Clinical practice guidelines standardisation of immunosuppressive and anti-infective drug regimens in UK paediatric renal transplantation: the harmonisation programme

Dudley, Jan; Christian, Martin; Andrews, Alice; Andrews, Nicola; Baker, Julie; Boyle, Sheila; Convery, Mairead; Gamston, Fiona; Garcia, M. T. Alvarez de Eulate; Haq, Shuman; Hegde, Shivaram; Holt, Richard C. L.; Jones, Helen; Khan, Sualeh Muslim; McCaughan, Jennifer; Milford, David V.; Pickles, Charlie; Reynolds, B. C.; Sathyanarayana, Vijaya; Stojanović, Jelena; Tse, Yincent; Wallace, Dean; Walsh, Grainne; Ware, Nick; Williams, A.R.; Yadav, Pallavi; Marks, Stephen D.

doi:10.1186/s12882-021-02460-5

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Few paediatric KTRs receive pre-transplant dental assessment although this has not been mandated by national guidance. However, given the rigorous treatment required [ 13 , 35 ], it would seem reasonable as our survey showed that some patients had transplants delayed for dental treatment. There is clear guidance for paediatric oncology [ 36 ] and cardiac [ 37 ] patients to have an oral screen prior to any treatment or transplant.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of UK paediatric nephrology teams’ understanding, experience and perceptions of oral health outcomes and accessibility to dental care: a mixed-methods study

Wallace,

Vernazza,

Emmet

et al. 2024

Pediatr Nephrol

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Background Oral health conditions are common in children and young people (CYP) with kidney disorders. There is currently limited literature on how confident paediatric nephrology teams feel to identify and manage oral health concerns for their patients. Method An exploratory mixed-method survey was distributed across all 13 UK specialist paediatric nephrology centres with responses received from consultants, registrars, specialist nurses and special interest (SPIN) paediatricians. Results Responses received from 109 multidisciplinary team members of 13/13 (100%) UK tertiary units. Ninety-two percent (n = 100) of respondents reported they had never received any training in oral health and 87% (n = 95) felt that further training would be beneficial to optimise care for patients and improve communication between medical and dental teams. Most respondents reported that they did not regularly examine, or enquire about, their patients’ oral health. Only 16% (n = 17) reported that all their paediatric kidney transplant recipients underwent routine dental assessment prior to transplant listing. Severe adverse oral health outcomes were rarely reported and only 11% (n = 12) of respondents recalled having a patient who had a kidney transplant delayed or refused due to concerns about oral infection. Seventy-eight percent (n = 85) felt that joint working with a dental team would benefit patients at their unit; however, 17% (n = 18) felt that current infrastructure does not currently support effective joint working. Conclusions Across the UK, paediatric kidney health professionals report lack of confidence and training in oral health. Upskilling subspecialty teams and creating dental referral pathways are recommended to maximise oral health outcomes for CYP with kidney diseases. Graphical abstract

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Section: Discussionmentioning

confidence: 99%

Evaluation of UK paediatric nephrology teams’ understanding, experience and perceptions of oral health outcomes and accessibility to dental care: a mixed-methods study

Wallace,

Vernazza,

Emmet

et al. 2024

Pediatr Nephrol

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“…The XGBoost algorithm has garnered substantial attention from researchers and has been extensively explored for application in predicting various diseases, including, but not limited to, forecasting and classifying heart diseases [ 11 ], establishing diagnostic models for breast cancer [ 12 ], staging liver cancer [ 13 ], optimizing the dosage of immunosuppressive drugs in kidney transplant patients [ 14 ], and analyzing survival factors influencing early mortality in colorectal cancer patients [ 15 ]. The XGBoost algorithm, a machine learning method based on gradient boosting trees, is renowned for its efficiency, flexibility, and scalability.…”

Section: Discussionmentioning

confidence: 99%

Online COVID-19 diagnosis prediction using complete blood count: an innovative tool for public health

Teng,

Wang

2023

BMC Public Health

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Background COVID-19, caused by SARS-CoV-2, presents distinct diagnostic challenges due to its wide range of clinical manifestations and the overlapping symptoms with other common respiratory diseases. This study focuses on addressing these difficulties by employing machine learning (ML) methodologies, particularly the XGBoost algorithm, to utilize Complete Blood Count (CBC) parameters for predictive analysis. Methods We performed a retrospective study involving 2114 COVID-19 patients treated between December 2022 and January 2023 at our healthcare facility. These patients were classified into fever (1057 patients) and pneumonia groups (1057 patients), based on their clinical symptoms. The CBC data were utilized to create predictive models, with model performance evaluated through metrics like Area Under the Receiver Operating Characteristics Curve (AUC), accuracy, sensitivity, specificity, and precision. We selected the top 10 predictive variables based on their significance in disease prediction. The data were then split into a training set (70% of patients) and a validation set (30% of patients) for model validation. Results We identified 31 indicators with significant disparities. The XGBoost model outperformed others, with an AUC of 0.920 and high precision, sensitivity, specificity, and accuracy. The top 10 features (Age, Monocyte%, Mean Platelet Volume, Lymphocyte%, SIRI, Eosinophil count, Platelet count, Hemoglobin, Platelet Distribution Width, and Neutrophil count.) were crucial in constructing a more precise predictive model. The model demonstrated strong performance on both training (AUC = 0.977) and validation (AUC = 0.912) datasets, validated by decision curve analysis and calibration curve. Conclusion ML models that incorporate CBC parameters offer an innovative and effective tool for data analysis in COVID-19. They potentially enhance diagnostic accuracy and the efficacy of therapeutic interventions, ultimately contributing to a reduction in the mortality rate of this infectious disease.

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Clinical practice guidelines standardisation of immunosuppressive and anti-infective drug regimens in UK paediatric renal transplantation: the harmonisation programme

Cited by 2 publications

References 6 publications

Evaluation of UK paediatric nephrology teams’ understanding, experience and perceptions of oral health outcomes and accessibility to dental care: a mixed-methods study

Evaluation of UK paediatric nephrology teams’ understanding, experience and perceptions of oral health outcomes and accessibility to dental care: a mixed-methods study

Online COVID-19 diagnosis prediction using complete blood count: an innovative tool for public health

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