scenarios. In addition, when the disease prevalence is close to or greater than 50%, the Convolutional Neural Network model achieved a comparable performance to the Transformer encoder, and its training time was 17.6% shorter than the second fastest model, 91.3% shorter than the Transformer encoder, and 94.7% shorter than the pre-trained BERT-Base model. The BioWordVec embeddings slightly improved the performance of the Bi-LSTM model in most disease prevalence scenarios, while the CNN model performed better without pre-trained word embeddings. In addition, the training time was significantly reduced with the GloVe embeddings for all models. CONCLUSIONS: For classification tasks on medical notes, Transformer encoders are the best choice if the computation resource is not an issue. Otherwise, when the classes are relatively balanced, CNNs are a leading candidate because of their competitive performance and computational efficiency.

A High Precision Machine Learning-Enabled System for Predicting Idiopathic Ventricular Arrhythmia Origins.

Background: Radiofrequency catheter ablation (CA) is an efficient antiarrhythmic treatment with a class I indication for idiopathic ventricular arrhythmia (IVA), only when drugs are ineffective or have unacceptable side effects. The accurate prediction of the origins of IVA can significantly increase the operation success rate, reduce operation duration and decrease the risk of complications. The present work proposes an artificial intelligence-enabled ECG analysis algorithm to estimate possible origins of idiopathic ventricular arrhythmia at a clinical-grade level accuracy. Method: A total of 18,612 ECG recordings extracted from 545 patients who underwent successful CA to treat IVA were proportionally sampled into training, validation and testing cohorts. We designed four classification schemes responding to different hierarchical levels of the possible IVA origins. For every classification scheme, we compared 98 distinct machine learning models with optimized hyperparameter values obtained through extensive grid search and reported an optimal algorithm with the highest accuracy scores attained on the testing cohorts. Results: For classification scheme 4, our pioneering study designs and implements a machine learning-based ECG algorithm to predict 21 possible sites of IVA origin with an accuracy of 98.24% on a testing cohort. The accuracy and F1-score for the left three schemes surpassed 99%. Conclusion: In this work, we developed an algorithm that precisely predicts the correct origins of IVA (out of 21 possible sites) and outperforms the accuracy of all prior studies and human experts.

Sensitized by a sea slug: Site-specific short-term and general long-term sensitization in Aplysia following Navanax attack.

Neurobiological studies of the model species, Aplysia californica (Mollusca, Gastropoda, Euopisthobranchia), have helped advance our knowledge of the neural bases of different forms of learning, including sensitization, a non-associative increase in withdrawal behaviors in response to mild innocuous stimuli. However, our understanding of the natural context for this learning has lagged behind the mechanistic studies. Previous studies, which exclusively used artificial stimuli, such as electric shock, to produce sensitization, left open the question of which stimuli might cause sensitization in nature. Our laboratory first addressed this question by testing for short and long-term sensitization after predatory attack by a natural predator, the spiny lobster. In the present study, we tested for sensitization after attack by a very different predator, the predacious sea-slug, Navanax inermis (Mollusca, Gastropoda, Euopisthobranchia). Unlike the biting and prodding action of lobster attack, Navanax uses a rapid strike that sucks and squeezes its prey in an attempt to swallow it whole. We found that Navanax attack to the head of Aplysia caused strong immediate sensitization of head withdrawal, and weaker, delayed, sensitization of tail-mantle withdrawal. By contrast, attack to the tail of Aplysia resulted in no sensitization of either reflex. We also developed an artificial attack stimulus that allowed us to mimick a more consistently strong attack. This artificial attack produced stronger but qualitatively similar sensitization: Strong immediate sensitization of head withdrawal and weaker sensitization of tail-mantle withdrawal after head attack, immediate sensitization in tail-mantle withdrawal, but no sensitization of head withdrawal after tail attack. We conclude that Navanax attack causes robust site-specific sensitization (enhanced sensitization near the site of attack), and weaker general sensitization (sensitization of responses to stimuli distal to the attack site). We also tested for long-term sensitization (lasting longer than 24 h) after temporally-spaced delivery of four natural Navanax attacks to the head of subject Aplysia. Surprisingly, these head attacks, any one of which strongly sensitizes head withdrawal in the short term, failed to sensitize head-withdrawal in the long term. Paradoxically, these repeated head attacks produced long-term sensitization in tail-mantle withdrawal. These experiments and observations confirm that Navanax attack causes short, and long-term sensitization of withdrawal reflexes of Aplysia. They add site-specific sensitization as well as paradoxical long-term sensitization of tail-mantle withdrawal to a short list of naturally induced learning phenotypes in this model species. Together with previous observations of sensitization after lobster attack, these data strongly support the premise that sensitization in Aplysia is an adaptive response to sub-lethal predator attack.

Increased Risks of Re-identification For Patients Posed by Deep Learning-Based ECG Identification Algorithms.

ECGs analysis is an important tool in cardiac diagnosis. ECG data also have the potential to be used as a biometric source that allows precise person identification similar to the widely used fingerprint and iris recognition techniques. However, this phenomenon also raises serious privacy concerns. In this study, we provide a complete, multi-step ECG identification algorithm using a private database of ECG recordings. We train and validate our AI model on approximately 40k patients which makes this study by far the largest research project in this field. Moreover, our best model attained an exceptionally high accuracy of 94.56%. In addition to discussing the general implications of the deployment of such systems related to privacy, for the first time, we also assess the accuracy of ECG-based identification for distinct heart condition groups (and combinations of such conditions) and the corresponding privacy implications. For instance, we discovered that in contrast to initial expectation that identification accuracy for healthy normal sinus rhythm should be the highest, the identification accuracy is higher for patients with sinus tachycardia or patients who are diagnosed with both ST changes and supraventricular tachycardia. This puts these patients at a higher risk of privacy issues due to re-identification. On the other hand, we observed that patients with premature ventricular contractions have an identification accuracy as low as 78.54%. The identification rate for patients with a pacemaker is 80.2%.Clinical relevance-While ECG as a biometric can be a potentially useful technology, it also raises serious concerns regarding the privacy of cardiac patients. Especially, the ECG Identification algorithms empowered by deep learning can increase the risk of re-identification.

A High-Precision Machine Learning Algorithm to Classify Left and Right Outflow Tract Ventricular Tachycardia.

INTRODUCTION: Multiple algorithms based on 12-lead ECG measurements have been proposed to identify the right ventricular outflow tract (RVOT) and left ventricular outflow tract (LVOT) locations from which ventricular tachycardia (VT) and frequent premature ventricular complex (PVC) originate. However, a clinical-grade machine learning algorithm that automatically analyzes characteristics of 12-lead ECGs and predicts RVOT or LVOT origins of VT and PVC is not currently available. The effective ablation sites of RVOT and LVOT, confirmed by a successful ablation procedure, provide evidence to create RVOT and LVOT labels for the machine learning model. METHODS: We randomly sampled training, validation, and testing data sets from 420 patients who underwent successful catheter ablation (CA) to treat VT or PVC, containing 340 (81%), 38 (9%), and 42 (10%) patients, respectively. We iteratively trained a machine learning algorithm supplied with 1,600,800 features extracted via our proprietary algorithm from 12-lead ECGs of the patients in the training cohort. The area under the curve (AUC) of the receiver operating characteristic curve was calculated from the internal validation data set to choose an optimal discretization cutoff threshold. RESULTS: The proposed approach attained the following performance: accuracy (ACC) of 97.62 (87.44-99.99), weighted F1-score of 98.46 (90-100), AUC of 98.99 (96.89-100), sensitivity (SE) of 96.97 (82.54-99.89), and specificity (SP) of 100 (62.97-100). CONCLUSIONS: The proposed multistage diagnostic scheme attained clinical-grade precision of prediction for LVOT and RVOT locations of VT origin with fewer applicability restrictions than prior studies.

A multicenter mixed-effects model for inference and prediction of 72-h return visits to the emergency department for adult patients with trauma-related diagnoses.

OBJECTIVE: Emergency department (ED) return visits within 72 h may be a sign of poor quality of care and entail unnecessary use of healthcare resources. In this study, we compare the performance of two leading statistical and machine learning classification algorithms, and we use the best performing approach to identify novel risk factors of ED return visits. METHODS: We analyzed 3.2 million ED encounters with at least one diagnosis under "injury, poisoning and certain other consequences of external causes" and "external causes of morbidity." These encounters included patients 18 years or older from across 128 emergency room facilities in the USA. For each encounter, we calculated the 72-h ED return status and retrieved 57 features from demographics, diagnoses, procedures, and medications administered during the process of administration of medical care. We implemented a mixed-effects model to assess the effects of the covariates while accounting for the hierarchical structure of the data. Additionally, we investigated the predictive accuracy of the extreme gradient boosting tree ensemble approach and compared the performance of the two methods. RESULTS: The mixed-effects model indicates that certain blunt force and non-blunt trauma inflates the risk of a return visit. Notably, patients with trauma to the head and patients with burns and corrosions have elevated risks. This is in addition to 11 other classes of both blunt force and non-blunt force traumas. In addition, prior healthcare resource utilization, patients who have had one or more prior return visits within the last 6 months, prior ED visits, and the number of hospitalizations within the 6 months are associated with increased risk of returning to the ED after discharge. On the one hand, the area under the receiver characteristic curve (AUROC) of the mixed-effects model was 0.710 (0.707, 0.712). On the other hand, the gradient boosting tree ensemble had a lower AUROC of 0.698 CI (0.696, 0.700) on the independent test model. CONCLUSIONS: The proposed mixed-effects model achieved the highest known AUC and resulted in the identification of novel risk factors. The model outperformed one of the leading machine learning ensemble classifiers, the extreme gradient boosting tree in terms of model performance. The risk factors we identified can assist emergency departments to decrease the number of unplanned return visits within 72 h.

A 12-Lead ECG database to identify origins of idiopathic ventricular arrhythmia containing 334 patients.

Cardiac catheter ablation has shown the effectiveness of treating the idiopathic premature ventricular complex and ventricular tachycardia. As the most important prerequisite for successful therapy, criteria based on analysis of 12-lead ECGs are employed to reliably speculate the locations of idiopathic ventricular arrhythmia before a subsequent catheter ablation procedure. Among these possible locations, right ventricular outflow tract and left outflow tract are the major ones. We created a new 12-lead ECG database under the auspices of Chapman University and Ningbo First Hospital of Zhejiang University that aims to provide high quality data enabling detection of the distinctions between idiopathic ventricular arrhythmia from right ventricular outflow tract to left ventricular outflow tract. The dataset contains 334 subjects who successfully underwent a catheter ablation procedure that validated the accurate origins of idiopathic ventricular arrhythmia.

A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients.

This newly inaugurated research database for 12-lead electrocardiogram signals was created under the auspices of Chapman University and Shaoxing People's Hospital (Shaoxing Hospital Zhejiang University School of Medicine) and aims to enable the scientific community in conducting new studies on arrhythmia and other cardiovascular conditions. Certain types of arrhythmias, such as atrial fibrillation, have a pronounced negative impact on public health, quality of life, and medical expenditures. As a non-invasive test, long term ECG monitoring is a major and vital diagnostic tool for detecting these conditions. This practice, however, generates large amounts of data, the analysis of which requires considerable time and effort by human experts. Advancement of modern machine learning and statistical tools can be trained on high quality, large data to achieve exceptional levels of automated diagnostic accuracy. Thus, we collected and disseminated this novel database that contains 12-lead ECGs of 10,646 patients with a 500 Hz sampling rate that features 11 common rhythms and 67 additional cardiovascular conditions, all labeled by professional experts. The dataset consists of 10-second, 12-dimension ECGs and labels for rhythms and other conditions for each subject. The dataset can be used to design, compare, and fine-tune new and classical statistical and machine learning techniques in studies focused on arrhythmia and other cardiovascular conditions.

Optimal Multi-Stage Arrhythmia Classification Approach.

Arrhythmia constitutes a problem with the rate or rhythm of the heartbeat, and an early diagnosis is essential for the timely inception of successful treatment. We have jointly optimized the entire multi-stage arrhythmia classification scheme based on 12-lead surface ECGs that attains the accuracy performance level of professional cardiologists. The new approach is comprised of a three-step noise reduction stage, a novel feature extraction method and an optimal classification model with finely tuned hyperparameters. We carried out an exhaustive study comparing thousands of competing classification algorithms that were trained on our proprietary, large and expertly labeled dataset consisting of 12-lead ECGs from 40,258 patients with four arrhythmia classes: atrial fibrillation, general supraventricular tachycardia, sinus bradycardia and sinus rhythm including sinus irregularity rhythm. Our results show that the optimal approach consisted of Low Band Pass filter, Robust LOESS, Non Local Means smoothing, a proprietary feature extraction method based on percentiles of the empirical distribution of ratios of interval lengths and magnitudes of peaks and valleys, and Extreme Gradient Boosting Tree classifier, achieved an F1-Score of 0.988 on patients without additional cardiac conditions. The same noise reduction and feature extraction methods combined with Gradient Boosting Tree classifier achieved an F1-Score of 0.97 on patients with additional cardiac conditions. Our method achieved the highest classification accuracy (average 10-fold cross-validation F1-Score of 0.992) using an external validation data, MIT-BIH arrhythmia database. The proposed optimal multi-stage arrhythmia classification approach can dramatically benefit automatic ECG data analysis by providing cardiologist level accuracy and robust compatibility with various ECG data sources.

A Statistical-Learning Model for Unplanned 7-Day Readmission in Pediatrics.

OBJECTIVES: The rate of pediatric 7-day unplanned readmissions is often seen as a measure of quality of care, with high rates indicative of the need for improvement of quality of care. In this study, we used machine learning on electronic health records to study predictors of pediatric 7-day readmissions. We ranked predictors by clinical significance, as determined by the magnitude of the least absolute shrinkage and selection operator regression coefficients. METHODS: Data consisting of 50 241 inpatient and observation encounters at a single tertiary pediatric hospital were retrieved; 50% of these patients' data were used for building a least absolute shrinkage and selection operator regression model, whereas the other half of the data were used for evaluating model performance. The categories of variables included were demographics, social determinants of health, severity of illness and acuity, resource use, diagnoses, medications, psychosocial factors, and other variables such as primary care no show. RESULTS: Previous hospitalizations and readmissions, medications, multiple comorbidities, longer current and previous lengths of stay, certain diagnoses, and previous emergency department use were the most significant predictors modifying a patient's risk of 7-day pediatric readmission. The model achieved an area under the curve of 0.778 (95% confidence interval 0.763-0.793). CONCLUSIONS: Predictors such as medications, previous and current health care resource use, history of readmissions, severity of illness and acuity, and certain psychosocial factors modified the risk of unplanned 7-day readmissions. These predictors are mostly unmodifiable, indicating that intervention plans on high-risk patients may be developed through discussions with patients and parents to identify underlying modifiable causal factors of readmissions.

A Novel Model for Enhanced Prediction and Understanding of Unplanned 30-Day Pediatric Readmission.

OBJECTIVES: To develop a model to assist clinicians in reducing 30-day unplanned pediatric readmissions and to enhance understanding of risk factors leading to such readmissions. METHODS: Data consisting of 38 143 inpatient clinical encounters at a tertiary pediatric hospital were retrieved, and 50% were used for training on a multivariate logistic regression model. The pediatric Rothman Index (pRI) was 1 of the novel candidate predictors considered. Multivariate model selection was conducted by minimization of Akaike Information Criteria. The area under the receiver operator characteristic curve (AUC) and values for sensitivity, specificity, positive predictive value, relative risk, and accuracy were computed on the remaining 50% of the data. RESULTS: The multivariate logistic regression model of readmission consists of 7 disease diagnosis groups, 4 measures of hospital resource use, 3 measures of disease severity and/or medical complexities, and 2 variables derived from the pRI. Four of the predictors are novel, including history of previous 30-day readmissions within last 6 months (P < .001), planned admissions (P < .001), the discharge pRI score (P < .001), and indicator of whether the maximum pRI occurred during the last 24 hours of hospitalization (P = .005). An AUC of 0.79 (0.77-0.80) was obtained on the independent test data set. CONCLUSIONS: Our model provides significant performance improvements in the prediction of unplanned 30-day pediatric readmissions with AUC higher than the LACE readmission model and other general unplanned 30-day pediatric readmission models. The model is expected to provide an opportunity to capture 39% of readmissions (at a selected operating point) and may therefore assist clinicians in reducing avoidable readmissions.

Assessing the Degree of Compassion Satisfaction and Compassion Fatigue Among Critical Care, Oncology, and Charge Nurses.

OBJECTIVES: The aim of this study was to assess the degree of compassion satisfaction and compassion fatigue (CF) among critical care, oncology and charge nurses. BACKGROUND: Cumulative grief resulting from caring for critically/terminally ill patients may result in CF, leading to lower quality care and higher nurse attrition. METHOD: Data were collected from 38 direct care nurses and 10 charge nurses, using the Professional Quality of Life. RESULTS: Charge nurses had higher secondary traumatic stress (STS) than direct care nurses. Nurses with less than 10 years of experience had lower CS than experienced nurses. Higher levels of burnout (BO) and STS were reported among charge nurses, whereas less direct care nurses had average to high BO and STS ratings. CONCLUSIONS: Previous studies focused on direct care nurses; our findings suggest that CF is prevalent among charge nurses as well. Interventions should be considered for clinical providers and charge nurses including debriefing, stress reduction, peer support, and team building.

The Effect of Malnutrition on the Risk of Unplanned 7-Day Readmission in Pediatrics.

BACKGROUND: Malnutrition is known to be associated with higher morbidity and a risk factor of readmissions in the adult population. In this study, we explore the effect of malnutrition in pediatrics because it may differ from the adult population. METHODS: Data for all inpatient encounters at a tertiary children's hospital within a 2-year period corresponding to 19 702 visits were obtained. The data included demographics, socioeconomic status, registered dietitian diagnosis of malnutrition, and variables of the LACE readmission model. We excluded all neonates and patients older than 21 years. A multivariable logistic model was obtained by implementing best subset regression on these variables, controlling for demographics and socioeconomic status, and considering all possible 2-way statistical interactions between malnutrition and the variables for demographics and socioeconomic status. RESULTS: We discovered a statistical interaction effect between a patient's age and malnutrition status (P value = .002) with respect to odds of unplanned 7-day readmission. It is indicated in this interaction term that patients who were malnourished had higher odds of readmission than patients who were not malnourished. Furthermore, younger patients who were malnourished were at increased odds of readmission than their older peers, whereas among patients who were not malnourished, younger patients were at reduced odds of readmission. CONCLUSIONS: The statistical interaction effect revealed that a patient's risk of readmission is jointly modified by the patient's age and malnutrition status. This finding advances our understanding of the complex picture of the simultaneous risk factor of unplanned 7-day readmissions in pediatrics.

The Effect of Offloading Heels on Sacral Pressure.

Offloading a patient's heels during supine surgical procedures is a common practice to prevent heel pressure injuries. This practice may increase sacral pressure and jeopardize sacral skin integrity, but prophylactic dressings may help protect sacral skin. The purpose of this study was to examine the effects of offloading the heels and of multilayered silicone foam dressings on sacral pressure. We measured the sacral pressure of 50 healthy volunteers using a pressure-mapping system under four conditions: heels not offloaded and sacral dressing applied, heels offloaded and dressing applied, heels not offloaded and no dressing, and heels offloaded and no dressing. We used linear mixed-effects modeling to compare the effects of these conditions on sacral pressure. Offloading the heels significantly increased sacral pressure (P < .001), whereas the dressing had no effect on sacral pressure (P = .49). Offloading a patient's heels may increase the risk of sacral pressure injuries.