ABSTRACT
Background: The aim of the study is to assess the effect of chronic lung disease on mortality in patients hospitalized with the diagnosis of prevariant COVID-19 Pneumonia compared to patients without chronic lung disease. Research design and methods: A cohort of 1,549 patients admitted to the pandemic clinic with a COVID-19 Pneumonia diagnosis was analyzed. Group 1 and Group 2 were compared in terms of the treatment they received, admission to intensive care, mortality and follow-up parameters. Results: The patient group with COVID-19 and lung disease consisted of 231 participants (14.91%) (Group 1). The patient group with COVID-19 but without lung disease had 1,318 participants (85.19%). Group 1 cases were found to receive more oxygen therapy and mechanical ventilation than Group 2 cases (p ≤ 0.001), Following univariate and multiple logistic regression analyses, it was determined that patients with chronic lung disease had a 25.76% higher mortality risk [OR: 25.763, 95% CI (Lower-Upper) (2.445-271.465), p = 0.007]. Conclusion: It was found that chronic lung disease contributed significantly to mortality in this study. Among chronic lung diseases, Chronic Obstructive Pulmonary Disease (COPD), lung cancer and interstitial lung diseases (ILDs) were shown to be more effective than other chronic lung diseases in patients with prevariant COVID-19 population.
ABSTRACT
BACKGROUND: Early identification of severe COVID-19 patients who will need intensive care unit (ICU) follow-up and providing rapid, aggressive supportive care may reduce mortality and provide optimal use of medical resources. We aimed to develop and validate a nomogram to predict severe COVID-19 cases that would need ICU follow-up based on available and accessible patient values. METHODS: Patients hospitalized with laboratory-confirmed COVID-19 between March 15, 2020, and June 15, 2020, were enrolled in this retrospective study with 35 variables obtained upon admission considered. Univariate and multivariable logistic regression models were constructed to select potential predictive parameters using 1000 bootstrap samples. Afterward, a nomogram was developed with 5 variables selected from multivariable analysis. The nomogram model was evaluated by Area Under the Curve (AUC) and bias-corrected Harrell's C-index with 95% confidence interval, Hosmer-Lemeshow Goodness-of-fit test, and calibration curve analysis. RESULTS: Out of a total of 1022 patients, 686 cases without missing data were used to construct the nomogram. Of the 686, 104 needed ICU follow-up. The final model includes oxygen saturation, CRP, PCT, LDH, troponin as independent factors for the prediction of need for ICU admission. The model has good predictive power with an AUC of 0.93 (0.902-0.950) and a bias-corrected Harrell's C-index of 0.91 (0.899-0.947). Hosmer-Lemeshow test p-value was 0.826 and the model is well-calibrated (p = 0.1703). CONCLUSION: We developed a simple, accessible, easy-to-use nomogram with good distinctive power for severe illness requiring ICU follow-up. Clinicians can easily predict the course of COVID-19 and decide the procedure and facility of further follow-up by using clinical and laboratory values of patients available upon admission.