Effective vaccination against coronavirus mitigates the risk of hospitalisation and mortality; however, it is unclear whether vaccination status influences long COVID symptoms in patients who require ...hospitalisation. The available evidence is limited to outpatients with mild disease. Here, we evaluated 412 patients (age: 60 ± 16 years, 65% males) consecutively admitted to two Hospitals in Brazil due to confirmed coronavirus disease 2019 (COVID-19). Compared with patients with complete vaccination (n = 185) before infection or hospitalisation, those with no or incomplete vaccination (n = 227) were younger and had a lower frequency of several comorbidities. Data during hospitalisation revealed that the no or incomplete vaccination group required more admissions to the intensive care unit (ICU), used more corticosteroids, and had higher rates of pulmonary embolism or deep venous thrombosis than the complete vaccination group. Ninety days after hospital discharge, patients with no or incomplete vaccination presented a higher frequency of symptoms (≥ 1) than patients with complete vaccination (40 vs. 27%; p = 0.013). After adjusting for confounders, no or incomplete vaccination (odds ratio OR 1.819; 95% confidence interval CI 1.175-2.815), female sex (OR 2.435; 95% CI 1.575-3.764) and ICU admission during hospitalisation (OR 1.697; 95% CI 1.062-2.712) were independently associated with ≥ 1 symptom 90 days after hospital discharge. In conclusion, even in patients with severe COVID-19, vaccination mitigates the probability of long COVID symptoms.
Most of the evidence about the impact of the post-acute COVID-19 Syndrome (PACS) reports individual symptoms without correlations with related imaging.
To evaluate cardiopulmonary symptoms, their ...predictors and related images in COVID-19 patients discharged from hospital.
Consecutive patients who survived COVID-19 were contacted 90 days after discharge. The Clinic Outcome Team structured a questionnaire evaluating symptoms and clinical status (blinded for hospitalization data). A multivariate analysis was performed to address the course of COVID-19, comorbidities, anxiety, depression, and post-traumatic stress during hospitalization, and cardiac rehabilitation after discharge. The significance level was set at 5%.
A total of 480 discharged patients with COVID-19 (age: 59±14 years, 67.5% males) were included; 22.3% required mechanical ventilation. The prevalence of patients with PACS-related cardiopulmonary symptoms (dyspnea, tiredness/fatigue, cough, and chest discomfort) was 16.3%. Several parameters of chest computed tomography and echocardiogram were similar in patients with and without cardiopulmonary symptoms. The multivariate analysis showed that PACS-related cardiopulmonary-symptoms were independently related to female sex (OR 3.023; 95% CI 1.319-6.929), in-hospital deep venous thrombosis (OR 13.689; 95% CI 1.069-175.304), elevated troponin I (OR 1.355; 95% CI 1.048-1.751) and C-reactive protein during hospitalization (OR 1.060; 95% CI 1.023-1.097) and depression (OR 6.110; 95% CI 2.254-16.558).
PACS-related cardiopulmonary symptoms 90 days post-discharge are common and multifactorial. Beyond thrombotic and markers of inflammation/myocardial injury during hospitalization, female sex and depression were independently associated with cardiopulmonary-related PACS. These results highlighted the need for a multifaceted approach targeting susceptible patients.
Resumo Fundamento A maioria da evidência sobre o impacto da síndrome COVID pós-aguda (PACS, do inglês, post-acute COVID-19 syndrome) descreve sintomas individuais sem correlacioná-los com exames de ...imagens. Objetivos Avaliar sintomas cardiopulmonares, seus preditores e imagens relacionadas em pacientes com COVID-19 após alta hospitalar. Métodos Pacientes consecutivos, que sobreviveram à COVID-19, foram contatados 90 dias após a alta hospitalar. A equipe de desfechos clínicos (cega quanto aos dados durante a internação) elaborou um questionário estruturado avaliando sintomas e estado clínico. Uma análise multivariada foi realizada abordando a evolução da COVID-19, comorbidades, ansiedade, depressão, e estresse pós-traumático durante a internação, e reabilitação cardíaca após a alta. O nível de significância usado nas análises foi de 5%. Resultados Foram incluídos 480 pacientes (idade 59±14 anos, 67,5% do sexo masculino) que receberam alta hospitalar por COVID-19; 22,3% necessitaram de ventilação mecânica. A prevalência de pacientes com sintomas cardiopulmonares relacionados à PACS (dispneia, cansaço/fadiga, tosse e desconforto no peito) foi de 16,3%. Vários parâmetros de tomografia computadorizada do tórax e de ecocardiograma foram similares entre os pacientes com e sem sintomas cardiopulmonares. A análise multivariada mostrou que sintomas cardiopulmonares foram relacionados de maneira independente com sexo feminino (OR 3,023; IC95% 1,319-6,929), trombose venosa profunda durante a internação (OR 13,689; IC95% 1,069-175,304), nível elevado de troponina (OR 1,355; IC95% 1,048-1,751) e de proteína C reativa durante a internação (OR 1,060; IC95% 1,023-1,097) e depressão (OR 6,110; IC95% 2,254-16,558). Conclusão Os sintomas cardiopulmonares relacionados à PACS 90 dias após a alta hospitalar são comuns e multifatoriais. Além dos marcadores trombóticos, inflamatórios e de lesão miocárdica durante a internação, sexo feminino e depressão foram associados independentemente com sintomas cardiopulmonares relacionados à PACS. Esses resultados destacaram a necessidade de uma abordagem multifacetada direcionada a pacientes susceptíveis.
The term bagging refers to a class of techniques in which an ensemble model is obtained by combining different member models generated by resampling the available data set. It has been shown that ...bagging can lead to substantial gains in accuracy for both classification and regression models, specially when alterations in the training set cause significant changes in the outcome of the modelling procedure. However, in the context of chemometrics, the use of bagging for quantitative multicomponent analysis is still incipient. More recently, an alternative aggregation scheme termed subagging, which is based on subsampling without replacement, has been shown to provide performance improvements similar to bagging at a smaller computational cost. The present paper proposes a strategy for using subagging in conjunction with three multivariate calibration methods, namely Partial Least Squares (PLS) and Multiple Linear Regression with variable selection by using either the Successive Projections Algorithm (MLR-SPA) or a Genetic Algorithm (MLR-GA). The subagging member models are generated by subsampling the pool of samples available for modelling and then forming new calibration sets. Such a strategy is of value in analytical problems involving complex matrices, in which reproducing the composition variability of real samples by means of optimized experimental designs may be a difficult task. The efficiency of the proposed strategy is illustrated in a problem involving the NIR spectrometric determination of four diesel quality parameters (specific mass, sulphur content, and the distillation temperatures T10% and T90% at which 10% and 90% of the sample has evaporated, respectively). In this case study, the use of 30 subsampling iterations provides relative improvements of up to 16%, 33%, and 35% in the prediction accuracy of PLS, MLR-SPA, and MLR-GA models, respectively, with respect to the expected results of individual (non-ensemble) models.
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