Diagnosis of acute appendicitis in children using urinary 5-hydroxy indol acetic acid and pediatric appendicitis score: A randomized control trial.
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ABSTRACT: Purpose
Acute appendicitis in children represents a common problem. Diagnosis may be difficult due to lack of clinical data. Several scoring systems and laboratory investigations are used for diagnosis. This study aimed to build a model for diagnosis of acute appendicitis in children using urinary 5-hydroxyindoleacetic acid (5-HIAA) and pediatric appendicitis score.Methods
This study was conducted on 191 children with suspicion of acute appendicitis. They were divided into two groups A and B. Children were evaluated in group A with pediatric appendicitis score, ultrasound, and CRP. In group B children were evaluated in the same manner of group A plus measuring of 5-HIAA.Results
mean age was 13.3 ± 5.2 years. The mean duration of symptoms was 2.2 ± 1.4 days. The mean level of urinary 5-HIAA was 43.53 ± 24.05 in appendicitis patients in group B. In group A there were 65 cases who had appendectomy. Seventy-five children were operated in group B. Negative appendectomies were found in 13 and 7 cases in groups A and B respectively. Thirteen cases were readmitted in group A with diagnosis of acute appendicitis while seven cases were readmitted in group B.Conclusion
This combination of urinary 5-HIAA and pediatric appendicitis score builds a model for diagnosis of acute appendicitis in children. This model improves the accuracy of diagnosis of acute appendicitis, reduces both the incidence of negative appendectomies and the incidence of readmission or missed cases in children.
SUBMITTER: Khirallah MG
PROVIDER: S-EPMC8058523 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundDiagnosis of appendicitis is still clinically challenging where resources are limited. The purpose of this study was to develop and externally validate Ramathibodi Appendicitis Score (RAMA-AS) in aiding diagnosis of appendicitis.MethodsA two-phase cross-sectional study (i.e., derivation and validation) was conducted at Ramathibodi Hospital (for derivation) and at Thammasat University Hospital and Chaiyaphum Hospital (for validation). Patients with abdominal pain and suspected of having appendicitis were enrolled. Multiple logistic regression was applied to develop a parsimonious model. Calibration and discrimination performances were assessed. In addition, our RAMA-AS was compared with Alvarado's score performances using ROC curve analysis.ResultsThe RAMA-AS consisted of three domains with seven predictors including symptoms (i.e., progression of pain, aggravation of pain, and migration of pain), signs (i.e., fever and rebound tenderness), and laboratory tests (i.e., white blood cell count (WBC) and neutrophil). The model fitted well with data, and it performed better discrimination than the Alvarado score with C-statistics of 0.842 (95% CI 0.804, 0.881) versus 0.760 (0.710, 0.810). Internal validation by bootstrap yielded Sommer's D of 0.686 (0.608, 0.763) and C-statistics of 0.848 (0.846, 0.849). The C-statistics of two external validations were 0.853 (0.791, 0.915) and 0.813 (0.736, 0.892) with fair calibrations.ConclusionRAMA-AS should be a useful tool for aiding diagnosis of appendicitis with good calibration and discrimination performances.
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Project description:Gene expression profiling of pediatric patients with simple appendicitis (SA) compared to perforated appendicitis (PA) and non-appendicitis abdominal pain (NAAP).
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Project description: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.
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Project description:The objectives were to assess the test characteristics of ultrasound (US) in diagnosing appendicitis in children and to evaluate site-related variations based on the frequency of its use. Additionally, the authors assessed the test characteristics of US when the appendix was clearly visualized.This was a secondary analysis of a prospective, 10-center observational study. Children aged 3 to 18 years with acute abdominal pain concerning for appendicitis were enrolled. US was performed at the discretion of the treating physician.Of 2,625 patients enrolled, 965 (36.8%) underwent abdominal US. US had an overall sensitivity of 72.5% (95% confidence interval [CI] = 58.8% to 86.3%) and specificity 97.0% (95% CI = 96.2% to 97.9%) in diagnosing appendicitis. US sensitivity was 77.7% at the three sites (combined) that used it in 90% of cases, 51.6% at a site that used it in 50% of cases, and 35% at the four remaining sites (combined) that used it in 9% of cases. US retained a high specificity of 96% to 99% at all sites. Of the 469 (48.6%) cases across sites where the appendix was clearly visualized on US, its sensitivity was 97.9% (95% CI = 95.2% to 99.9%), with a specificity of 91.7% (95% CI = 86.7% to 96.7%).Ultrasound sensitivity and the rate of visualization of the appendix on US varied across sites and appeared to improve with more frequent use. US had universally high sensitivity and specificity when the appendix was clearly identified. Other diagnostic modalities should be considered when the appendix is not definitively visualized by US.
| S-EPMC5562364 | biostudies-other
Project description:ImportanceDelayed appendicitis diagnosis is associated with worse outcomes. Appendicitis hospital care costs associated with delayed diagnosis are unknown.ObjectiveTo determine whether delayed appendicitis diagnosis was associated with increased appendicitis hospital care costs.Design, setting, and participantsThis cohort study used data from patients receiving an appendectomy aged 18 to 64 years in 5 states (Florida, Maryland, Massachusetts, New York, Wisconsin) that were captured in the Healthcare Cost and Utilization Project State Inpatient and Emergency Department databases for the years 2016 and 2017 with no additional follow-up. Data were analyzed January through April 2023.ExposuresDelayed diagnosis was defined as a previous emergency department or inpatient hospital encounter with an abdominal diagnosis other than appendicitis, and no intervention 7 days prior to appendectomy encounter.Main outcomes and measuresThe main outcome was appendicitis hospital care costs. This was calculated from aggregated charges of encounters 7 days prior to appendectomy, the appendectomy encounter, and 30 days postoperatively. Cost-to-charge ratios were applied to charges to obtain costs, which were then adjusted for wage index, inflation to 2022 US dollar, and with extreme outliers winsorized. A multivariable Poisson regression estimated appendicitis hospital care costs associated with a delayed diagnosis while controlling for age, sex, race and ethnicity, insurance status, care discontinuity, income quartile, hospital size, teaching status, medical school affiliation, percentage of Black and Hispanic patient discharges, core-based statistical area, and state.ResultsThere were 76 183 patients (38 939 female [51.1%]; 2192 Asian or Pacific Islander [2.9%], 14 132 Hispanic [18.5%], 8195 non-Hispanic Black [10.8%], 46 949 non-Hispanic White [61.6%]) underwent appendectomy, and 2045 (2.7%) had a delayed diagnosis. Delayed diagnosis patients had median (IQR) unadjusted cost of $11 099 ($6752-$17 740) compared with $9177 ($5575-$14 481) for nondelayed (P < .001). Patients with delayed diagnosis had 1.23 times (95% CI, 1.16-1.28 times) adjusted increased appendicitis hospital care costs. The mean marginal cost of delayed diagnosis was $2712 (95% CI, $2083-$3342). Even controlling for delayed diagnosis, non-Hispanic Black patients had 1.22 times (95% CI, 1.17-1.28 times) the adjusted increased appendicitis hospital care costs compared with non-Hispanic White patients.Conclusions and relevanceIn this cohort study, delayed diagnosis of appendicitis was associated with increased hospital care costs.
| S-EPMC11019393 | biostudies-literature