Etiologies of bloody diarrhea in children presenting with acute gastroenteritis to US emergency departments.

Among 111 children presenting with bloody diarrhea in a multicenter study of molecular testing in US emergency departments, we found viral pathogens in 18%, bacteria in 48%, protozoa in 2%, and no pathogens detected in 38%.

A pipeline for evaluation of machine learning/AI models to quantify PD-L1 immunohistochemistry.

Immunohistochemistry (IHC) is used to guide treatment decisions in multiple cancer types. For treatment with checkpoint inhibitors, PD-L1 IHC is used as a companion diagnostic. However, the scoring of PD-L1 is complicated by its expression in cancer and immune cells. Separation of cancer and non-cancer regions is needed to calculate tumor proportion scores (TPS) of PD-L1, which is based on the percentage of PD-L1 positive cancer cells. Evaluation of PD-L1 expression requires highly experienced pathologists and is often challenging and time consuming. Here we used a multi-institutional cohort of 77 lung cancer cases stained centrally with the PD-L1 22C3 clone. We developed a four-step pipeline for measuring TPS that includes the co-registration of H&E, PD-L1 and negative control (NC) digital slides for exclusion of necrosis, segmentation of cancer regions and quantification of PD-L1+ cells. As cancer segmentation is a challenging step for TPS generation, we trained DeepLab V3 in the Visiopharm software package to outline cancer regions in PD-L1 and negative control (NC) images and evaluated the model performance by mean intersection over union (mIoU) against manual outlines. Only 14 cases were required to accomplish an mIoU of 0.82 for cancer segmentation in hematoxylin stained NC cases. For PD-L1 stained slides, a model trained on PD-L1 tiles augmented by registered NC tiles achieved an mIoU of 0.79. In segmented cancer regions from whole slide images, the digital TPS achieved an accuracy of 75% against the manual TPS scores from the pathology report. Major reasons for algorithmic inaccuracies include the inclusion of immune cells in cancer outlines and poor nuclear segmentation of cancer cells. Our transparent and stepwise approach and performance metrics can be applied to any IHC assay to provide pathologists with important insights when to apply and how to evaluate commercial automated IHC scoring systems.

Integrating molecular and clinical variables to predict myocardial recovery.

Mechanical unloading and circulatory support with left ventricular assist devices (LVADs) mediate significant myocardial improvement in a subset of advanced heart failure (HF) patients. The clinical and biological phenomena associated with cardiac recovery are under intensive investigation. Left ventricular (LV) apical tissue, alongside clinical data, were collected from HF patients at the time of LVAD implantation (n=208). RNA was isolated and mRNA transcripts were identified through RNA sequencing and confirmed with RT-qPCR. To our knowledge this is the first study to combine transcriptomic and clinical data to derive predictors of myocardial recovery. We used a bioinformatic approach to integrate 59 clinical variables and 22,373 mRNA transcripts at the time of LVAD implantation for the prediction of post-LVAD myocardial recovery defined as LV ejection fraction (LVEF) ≥40% and LV end-diastolic diameter (LVEDD) ≤5.9cm, as well as functional and structural LV improvement independently by using LVEF and LVEDD as continuous variables, respectively. To substantiate the predicted variables, we used a multi-model approach with logistic and linear regressions. Combining RNA and clinical data resulted in a gradient boosted model with 80 features achieving an AUC of 0.731±0.15 for predicting myocardial recovery. Variables associated with myocardial recovery from a clinical standpoint included HF duration, pre-LVAD LVEF, LVEDD, and HF pharmacologic therapy, and LRRN4CL (ligand binding and programmed cell death) from a biological standpoint. Our findings could have diagnostic, prognostic, and therapeutic implications for advanced HF patients, and inform the care of the broader HF population.

Integration of population-level data sources into an individual-level clinical prediction model for dengue virus test positivity.

The differentiation of dengue virus (DENV) infection, a major cause of acute febrile illness in tropical regions, from other etiologies, may help prioritize laboratory testing and limit the inappropriate use of antibiotics. While traditional clinical prediction models focus on individual patient-level parameters, we hypothesize that for infectious diseases, population-level data sources may improve predictive ability. To create a clinical prediction model that integrates patient-extrinsic data for identifying DENV among febrile patients presenting to a hospital in Thailand, we fit random forest classifiers combining clinical data with climate and population-level epidemiologic data. In cross-validation, compared to a parsimonious model with the top clinical predictors, a model with the addition of climate data, reconstructed susceptibility estimates, force of infection estimates, and a recent case clustering metric significantly improved model performance.

Assessment of Model Estimated and Directly Observed Weather Data for Etiological Prediction of Diarrhea.

Recent advances in clinical prediction for diarrheal etiology in low- and middle-income countries have revealed that addition of weather data improves predictive performance. However, the optimal source of weather data remains unclear. We aim to compare model estimated satellite- and ground-based observational data with weather station directly-observed data for diarrheal prediction. We used clinical and etiological data from a large multi-center study of children with diarrhea to compare these methods. We show that the two sources of weather conditions perform similarly in most locations. We conclude that while model estimated data is a viable, scalable tool for public health interventions and disease prediction, directly observed weather station data approximates the modeled data, and given its ease of access, is likely adequate for prediction of diarrheal etiology in children in low- and middle-income countries.

Integration of population-level data sources into an individual-level clinical prediction model for dengue virus test positivity.

The differentiation of dengue virus (DENV) infection, a major cause of acute febrile illness in tropical regions, from other etiologies, may help prioritize laboratory testing and limit the inappropriate use of antibiotics. While traditional clinical prediction models focus on individual patient-level parameters, we hypothesize that for infectious diseases, population-level data sources may improve predictive ability. To create a clinical prediction model that integrates patient-extrinsic data for identifying DENV among febrile patients presenting to a hospital in Thailand, we fit random forest classifiers combining clinical data with climate and population-level epidemiologic data. In cross validation, compared to a parsimonious model with the top clinical predictors, a model with the addition of climate data, reconstructed susceptibility estimates, force of infection estimates, and a recent case clustering metric, significantly improved model performance.

Derivation and validation of a clinical prediction model for risk-stratification of children hospitalized with severe pneumonia in Bangladesh.

Children with severe pneumonia in low- and middle-income countries (LMICs) suffer from high rates of treatment failure despite appropriate World Health Organization (WHO)-directed antibiotic treatment. Developing a clinical prediction rule for treatment failure may allow early identification of high-risk patients and timely intervention to decrease mortality. We used data from two separate studies conducted at the Dhaka Hospital of the International Centre for Diarrheal Disease Research, Bangladesh (icddr,b) to derive and externally validate a clinical prediction rule for treatment failure of children hospitalized with severe pneumonia. The derivation dataset was from a randomized clinical trial conducted from 2018 to 2019, studying children aged 2 to 59 months hospitalized with severe pneumonia as defined by WHO. Treatment failure was defined by the persistence of danger signs at the end of 48 hours of antibiotic treatment or the appearance of any new danger signs within 24 hours of enrollment. We built a random forest model to identify the top predictors. The top six predictors were the presence of grunting, room air saturation, temperature, the presence of lower chest wall indrawing, the presence of respiratory distress, and central cyanosis. Using these six predictors, we created a parsimonious model with a discriminatory performance of 0.691, as measured by area under the receiving operating curve (AUC). We performed external validation using a temporally distinct dataset from a cohort study of 191 similarly aged children with severe acute malnutrition and pneumonia. In external validation, discriminatory performance was maintained with an improved AUC of 0.718. In conclusion, we developed and externally validated a parsimonious six-predictor model using random forest methods to predict treatment failure in young children with severe pneumonia in Bangladesh. These findings can be used to further develop and validate parsimonious and pragmatic prognostic clinical prediction rules for pediatric pneumonia, particularly in LMICs.

Skin Color Representation in Teaching Photographs: One Institution's Approach to Addressing Visual Racism in Medical Education.

Problem: Visual racism refers to both the underrepresentation and inappropriate representation of darker skin types in medical education. By not teaching medical students and resident physicians to recognize common conditions in darker skin, it perpetuates biases that contribute to healthcare disparities for racial and ethnic minoritized groups. In this paper we describe our efforts to engage in institutional anti-racism work by addressing imbalances in representation of darker skin types in visual teaching images within our institution's curriculum. Intervention: We initially surveyed preclinical medical students regarding their perceptions of skin color representation in two courses. Researchers recorded the skin types of all teaching photographs in these courses in 2020. We then provided feedback and education to faculty, proposing that they increase brown and black skin color representation in educational content. During 2021, we reviewed the same courses and surveyed students again to ascertain the implementation and impact of our proposal. Context: We applied our intervention to two courses, Host & Defense (H&D) and Skin, Muscle, Bone, and Joint (SMBJ) since both courses utilize a large number of teaching images. Impact: From 2020 to 2021, both H&D and SMBJ significantly increased the proportion of visual teaching images that included darker skin types, with an increase from 28% to 42% in H&D and 20% to 30% in SMBJ. Significantly more students in the courses' 2021 iterations (73% in H&D, 93% in SMBJ) felt that lectures had appropriate representations of darker skin types when compared to students who took the course in 2020 (8% in H&D, 51% in SMBJ). Students in 2021 felt more confident in recognizing dermatological signs and symptoms in patients with darker skin than students in 2020. The majority of students in both 2020 and 2021 reported wanting to see a gradient of skin types for every dermatological condition discussed. Lessons learned: Our work suggests that addressing visual racism can be achieved partly by setting expectations for increased visual representation, collaborating across educational departments, and establishing clear metrics for assessing implementation. Future interventions will require a continual feedback loop of monitoring learning material, assessing faculty and student perception, refining resources, and recommending revisions to improve visual representation across the entire curriculum.

Derivation and External Validation of a Clinical Prediction Model for Viral Diarrhea Etiology in Bangladesh.

Background: Antibiotics are commonly overused for diarrheal illness in many low- and middle-income countries, partly due to a lack of diagnostics to identify viral cases, in which antibiotics are not beneficial. This study aimed to develop clinical prediction models to predict risk of viral-only diarrhea across all ages, using routinely collected demographic and clinical variables. Methods: We used a derivation dataset from 10 hospitals across Bangladesh and a separate validation dataset from the icddr,b Dhaka Hospital. The primary outcome was viral-only etiology determined by stool quantitative polymerase chain reaction. Multivariable logistic regression models were fit and externally validated; discrimination was quantified using area under the receiver operating characteristic curve (AUC) and calibration assessed using calibration plots. Results: Viral-only diarrhea was common in all age groups (<1 year, 41.4%; 18-55 years, 17.7%). A forward stepwise model had AUC of 0.82 (95% confidence interval [CI], .80-.84) while a simplified model with age, abdominal pain, and bloody stool had AUC of 0.81 (95% CI, .78-.82). In external validation, the models performed adequately although less robustly (AUC, 0.72 [95% CI, .70-.74]). Conclusions: Prediction models consisting of 3 routinely collected variables can accurately predict viral-only diarrhea in patients of all ages in Bangladesh and may help support efforts to reduce inappropriate antibiotic use.

Derivation and external validation of a clinical prognostic model identifying children at risk of death following presentation for diarrheal care.

Diarrhea continues to be a leading cause of death for children under-five. Amongst children treated for acute diarrhea, mortality risk remains elevated during and after acute medical management. Identification of those at highest risk would enable better targeting of interventions, but available prognostic tools lack validation. We used clinical and demographic data from the Global Enteric Multicenter Study (GEMS) to build clinical prognostic models (CPMs) to predict death (in-treatment, after discharge, or either) in children aged ≤59 months presenting with moderate-to-severe diarrhea (MSD), in Africa and Asia. We screened variables using random forests, and assessed predictive performance with random forest regression and logistic regression using repeated cross-validation. We used data from the Kilifi Health and Demographic Surveillance System (KHDSS) and Kilifi County Hospital (KCH) in Kenya to externally validate our GEMS-derived CPM. Of 8060 MSD cases, 43 (0.5%) children died in treatment and 122 (1.5% of remaining) died after discharge. MUAC at presentation, respiratory rate, age, temperature, number of days with diarrhea at presentation, number of people living in household, number of children <60 months old living in household, and how much the child had been offered to drink since diarrhea started were predictive of death both in treatment and after discharge. Using a parsimonious 2-variable prediction model, we achieved an area under the ROC curve (AUC) of 0.84 (95% CI: 0.82, 0.86) in the derivation dataset, and an AUC = 0.74 (95% CI 0.71, 0.77) in the external dataset. Our findings suggest it is possible to identify children most likely to die after presenting to care for acute diarrhea. This could represent a novel and cost-effective way to target resources for the prevention of childhood mortality.

Structured Case-Based Ethics Discussion for Trainees and Faculty on Dermatopathology.

Introduction: Ethical and professional dilemmas are part of the day-to-day practice of medicine, including within dermatopathology (e.g., ethical implications of self-referring skin biopsies for pathology interpretation). There is a need for teaching aids that dermatology educators can easily access to help provide ethics education. Methods: We held an hour-long, faculty-facilitated, interactive, virtual discussion about ethical issues in dermatopathology. The session followed a structured, case-based format. We administered anonymous online feedback surveys after the session and used the Wilcoxon signed rank test to compare participants' before and after responses. Results: Seventy-two individuals from two academic institutions participated in the session. We collected 35 total responses (49%) from dermatology residents (n = 15), dermatology faculty (n = 14), medical students (n = 2), and other providers and learners (n = 4). Feedback was largely positive, with 21 attendees (60%) indicating they learned a few things and 11 (31%) indicating they learned a great deal. Additionally, 32 participants (91%) indicated they would recommend the session to a colleague. Our analysis showed that attendees had a greater self-perceived level of achievement for each of our three objectives after the session. Discussion: This dermatoethics session is structured so as to be easily shared, deployed, and built on by other institutions. We hope that other institutions will use our materials and results to improve upon the foundation presented here and that this framework will be used by other medical specialties seeking to foster ethics education in their training programs.

Development of a prediction model for the acquisition of extended spectrum beta-lactam-resistant organisms in U.S. international travellers.

BACKGROUND: Extended spectrum beta-lactamase producing Enterobacterales (ESBL-PE) present a risk to public health by limiting the efficacy of multiple classes of beta-lactam antibiotics against infection. International travellers may acquire these organisms and identifying individuals at high risk of acquisition could help inform clinical treatment or prevention strategies. METHODS: We used data collected from a cohort of 528 international travellers enrolled in a multicentre US-based study to derive a clinical prediction rule (CPR) to identify travellers who developed ESBL-PE colonization, defined as those with new ESBL positivity in stool upon return to the United States. To select candidate features, we used data collected from pre-travel and post-travel questionnaires, alongside destination-specific data from external sources. We utilized LASSO regression for feature selection, followed by random forest or logistic regression modelling, to derive a CPR for ESBL acquisition. RESULTS: A CPR using machine learning and logistic regression on 10 features has an internally cross-validated area under the receiver operating characteristic curve (cvAUC) of 0.70 (95% confidence interval 0.69-0.71). We also demonstrate that a four-feature model performs similarly to the 10-feature model, with a cvAUC of 0.68 (95% confidence interval 0.67-0.69). This model uses traveller's diarrhoea, and antibiotics as treatment, destination country waste management rankings and destination regional probabilities as predictors. CONCLUSIONS: We demonstrate that by integrating traveller characteristics with destination-specific data, we could derive a CPR to identify those at highest risk of acquiring ESBL-PE during international travel.

Clinical Prediction Rule to Guide Diagnostic Testing for Shigellosis and Improve Antibiotic Stewardship for Pediatric Diarrhea.

Background: Diarrheal diseases are a leading cause of death for children aged <5 years. Identification of etiology helps guide pathogen-specific therapy, but availability of diagnostic testing is often limited in low-resource settings. Our goal is to develop a clinical prediction rule (CPR) to guide clinicians in identifying when to use a point-of-care (POC) diagnostic for Shigella in children presenting with acute diarrhea. Methods: We used clinical and demographic data from the Global Enteric Multicenter Study (GEMS) study to build predictive models for diarrhea of Shigella etiology in children aged ≤59 months presenting with moderate to severe diarrhea in Africa and Asia. We screened variables using random forests, and assessed predictive performance with random forest regression and logistic regression using cross-validation. We used the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) study to externally validate our GEMS-derived CPR. Results: Of the 5011 cases analyzed, 1332 (27%) had diarrhea of Shigella etiology. Our CPR had high predictive ability (area under the receiver operating characteristic curve = 0.80 [95% confidence interval, .79-.81]) using the top 2 predictive variables, age and caregiver-reported bloody diarrhea. We show that by using our CPR to triage who receives diagnostic testing, 3 times more Shigella diarrhea cases would have been identified compared to current symptom-based guidelines, with only 27% of cases receiving a POC diagnostic test. Conclusions: We demonstrate how a CPR can be used to guide use of a POC diagnostic test for diarrhea management. Using our CPR, available diagnostic capacity can be optimized to improve appropriate antibiotic use.

Derivation and external validation of a clinical prognostic model identifying children at risk of death following presentation for diarrheal care.

Diarrhea continues to be a leading cause of death for children under-five. Amongst children treated for acute diarrhea, mortality risk remains elevated during and after acute medical management. Identification of those at highest risk would enable better targeting of interventions, but available prognostic tools lack validation. We used clinical and demographic data from the Global Enteric Multicenter Study (GEMS) to build predictive models for death (in-treatment, after discharge, or either) in children aged ≤59 months presenting with moderate-to-severe diarrhea (MSD), in Africa and Asia. We screened variables using random forests, and assessed predictive performance with random forest regression and logistic regression using repeated cross-validation. We used data from the Kilifi Health and Demographic Surveillance System (KHDSS) and Kilifi County Hospital (KCH) in Kenya to externally validate our GEMS-derived clinical prognostic model (CPM). Of 8060 MSD cases, 43 (0.5%) children died in treatment and 122 (1.5% of remaining) died after discharge. MUAC at presentation, respiratory rate, age, temperature, number of days with diarrhea at presentation, number of people living in household, number of children <60 months old living in household, and how much the child had been offered to drink since diarrhea started were predictive of death both in treatment and after discharge. Using a parsimonious 2-variable prediction model, we achieve an AUC=0.84 (95% CI: 0.82, 0.86) in the derivation dataset, and an AUC=0.74 (95% CI 0.71, 0.77) in the external dataset. Our findings suggest it is possible to identify children most likely to die after presenting to care for acute diarrhea. This could represent a novel and cost-effective way to target resources for the prevention of childhood mortality.

Predicting IHC staining classes of NF1 using features in the hematoxylin channel.

Immunohistochemistry (IHC) highlights specific cell types in tissues and traditionally involves antibody staining together with a hematoxylin counterstain. The intensity and pattern of hematoxylin staining differs between cell types and reveals morphological characteristics of cells. Here, we propose that features in the hematoxylin stain can be used to predict IHC labels, such as Neurofibromin (encoded by the gene NF1). The dataset consists of 7.2 million cells from benign and kidney cancer cores in a tissue microarray. Morphology and hematoxylin (H&M) features defined within QuPath are subjected to a clustering analysis in CytoMap. H&M features are also used to train 4 different XGBoost models to predict high, low, and negative NF1 stain classes in benign renal tubules, clear cell (ccRCC), papillary (PRCC), and chromophobe (ChRCC) renal carcinoma. The prediction accuracies of NF1 staining classes in benign, ccRCC, ChRCC, and PRCC range between 70% and 90% with areas under the precision recall curve PRAUCNF1-high = 0.82+0.12, PRAUCNF1-low = 0.62+0.25, and PRAUCNF1-negative = 0.83+0.16. The most important feature for predicting the NF1 class involves the minimum cellular hematoxylin staining intensity. Together, these results demonstrate the feasibility to predict NF1 expression solely from features in hematoxylin staining using open source software. Since the hematoxylin features can be obtained from regular H&E and IHC slides, the proposed workflow has broad applicability.

Association between specific unmet functional needs and desire to institutionalize among caregivers of older veterans.

OBJECTIVES: To evaluate the associations between specific functional needs of older Veterans and the desire to institutionalize (DTI) among their caregivers. METHODS: Cross-sectional multivariable logistic regression analysis of 3579 Hero Care survey responses from caregivers of Veterans at five US sites from July to December 2021. Unmet needs were areas in which the caregiver reported the Veteran needed a little more or a lot more help. Caregiver DTI was defined as the caregiver reporting that they had discussed, considered, or taken steps toward a nursing home or assisted living placement for the Veteran or that they felt the Veteran would be better off in such a setting or they were likely to move the Veteran to another living arrangement. RESULTS: Caregivers were largely white, retired, females with an average age of 71 and with some college education who spent an average of 8-9 h per day 6 days a week caring for a Veteran spouse. There was evidence of associations between the following needs and a DTI: managing incontinence, using the telephone, transportation, and arranging services in the home such as visiting nurses, home care aides, or meals on wheels. Unmet functional needs in other selected domains were not associated with the DTI. CONCLUSION: Among caregivers of older Veterans, a need for more assistance managing incontinence, telephone use, transportation, and arranging in-home services were associated with the DTI. These may represent functional markers of important clinical determinants for institutionalization as well as potential targets for intervention to reduce caregiver DTI, such as programs that provide more caregiver or Veteran support in the home to meet these needs and reduce caregiver burden.

Derivation and external validation of clinical prediction rules identifying children at risk of linear growth faltering.

Background: Nearly 150 million children under-5 years of age were stunted in 2020. We aimed to develop a clinical prediction rule (CPR) to identify children likely to experience additional stunting following acute diarrhea, to enable targeted approaches to prevent this irreversible outcome. Methods: We used clinical and demographic data from the Global Enteric Multicenter Study (GEMS) to build predictive models of linear growth faltering (decrease of ≥0.5 or ≥1.0 in height-for-age z-score [HAZ] at 60-day follow-up) in children ≤59 months presenting with moderate-to-severe diarrhea, and community controls, in Africa and Asia. We screened variables using random forests, and assessed predictive performance with random forest regression and logistic regression using fivefold cross-validation. We used the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) study to (1) re-derive, and (2) externally validate our GEMS-derived CPR. Results: Of 7639 children in GEMS, 1744 (22.8%) experienced severe growth faltering (≥0.5 decrease in HAZ). In MAL-ED, we analyzed 5683 diarrhea episodes from 1322 children, of which 961 (16.9%) episodes experienced severe growth faltering. Top predictors of growth faltering in GEMS were: age, HAZ at enrollment, respiratory rate, temperature, and number of people living in the household. The maximum area under the curve (AUC) was 0.75 (95% confidence interval [CI]: 0.75, 0.75) with 20 predictors, while 2 predictors yielded an AUC of 0.71 (95% CI: 0.71, 0.72). Results were similar in the MAL-ED re-derivation. A 2-variable CPR derived from children 0-23 months in GEMS had an AUC = 0.63 (95% CI: 0.62, 0.65), and AUC = 0.68 (95% CI: 0.63, 0.74) when externally validated in MAL-ED. Conclusions: Our findings indicate that use of prediction rules could help identify children at risk of poor outcomes after an episode of diarrheal illness. They may also be generalizable to all children, regardless of diarrhea status. Funding: This work was supported by the National Institutes of Health under Ruth L. Kirschstein National Research Service Award NIH T32AI055434 and by the National Institute of Allergy and Infectious Diseases (R01AI135114).

Analysis of Readmissions and Reoperations in Pediatric Microvascular Reconstruction.

BACKGROUND: Free tissue transfer is utilized as a reconstructive option for various anatomic defects. While it has long been performed in adults, reconstructive surgeons have used free tissue transfer to a lesser degree in children. As such, there are few analyses of factors associated with complications in free tissue transfer within this population. The aim of this study is to assess factors associated with readmission and reoperation in pediatric free flap patients utilizing the pediatric National Surgical Quality Improvement Program database. METHODS: Pediatric patients who underwent microvascular reconstruction between 2015 and 2020 were included. Patients were identified by five microvascular reconstruction Current Procedural Terminology codes and were then stratified by flap site (head and neck, extremities, trunk) and defect etiology (congenital, trauma, infection, neoplasm). Multivariate logistic regression was performed to identify factors associated with readmissions and reoperations. RESULTS: The study cohort consisted of 258 patients. The average age was 10.0 ± 4.7 years and the majority of patients were male (n = 149, 57.8%), were of white race (n = 164, 63.6%), and had a normal body mass index. Twenty-two patients (8.5%) experienced an unplanned readmission within 30 days of the initial operation, most commonly for wound disruption (31.8% of readmissions). The overall rate of unplanned reoperation within 30 days was 11.6% (n = 30) for all patients, with an average of 8.9 ± 7.5 days to reoperation. On multivariate regression analysis, each hour increase in operative time was associated with an increased odds of reoperation (odds ratio [OR]: 1.27; 95% confidence interval [CI]: 1.12, 1.45) and readmission (OR: 1.16; 95% CI: 1.02, 1.34). CONCLUSION: In pediatric patients undergoing free tissue transfer, higher readmission and reoperation risk was associated with longer operative duration. Overall, free tissue transfer is safe in the pediatric population with relatively low rates of readmission and reoperation.

Incidence and severity of asymptomatic ocular injury in adult and pediatric orbital fractures.

PURPOSE: To evaluate the incidence of severe ocular injury requiring emergent ophthalmic evaluation in visually asymptomatic patients presenting with orbital fractures. METHODS: We performed a retrospective chart review of all adult and pediatric orbital fractures between 2012-2022 at a level 1 trauma center. Ocular injuries were categorized into severe, moderate, and mild. We evaluated symptoms, mechanism of injury, visual acuity (VA), and severity of injuries using the Cochran-Armitage and linear-by-linear tests. RESULTS: Of the 2495 cases, 1534 had ophthalmology evaluation. The mean ± standard deviation age was 40.4 ± 20.4 years. Most patients were male (73.1%) and Caucasian (75.9%). The mean time to evaluation was 0.6 ± 2.5 days. 486 (31.7%) were visually symptomatic, 760 (49.5%) were asymptomatic, and 288 (18.8%) were unknown. Of the symptomatic, 135 (27.8%) had severe injuries, 108 (22.2%) had moderate injuries, 216 (44.4%) had mild injuries, and 27 (5.6%) had no injuries. Of the asymptomatic, 67 (8.8%) had severe injuries, 183 (24.1%) had moderate injuries, 468 (61.6%) had mild injuries, and 42 (5.5%) had no injuries. Symptoms correlated with injury severity (p-value <.001). The most common mechanism of injury were falls (24.0%), assaults (22.2%), and motor vehicle accidents (14.8%). CONCLUSIONS: Visually asymptomatic orbital fractures were less likely to have severe ocular injuries; however, many patients were unable to express symptoms. Emergent ophthalmology evaluation should be considered in all patients presenting with orbital fractures, especially patients with visual symptoms or are unable to report symptoms.

Staphylococcal scalded skin syndrome: Clinical features, ancillary testing, and patient management.

BACKGROUND/OBJECTIVES: The utility of ancillary testing in improving diagnostic precision or improving patient outcomes in staphylococcal scalded skin syndrome (SSSS) is unclear. Similarly, an optimal antibiotic regimen has yet to be established. Our goal was to describe clinical characteristics and ancillary work-up of SSSS, report bacterial resistance patterns, and examine patient outcomes under varying therapeutic strategies with the aim of developing an evidence-based management algorithm. METHODS: We performed a retrospective review of pediatric patients diagnosed with SSSS at Intermountain Healthcare facilities between 2010 and 2021. A Kruskal-Wallis rank sum test was used to assess median length of stay between different antibiotic regimens. RESULTS: Eighty-five cases were identified. The most common ancillary tests obtained were a complete blood count (88%), followed by chemistry analysis (80%). Blood cultures were collected in more patients (79%) compared to aerobic cultures (60%). No blood culture was positive for Staphylococcus aureus. All S. aureus isolates were methicillin-sensitive. Of those found resistant to clindamycin (36%), all demonstrated macrolide-induced clindamycin resistance. None were constitutively resistant to clindamycin. There was no statistical difference between antibiotic regimen and length of stay (p = .691). Receiving opiate medications was the only risk factor associated with prolonged hospitalization (p = .001). CONCLUSIONS: Ancillary testing does not improve diagnostic precision and can be reduced. Clindamycin does not improve patient outcomes, suggesting beta-lactams should be considered first line. Susceptibility patterns in our cohort demonstrate inducible clindamycin resistance as opposed to constitutive.