RESUMO
The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.
RESUMO
BACKGROUND: Neurocysticercosis, infection of the brain with larvae of Taenia solium (pork tapeworm), is one of several forms of human cysticercosis caused by this organism. We investigated the role of albendazole and praziquantel in the treatment of patients with parenchymal neurocysticercosis by performing a meta-analysis of comparative trials of their effectiveness and safety. METHODS AND PRINCIPAL FINDINGS: We performed a search in the PubMed database, Cochrane Database of Controlled Trials, and in references of relevant articles. Six studies were included in the meta-analysis. Albendazole was associated with better control of seizures than praziquantel in the pooled data analysis, when the generic inverse variance method was used to combine the incidence of seizure control in the included trials (patients without seizures/[patients x years at risk]) (156 patients in 4 studies, point effect estimate [incidence rate ratio] = 4.94, 95% confidence interval 2.45-9.98). In addition, albendazole was associated with better effectiveness than praziquantel in the total disappearance of cysts (335 patients in 6 studies, random effects model, OR = 2.30, 95% CI 1.06-5.00). There was no difference between albendazole and praziquantel in reduction of cysts, proportion of patients with adverse events, and development of intracranial hypertension due to the administered therapy. CONCLUSIONS: A critical review of the available data from comparative trials suggests that albendazole is more effective than praziquantel regarding clinically important outcomes in patients with neurocysticercosis. Nevertheless, given the relative scarcity of trials, more comparative interventional studies--especially randomized controlled trials--are required to draw a safe conclusion about the best regimen for the treatment of patients with parenchymal neurocysticercosis.
