In-hospital mortality from Coronavirus Disease 2019 in a northern Italian centre:results of a competing risk analysis from SMAtteo COvid19 REgistry (SMACORE)

Objectives An accurate prediction of the clinical outcomes of European patients requiring hospitalisation for Coronavirus Disease 2019 (COVID-19) is lacking. The aim of the study is to identify predictors of in-hospital mortality and discharge in a cohort of Lombardy patients with COVID-19. MethodsAll consecutive hospitalised patients from February 21 st to March 30 th , 2020, with conrmed COVID19 from the IRCCS Policlinico San Matteo, Pavia, Lombardy, Italy, were included. In-hospital mortality and discharge were evaluated by competing risk analysis. The Fine and Gray model was tted in order to estimate the effect of covariates on the cumulative incidence functions (CIFs) for in-hospital mortality and discharge. Results 426 adult patients (median age 68 (IQR, 56 to 77 years) were admitted with conrmed COVID-19 over a 5-week period; 292 (69%) were male. By 21 April 2020, 141 (33%) of these patients had died, 239 (56%) patients had been discharged and 46 (11%) were still hospitalised. Regression on the CIFs for in-hospital mortality showed that older age, male sex, number of comorbidities and hospital admission after March 4 th were independent risk factors associated with in-hospital mortality. Conclusions Olderage, male sex and number of comorbidities denitively predicted in-hospital mortality in hospitalised patients with COVID-19 on clinical outcomes and treatment of in observed to identify risks

March 2020, has spread rapidly all over the world. 1 Outside China, the rst western country to be affected was Italy, where the epidemic began on 21 February 2020 and quickly affected thousands of people, practically overwhelming the capacity of the National Health System to respond to it in terms of availability of hospital, ICU beds and ER spaces to receive and manage patients. 2 One of the rst factors limiting the ability to best manage COVID-19 patients was the di culty in allocating them, because clinical criteria to de ne the evolution of the disease were, and still are, missing.
To date, most of the studies that have extensively reported the clinical and laboratory characteristics of patients infected by COVID-19 have been carried out in China. 3 Data on clinical outcomes and treatment of COVID-19 outside China are lacking and the high heterogeneity in observed case-fatality ratios between and within different countries still remains unexplained. Because COVID-19 shows an array of clinical presentations and the lack of effective treatment makes it di cult to predict its outcome, the identi cation of risk factors for clinical outcomes, such as death, ICU admission and hospital discharge is crucial in order to improve the organisation of healthcare and to identify patients who may bene t the most from the available treatment strategies. Moreover, in such a complex epidemiological and clinical scenario, competing risks might help in the assessment of the impact of treatment strategies on meaningful clinical endpoints, such as in-hospital death and discharge. 4 The aim of this study was to explore and explain, in a cohort of Lombardy patients with COVID-19 in Pavia, Italy, the heterogeneity of clinical outcomes and to identify predictors of inhospital mortality and discharge by competing risks analysis. Patients who died were older, had higher Charlson comorbidity index, higher CRP and LDH levels and lower lymphocyte count compared to survivor patients (Table 1).. Hydroxycloroquine and antibiotics were used more frequently in patients who died compared to those who did not. The frequency of complications, such as respiratory failure, acute kidney injury, acute cardiac injury and septic shock was signi cantly higher in patients who died as compared to survivors. (Table S1)..

Results
The CIF for in-hospital mortality is showed in Figure 1. The estimated probability of in-hospital death was 24.4% during the rst 10 days from hospitalization, 31.0% during the rst 20 days and 33.7% at the end of follow-up. Univariate analysis for in-hospital mortality is reported in independently associated with higher in-hospital mortality, while time to ICU admission longer than 7 days (HR 0.19, 95%CI 0.05-0.67, p = 0.01) were independently associated with lower in-hospital mortality ( Table  3). The CIFs for in-hospital mortality performed using the parameter estimates of the Fine and Gray model for each of these covariates are showed in Figures S1-S4.
These risk factors were then used to construct a model encompassing all patients grouped into a "best" and a "worst" class according to the presence or not of these factors. CIFs for the best class (female patients with less than 3 comorbidities, admitted between February, 21 and March, 3) and for the worst class (male patients with more than 3 comorbidities, hospitalized between 4 March and 16 March) strati ed by age group are showed in Figure 2. At the end of follow-up, the probability of in-hospital death in patients younger than 70 years was 1.8% in the best class and 18.6% in the worst class. In patients with 70-79 years, the probability of in-hospital death at the end of follow-up was 8.3% in the best class and 62.5% in the worst class. In patients older than 80 years, the probability of in-hospital death at the end of follow-up was 13.7% in the best class and 80.8% in the worst class.
The characteristics and outcomes of patients according to the discharge status are reported in Tables S3-S4.
The CIF for discharge is showed in Figure 1. The estimated probability of discharge was 30.5% during the rst 10 days from hospitalization, 48.8% during the rst 20 days and 61.4% at the end of follow-up Univariate analysis is reported in Table S2. Tocilizumab use was signi cantly associated with a lower probability to be discharged at univariate analysis, however it was not included in the multivariate model because only 22 patients received Tocilizumab. Using the Fine and Gray model, we observed that lymphocytes count (HR 1.13, 95% CI 1.06-1.19, p = 0.0001) was independently associated with higher probability to be discharged,  Table 3). The CIFs for discharge performed using the parameter estimates of the Fine and Gray model for each of these covariates are showed in Figures S5-S9. The CIFs for the best class and for the worst class according to age are showed in Figures S10-S12. At the end of follow-up, the probability of discharge in patients younger than 70 years was 99.5% in the best class and 31.5% in the worst class. In patients with 70-79 years, the probability of discharge at the end of follow-up was 84.6% in the best class and 12.6% in the worst class. In patients older than 80 years, the probability of discharge at the end of follow-up was 75.3% in the best class and 9.6% in the worst class.

Discussion
This report, to our knowledge, is the rst large retrospective study of consecutive hospitalised patients with con rmed 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 signi cant to compare them with controls; however, this possibility is currently nonexistent 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 signi cantly 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 brillation, 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, re ecting a signi cant comorbidity burden 4 . 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 inhospital 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][7][8] While 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-in ammatory 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 rst weeks of the emergency had a signi cantly 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 ndings may be explained by also taking into consideration that during the rst 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 ndings con rm, in a European cohort collected in a real-world setting, the results of a recent randomised controlled trial that did not show any bene ts from lopinavir/ritonavir treatment beyond standard care in a Chinese population. 13 Similarly, we did not observe any signi cant differences in the in-hospital mortality between patients exposed or unexposed to hydroxichloroquine. Although Tocilizumab was signi cantly 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 signi cantly 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 bene t of this treatment di cult 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 rst 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 realworld comparative effectiveness studies, we are convinced that effective treatments for COVID-19 are still lacking and that therapies, such as speci c 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 con rmed 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.
In conclusion, our ndings indicate that in a Lombardy cohort of elderly hospitalized patients, for the most part male with a high prevalence of comorbidities, COVID-19 is characterised by high in-hospital mortality.
Older age, male sex, comorbidities and time of admission were found to be signi cant risk factors for inhospital mortality and associated with a lower probability of being discharged.

Study setting
The SMatteo COvid19 Registry (SMACORE) is a cohort of patients with a con rmed diagnosis of COVID-19 disease referred to the IRCCS Policlinico San Matteo Hospital of Pavia, Italy from February 2020. The SMACORE database includes demographic, clinical laboratory tests, treatment, and outcome data. Ethics approval for observational research using SMACORE data was obtained from the local ethics committee. This is a single centre, retrospective, observational cohort study and all patients of SMACORE cohort consecutively admitted to the Infectious Diseases Unit between 22 February and 30 March 2020, with a diagnosis of COVID-19 were enrolled. ICD-9 CM codes were reviewed, and clinical data were extracted.

Statement
All methods were carried out in accordance with relevant guidelines and regulations and ethics approval for observational research using SMACORE data was obtained from the local ethics committee and the informed consent has been obtained as by internal procedures.

Data source
Demographic, clinical, laboratory, treatment, and outcome data were extracted from medical records using a standardised data collection form. The Charlson comorbidity index was used to assess comorbidity. It includes 16 comorbidities, predicting 10-year survival in patients with multiple comorbidities and was used as a measure of the total comorbidity burden. 18 Imaging examinations were based on chest X-ray results.
Although the bene ts of a chest CT scan in achieving an early diagnosis of COVID-19 and in the follow-up of pneumonia evolution are well known, 19 we did not have the opportunity to include them in our clinical workout.

Laboratory tests
Respiratory samples from the upper respiratory tract were prospectively collected and analysed at the Molecular Virology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy, as part of the Regional SARS-CoV-2 surveillance and diagnosis plan in the Lombardy region. Total nucleic acids (DNA/RNA) were extracted from 200 ul of UTM™ using the QIAsymphon® instrument with QIAsymphony® DSP Virus/Pathogen Midi Kit (Complex 400 protocol) according to the manufacturer's instructions (QIAGEN, Qiagen, Hilden, Germany). Speci c RT-PCR targeting RNA-dependent RNA polymerase and E genes were used to detect the presence of SARS-CoV-2 in respiratory samples according to the WHO guidelines and published protocols. 20,21 Routine blood examinations included complete blood count, serum creatinine, glutamic oxaloacetic transaminase (GOT) and glutamic pyruvic transaminase (GPT), lactate dehydrogenase (LDH), C-reactive protein (CRP) and troponin. Lymphocitopenia was de ned as lymphocyte count <1.5 x 10 9 /L. CRP was considered elevated above 10 mg/dL. LDH levels were considered elevated above 245 U/L. Blood cultures were performed in each patient and arterial-blood gas analysis (ABG) was performed when clinical signs of oxygen impairment were detected (e.g. tachypnoea and hypoxia).

Treatment data
Treatment data included use of lopinavir/ritonavir, hydroxychloroquine, corticosteroids, tocilizumab and antibiotic drugs. Lopinavir/ritonavir 400/100 mg was administered orally twice daily for 14 days.
Hydroxychloroquine (HCQ) 600 mg twice on day 1, then 400 mg daily for 7 days. Corticosteroid treatment consisted of dexamethasone 20 mg daily for 5 days in patients admitted from 22 February to 20 March and methylprednisolone 1 mg/kg intravenously daily for 5 days from 21 March to the end of follow-up. Tocilizumab 8 mg/kg was given intravenously in 1 or 2 doses from 13 March to the end of follow-up. A second dose was given 8-12 hours after the rst dose in patients with inadequate response. Antibiotic therapy consisted of a combination of piperacillin/tazobactam and doxycycline. Low (cannula and simple masks) and high (Venturi and reservoir masks, Nasal High Flow (NHF), helmet continuous positive airway pressure (CPAP)) ow oxygen support was provided when hypoxia was detected. Time to ICU admission was de ned as the time from hospitalisation to ICU admission.

Outcomes
The primary disease event was in-hospital mortality. Discharge was analysed as a competing event by competing risks analysis.
The criteria for discharge were absence of fever, clinical remission of respiratory symptoms, oxygen saturation greater than 94% and two nasal swab samples negative for SARS-CoV-2 RNA obtained at least 24 hours apart.
Septic shock was de ned according to the 2016 Third International Consensus De nition for Sepsis and Septic Shock. 22 Acute kidney injury was de ned according to the KDIGO clinical practice guidelines 23 and acute cardiac injury was diagnosed if serum levels of cardiac biomarkers (troponin) was above the 99 th percentile upper reference limit, or if new abnormalities were shown in electrocardiography and echocardiography. 24 Statistical analysis Data for continuous variables are presented as mean and standard deviation or median and interquartile ranges (IQR), and data for categorical variables are presented as frequency and percentage. Differences between continuous data were analysed by Student t test or by Mann-Whitney U test. Differences between categorical variables were analysed by χ 2 test.
In-hospital mortality and discharge were evaluated by competing risks analysis, using cumulative incidence function (CIF). 4 The proportional sub-distribution hazard model by Fine and Gray was tted in order to estimate the effect of covariates on CIFs in-hospital death and discharge, including ICU admission as a timedependent covariate. 25  Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: no support from any organisation for the submitted work; no nancial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have in uenced the submitted work.
Ethics Approval The study was approved by Fondazione IRCCS Policlinico San Matteo institutional review board for observational research using SMACORE data.
Data sharing The authors agree to share relevant, anonymized data generated as part of the SMAtteo COvid19 REgistry (SMACORE) upon reasonable request.
Transparency statement The manuscript's guarantor (RB) a rms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as originally planned have been explained. Data are expressed as median (interquartile range) or n (%).  Figure 1 Cumulative incidence functions for in-hospital mortality and discharge of patients with Coronavirus Disease-19.

Figure 2
Cumulative incidence functions for in-hospital mortality performed using the parameter estimates of the Fine and Gray model and considering the best patient pro le (female sex, number of comorbidities lower than 3 admitted between 21 February to 3 March 2020) and the worst patient pro le (male sex, number of