Sacral neuromodulation in children and adolescents with defecation disorders.

BACKGROUND: Even if understanding of neuronal enteropathies, such as Hirschsprung's disease and functional constipation, has been improved, specialized therapies are still missing. Sacral neuromodulation (SNM) has been established in the treatment of defecation disorders in adults. The aim of the study was to investigate effects of SNM in children and adolescents with refractory symptoms of chronic constipation. METHODS: A two-centered, prospective trial has been conducted between 2019 and 2022. SNM was applied continuously at individually set stimulation intensity. Evaluation of clinical outcomes was conducted at 3, 6, and 12 months after surgery based on the developed questionnaires and quality of life analysis (KINDLR). Primary outcome was assessed based on predefined variables of fecal incontinence and defecation frequency. KEY RESULTS: Fifteen patients enrolled in the study and underwent SNM (median age 8.0 years (range 4-17 years)): eight patients were diagnosed with Hirschsprung's disease (53%). Improvement of defecation frequency was seen in 8/15 participants (53%) and an improvement of fecal incontinence in 9/12 patients (75%). We observed stable outcome after 1 year of treatment. Surgical revision was necessary in one patient after electrode breakage. Urinary incontinence was observed as singular side effect of treatment in two patients (13%), which was manageable with the reduction of stimulation intensity. CONCLUSIONS: SNM shows promising clinical results in children and adolescents presenting with chronic constipation refractory to conservative therapy. Indications for patients with enteral neuropathies deserve further confirmation.

Suspected Simple Appendicitis in Children: Should We Use a Nonoperative, Antibiotic-Free Approach? An Observational Study.

BACKGROUND: Simple appendicitis may be self-limiting or require antibiotic treatment or appendectomy. The aim of this study was to assess the feasibility and safety of a nonoperative, antibiotic-free approach for suspected simple appendicitis in children. METHODS: This single-center, retrospective study included patients (0-17 years old) who were hospitalized at the pediatric surgery department due to suspected appendicitis between 2011 and 2012. Data from patients who primarily underwent appendectomy were used as controls. The follow-up of nonoperatively managed patients was conducted in 2014. The main outcome of interest was appendicitis recurrence. RESULTS: A total of 365 patients were included: 226 were treated conservatively and 139 underwent appendectomy. Fourteen (6.2% of 226) of the primarily nonoperatively treated patients required secondary appendectomy during follow-up, and histology confirmed simple, uncomplicated appendicitis in 10 (4.4% of 226) patients. Among a subset of 53 patients managed nonoperatively with available Alvarado and/or Pediatric Appendicitis Scores and sonographic appendix diameters in clinical reports, 29 met the criteria for a high probability of appendicitis. Three of these patients (10.3% of 29) underwent secondary appendectomy. No complications were reported during follow-up. CONCLUSIONS: A conservative, antibiotic-free approach may be considered for pediatric patients with suspected uncomplicated appendicitis in a hospital setting. Only between 6 and 10% of these patients required secondary appendectomy. Nevertheless, the cohort of patients treated nonoperatively was likely to have also included individuals with further abdominal conditions other than appendicitis. Active observation and clinical support during the disease course may help patients avoid unnecessary procedures and contribute to spontaneous resolution of appendicitis or other pediatric conditions as the cause of abdominal pain. However, further studies are needed to define validated diagnostic and management criteria.

Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis.

Appendicitis is among the most frequent reasons for pediatric abdominal surgeries. Previous decision support systems for appendicitis have focused on clinical, laboratory, scoring, and computed tomography data and have ignored abdominal ultrasound, despite its noninvasive nature and widespread availability. In this work, we present interpretable machine learning models for predicting the diagnosis, management and severity of suspected appendicitis using ultrasound images. Our approach utilizes concept bottleneck models (CBM) that facilitate interpretation and interaction with high-level concepts understandable to clinicians. Furthermore, we extend CBMs to prediction problems with multiple views and incomplete concept sets. Our models were trained on a dataset comprising 579 pediatric patients with 1709 ultrasound images accompanied by clinical and laboratory data. Results show that our proposed method enables clinicians to utilize a human-understandable and intervenable predictive model without compromising performance or requiring time-consuming image annotation when deployed. For predicting the diagnosis, the extended multiview CBM attained an AUROC of 0.80 and an AUPR of 0.92, performing comparably to similar black-box neural networks trained and tested on the same dataset.

Using Machine Learning to Predict the Diagnosis, Management and Severity of Pediatric Appendicitis.

Background: Given the absence of consolidated and standardized international guidelines for managing pediatric appendicitis and the few strictly data-driven studies in this specific, we investigated the use of machine learning (ML) classifiers for predicting the diagnosis, management and severity of appendicitis in children. Materials and Methods: Predictive models were developed and validated on a dataset acquired from 430 children and adolescents aged 0-18 years, based on a range of information encompassing history, clinical examination, laboratory parameters, and abdominal ultrasonography. Logistic regression, random forests, and gradient boosting machines were used for predicting the three target variables. Results: A random forest classifier achieved areas under the precision-recall curve of 0.94, 0.92, and 0.70, respectively, for the diagnosis, management, and severity of appendicitis. We identified smaller subsets of 6, 17, and 18 predictors for each of targets that sufficed to achieve the same performance as the model based on the full set of 38 variables. We used these findings to develop the user-friendly online Appendicitis Prediction Tool for children with suspected appendicitis. Discussion: This pilot study considered the most extensive set of predictor and target variables to date and is the first to simultaneously predict all three targets in children: diagnosis, management, and severity. Moreover, this study presents the first ML model for appendicitis that was deployed as an open access easy-to-use online tool. Conclusion: ML algorithms help to overcome the diagnostic and management challenges posed by appendicitis in children and pave the way toward a more personalized approach to medical decision-making. Further validation studies are needed to develop a finished clinical decision support system.