Diagnostic value of endothelial markers and HHV-8 staining in gastrointestinal Kaposi sarcoma and its difference in endoscopic tumor staging

AIM: To clarify the diagnostic values of hematoxylin and eosin (HE), D2-40, CD31, CD34, and HHV-8 immunohistochemical (IHC) staining in gastrointestinal Kaposi's sarcoma (GI-KS) in relation to endoscopic tumor staging. METHODS: Biopsy samples (n = 133) from 41 human immunodeficiency virus-infected patients were reviewed. GI-KS was defined as histologically negative for other GI diseases and as a positive clinical response to KS therapy. The receiver operating characteristic area under the curve (ROC-AUC) was compared in relation to lesion size, GI location, and macroscopic appearances on endoscopy. RESULTS: GI-KS was confirmed in 84 lesions (81.6%). Other endoscopic findings were polyps (n = 9), inflammation (n = 4), malignant lymphoma (n = 4), and condyloma (n = 2), which mimicked GI-KS on endoscopy. ROC-AUC of HE, D2-40, blood vessel markers, and HHV-8 showed results of 0.83, 0.89, 0.80, and 0.82, respectively. For IHC staining, the ROC-AUC of D2-40 was significantly higher (P < 0.05) than that of HE staining only. In the analysis of endoscopic appearance, the ROC-AUC of HE and IHC showed a tendency toward an increase in tumor staging (e.g. , small to large, patches, and polypoid to SMT appearance). D2-40 was significantly (P < 0.05) advantageous in the upper GI tract and for polypoid appearance compared with HE staining. CONCLUSION: The diagnostic value of endothelial markers and HHV-8 staining was found to be high, and its accuracy tended to increase with endoscopic tumor staging. D2-40 will be useful for complementing HE staining in the diagnosis of GI-KS, especially in the upper GI tract and for polypoid appearance.

Predictive model of risk factors of High Flow Nasal Cannula using machine learning in COVID-19

With the rapid increase in the number of COVID-19 patients in Japan,the number of patients receiving oxygen at home has also increased rapidly,and some of these patients have died.An efficient approach to identify high-risk patients with slowly progressing and rapidly worsening COVID-19,and to avoid missing the timing of therapeutic intervention will improve patient prognosis and prevent medical complications.Patients admitted to medical institutions in Japan from November 14,2020 to April 11,2021 and registered in the COVID-19 Registry Japan were included.Risk factors for patients with High Flow Nasal Cannula invasive respiratory management or higher were comprehensively explored using machine learning.Age-specific cohorts were created,and severity prediction was performed for the patient surge period.We were able to obtain a model that was able to predict severe disease with a sensitivity of 57%when the specificity was set at 90%for those aged 40e59 years,and with a specificity of 50%and 43%when the sensitivity was set at 90%for those aged 60e79 years and 80 years and older,respectively.We were able to identify lactate dehydrogenase level(LDH)as an important factor in predicting the severity of illness in all age groups.Using machine learning,we were able to identify risk factors with high accuracy,and predict the severity of the disease.We plan to develop a tool that will be useful in determining the indications for hospitalisation for patients undergoing home care and early hospitalisation.