ABSTRACT
Background Although dementia has emerged as an important risk factor for severe SARS-CoV-2 infection, results on COVID-19-related complications and mortality are not consistent. We examined the clinical presentations and outcomes of COVID-19 in a multicentre cohort of in-hospital patients, comparing those with and without dementia. Methods This retrospective observational study comprises COVID-19 laboratory-confirmed patients aged ≥60 years admitted to 38 hospitals from 19 cities in Brazil. Data were obtained from electronic hospital records. A propensity score analysis was used to match patients with and without dementia (up to 3:1) according to age, sex, comorbidities, year and hospital of admission. Our primary outcome was in-hospital mortality. We also assessed admission to the intensive care unit (ICU), invasive mechanical ventilation (IMV), kidney replacement therapy (KRT), sepsis, nosocomial infection, and thromboembolic events. Results Among 8,947 eligible patients, 405 (4.5%) had a diagnosis of dementia and were matched with 1,151 patients without dementia. Compared to a group of similar demographics and comorbidities, patients with dementia presented a lower duration of symptoms (5.0 vs. 7.0 days; p<0.001) and frequency of dyspnoea, cough, myalgia, headache, ageusia, and anosmia. Fever and delirium were more frequent in patients with dementia than the control group. Patients with dementia also received more palliative care than the control group. Dementia was associated with lower admission (32.7% vs. 47.1%, p<0.001) and length of stay (7 vs. 9 days, p<0.026) in the ICU, frequency of sepsis (17% vs. 24%, p=0.005), KRT (6.4% vs. 13%, p<0.001), and IVM (4.6% vs. 9.8%, p=0.002). We did not find differences in hospital mortality among those with and without dementia. Conclusion Clinical manifestations of COVID-19 differ in older patients with and without dementia in the hospital, with delirium being highly prevalent among those with dementia. Our findings indicate that dementia alone might not explain higher short-term mortality after severe COVID-19. Clinicians should include other risk factors such as acute morbidity severity and baseline frailty when evaluating the prognosis of COVID-19 in the hospital.
Subject(s)
COVID-19ABSTRACT
The majority prognostic scores proposed for early assessment of coronavirus disease 19 (COVID-19) patients are bounded by methodological flaws. Our group recently developed a new risk score - ABC 2 SPH - using traditional statistical methods (least absolute shrinkage and selection operator logistic regression - LASSO). In this article, we provide a thorough comparative study between modern machine learning (ML) methods and state-of-the-art statistical methods, represented by ABC 2 SPH, in the task of predicting in-hospital mortality in COVID-19 patients using data upon hospital admission. We overcome methodological and technological issues found in previous similar studies, while exploring a large sample (5,032 patients). Additionally, we take advantage of a large and diverse set of methods and investigate the effectiveness of applying meta-learning, more specifically Stacking, in order to combine the methods' strengths and overcome their limitations. In our experiments, our Stacking solutions improved over previous state-of-the-art by more than 26% in predicting death, achieving 87.1% of AUROC and MacroF1 of 73.9%. We also investigated issues related to the interpretability and reliability of the predictions produced by the most effective ML methods. Finally, we discuss the adequacy of AUROC as an evaluation metric for highly imbalanced and skewed datasets commonly found in health-related problems.
Subject(s)
COVID-19 , Coronavirus InfectionsABSTRACT
Background: It is not clear whether previous thyroid diseases influence the course and outcomes of COVID-19. The study aims to compare clinical characteristics and outcomes of COVID-19 patients with and without hypothyroidism. Methods: The study is a part of a multicentric cohort of patients with confirmed COVID-19 diagnosis, including data collected from 37 hospitals. Matching for age, sex, number of comorbidities and hospital was performed to select the patients without hypothyroidism for the paired analysis. Results: From 7,762 COVID-19 patients, 526 had previously diagnosed hypothyroidism (50%) and 526 were selected as matched controls. The median age was 70 (interquartile range 59.0-80.0) years-old and 68.3% were females. The prevalence of underlying comorbidities were similar between groups, except for coronary and chronic kidney diseases, that had a higher prevalence in the hypothyroidism group (9.7% vs. 5.7%, p=0.015 and 9.9% vs. 4.8%, p=0.001, respectively). At hospital presentation, patients with hypothyroidism had a lower frequency of respiratory rate > 24 breaths per minute (36.1% vs 42.0%; p=0.050) and need of mechanical ventilation (4.0% vs 7.4%; p=0.016). D-dimer levels were slightly lower in hypothyroid patients (2.3 times higher than the reference value vs 2.9 times higher; p=0.037). In-hospital management was similar between groups, but hospital length-of-stay (8 vs 9 days; p=0.029) and mechanical ventilation requirement (25.4% vs. 33.1%; p=0.006) were lower for patients with hypothyroidism. There was a trend of lower in-hospital mortality in patients with hypothyroidism (22.1% vs. 27.0%; p=0.062). Conclusion: In this large Brazilian COVID-19 Registry, patients with hypothyroidism had a lower requirement of mechanical ventilation, and showed a trend of lower in-hospital mortality. Therefore, hypothyroidism does not seem to be associated with a worse prognosis, and should not be considered among the comorbidities that indicate a risk factor for COVID-19 severity.
Subject(s)
COVID-19 , Thyroid Diseases , Renal Insufficiency, Chronic , HypothyroidismABSTRACT
Objective: To provide a thorough comparative study among state ofthe art machine learning methods and statistical methods for determining in-hospital mortality in COVID 19 patients using data upon hospital admission; to study the reliability of the predictions of the most effective methods by correlating the probability of the outcome and the accuracy of the methods; to investigate how explainable are the predictions produced by the most effective methods. Materials and Methods: De-identified data were obtained from COVID 19 positive patients in 36 participating hospitals, from March 1 to September 30, 2020. Demographic, comorbidity, clinical presentation and laboratory data were used as training data to develop COVID 19 mortality prediction models. Multiple machine learning and traditional statistics models were trained on this prediction task using a folded cross validation procedure, from which we assessed performance and interpretability metrics. Results: The Stacking of machine learning models improved over the previous state of the art results by more than 26% in predicting the class of interest (death), achieving 87.1% of AUROC and macroF1 of 73.9%. We also show that some machine learning models can be very interpretable and reliable, yielding more accurate predictions while providing a good explanation for the why. Conclusion: The best results were obtained using the meta learning ensemble model Stacking. State of the art explainability techniques such as SHAP values can be used to draw useful insights into the patterns learned by machine-learning algorithms. Machine learning models can be more explainable than traditional statistics models while also yielding highly reliable predictions. Key words: COVID-19; prognosis; prediction model; machine learning