COVID-19 in children and adolescents has low frequency, severity, and fatality rate all over the world. A cross-sectional study was conducted to assess the epidemiological and clinical aspects of COVID-19 in patients younger than 20 years in Pernambuco (Brazil), with cases confirmed by reverse-transcriptase–PCR SARS-CoV-2 between 13 February and June 19, 2020, reported on information systems. Data regarding age (< 30 days, 1–11 months, 1–4 years, 5–9 years, 10–14 years, and 15–19 years), gender, color/race, symptoms, pregnancy or puerperium, comorbidities, hospitalization, and death were investigated. Fatality rate and mortality coefficient were calculated, and a multiple logistic regression analysis was performed to determine if gender, age, and comorbidities were factors associated with death. Of 682 pediatric cases, 52.8% were female, with a mean age of 9 ± 7.2 years. The most frequent symptoms were fever (64.4%), cough (52.4%), and respiratory distress (32.4%). Hospitalization was reported in 46.2% of cases, mainly among neonates (80.3%) and infants (73.8%). Thirty-eight deaths were notified, and a fatality rate of 5.6% (95% CI: 3.9–7.3) was found, with higher fatality rates among neonates 11.5% (7 of 61) and 9.5% (8 of 84) infants. The mortality coefficient was 10.9 per 100,000 inhabitants < 1 year of age, whereas comorbidities (Odds ratio [OR] = 14.13, 95% CI: 6.35–31.44), age < 30 days (OR = 5.17, 95% CI: 1.81–14.77), and age 1–11 months (OR = 3.28, 95% CI: 1.21–8.91) were independent factors associated with death. The results demonstrate the vulnerability of neonates and infants with severe conditions, need hospitalization, and high fatality rate, indicating the necessity to adapt public health policies for these age-groups.

Background The importance of classifying cancer patients into high- or low-risk groups has led many research teams, from the biomedical and bioinformatics fields, to study the application of machine learning (ML) algorithms. The International Society of Geriatric Oncology recommends the use of the comprehensive geriatric assessment (CGA), a multidisciplinary tool to evaluate health domains, for the follow-up of elderly cancer patients. However, no applications of ML have been proposed using CGA to classify elderly cancer patients. Objective The aim of this study was to propose and develop predictive models, using ML and CGA, to estimate the risk of early death in elderly cancer patients. Methods The ability of ML algorithms to predict early mortality in a cohort involving 608 elderly cancer patients was evaluated. The CGA was conducted during admission by a multidisciplinary team and included the following questionnaires: mini-mental state examination (MMSE), geriatric depression scale-short form, international physical activity questionnaire-short form, timed up and go, Katz index of independence in activities of daily living, Charlson comorbidity index, Karnofsky performance scale (KPS), polypharmacy, and mini nutritional assessment-short form (MNA-SF). The 10-fold cross-validation algorithm was used to evaluate all possible combinations of these questionnaires to estimate the risk of early death, considered when occurring within 6 months of diagnosis, in a variety of ML classifiers, including Naive Bayes (NB), decision tree algorithm J48 (J48), and multilayer perceptron (MLP). On each fold of evaluation, tiebreaking is handled by choosing the smallest set of questionnaires. Results It was possible to select CGA questionnaire subsets with high predictive capacity for early death, which were either statistically similar (NB) or higher (J48 and MLP) when compared with the use of all questionnaires investigated. These results show that CGA questionnaire selection can improve accuracy rates and decrease the time spent to evaluate elderly cancer patients. Conclusions A simplified predictive model aiming to estimate the risk of early death in elderly cancer patients is proposed herein, minimally composed by the MNA-SF and KPS. We strongly recommend that these questionnaires be incorporated into regular geriatric assessment of older patients with cancer.

Objectives: train a Random Forest (RF) classifier to estimate death risk in elderly people (over 60 years old) diagnosed with COVID-19 in Pernambuco. A "feature" of this classifier, called feature importance, was used to identify the attributes (main risk factors) related to the outcome (cure or death) through gaining information. Methods: data from confirmed cases of COVID-19 was obtained between February 13 and June 19, 2020, in Pernambuco, Brazil. The K-fold Cross Validation algorithm (K=10) assessed RF performance and the importance of clinical features. Results: the RF algorithm correctly classified 78.33% of the elderly people, with AUC of 0.839. Advanced age was the factor representing the highest risk of death. The main comorbidity and symptom were cardiovascular disease and oxygen saturation ≤ 95%, respectively. Conclusion: this study applied the RF classifier to predict risk of death and identified the main clinical features related to this outcome in elderly people with COVID-19 in the state of Pernambuco.