Brit J Hosp Med
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With the rapid ageing of the population, the number of older adults with two or more chronic diseases is increasing. There are individual differences in health assessment, diagnosis, treatment, health management, and medication safety for older adults with chronic conditions and multiple morbidities. ⋯ Developing effective community health management models specifically designed for older adults with multiple chronic diseases is crucial for improving their overall health. This study provides a comprehensive review of the progress in research on community health management models for older adults with multiple chronic diseases, aiming to offer valuable insights for health management in this population.
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Aims/Background Accurate prediction of recurrence after treatment is crucial for controlling the progression and improving the prognosis of active ulcerative colitis (UC) patients. Previous studies have evaluated the therapeutic response in UC patients by assessing mucosal healing, using measures such as the Paddington International Virtual ChromoendoScopy Score (PICaSSO) and the PICaSSO Histological Remission Index (PHRI). The PHRI is effective for evaluating treatment response and disease control in UC patients, but its predictive value for short-term recurrence has not been reported in the literature. ⋯ The results of ROC curve analysis showed that the area under the curve of the PHRI score in predicting the recurrence of UC patients was 0.838 (95% CI: 0.760-0.916). When the optimal cut-off value was 1 point, the sensitivity and specificity were the highest, which were 89.58% and 65.58%, respectively, indicating that PHRI score had good predictive value. Conclusion The lesion extent, disease severity, endoscopic score, and PHRI score are associated with recurrence within one year in UC patients in the clinical remission stage, and the PHRI score has good predictive value.
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Aims/Background Acute radiation dermatitis is the most common complication of radiotherapy in patients with breast cancer, with mild severity relieved by symptomatic treatment and moderate-to-severe severity leading to compromised skin integrity and affecting the patient's quality of life. Therefore, this study aims to develop a prediction model for moderate-to-severe acute radiation dermatitis in patients with breast cancer to reduce its severity. Methods A retrospective analysis of 713 patients receiving radiotherapy for breast cancer at the Affiliated Cancer Hospital of Xinjiang Medical University from January 2019 to December 2023 was conducted, with January 2019 to December 2021 serving as the training group (497 patients) and January 2022 to December 2023 serving as the validation group (216 patients). ⋯ A nomogram prediction model was constructed, and the area under the receiver operating characteristic curve of the model was 0.814 and 0.743 for internal and external validation, respectively. The calibration curve showed that the model predicted moderate-to-severe acute radiation dermatitis better, and the decision curve analysis curve showed that the model had a high clinical benefit. Conclusion This risk prediction model can predict moderate-to-severe acute radiation dermatitis in patients with breast cancer, and help clinical providers screen high-risk patients and reduce acute radiation dermatitis severity.
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Aims/Background The clinical presentation of non-lactational mastitis (NLM) shares similarities with some symptoms and examination results of breast cancer (BC), which can lead to misdiagnosis or delayed treatment. Current studies on breast lesions mostly focus on the diagnostic performance of a single imaging technique. This study aims to construct a discrimination diagnostic model for NLM and BC based on such imaging features as ultrasound and magnetic resonance imaging (MRI) and to validate the application value of the model, assisting clinicians in improving disease diagnosis and refining medical decisions. ⋯ The DCA results showed that the model had high net benefits for discriminating NLM and BC. The calibration curve analysis showed that the model had good consistency with the actual diagnosis of NLM and BC, with a chi-square value of 4.545 and a p-value of 0.155 according to the Hosmer-Lemeshow test. Conclusion Age, internal blood flow, calcification, edge, enhancement characteristics, ADC, and TIC curve types are important factors in distinguishing NLM and BC, and the model based on the above characteristics to distinguish NLM and BC has a high net benefit in distinguishing the two.
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Aims/Background Patients receiving treatment in specialized cancer hospitals are particularly susceptible to multidrug-resistant organisms (MDRO) infections due to factors such as weakened immune systems caused by intensive treatments and prolonged hospital stays. This study aims to investigate the risk factors for MDRO infections in the cancer specialty hospital setting and to develop a corresponding risk prediction model. Methods Patients diagnosed with MDRO infections were selected for the MDRO infection group (n = 238), and those without for the non-MDRO infection group (n = 238). ⋯ The constructed nomogram prediction model for patients with MDRO infection has a C-index of 0.8640. The ROC curve results showed that the prediction model has a specificity of 0.7700, a sensitivity of 0.8800, and an area under the curve (AUC) of 0.8800. Conclusion This study identifies significant risk factors for MDRO infections in a cancer specialty hospital setting and offers a clinically useful prediction model, which may aid in targeted preventive measures and optimization of antibiotics usage, thereby potentially reducing the incidence and impact of these infections.