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- COVID-19: nothing is normal in this pandemicPublication . Gonçalves, Luzia; Turkman; Brás-Geraldes, Carlos; Marques, Tiago A.; SOUSA, LISETEThis manuscript brings attention to inaccurate epidemiological concepts that emerged during the COVID-19 pandemic. In social media and scientific journals, some wrong references were given to a "normal epidemic curve" and also to a "log-normal curve/distribution". For many years, textbooks and courses of reputable institutions and scientific journals have disseminated misleading concepts. For example, calling histogram to plots of epidemic curves or using epidemic data to introduce the concept of a Gaussian distribution, ignoring its temporal indexing. Although an epidemic curve may look like a Gaussian curve and be eventually modelled by a Gauss function, it is not a normal distribution or a log-normal, as some authors claim. A pandemic produces highly-complex data and to tackle it effectively statistical and mathematical modelling need to go beyond the "onesize-fits-all solution". Classical textbooks need to be updated since pandemics happen and epidemiology needs to provide reliable information to policy recommendations and actions.
- Wind power forecasting with machine learning: single and combined methodsPublication . Rosa, J.; Pestana, Rui; Leandro, Carlos; Brás-Geraldes, Carlos; Esteves, João; Carvalho, D.In Portugal, wind power represents one of the largest renewable sources of energy in the national energy mix. The investment in wind power started several decades ago and is still on the roadmap of political and industrial players. One example is that by 2030 it is estimated that wind power is going to represent up to 35% of renewable energy production in Portugal. With the growth of the installed wind capacity, the development of methods to forecast the amount of energy generated becomes increasingly necessary. Historically, Numerical Weather Prediction (NWP) models were used. However, forecasting accuracy depends on many variables such as on-site conditions, surrounding terrain relief, local meteorology, etc. Thus, it becomes a challenge to obtain improved results using such methods. This article aims to report the development of a machine learning pipeline with the objective of improving the forecasting capability of the NWP’s to obtain an error lower than 10%.
- A multivariable prediction model to select colorectal surgical patients for co-managementPublication . Bayão Horta, A.; Brás-Geraldes, Carlos; Salgado, Cátia; Vieira, Susana; Xavier, Miguel; Papoila, Ana LuisaIntroduction: Increased life expectancy leads to older and frailer surgical patients. Co-management between medical and surgical specialities has proven favourable in complex situations. Selection of patients for co-management is full of difficulties. The aim of this study was to develop a clinical decision support tool to select surgical patients for co-management. Material and Methods: Clinical data was collected from patient electronic health records with an ICD-9 code for colorectal surgery from January 2012 to December 2015 at a hospital in Lisbon. The outcome variable consists in co-management signalling. A dataset from 344 patients was used to develop the prediction model and a second data set from 168 patients was used for external validation. Results: Using logistic regression modelling the authors built a five variable (age, burden of comorbidities, ASA-PS status, surgical risk and recovery time) predictive referral model for co-management. This model has an area under the curve (AUC) of 0.86 (95% CI: 0.81 - 0.90), a predictive Brier score of 0.11, a sensitivity of 0.80, a specificity of 0.82 and an accuracy of 81.3%. Discussion: Early referral of high-risk patients may be valuable to guide the decision on the best level of post-operative clinical care. We developed a simple bedside decision tool with a good discriminatory and predictive performance in order to select patients for comanagement. Conclusion: A simple bed-side clinical decision support tool of patients for co-management is viable, leading to potential improvement in early recognition and management of postoperative complications and reducing the ‘failure to rescue’. Generalizability to other clinical settings requires adequate customization and validation.
- The potential role of peak nasal inspiratory flow to evaluate active sinonasal inflammation and disease severityPublication . Araújo-Martins, José; Brás-Geraldes, Carlos; Neuparth, NunoAlthough the pathophysiology of nasal polyposis is incompletely understood, rhinologists have seldom studied it with rhinomanometry or peak nasal inspiratory flow (PNIF) due to technical limitations and the perception that polyp size might impair reproducibility and the usefulness of recordings. The objective of this study is to assess how measures of rhinomanometry and PNIF relate to disease activity. Nineteen patients with polyps, 15 patients with chronic sinusitis without polyps and 11 negative controls were evaluated with active anterior rhinomanometry and PNIF. Sinusitis and polyp patients were re-evaluated after medical treatment. Polyp patients had the highest median Lund-Mackay score (14) and a median Johansen score of 1. PNIF and its variation after treatment were also lowest in this group (median 90 L/min before and after treatment; median variation of 0 L/min). Nasal resistance was similar between groups, and only correlated with Johansen score (Spearman=0.517, p=0.048) after treatment. Our study suggests that evaluating polyp patients using rhinomanometry and PNIF may provide useful and reproducible data. Several findings considered together suggest that polyp size is not the main determinant of nasal functional changes in these patients, warranting further studies to verify whether PNIF changes reflect sinus inflammation or merely airway obstruction.
- An interactive dashboard for statistical analysis of intensive care unit COVID-19 dataPublication . Dias, Rúben; Ferreira, Artur; Pinto, Iola; Brás-Geraldes, Carlos; Von Rekowski, Cristiana; Bento, LuísCOVID-19 caused a pandemic, due to its ease of transmission and high number of infections. The evolution of the pandemic and its consequences for the mortality and morbidity of populations, especially the elderly, generated several scientific studies and many research projects. Among them, we have the Predictive Models of COVID-19 Outcomes for Higher Risk Patients Towards a Precision Medicine (PREMO) research project. For such a project with many data records, it is necessary to provide a smooth graphical analysis to extract value from it. Methods: In this paper, we present the development of a full-stack Web application for the PREMO project, consisting of a dashboard providing statistical analysis, data visualization, data import, and data export. The main aspects of the application are described, as well as the diverse types of graphical representations and the possibility to use filters to extract relevant information for clinical practice. Results: The application, accessible through a browser, provides an interactive visualization of data from patients admitted to the intensive care unit (ICU), throughout the six waves of COVID-19 in two hospitals in Lisbon, Portugal. The analysis can be isolated per wave or can be seen in an aggregated view, allowing clinicians to create many views of the data and to study the behavior and consequences of different waves. For instance, the experimental results show clearly the effect of vaccination as well as the changes on the most relevant clinical parameters on each wave. Conclusions: The dashboard allows clinicians to analyze many variables of each of the six waves as well as aggregated data for all the waves. The application allows the user to extract information and scientific knowledge about COVID-19’s evolution, yielding insights for this pandemic and for future pandemics.
- The characteristics and laboratory findings of SARS-CoV-2 infected patients during the first three COVID-19 waves in Portugal – a retrospective single-center studyPublication . Von Rekowski, Cristiana; Fonseca, Tiago AH; Araújo, Rúben; Brás-Geraldes, Carlos; Calado, Cecília; Bento, Luís; Pinto, IolaBackground and Objectives: Given the wide spectrum of clinical and laboratory manifestations of the coronavirus disease 2019 (COVID-19), it is imperative to identify potential contributing factors to patients’ outcomes. However, a limited number of studies have assessed how the different waves affected the progression of the disease, more so in Portugal. Therefore, our main purpose was to study the clinical and laboratory patterns of COVID-19 in an unvaccinated population admitted to the intensive care unit, identifying characteristics associated with death, in each of the first three waves of the pandemic. Materials and Methods: This study included 337 COVID-19 patients admitted to the intensive care unit of a single-center hospital in Lisbon, Portugal, between March 2020 and March 2021. Comparisons were made between three COVID-19 waves, in the second (n = 325) and seventh (n = 216) days after admission, and between discharged and deceased patients. Results: Deceased patients were considerably older (p = 0.021) and needed greater ventilatory assistance (p = 0.023), especially in the first wave. Differences between discharged and deceased patients’ biomarkers were minimal in the first wave, on both analyzed days. In the second wave significant differences emerged in troponins, lactate dehydrogenase, procalcitonin, C-reactive protein, and white blood cell subpopulations, as well as platelet-to-lymphocyte and neutrophil-to-lymphocyte ratios (all p < 0.05). Furthermore, in the third wave, platelets and D-dimers were also significantly different between patients’ groups (all p < 0.05). From the second to the seventh days, troponins and lactate dehydrogenase showed significant decreases, mainly for discharged patients, while platelet counts increased (all p < 0.01). Lymphocytes significantly increased in discharged patients (all p < 0.05), while white blood cells rose in the second (all p < 0.001) and third (all p < 0.05) waves among deceased patients. Conclusions: This study yields insights into COVID-19 patients’ characteristics and mortality-associated biomarkers during Portugal’s first three COVID-19 waves, highlighting the importance of considering wave variations in future research due to potential significant outcome differences.