Clinical outcome, risk assessment, and seasonal variation in hospitalized COVID-19 patients-Results from the CORONA Germany study

Abstract

Background: After one year of the pandemic and hints of seasonal patterns, temporal variations of in-hospital mortality in COVID-19 are widely unknown. Additionally, heterogeneous data regarding clinical indicators predicting disease severity has been published. However, there is a need for a risk stratification model integrating the effects on disease severity and mortality to support clinical decision-making.

Methods: We conducted a multicenter, observational, prospective, epidemiological cohort study at 45 hospitals in Germany. Until 1 January 2021, all hospitalized SARS CoV-2 positive patients were included. A comprehensive data set was collected in a cohort of seven hospitals. The primary objective was disease severity and prediction of mild, severe, and fatal cases. Ancillary analyses included a temporal analysis of all hospitalized COVID-19 patients for the entire year 2020.

Findings: A total of 4704 COVID-19 patients were hospitalized with a mortality rate of 19% (890/4704). Rates of mortality, need for ventilation, pneumonia, and respiratory insufficiency showed temporal variations, whereas age had a strong influence on the course of mortality. In cohort conducting analyses, prognostic factors for fatal/severe disease were: age (odds ratio (OR) 1.704, CI:[1.221-2.377]), respiratory rate (OR 1.688, CI:[1.222-2.333]), lactate dehydrogenase (LDH) (OR 1.312, CI:[1.015-1.695]), C-reactive protein (CRP) (OR 2.132, CI:[1.533-2.965]), and creatinine values (OR 2.573, CI:[1.593-4.154].

Conclusions: Age, respiratory rate, LDH, CRP, and creatinine at baseline are associated with all cause death, and need for ventilation/ICU treatment in a nationwide series of COVID 19 hospitalized patients. Especially age plays an important prognostic role. In-hospital mortality showed temporal variation during the year 2020, influenced by age.

Trial registration number: NCT04659187.

Conflict of interest statement

The authors have declared that no competing interests exist that could be perceived to bias this work. The commercial affiliation does not alter our adherence to PLOS ONE policies on sharing data and materials. SW reports grants and personal fees from Abbott and personal feels from Abbott, Boston Scientific, Boehringer Ingelheim, Bristol Myers Squibb, Bayer Vital, Acutus, and Daiichi Sankyo, outside the submitted work.

Figures

Fig 1. Patient distribution of the 4704 included patients in Germany.

The map of Germany is divided into northern, northeastern, northwestern, south, southeastern and southwestern Germany.

Fig 2. Flowchart of the trial showing the entire study population and the cohort Hamburg/Gauting with the primary endpoint mortality and severity of disease (mild, severe and fatal course).

Fig 3. Daily admissions of COVID-19 patients to the participating 45 centers in 2020 (blue color) and daily incidence of Germany in 2020 (red color, data used with kind approval by RKI, [13]).

1Lockdown: Closing of restaurants, most shops, schools, kindergardens, etc.; 2Restrictions: First openings of shops (with restrictions), partial opening of schools, restaurants remained closed; 3Restrictions: Closing of restaurants, hairdressing salons, etc.; 4Lockdown: Additionally, closing of schools, kindergardens, several shops, etc.

Fig 4. Temporal trend of mortality and age during the year 2020, based on the total study cohort (n = 4704).

Dot size: small = up to 20 cases, medium = 21–40 cases, big = 41–60 cases.

Fig 5. Temporal trend of pneumonia, respiratory insufficiency and need for ventilation during the year 2020, based on the total study cohort (n = 4704).

Dot size: small = up to 20 cases, medium = 21–40 cases, big = 41–60 cases.

Fig 6. Predicting severity of disease from baseline data: Fig 6A (above) shows the interquartile-range odds ratios for continuous predictors (upper quartile: lower quartile) and simple odds ratios for categorical predictors (current category: reference category) with regard to a severe or fatal disease.

Fig 6A (below) shows the nomogram (risk score): calculating the probability for a severe or fatal disease. For each predictor, points (0–100) are assigned. The total points are associated to event prediction. E.g. In case of an 80 years old (= 40 points) patient with CRP 150 (= 30 points), respiratory rate 24 (= 30 points), LDH 400 (= 10 points), and creatinine 2 (= 73 points), the total point score is 183 and therefore the risk for ventilation, ICU, or death is elevated with a probability of approximately 0.7. Fig 6B (above) shows the interquartile-range odds ratios for continuous predictors (upper quartile: lower quartile) and simple odds ratios for categorical predictors (current category: reference category) with regard to a fatal disease conditional of patients with at least severe diseases (mild courses are not taken into account). Fig 6B (below) shows the nomogram (risk score): calculating the probability for a fatal course of patients with severe diseases. Fig 6C This figure shows comparing effects of the above mentioned two models (model 1 for prediction of the combined endpoint versus model 2 for prediction of death in mechanical ventilated/ICU patients) by baseline parameters. The interquartile-range odds ratios for continuous predictors (upper quartile: lower quartile) and simple odds ratios for categorical predictors (current category: reference category) with regard to disease severity (Model 1) and mortality (Model 2) are compared.