| Autor: |
Hazal Cansu Acar, Günay Can, Rıdvan Karaali, Şermin Börekçi, İlker İnanç Balkan, Bilun Gemicioğlu, Dildar Konukoğlu, Ethem Erginöz, Mehmet Sarper Erdoğan, Fehmi Tabak |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
BMC Infectious Diseases, Vol 21, Iss 1, Pp 1-12 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1471-2334 |
| DOI: |
10.1186/s12879-021-05845-x |
| Popis: |
Abstract Background One-fifth of COVID-19 patients are seriously and critically ill cases and have a worse prognosis than non-severe cases. Although there is no specific treatment available for COVID-19, early recognition and supportive treatment may reduce the mortality. The aim of this study is to develop a functional nomogram that can be used by clinicians to estimate the risk of in-hospital mortality in patients hospitalized and treated for COVID-19 disease, and to compare the accuracy of model predictions with previous nomograms. Methods This retrospective study enrolled 709 patients who were over 18 years old and received inpatient treatment for COVID-19 disease. Multivariable Logistic Regression analysis was performed to assess the possible predictors of a fatal outcome. A nomogram was developed with the possible predictors and total point were calculated. Results Of the 709 patients treated for COVID-19, 75 (11%) died and 634 survived. The elder age, certain comorbidities (cancer, heart failure, chronic renal failure), dyspnea, lower levels of oxygen saturation and hematocrit, higher levels of C-reactive protein, aspartate aminotransferase and ferritin were independent risk factors for mortality. The prediction ability of total points was excellent (Area Under Curve = 0.922). Conclusions The nomogram developed in this study can be used by clinicians as a practical and effective tool in mortality risk estimation. So that with early diagnosis and intervention mortality in COVID-19 patients may be reduced. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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