Evaluation of the efficacy and impact of a clinical prediction tool to identify maltreatment associated with children’s burns

BackgroundAn estimated 10%–24% of children attending emergency departments with a burn are maltreated.ObjectiveTo test whether a clinical prediction tool (Burns Risk assessment for Neglect or abuse Tool; BuRN-Tool) improved the recognition of maltreatment and increased the referral of high-risk children to safeguarding services for assessment.MethodsA prospective study of children presenting with burns to four UK hospitals (2015–2018), each centre providing a minimum of 200 cases before and after the introduction of the BuRN-Tool. The proportions of children referred to safeguarding services were compared preintervention and postintervention, and the relationship between referral and the recommended cut-off for concern (BuRN-Tool score (BT-score) ≥3) was explored.ResultsThe sample was 2443 children (median age 2 years). Nurses and junior doctors mainly completed the BuRN-Tool, and a BT-score was available for 90.8% of cases. After intervention, 28.4% (334/1174) had a BT-score ≥3 and were nearly five times more likely to be discussed with a senior clinician than those with a BT-score <3 (65.3% vs 13.4%, p<0.001). There was no overall difference in the proportion of safeguarding referrals preintervention and postintervention. After intervention, the proportion of referrals for safeguarding concerns was greater when the BT-score was ≥3 (p=0.05) but not for scores <3 (p=0.60). A BT-score of 3 as a cut-off for referral had a sensitivity of 72.1, a specificity of 82.7 and a positive likelihood ratio of 4.2.ConclusionsA BT-score ≥3 encouraged discussion of cases of concern with senior colleagues and increased the referral of <5 year-olds with safeguarding concerns to children’s social care.

Validation of the appendicitis inflammatory response score in a provincial population

Background: The appendicitis inflammatory response (AIR) score is a high performing, and easy to use clinical prediction tool for the evaluation of appendicitis, but its efficacy has not been studied in the provincial setting. This retrospective, single centre study aims to validate the AIR score, estimate the effect AIR score-based risk stratification would have on the negative appendicectomy rate and compare it against other well-known clinical prediction tools for appendicitis.Methods: 425 patients treated with appendicectomy or laparoscopy between 1st Jan 2015 and Dec 31st 2017 were retrospectively provided with an AIR score. This score was compared against the final macroscopic and histological results to determine its accuracy in the local population.Results: The AIR score did not perform as well as in other published series, with an area under the receiver operating characteristic curve (AUC) of 0.836. The AIR score performed favourably in comparison to the Alvarado score (0.761), APPEND score (0.747) and adult appendicitis score (AAS) (0.828).Conclusions: This study showed that the AIR score has a high accuracy, and validates its use in a provincial setting. AIR score-based management of appendicitis would be expected to reduce non-therapeutic explorations by a minimum of 50%.

Symptom clusters in Covid19: A potential clinical prediction tool from the COVID Symptom study app

As no one symptom can predict disease severity or the need for dedicated medical support in COVID-19, we asked if documenting symptom time series over the first few days informs outcome. Unsupervised time series clustering over symptom presentation was performed on data collected from a training dataset of completed cases enlisted early from the COVID Symptom Study Smartphone application, yielding six distinct symptom presentations. Clustering was validated on an independent replication dataset between May 1-May 28th, 2020. Using the first 5 days of symptom logging, the ROC-AUC of need for respiratory support was 78.8%, substantially outperforming personal characteristics alone (ROC-AUC 69.5%). Such an approach could be used to monitor at-risk patients and predict medical resource requirements days before they are required.One sentence summaryLongitudinal clustering of symptoms can predict the need for respiratory support in severe COVID-19.