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Acute Medicine ; 20(1):4-14, 2021.
Article in English | MEDLINE | ID: covidwho-1143987


BACKGROUND: A recent systematic review recommends against the use of any of the current COVID-19 prediction models in clinical practice. To enable clinicians to appropriately profile and treat suspected COVID-19 patients at the emergency department (ED), externally validated models that predict poor outcome are desperately needed. OBJECTIVE: Our aims were to identify predictors of poor outcome, defined as mortality or ICU admission within 30 days, in patients presenting to the ED with a clinical suspicion of COVID-19, and to develop and externally validate a prediction model for poor outcome. METHODS: In this prospective, multi-center study, we enrolled suspected COVID-19 patients presenting at the EDs of two hospitals in the Netherlands. We used backward logistic regression to develop a prediction model. We used the area under the curve (AUC), Brier score and pseudo-R2 to assess model performance. The model was externally validated in an Italian cohort. RESULTS: We included 1193 patients between March 12 and May 27 2020, of whom 196 (16.4%) had a poor outcome. We identified 10 predictors of poor outcome: current malignancy (OR 2.774;95%CI 1.682-4.576), systolic blood pressure (OR 0.981;95%CI 0.964-0.998), heart rate (OR 1.001;95%CI 0.97-1.028), respiratory rate (OR 1.078;95%CI 1.046-1.111), oxygen saturation (OR 0.899;95%CI 0.850-0.952), body temperature (OR 0.505;95%CI 0.359-0.710), serum urea (OR 1.404;95%CI 1.198-1.645), C-reactive protein (OR 1.013;95%CI 1.001-1.024), lactate dehydrogenase (OR 1.007;95%CI 1.002-1.013) and SARS-CoV-2 PCR result (OR 2.456;95%CI 1.526-3.953). The AUC was 0.86 (95%CI 0.83-0.89), with a Brier score of 0.32 and, and R2 of 0.41. The AUC in the external validation in 500 patients was 0.70 (95%CI 0.65-0.75). CONCLUSION: The COVERED risk score showed excellent discriminatory ability, also in an external validation. It may aid clinical decision making, and improve triage at the ED in health care environments with high patient throughputs.

Eur Rev Med Pharmacol Sci ; 24(18): 9698-9704, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-814890


OBJECTIVE: To investigate patient characteristics and factors that increase the risk of being admitted to intensive care and that influence survival in cases of SARS-CoV-2 pneumonia. PATIENTS AND METHODS: One-hundred and ninety-one SARS-CoV-2 patients were admitted to the "Fondazione Poliambulanza di Brescia" Hospital (Brescia, Lombardy, Italy) in the period 1st March 2020 to 11th April 2020. Data on demographics, clinical presentation at admission, co-morbidities, pharmacological treatment, admission to intensive care and death was recorded. Logistic regression and survival analysis were carried out to investigate the risk of being admitted to intensive care and the risk of death. RESULTS: The mean age of the study cohort was 64.6±9.9 years (range 20-88). Median BMI was 28.5±5 kg/m2. Fever (81%) and dyspnea (65%) were the most common symptoms on admission. Most of patients (63%) had at least one co-existing disease. The 157 (82%) patients admitted to intensive care were more likely to be of intermediate age (60-69 years; OR 3.23, 95% CI 1.32-8.38), overweight (OR 2.66, 95% CI 1.02-7.07) or obese (OR 5.63, 95% CI 1.73-21.09) and with lymphocytopenia (OR 2.75, 95% CI 1.17-6.89) than the 34 patients admitted to the ordinary ward. During intensive care, 50% of patients died and their death was associated with older age (HR 2.06, 95% CI 1.07-3.97), obesity (HR 2.23, 95% CI 1.15-4.35) and male gender (HR 1.9, 95% CI 1.02-3.57). CONCLUSIONS: We found that admission to intensive care and poor survival were associated with advanced age and higher body mass index, albeit with differences in statistical significance. Pre-existing diseases and symptoms on admission were not associated with different clinical outcomes. Interestingly, male gender was more prevalent among SARS-CoV-2 patients and was related negatively to survival, but it was not associated with more frequent admission to intensive care.

Coronavirus Infections/mortality , Hospitalization/statistics & numerical data , Intensive Care Units/statistics & numerical data , Pneumonia, Viral/mortality , Adult , Age Factors , Aged , Aged, 80 and over , Betacoronavirus , COVID-19 , Female , Humans , Italy , Male , Middle Aged , Pandemics , Risk Factors , SARS-CoV-2 , Sex Factors , Young Adult