The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is one of the world’s most disruptive health crises. The presence of diabetes plays an important role in the severity of the infection, and a rise in newly diagnosed diabetes cases has been identified. The aim of this retrospective study was to determine the incidence of new-onset diabetes (NOD) and predictive factors with their cut-off values for patients hospitalized with COVID-19. All patients (n = 219) hospitalized for COVID-19 during three consecutive months were included. NOD was diagnosed in 26.48% of patients. The severity of the infection, hospital admission values for fasting plasma glucose, lactate dehydrogenase (LDH), PaO2/FiO2 ratio, the peak values for leucocytes, neutrophils, C-reactive protein, triglycerides, and the need for care in the intensive care unit were predictors for the occurrence of NOD in univariate analysis, while only LDH level remained a significant predictor in the multivariable analysis. In conclusion, the results of the study showed a high incidence of NOD in patients hospitalized with COVID-19 and identified LDH levels at hospital admission as a significant predictor of NOD during SARS-CoV-2 infection. However, the persistence of NOD after the COVID-19 infection is not known, therefore, the results must be interpreted with caution.
Some studies have reported that chronic respiratory illnesses in patients with COVID-19 result in an increase in hospitalization and death rates, while other studies reported to the contrary. The present research aims to determine if a predictive model (developed by combing different clinical, imaging, or blood markers) could be established for patients with both chronic obstructive pulmonary disease (COPD) and COVID-19, in order to be able to foresee the outcomes of these patients. A prospective observational cohort of 165 patients with both diseases was analyzed in terms of clinical characteristics, blood tests, and chest computed tomography results. The beta-coefficients from the logistic regression were used to create a score based on the significant identified markers for poor outcomes (transfers to an intensive care unit (ICU) for mechanical ventilation, or death). The severity of COVID-19, renal failure, diabetes, smoking status (current or previous), the requirement for oxygen therapy upon admission, high lactate dehydrogenase (LDH) and C-reactive protein level (CRP readings), and low eosinophil and lymphocyte counts were all identified as being indicators of a poor prognosis. Higher mortality was linked to the occurrence of renal failure, the number of affected lobes, the need for oxygen therapy upon hospital admission, high LDH, and low lymphocyte levels. Patients had an 86.4% chance of dying if their mortality scores were −2.80 or lower, based on the predictive model. The factors that were linked to a poor prognosis in patients who had both COPD and COVID-19 were the same as those that were linked to a poor prognosis in patients who had only COVID-19.
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