Evolving mortality and clinical outcomes of hospitalized subjects during successive COVID-19 waves in Catalonia, Spain

Abstract

Background: The changes in shield strategies, treatments, emergence variants, and healthcare pathways might shift the profile and outcome of patients hospitalized with COVID-19 in successive waves of the outbreak.

Methods: We retrospectively analysed the characteristics and in-hospital outcomes of all patients admitted with COVID-19 in eight university hospitals of Catalonia (North-East Spain) between Feb 28, 2020 and Feb 28, 2021. Using a 7-joinpoint regression analysis, we split admissions into four waves. The main hospital outcomes included 30-day mortality and admission to intensive care unit (ICU).

Findings: The analysis included 17,027 subjects admitted during the first wave (6800; 39.9%), summer wave (1807; 10.6%), second wave (3804; 22.3%), and third wave (4616; 27.1%). The highest 30-day mortality rate was reported during the first wave (17%) and decreased afterwards, remaining stable at 13% in the second and third waves (overall 30% reduction); the lowest mortality was reported during the summer wave (8%, 50% reduction). ICU admission became progressively more frequent during successive waves. In Cox regression analysis, the main factors contributing to differences in 30-day mortality were the epidemic wave, followed by gender, age, diabetes, chronic kidney disease, and neoplasms.

Interpretation: Although in-hospital COVID-19 mortality remains high, it decreased substantially after the first wave and is highly dependent of patient's characteristics and ICU availability. Highest mortality reductions occurred during a wave characterized by younger individuals, an increasingly frequent scenario as vaccination campaigns progress.

Funding: This work did not receive specific funding.

Keywords: Clinical characteristics; Coronavirus disease 2019 (Covid-19); Hospital mortality; Risk factors; Socioeconomic characteristics.

Fig. 1

Distribution of hospital admissions due to COVID-19 across the analysed period (Feb 28, 2020 to Feb 28, 2021). The 7-joinpoint regression analysis revealed the presence of four waves, one of them below 33 daily admissions.

Fig. 2

Survival curve (Kaplan-Meier estimate) of patients admitted to hospital because of COVID-19 during the first wave (Feb 28 to Jun 6, red line), summer wave (Jun 7 to Sep 22, green line), second wave (Sep 23 to Dec 12, blue line), and third wave (Dec 13, 2020 to Feb 28, 2021) of the COVID-19 outbreak. A: In-hospital survival. B: 30-day survival. The p-value corresponds to the Log-rank test for survival differences between the two curves. C: competing risk analysis for 30-day mortality. D: competing risk analysis for in-hospital mortality. The censoring proportion for 30-day and in-hospital survival were 86.2% and 86.9%, all due to end of follow-up. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Survival curve (Kaplan-Meier estimate) of patients admitted to hospital because of COVID-19 during the first wave (Feb 28 to Jun 6, red line), summer wave (Jun 7 to Sep 22, green line), second wave (Sep 23 to Dec 12, blue line), and third wave (Dec 13, 2020 to Feb 28, 2021) of the COVID-19 outbreak. A: In-hospital survival. B: 30-day survival. The p-value corresponds to the Log-rank test for survival differences between the two curves. C: competing risk analysis for 30-day mortality. D: competing risk analysis for in-hospital mortality. The censoring proportion for 30-day and in-hospital survival were 86.2% and 86.9%, all due to end of follow-up. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

Cox proportional-hazard model for hospital outcomes during the investigated period (fully adjusted model with 10 multiple imputations). A: 30-day mortality. B: transfer to intensive care unit. Horizontal bars show the 95% confidence interval of the hazard ratio, adjusted (continuous line) and unadjusted (dashed grey line). The corresponding models with age included as restricted cubic spline for linearity is provided in Fig. S3 (Supplementary file 1).