Contemporary Epidemiology of and Risk Factors Associated with Removal of a Pathologically Normal Appendix in Children with Suspected Appendicitis

BackgroundThe goal of this study was to characterize contemporary performance benchmarks and risk factors associated with negative appendectomy (NA) in children with suspected appendicitis.MethodsA multicenter retrospective cohort analysis of children undergoing appendectomy for suspected appendicitis was performed using data from the 2016–2021 NSQIP-Pediatric Appendectomy Targeted Public Use Files. Multivariable regression was used to evaluate the influence of year, age, sex, and WBC count on NA rate, and to generate rate estimates for NA based on different combinations of demographic characteristics and WBC profiles.Results100,322 patients were included from 140 hospitals. The overall NA rate was 2.4%, and rates decreased significantly during the study period (2016: 3.1% vs. 2021: 2.3%, p < 0.001). In adjusted analyses, the highest risk for NA was associated with a normal WBC (<9000/mm³; OR 5.31 [95% CI: 4.87–5.80]), followed by female sex (OR 1.55 [95% CI: 1.42–1.68]) and age <5 years (OR 1.64 [95% CI 1.39, 1.94]). Model-estimated risk for NA varied significantly across demographic and WBC strata, with a 14.4-fold range in rates between subgroups with the lowest and highest predicted risk (males 13–17 years with elevated WBC [1.1%] vs. females 3–4 years with normal WBC [15.8%]).ConclusionsContemporary NA rates have decreased over time, however NA risk remains high in children without a leukocytosis, particularly for girls and children <5 years of age. These data provide contemporary performance benchmarks for NA in children with suspected appendicitis and identify high-risk populations where further efforts to mitigate NA risk should be targeted.Level of EvidenceIII.     

Hip Displacement in Cerebral Palsy: The Role of Surveillance

IntroductionHip displacement is common in cerebral palsy (CP) and is related to the severity of neurological and functional impairment. It is a silent, but progressive disease, and can result in significant morbidity and decreased quality of life, if left untreated. The pathophysiology of hip displacement in CP is a combination of hip flexor-adductor muscle spasticity, abductor muscle weakness, and delayed weight-bearing, resulting in proximal femoral deformities and progressive acetabular dysplasia. Due to a lack of symptoms in the early stages of hip displacement, the diagnosis is easily missed. Awareness of this condition and regular surveillance by clinical examination and serial radiographs of the hips are the key to early diagnosis and treatment.Hip surveillance programmesSeveral population-based studies from around the world have demonstrated that universal hip surveillance in children with CP allows early detection of hip displacement and appropriate early intervention, with a resultant decrease in painful dislocations. Global hip surveillance models are based upon the patients’ age, functional level determined by the Gross Motor Function Classification system (GMFCS), gait classification, standardized clinical exam, and radiographic indices such as the migration percentage (MP), as critical indicators of progressive hip displacement.ConclusionDespite 25 years of evidence showing the efficacy of established hip surveillance programmes, there is poor awareness among healthcare professionals in India about the importance of regular hip surveillance in children with CP. There is a need for professional organizations to develop evidence-based guidelines for hip surveillance which are relevant to the Indian context.     

Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer：A multicenter study

IntroductionPredicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic information of the tumor, which is important for therapeutic response effectiveness. In this study, we aimed to investigate whether predictive information for pCR could be detected from WSIs.Materials and methodsWe retrospectively collected data from four cohorts of 874 patients diagnosed with biopsy-proven breast cancer. A deep learning pathological model (DLPM) was constructed to predict pCR using biopsy WSIs in the primary cohort, and it was then validated in three external cohorts. The DLPM could generate a deep learning pathological score (DLPs) for each patient; stromal tumor-infiltrating lymphocytes (TILs) were selected for comparison with DLPs.ResultsThe WSI feature-based DLPM showed good predictive performance with the highest area under the curve (AUC) of 0.72 among the cohorts. Alternatively, the combination of the DLPM and clinical characteristics offered a better prediction performance (AUC >0.70) in all cohorts. We also evaluated the performance of DLPM in three different breast subtypes with the best prediction for the triple-negative breast cancer (TNBC) subtype (AUC: 0.73). Moreover, DLPM combined with clinical characteristics and stromal TILs achieved the highest AUC in the primary cohort (AUC: 0.82) and validation cohort 1 (AUC: 0.80).ConclusionOur study suggested that WSIs integrated with deep learning could potentially predict pCR to NAC in breast cancer. The predictive performance will be improved by combining clinical characteristics. DLPs from DLPM can provide more information compared to stromal TILs for pCR prediction.     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

Locally advanced cervical carcinoma patients treated with chemoradiation followed by radical surgery: clinical response and oncological outcomes according to histotype after propensity score analysis

IntroductionThe aims of this study were to analyze the pathological response, and survival outcomes of adenocarcinoma/adenosquamous (AC/ASC) versus squamous cell carcinoma (SCC) in patients with locally advanced cervical cancer (LACC) managed by chemoradiotherapy followed by radical surgery.MethodsRetrospective, multicenter, observational study, including patients with SCC and AC/ACS LACC patients treated with preoperative CT/RT followed by tailored radical surgery (RS) between 06/2002 and 05/2017. Clinical-pathological characteristics were compared between patients with SCC versus AC/ASC. A 1:3 ratio propensity score (PS) matching was applied to remove the variables imbalance between the two groups.ResultsAfter PS, 320 patients were included, of which 240 (75.0%) in the SCC group, and 80 (25.0%) in the AC/ASC group. Clinico-pathological and surgical baseline characteristics were balanced between the two study groups. Percentage of pathologic complete response was 47.5% in SCC patients versus 22.4% of AC/ASC ones (p < 0.001). With a median follow-up of 51 months (range:1–199), there were 54/240 (22.5%) recurrences in SCC versus 28/80 (35.0%) in AC/ASC patients (p = 0.027). AC/ASC patients experienced worse disease free (DFS), and overall survival (OS) compared to SCC patients (p = 0.019, and p = 0.048, respectively). In multivariate analysis, AC/ACS histotype, and FIGO stage were associated with worse DFS and OS.ConclusionIn LACC patients treated with CT/RT followed by RS, AC/ASC histology was associated with lower pathological complete response to CT/RT, and higher risk of recurrence and death compared with SCC patients. This highlights the need for specific therapeutic strategies based on molecular characterization to identify targets and develop novel treatments.