Post-COVID-19 Cardiopulmonary Symptoms: Predictors and Imaging Features in Patients after Hospital Discharge. / Sintomas Cardiopulmonares Pós-COVID-19: Preditores e Características de Imagem de Pacientes após a Alta Hospitalar.

BACKGROUND: Most of the evidence about the impact of the post-acute COVID-19 Syndrome (PACS) reports individual symptoms without correlations with related imaging. OBJECTIVES: To evaluate cardiopulmonary symptoms, their predictors and related images in COVID-19 patients discharged from hospital. METHODS: 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%. RESULTS: 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). CONCLUSION: 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.

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.

Sintomas Cardiopulmonares Pós-COVID-19: Preditores e Características de Imagem de Pacientes após a Alta Hospitalar / Post-COVID-19 Cardiopulmonary Symptoms: Predictors and Imaging Features in Patients after Hospital Discharge

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.

Abstract Background Most of the evidence about the impact of the post-acute COVID-19 Syndrome (PACS) reports individual symptoms without correlations with related imaging. Objectives To evaluate cardiopulmonary symptoms, their predictors and related images in COVID-19 patients discharged from hospital. Methods 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%. Results 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). Conclusion 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.

Tear Film Classification in Interferometry Eye Images Using Phylogenetic Diversity Indexes and Ripley's K Function.

Dry eye syndrome is one of the most frequently reported eye diseases in ophthalmological practice. The diagnosis of this disease is a challenging task due to its multifactorial etiology. One of the most applied tests is the manual classification of tear film images captured with the Doane interferometer. The interference phenomena in these images can be characterized as texture patterns, which can be automatically classified into one of the following categories: strong fringes, coalescing strong fringes, fine fringes, coalescing fine fringes, and debris. This work presents a method for classifying tear film images based on texture analysis using phylogenetic diversity indexes and Ripley's K function. The proposed method consists of six main steps: acquisition of the image dataset; segmentation of the region of interest; feature extraction using phylogenetic diversity indexes and Ripley's K function; feature selection using Greedy Stepwise; classification using the algorithms Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), Multilayer Perceptron (MLP), Random Tree (RT) and Radial Basis Function Network (RBFNet); and (6) validation of results. The best result, using the RF classifier, we obtained classification rates higher than 99% of accuracy with 0.843% of standard deviation, 0.999 of the area under the Receiver Operating Characteristics (ROC) curve, 0.995 of Kappa and 0.996 of F-Measure. The experimental results demonstrate that the proposed method is promising and can potentially be used by experts to accurately diagnose dry eye syndrome in tear film images.

Interferometer eye image classification for dry eye categorization using phylogenetic diversity indexes for texture analysis.

Background and Objective Dry eye syndrome disease negatively impacts many people in various ways. Several tests are required to diagnose it for evaluating different physiological characteristics. One of the most applied tests for this is the manual classification of tear film images captured with Doane interferometer. Interferometry images can be categorized into five groups: debris, fine fringes, coalescing fine fringes, strong fringes, and coalescing strong fringes. Instability in the tear film creates the need for an automatic system to provide experts with diagnostic support. Therefore, the purpose of this study was to propose a method for automatic classification of the tear film lipid layer using phylogenetic diversity indexes for feature extraction and several classifiers. Methods The proposed method consisted of five main steps: (1) acquisition of VOPTICAL_GCU image dataset, (2) segmentation of the region of interest, (3) feature extraction using phylogenetic diversity indexes, (4) classification using the algorithms Support Vector Machines, Random Forest, Naive Bayes, Multilayer Perceptron, Random Tree, and RBFNetwork, and, (5) validation of results. Results The best result was obtained using Random Forest classifier, reaching an accuracy of over 97%, standard deviation of 0.51%, an area under the receiver operating characteristic curve of 0.99, a Kappa index of 0.96, and an F-Measure of 0.97. Conclusions The proposed method demonstrated that the tear film lipid layer classification problem can be resolved efficiently by using phylogenetic diversity indexes.

An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks.

BACKGROUND AND OBJECTIVE: Chest X-ray (CXR) is one of the most used imaging techniques for detection and diagnosis of pulmonary diseases. A critical component in any computer-aided system, for either detection or diagnosis in digital CXR, is the automatic segmentation of the lung field. One of the main challenges inherent to this task is to include in the segmentation the lung regions overlapped by dense abnormalities, also known as opacities, which can be caused by diseases such as tuberculosis and pneumonia. This specific task is difficult because opacities frequently reach high intensity values which can be incorrectly interpreted by an automatic method as the lung boundary, and as a consequence, this creates a challenge in the segmentation process, because the chances of incomplete segmentations are increased considerably. The purpose of this work is to propose a method for automatic segmentation of lungs in CXR that addresses this problem by reconstructing the lung regions "lost" due to pulmonary abnormalities. METHODS: The proposed method, which features two deep convolutional neural network models, consists of four steps main steps: (1) image acquisition, (2) initial segmentation, (3) reconstruction and (4) final segmentation. RESULTS: The proposed method was experimented on 138 Chest X-ray images from Montgomery County's Tuberculosis Control Program, and has achieved as best result an average sensitivity of 97.54%, an average specificity of 96.79%, an average accuracy of 96.97%, an average Dice coefficient of 94%, and an average Jaccard index of 88.07%. CONCLUSIONS: We demonstrate in our lung segmentation method that the problem of dense abnormalities in Chest X-rays can be efficiently addressed by performing a reconstruction step based on a deep convolutional neural network model.