This report, to our knowledge, is the first large retrospective study of consecutive hospitalised patients with confirmed COVID–19 in Europe. Thus far, descriptions of retrospective data of cohorts of COVID–19 patients show results which are, for the most part, limited by some biases, such as the heterogeneity of subjects enrolled, as well as that of medical interventions. Therefore, the interpretation of data should move from an analysis of the population and progress to describing the kind of interventions carried out and the outcomes obtained. It would be significant to compare them with controls; however, this possibility is currently non-existent due to the pandemic emergency.
The median age in our cohort was 68 years and 77 years in patients who died, which is higher than that observed in other studies. In-hospital mortality assessed by competing risks analysis was significantly higher in patients aged between 70 and 79 years and in those over 79, compared with patients younger than 70 years. By contrast, the probability of discharge was similar between patients of 70–79 years and those older than 79 years. Hypertension, diabetes and other cardiovascular comorbidities, such as coronary heart disease and atrial fibrillation, were the most common. Notably, the median score on the Charlson comorbidity index was 3 in our cohort, which corresponds to about an 80% estimated 10-year survival, reflecting a significant comorbidity burden4. Our results are in line with those of the Italian National Institute of Health, showing that approximately 61% of deceased Italian patients with COVID–19 had more than 3 comorbidities, while only 3.6% of patients who died had no comorbidity.5 Male sex was an independent risk factor for in-hospital mortality and a lower probability of discharge.
When comparing our cohort with those described in the literature we noted that mortality was higher than that observed in other studies conducted both in and outside China.6–8While the prevalence of comorbidities in our cohort was similar to that reported in the USA,7 it was higher than that observed in Chinese cohorts.6,8 The association between age and in-hospital mortality could be explained by the lower cardiopulmonary reserve, by the enhanced susceptibility to infections and by the inadequate control of anti-inflammatory mechanisms.9 The association between gender and worst outcomes in COVID–19 is not fully understood. It has been proposed that female sex could be associated with a lower susceptibility to viral infections, with sex hormones playing a relevant role in innate and adaptive immune response.10 A different expression of ACE 2 receptor has also been suggested as an explanation of the gender-associated mortality in COVID–19 patients.11 Conversely, it has been suggested that males could be more prone to being affected by COVID–19 due to the higher smoking rate and higher prevalence of cardiovascular comorbidities.12 However, our multivariate model suggested that sex was an independent predictor of mortality, and discharge regardless of comorbidities and evidence supporting smoking as a predisposing factor in men with COVID–19 are lacking. Unfortunately, we were unable to evaluate the association between smoking and clinical outcomes in COVID–19.
In-hospital mortality was high (33%), and patients who were admitted during the first weeks of the emergency had a significantly lower in-hospital mortality and a higher likelihood of discharge compared to those who were admitted during subsequent weeks, with the worst outcomes observed from 4 March to 16 March 2020. One factor that many reports have addressed is the sequence of phases into which the disease has been divided, each corresponding to a different pattern of viral and immunological factors. Patient presentation in late phase may also have occurred, leading to the admission of an exceptionally large number of patients who needed hospitalisation in a short time span, resulting in a critical overload in the Policlinico San Matteo, in both triage and the management of the disease. These findings may be explained by also taking into consideration that during the first week many admissions were made for epidemiological reasons, leading to the hospitalisation of patients with few symptoms or mild disease.
Notably, no antiviral treatment was found to be associated with any improvement in mortality and discharge. Regarding lopinavir/ritonavir in particular, our findings confirm, in a European cohort collected in a real- world setting, the results of a recent randomised controlled trial that did not show any benefits from lopinavir/ritonavir treatment beyond standard care in a Chinese population.13 Similarly, we did not observe any significant differences in the in-hospital mortality between patients exposed or unexposed to hydroxichloroquine. Although Tocilizumab was significantly associated with a lower probability of discharge at univariate analysis, the small sample size of treated patients and potential selection bias of physicians to give anticytokine agents to the most severe patients hampered robust conclusions regarding this drug. Although ICU admission after 7 days from hospitalisation was independently and significantly associated with a lower risk of in-hospital mortality, the rapidity with which patients entered the ICU often concurrently with initiating other treatments, making the benefit of this treatment difficult to assess. Moreover, results from observational studies of drug effects should be interpreted with caution as they may be biased by survivor treatment selection bias, including time-related biases.14,15
In the literature, the use of composite endpoints (i.e. death or ICU admission) and, on the other hand, the implementation of traditional survival and Cox models are not appropriate in a disaster medicine setting such as that of COVID–19. The first assumption considers ICU and death to be equal, which is not true, while the traditional Cox model neglects to model discharge as an alternative endpoint. Competing risks analysis may provide further insights into the effect of interventions on the separate endpoint components.16 We overcame this issue by performing a competing risks analysis taking into account two events (in-hospital death and discharge) and including ICU admission as a time-dependent covariate.17 We suggest the use of a standardised methodology to assess treatment effects in observational studies in the complex clinical scenario of COVID–19. Summarising all the available evidence from randomised controlled trials and real- world comparative effectiveness studies, we are convinced that effective treatments for COVID–19 are still lacking and that therapies, such as specific antiviral drugs and immunomodulatory agents, remain an unmet and urgent medical need.
The main limitation of our study is the retrospective design. Retrospective studies have many problems that reduce their internal and external validity. When assessing retrospective cohort studies, the most important bias is the likelihood of the inappropriate selection of patients, which can lead to incorrect results and spurious associations. However, we included only consecutive patients with confirmed COVID–19, therefore we believe that selection bias was not relevant. Moreover, some potential confounders associated with the severity of COVID–19 (i.e. P/F ratio or circulating cytokine levels) and not available for this modelling could affect our results. Thus, we performed multivariate competing risks analysis to overcome this issue. Other limitations are the generalisability of our results to different populations and settings, particularly regarding the demographic structure of our country, including European elderly patients with a high prevalence of comorbidities. Finally, mortality was limited to in-hospital death, and discharged patients were assumed to still be alive during the study period.