BackgroundOral lichen Planus (OLP) is a chronic inflammatory disease involving skin and mucous membranes. Its etiology is still uncertain whilst an autoimmune mechanism is known to be implicated. OLP is commonly considered a geriatric disease and gender differences in prevalence are clear, whereby females are generally more frequently affected than males more often during the 5th and 6th decades of life. Lesions are symmetrical and bilateral and the buccal mucosa is frequently involved. The risk of malignant transformation is extremely low.This study aims to describe both the clinical characteristics and the prevalence of OLP among a group of patients from Southern Italy. The results of the present study were compared to analogous retrospective studies.MethodsEighty-seven (31 man and 56 woman) cases of OLP were retrospectively reviewed and demographic and clinical data were collected. Data about OLP as clinical forms, oral and extraoral sites involved and Visual Analogue Scale were also recorded.ResultsThe average age of OLP onset was 59.2 years. The most common clinical presentation was the hyperkeratosic type. Symptomatic OLP was noted in 26.8 % of the patiens. The most frequently affected oral sites were buccal mucosa, tongue, gums. The most frequently associated systemic diseases were diabetes, hypertension, C hepatitis and thyroiditis. Only one patient developed a malignant transformation (1.2 %).ConclusionsPrevious retrospective studies report data partially comparable with our results. Different geographic area, number of enrolled patients and OLP classification criteria may justify the observed differences.
Introduction The favorable effects of bariatric surgery (BS) on overall pulmonary function and obesity-related comorbidities could influence SARS-CoV-2 clinical expression. This has been investigated comparing COVID-19 incidence and clinical course between a cohort of patients submitted to BS and a cohort of candidates for BS during the spring outbreak in Italy. Materials and Methods From April to August 2020, 594 patients from 6 major bariatric centers in Emilia-Romagna were administered an 87-item telephonic questionnaire. Demographics, COVID-19 incidence, suggestive symptoms, and clinical outcome parameters of operated patients and candidates to BS were compared. The incidence of symptomatic COVID-19 was assessed including the clinical definition of probable case, according to World Health Organization criteria. Results Three hundred fifty-three operated patients (Op) and 169 candidates for BS (C) were finally included in the statistical analysis. While COVID-19 incidence confirmed by laboratory tests was similar in the two groups (5.7% vs 5.9%), lower incidence of most of COVID-19-related symptoms, such as anosmia (p: 0.046), dysgeusia (p: 0.049), fever with rapid onset (p: 0.046) were recorded among Op patients, resulting in a lower rate of probable cases (14.4% vs 23.7%; p: 0.009). Hospitalization was more frequent in C patients (2.4% vs 0.3%, p: 0.02). One death in each group was reported (0.3% vs 0.6%). Previous pneumonia and malignancies resulted to be associated with symptomatic COVID-19 at univariate and multivariate analysis. Conclusion Patients submitted to BS seem to develop less severe SARS-CoV-2 infection than subjects suffering from obesity.
Background Risk stratification plays a central role in anesthetic evaluation. The use of Big Data and machine learning (ML) offers considerable advantages for collection and evaluation of large amounts of complex health-care data. We conducted a systematic review to understand the role of ML in the development of predictive post-surgical outcome models and risk stratification. Methods Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, we selected the period of the research for studies from 1 January 2015 up to 30 March 2021. A systematic search in Scopus, CINAHL, the Cochrane Library, PubMed, and MeSH databases was performed; the strings of research included different combinations of keywords: “risk prediction,” “surgery,” “machine learning,” “intensive care unit (ICU),” and “anesthesia” “perioperative.” We identified 36 eligible studies. This study evaluates the quality of reporting of prediction models using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist. Results The most considered outcomes were mortality risk, systemic complications (pulmonary, cardiovascular, acute kidney injury (AKI), etc.), ICU admission, anesthesiologic risk and prolonged length of hospital stay. Not all the study completely followed the TRIPOD checklist, but the quality was overall acceptable with 75% of studies (Rev #2, comm #minor issue) showing an adherence rate to TRIPOD more than 60%. The most frequently used algorithms were gradient boosting (n = 13), random forest (n = 10), logistic regression (LR; n = 7), artificial neural networks (ANNs; n = 6), and support vector machines (SVM; n = 6). Models with best performance were random forest and gradient boosting, with AUC > 0.90. Conclusions The application of ML in medicine appears to have a great potential. From our analysis, depending on the input features considered and on the specific prediction task, ML algorithms seem effective in outcomes prediction more accurately than validated prognostic scores and traditional statistics. Thus, our review encourages the healthcare domain and artificial intelligence (AI) developers to adopt an interdisciplinary and systemic approach to evaluate the overall impact of AI on perioperative risk assessment and on further health care settings as well.
The aim of this article is to review the current applications of artificial intelligence in thoracic surgery, from diagnosis and pulmonary disease management, to preoperative risk-assessment, surgical planning, and outcomes prediction. Background: Artificial intelligence implementation in healthcare settings is rapidly growing, though its widespread use in clinical practice is still limited. The employment of machine learning algorithms in thoracic surgery is wide-ranging, including all steps of the clinical pathway. Methods: We performed a narrative review of the literature on Scopus, PubMed and Cochrane databases, including all the relevant studies published in the last ten years, until March 2021. Conclusion:Machine learning methods are promising encouraging results throughout the key issues of thoracic surgery, both clinical, organizational, and educational. Artificial intelligence-based technologies showed remarkable efficacy to improve the perioperative evaluation of the patient, to assist the decisionmaking process, to enhance the surgical performance, and to optimize the operating room scheduling. Still, some concern remains about data supply, protection, and transparency, thus further studies and specific consensus guidelines are needed to validate these technologies for daily common practice.
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