Objectives: Our primary aim was to validate the Liverpool Peritonsillar abscess Score (LPS) externally in a new patient cohort. Our secondary aim was to modify the LPS in the light of the COVID-19 pandemic to produce a no-examination variant for use in this instance.
Design: Prospective multicentre external validation study.
Setting: Six different secondary care institutions across the United Kingdom.
Participants: Patients over 16 years old who were referred to ENT with any uncomplicated sore throat such a tonsillitis or peritonsillar abscess (PTA).
Main Outcome Measures: Sensitivity, specificity, positive predictive value and negative predictive value for both the original LPS model and the modified model for COVID-19.
Results: The LPS model had sensitivity and specificity calculated at 98% and 79%, respectively. The LPS has a high negative predictive value (NPV) of 99%. The positive predictive value (PPV) was slightly lower at 63%. Receiver operating characteristic (ROC) curve, including the area under the curve (AUROC), was 0.888 which indicates very good accuracy.
Conclusions: External validation of the LPS against an independent geographically diverse population yields high NPV. This may support non-specialist colleagues who may have concerns about mis-diagnosing a PTA. The COVID-19 modification of the LPS has a similar NPV, which may be of use where routine oral examination is to be avoided during the COVID-19 pandemic.
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http://dx.doi.org/10.1111/coa.13652 | DOI Listing |
Sci Rep
December 2024
School of Pharmacy, Jiangxi Medical College, Nanchang University, Nanchang, 330006, People's Republic of China.
Cuproptosis, a newly identified form of cell death, has drawn increasing attention for its association with various cancers, though its specific role in colorectal cancer (CRC) remains unclear. In this study, transcriptomic and clinical data from CRC patients available in the TCGA database were analyzed to investigate the impact of cuproptosis. Differentially expressed genes linked to cuproptosis were identified using Weighted Gene Co-Expression Network Analysis (WGCNA).
View Article and Find Full Text PDFBrief Bioinform
November 2024
School of Medicine, Institute of Biomedicine, University of Eastern Finland, Yliopistonranta 1, PO Box 1627, 70211 Kuopio, Finland.
The selection of biomarker panels in omics data, challenged by numerous molecular features and limited samples, often requires the use of machine learning methods paired with wrapper feature selection techniques, like genetic algorithms. They test various feature sets-potential biomarker solutions-to fine-tune a machine learning model's performance for supervised tasks, such as classifying cancer subtypes. This optimization process is undertaken using validation sets to evaluate and identify the most effective feature combinations.
View Article and Find Full Text PDFCureus
December 2024
General Surgery, Aneurin Bevan University Health Board, Newport, GBR.
Aim: To assess recent colonoscopies and CT scans in conjunction with the feacal immunochemical test (FIT) for possibly downgrading urgent suspected cancer (USC) referrals.
Methods: A retrospective single-centre study was conducted, including all USC referrals for colonoscopy in 2022, excluding anal cancers. The CT and colonoscopy findings for a two-year period prior to the referral, along with the FIT result (if done), were noted.
J Inflamm Res
December 2024
Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, 40014, People's Republic of China.
Purpose: Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive lung disease. PANoptosis, a unique inflammatory programmed cell death, it manifests as the simultaneous activation of signaling markers for pyroptosis, apoptosis, and necroptosis. However, research on the role of PANoptosis in the development of IPF is currently limited.
View Article and Find Full Text PDFBMC Med
December 2024
Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing, 100191, China.
Background: Risk prediction models can identify individuals at high risk of chronic liver disease (CLD), but there is limited evidence on the performance of various models in diverse populations. We aimed to systematically review CLD prediction models, meta-analyze their performance, and externally validate them in 0.5 million Chinese adults in the China Kadoorie Biobank (CKB).
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