Pediatric Hospital Admissions with Concern for Neglect: Correlations Between Neglect Types and Other Clinical and Environmental Risk Factors.

OBJECTIVE: Child neglect is a public health concern with negative consequences that impact children, families, and society. While neglect is involved with many pediatric hospitalizations, few studies explore characteristics associated with neglect types, social needs, and post-discharge care. METHODS: Data on neglect type, sociodemographics, social needs, inpatient consultations, and post-discharge care were collected from the electronic medical record for children aged 0-5 years who were hospitalized with concern for neglect during 2016-2020. Frequencies and percentages were calculated to determine sample characteristics. The Chi-square Test for Independence was used to evaluate associations between neglect type and other variables. RESULTS: The most common neglect types were inadequate nutrition (40%), inability to provide basic care (37%), intrauterine substance exposure (25%), combined types (23%), and inadequate medical care (10%). Common characteristics among neglect types included age less than 1 year, male sex, Hispanic ethnicity, public insurance, past involvement with Child Protective Services, and inpatient consultation services (social work, physical therapy, and occupational therapy), and post-discharge recommendations (primary care, physical therapy, and regional center). Neglect type groups varied by child medical history, social needs, and discharge recommendations. Statistically significant associations supported differences per neglect type. CONCLUSIONS: Our findings highlight five specific types of neglect seen in an impoverished and ethnically diverse geographic region. Post-discharge care needs should focus on removing social barriers and optimizing resources, in particular mental health, to mitigate the risk of continued neglect. Future studies should focus on prevention strategies, tailored interventions, and improved resource allocations per neglect type and discharge location.

Predicting Overall Survival for Patients with Malignant Mesothelioma Following Radiotherapy via Interpretable Machine Learning.

PURPOSE/OBJECTIVES: Malignant pleural mesothelioma (MPM) is a rare but aggressive cancer arising from the cells of the thoracic pleura with a poor prognosis. We aimed to develop a model, via interpretable machine learning (ML) methods, predicting overall survival for MPM following radiotherapy based on dosimetric metrics as well as patient characteristics. MATERIALS/METHODS: Sixty MPM (37 right, 23 left) patients treated on a Tomotherapy unit between 2013 and 2018 were retrospectively analyzed. All patients received 45 Gy (25 fractions). The multivariable Cox regression (Cox PH) model and Survival Support Vector Machine (sSVM) were applied to build predictive models of overall survival (OS) based on clinical, dosimetric, and combined variables. RESULTS: Significant differences in dosimetric endpoints for critical structures, i.e., the lung, heart, liver, kidney, and stomach, were observed according to target laterality. The OS was found to be insignificantly different (p = 0.18) between MPM patients who tested left- and right-sided, with 1-year OS of 77.3% and 75.0%, respectively. With Cox PH regression, considering dosimetric variables for right-sided patients alone, an increase in PTV_Min, Total_Lung_PTV_Mean, Contra_Lung_Volume, Contra_Lung_V20, Esophagus_Mean, and Heart_Volume had a greater hazard to all-cause death, while an increase in Total_Lung_PTV_V20, Contra_Lung_V5, and Esophagus_Max had a lower hazard to all-cause death. Considering clinical variables alone, males and increases in N stage had greater hazard to all-cause death; considering both clinical and dosimetric variables, increases in N stage, PTV_Mean, PTV_Min, and esophagus_Mean had greater hazard to all-cause death, while increases in T stage and Heart_V30 had lower hazard to all-cause-death. In terms of C-index, the Cox PH model and sSVM performed similarly and fairly well when considering clinical and dosimetric variables independently or jointly. CONCLUSIONS: Clinical and dosimetric variables may predict the overall survival of mesothelioma patients, which could guide personalized treatment planning towards a better treatment response. The identified predictors and their impact on survival offered additional value for translational application in clinical practice.

Evaluating current chest imaging practices of pediatric patients with COVID-19: A retrospective analysis.

PURPOSE: This retrospective analysis aims to examine the effectiveness of the current chest imaging guidelines regarding COVID-19 positive pediatric patients on our study group of patients aged 0 to 18. MATERIALS AND METHODS: We examined clinical and imaging data of 178 pediatric COVID-19 positive patients confirmed by PCR admitted to the Children's Hospital of Los Angeles between March 6, 2020 and June 23, 2020. RESULTS: Of 178 patients, only 46 (27%) patients underwent any form of chest imaging. Thirteen (28%) of 46 imaged patients had positive chest X-rays (CXR) or computed tomography (CT) chest findings, with 8 (62%) of the 13 patients suggesting pneumonia or multifocal pneumonia, 3 (23%) patients having acute respiratory distress syndrome, and 2 (15%) patients demonstrating left sided pleural effusions thought to be the result of ruptured appendicitis unrelated to their COVID-19 diagnosis. All but one patient had significant prior medical histories with an associated comorbid medical condition. Of the 46 imaged patients, 17 (37%) patients had a negative chest X-ray, and 15 (33%) patients had suggestive findings of viral etiology. 132 patients were not imaged. CONCLUSION: Our study population corroborated current chest imaging guidelines in pediatric patients. Chest imaging modalities such as CXR and CT should be reserved for patients who are severely symptomatic and/or possess prior comorbidities such as immunosuppression, diabetes, asthma, obesity, or where other differential etiologies must be entertained.