Characteristics and Prognosis of COVID-19 in Patients with COPD

Yiğitbaş

Expert Review of Respiratory Medicine

et al. 2021

Background: Although COPD is not one of the most common comorbidities in COVID-19 patients, it can be more fatal in this group. This study aimed to investigate the characteristics and prognosis of COPD patients among the population with COVID-19. Research design and methods: Patients diagnosed with positive PCR test were included in our multicentered, retrospective study. Patients with airway obstruction (previous spirometry) were included in 'COPD group'. Results: The prevalence of COPD in COVID-19 patients was 4.96%(53/1069). There was a significant difference between COPD and non-COPD COVID-19 patients in terms of gender, mean age, presence of dyspnea, tachypnea, tachycardia, hypoxemia and presence of pneumonia. The mortality rate was 13.2% in COPD, 7% in non-COPD patients(p = 0.092). The significant predictors of mortality were higher age, lymphopenia (p < 0.001), hypoxemia (p = 0.028), high D-dimer level (p = 0.011), and presence of pneumonia (p = 0.043) in COVID-19 patients. Conclusions: Our research is one of the first studies investigating characteristics of COPD patients with COVID-19 in Turkey. Although COPD patients had some poor prognostic features, there was no statistical difference between overall survival rates of two groups. Age, status of oxygenization, serum D-dimer level, lymphocyte count and pneumonia were significantly associated parameters with mortality in COVID-19.

Section: Discussionmentioning

confidence: 99%

Clinical characteristics and outcomes of hospitalized COVID-19 patients with COPD

Yiğitbaş

Expert Review of Respiratory Medicine

et al. 2021

“…COPD patients have both an increased risk of COVID-19 disease, and a poorer prognosis, with higher rates of hospitalization and mortality. (22) COPD is an independent predictor of mortality in patients admitted to ICU for COVID-19 pneumonia. (4) Diabetes Mellitus is associated with mortality in our results.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence to Predict Mortality in Critically ill COVID-19 Patients Using Data from the First 24h: A Case Study from Lombardy Outbreak

Greco

Angelotti

Caruso

et al. 2021

Preprint

Introduction: SARS-CoV-2 infection was first identified at the end of 2019 in China, and subsequently spread globally. COVID-19 disease frequently affects the lungs leading to bilateral viral pneumonia, progressing in some cases to severe respiratory failure requiring ICU admission and mechanical ventilation. Risk stratification at ICU admission is fundamental for resource allocation and decision making, considering that baseline comorbidities, age, and patient conditions at admission have been associated to poorer outcomes. Supervised machine learning techniques are increasingly diffuse in clinical medicine and can predict mortality and test associations reaching high predictive performance. We assessed performances of a machine learning approach to predict mortality in COVID-19 patients admitted to ICU using data from the Lombardy ICU Network.Methods: this is a secondary analysis of prospectively collected data from Lombardy ICU network. To predict survival at 7-,14- and 28 days we built two different models; model A included patient demographics, medications before admission and comorbidities, while model B also included the data of the first day since ICU admission. 10-fold cross validation was repeated 2500 times, to ensure optimal hyperparameter choice. The only constrain imposed to model optimization was the choice of logistic regression as final layer to increase clinical interpretability. Different imputation and over-sampling techniques were employed in model training.Results 1503 patients were included, with 766 deaths (51%). Exploratory analysis and Kaplan-Meier curves demonstrated mortality association with age and gender. Model A and B reached the greatest predictive performance at 28 days (AUC 0.77 and 0.79), with lower performance at 14 days (AUC 0.72 and 0.74) and 7 days (AUC 0.68 and 0.71). Male gender, age and number of comorbidities were strongly associated with mortality in both models. Among comorbidities, chronic kidney disease and chronic obstructive pulmonary disease demonstrated association. Mode of ventilatory assistance at ICU admission and Fraction of Inspired oxygen were associated with mortality in model B.Conclusions Supervised machine learning models demonstrated good performance in prediction of 28-day mortality. 7-days and 14-days predictions demonstrated lower performance. Machine learning techniques may be useful in emergency phases to reach higher predictive performance with reduced human supervision using complex data.

“…Indeed, there is a significant number of existing corpora, datasets and resources available in English. Yet, we observe an increasing number of publications dedicated to other languages and a greater variety of languages: Arabic [ 20 ], Chinese [ 21 22 23 24 25 26 ], Croatian [ 27 ], Finnish [ 28 , 29 ], French [ 30 , 31 ], German [ 32 33 34 ], Hebrew [ 35 ], Italian [ 36 37 38 ], Japanese [ 39 , 40 ], Korean [ 41 , 42 ], Norwegian [ 43 ], Portuguese [ 44 ], Spanish [ 45 46 47 48 ], Swedish [ 49 ], and Turkish [ 28 ]. Overall, we believe that the trend observed in previous years is continuing.…”

Section: Current Trends In Biomedical Nlpmentioning

confidence: 99%

“…In order to help such work, several papers present COVID-19 resources, such as the COVID-19 Open Research Dataset (CORD-19) produced by Wang et al [ 50 ] and available through the Kaggle 6 platform. Besides, several works are anchored in hospitals and propose to predict conditions and events related to COVID, such as prediction of admission of patients with COVID in an intensive care unit [ 51 ], detection of COVID cases from radiological text reports [ 46 ], prognosis prediction for patients with chronic obstructive pulmonary disease [ 45 ] or for patients with hypertension [ 31 ].…”

Section: Current Trends In Biomedical Nlpmentioning

confidence: 99%

Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing

Grabar

Grouin

2021

Yearb Med Inform

Summary Objectives: To analyze the content of publications within the medical NLP domain in 2020. Methods: Automatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues. Results: Three best papers have been selected in 2020. We also propose an analysis of the content of the NLP publications in 2020, all topics included. Conclusion: The two main issues addressed in 2020 are related to the investigation of COVID-related questions and to the further adaptation and use of transformer models. Besides, the trends from the past years continue, such as diversification of languages processed and use of information from social networks