To identify and quantify associations between baseline characteristics on hospital admission and mortality in patients with COVID-19 at a tertiary hospital in Spain.
Methods and findings
This retrospective case series included 238 patients hospitalized for COVID-19 at Hospital Universitario Clínico San Cecilio (Granada, Spain) who were discharged or who died. Electronic medical records were reviewed to obtain information on sex, age, personal antecedents, clinical features, findings on physical examination, and laboratory results for each patient. Associations between mortality and baseline characteristics were estimated as hazard ratios (HR) calculated with Cox regression models.
Series mortality was 25.6%. Among patients with dependence for basic activities of daily living, 78.7% died, and among patients residing in retirement homes, 80.8% died. The variables most clearly associated with a greater hazard of death were age (3% HR increase per 1-year increase in age; 95%CI 1–6), diabetes mellitus (HR 2.42, 95%CI 1.43–4.09), SatO2/FiO2 ratio (43% HR reduction per 1-point increase; 95%CI 23–57), SOFA score (19% HR increase per 1-point increase, 95%CI 5–34) and CURB-65 score (76% HR increase per 1-point increase, 95%CI 23–143).
Citation: Rivera-Izquierdo M, del Carmen Valero-Ubierna M, R-delAmo JL, Fernández-García MÁ, Martínez-Diz S, Tahery-Mahmoud A, et al. (2020) Sociodemographic, clinical and laboratory factors on admission associated with COVID-19 mortality in hospitalized patients: A retrospective observational study. PLoS ONE 15(6): e0235107. https://doi.org/10.1371/journal.pone.0235107
Editor: Muhammad Adrish, BronxCare Health System, Affiliated with Icahn School of Medicine at Mount Sinai, UNITED STATES
Received: May 6, 2020; Accepted: June 9, 2020; Published: June 25, 2020
Copyright: © 2020 Rivera-Izquierdo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: As specified now in the manuscript, data underlying the study cannot be made publicly available due to ethical concerns regarding participant privacy, as imposed by the Research Ethics Committee of Granada province (CEI). Qualified researchers may request access to this data to CEI, Granada, Spain: firstname.lastname@example.org.
Funding: This work was supported by the SEMERGEN-UGR Chair of Teaching and Research in Family Medicine (Cátedra de Docencia e Investigación en Medicina de Familia SEMERGEN-UGR), University of Granada, Spain to MRI. The funder played no role in the study design, data collection or analysis, and decision to publish.
Competing interests: The authors have declared that no competing interests exist.
Since 11 March 2020, when the World Health Organization (WHO) declared a worldwide SARS-CoV-2 pandemic, there have been more than 213,000 confirmed cases in Spain (as of 25 April 2020) and 22,157 deaths, representing a mortality rate of 47 per 100,000 inhabitants . Different reports on the status of the COVID-19 epidemic published in Spain by the Ministry of Health indicate that this disease is slightly more prevalent in women, in people older than 50 years, and in people with two or more previous illnesses, particularly hypertension (HT) (47.4%), cardiovascular disease (CVD) (32.3%), and diabetes mellitus (DM) (16.1%) [2,3].
Among confirmed cases of infection, 48.7% patients have required hospitalization mainly for pneumonia and adult respiratory distress syndrome (ARDS). Among patients with pneumonia, men, people older than 60 years, and people with CVD and/or DM are overrepresented [4,5]. Among Spanish patients the most frequent symptoms have been fever (75.7%), cough (75.5%) and dyspnoea (48.3%) . In 5% of hospitalized patients, the clinical course progressed to critical status requiring admission to the intensive care unit, and 2.3% have died [7,8].
Radiographic findings in patients with pneumonia are highly variable depending on–among other factors–the time elapsed since symptom onset [9,10]. However, the most frequent findings are bilateral infiltrates with an interstitial or ground glass pattern, or bilateral alveolar opacities [11,12]. Alterations in laboratory values are also frequent and varied, depending on disease severity, e.g. anaemia, leucocytosis, neutrophilia, lymphopenia, and elevated creatinine, erythrocyte sedimentation velocity (ESV), C-reactive protein (CRP), procalcitonin, lactate dehydrogenase (LDH), serum ferritin, troponins, and cytokines (among other markers) [12,13]. The most frequent laboratory findings in seriously ill patients are lymphopenia, hypoalbuminemia, and increased ferritin and D-dimer; most of these alterations probably reflect increases in cytokines levels or the “cytokine storm” phenomenon (IL-2R, IL-6, IL-10 and TNFα) [14,15].
Few studies to date have shed light on the main factors associated with fatality rates in hospitalized patients. Most notably, a retrospective cohort study of 191 patients at two hospitals in China reported 41 deaths, and analysed the association of sex, age, chronic obstructive lung disease, coronary disease, diabetes, hypertension, chronic kidney disease, and other comorbidities on the risk of death due to COVID-19. According to the multivariate models in this study, associations were detected only for patient age and two parameters measured on admission: Sequential Organ Failure Assessment (SOFA) score and D-dimer concentration greater than 1 μg/ml . Other studies in similar conditions were made for Middle East respiratory syndrome (MERS)  and severe acute respiratory syndrome (SARS)  but the number of studies analysing prognosis factors on COVID-19 remain insufficient to date.
The aim of this study was to identify and quantify the associations between baseline characteristics on hospital admission in patients with COVID-19 infection (e.g. personal antecedents, physical examination and clinical findings, and laboratory results) and mortality at a tertiary hospital in Spain.
Materials and methods
This retrospective case series comprised 238 patients admitted for COVID-19 to Hospital Universitario Clínico San Cecilio, and who died or were discharged, between 16 March and 10 April 2020. This 480-bed tertiary hospital is located in the city of Granada (southern Spain), and provides specialized health care services to a total population of 291,797 inhabitants. Only positive PCR test was considered as diagnosis criteria for the patients included in our series. Therefore, patients presenting compatible symptoms and negative test were not included in the present study. The information detailed below was collected from electronic medical records for each patient.
General information on admission and recorded in the emergency service report
Sex, age (analysed as a continuous variable and also stratified into groups of 30–39, 40–49, 50–59, 60–69, 70–79, and 80 years or older), presence of comorbidities (HT; DM; previous lung disease; chronic kidney failure; CVD, or active neoplasia), dependence for basic activities of daily living (BADL) (yes or no), clinical findings on admission (low-grade or higher fever, dry cough, productive cough, haemoptysis, dyspnoea, anosmia, ageusia, headache, nausea, diarrhoea, other gastrointestinal symptoms, myalgia, fatigue, tiredness, general malaise, exanthema, odynophagia), smoking status (never smoker, current smoker, or ex-smoker) and place of residence (own home or retirement facility).
Variables recorded from physical examination and laboratory tests on admission
Systolic and diastolic blood pressure, heart rate, temperature, basal oxygen saturation (SatO2), externally supplied oxygen (litres), fraction of inspired oxygen (FiO2), SatO2/FiO2 ratio, haemoglobin, lymphocyte count, neutrophil count, platelet count, D-dimer level, glycaemia, total bilirubin, LDH, ferritin, CRP, procalcitonin, troponins, creatinine, kidney function (elevated urea and creatinine), partial O2 and CO2 pressure in arterial blood (PO2 and PCO2), CO2, arterial blood pH, SOFA score, CURB-65 score, and X-ray findings on admission (categorized as dichotomous variables), e.g. diffuse opacities, interstitial opacities, hilar congestion or pleural effusion.
Analysis of outcome variables: Death and days of hospital stay
Central tendency and dispersion were estimated for quantitative variables, and frequency distribution was determined for categorical variables. For each category in the latter, we estimated the distribution of discharges and deaths. Because our case series was defined on the basis of outcomes (discharge or death), it cannot be considered a true cohort of admitted patients, given that by definition we excluded patients who, at the end of the recruitment period (10 April 2020), remained hospitalized (censured data). For these patients the mean duration of hospital stay can be assumed to be longer than for patients included in the present case series. Because the length of hospital stay tends to be inversely associated with mortality, this association would lead to selection bias, which in turn would result in overestimating the proportion of deaths in our sample. In addition, it could potentially skew (in either direction) the magnitude of the associations between patient characteristics on admission and death. To compensate for this source of bias we opted to use time to death as a dependent variable and used Cox proportional hazards regression models to calculate hazard ratios (HR) in order to quantify the magnitude of associations between hazard or instantaneous death rate and patients’ baseline characteristics.
Because of the low number of patients compared to the number of independent variables considered in this analysis, we used a three-step modelling process. First, we developed separate models for each baseline variable and for age. Next we defined subgroups of baseline variables (sociodemographic, symptoms, comorbidities, physical examination on admission, laboratory values, and X-ray findings), and used a stepwise process to build multivariate models for each variable (including age in all models). The variables selected in each model where then used in a stepwise procedure to build a final model that included baseline variables that were retained. In all cases the P value for inclusion in the model was set at 0.05, and the P value for exclusion was set at 0.10. The 95%CI was calculated for each HR. All data were analysed with STATA software (version 15.0) .
This study met the requirements for appropriateness to achieve the intended aims and complied with all ethical principles pertinent to this type of study design. All data were fully anonymized before the analyses. No informed consent was required due to the characteristics of the design. The study was approved by the Research Ethics Committee of Granada province (CEI) on 13 April 2020.
Slightly more than half of our patients were males (55.0%, n = 131). The age range was 24 to 97 years with a mean of 64.7, standard deviation 15.4, and median 67 years. Most patients (60.5%) were 60 years old or more, and the largest age stratum was 70 to 79 years. At least one comorbidity was present on admission in 56.7% of the sample. Among the most prevalent diseases were HT (48.7%), DM (21.9%), and CVD (22.7%). About one fifth of the patients (19.8%) had dependence for BADL, and 10.9% lived in retirement homes (Table 1). On admission, the most frequent clinical profile was characterized by low-grade fever (89.5%), dry cough (80.7%), general malaise (63.5%), dyspnoea (61.3%) and tiredness (59.2%) (Table 1).
Also shown in Table 1 are the proportions of deaths in the entire sample and for each category of baseline variables. About one fourth of the patients in the present series died (25.6%). No patients younger than 50 years died; as age increased, the proportion of deaths increased in an approximately exponential manner, e.g. 5.8% in patients aged 50 to 59 years (95%CI 1.2–15.9), 12.8% in those aged 60 to 69 (95%CI 4.8–25.7), 28.6% in those aged 70 to 79 (95%CI 16.6–43.3), and 79.2% in those 80 years old or more (95%CI 65.0–89.5). In our series only 5.8% (6 out of 103) of the patients with no prior comorbidities died. In contrast, 78.7% of our patients with dependence for BADL and 80.8% of those who resided in retirement homes died.
Table 2 show the baseline variables regarding demographic data, comorbidities and medications, symptoms, physical exam and X-Ray findings on admission. The variables included in the final model as associated with higher mortality were age (3% increase in HR per 1-year increase in age; 95%CI 1–6), DM (HR 2.42; 95%CI 1.43–4.09. Elevated SatO2/FiO2 ratio was inversely related with mortality (43% decrease per 1-point increase, 95%CI 23–57). None of the other baseline variables were independently associated with mortality.
Table 3. show the laboratory parameters and prognostic scales on admission. The variables included in the final model as associated with higher mortality were SOFA score (19% increase per 1-point increase, 95%CI 5–34) and CURB-65 score (73% increase per 1-point increase, 95%CI 23–143). None of the other baseline variables were independently associated with mortality.
The sex and age distribution in our series of patients differs from that reported by the WHO mission in China and from data published by the Spanish National Epidemiology Centre, both of which found very similar figures [7,20]. In the present series the proportion of men was larger (55% vs. 51% in China) and median age was greater (67 years vs. 51 years in China). Our figures can be attributed in part to the greater population aging in Spain, where people aged 65 years or more account for nearly 50% of the population vs. 12% in China . In addition, evolution of the pandemic in Spain has lagged behind China, and this may mean that because the patients in our series are among the first in Spain for whom these early outcomes (discharge or death) are reported, our sample comprises a high proportion of patients with factors potentially associated with more severe illness: male sex and older age [16,22,23].
Regarding the distribution of comorbidities and symptoms on hospital admission, our results are consistent with earlier studies in China [4,24,22,25], and with data from the Ministry of Health report on the status of COVID-19 in Spain as of 17 April 2020 . In all series studied to date, the comorbidities associated most frequently with COVID-19 were CVD, DM and HT, and the most common clinical characteristics on admission were low-grade fever, dry cough, general malaise, fatigue, and dyspnoea. In our series, the presence of ageusia showed better prognosis of patients (HR 0.11; 95%CI 0.02–0.83) in the stepwise regression model including age and the remaining symptomatologic variables. This symptom has reported to be more frequent in younger patients and in women [27,28]. Therefore, ageusia might bias the results as older age and male sex are associated with worse prognosis. Patients with cognitive impairment also would present difficulties to detect and express this specific symptom. This hypothesis should be tested in specific studies but, in any case, the association disappeared in our final models fitted for each group of variables. Regarding smoking, although several studies have pointed a worse progression and adverse outcomes of COVID-19 [29,30], in our study we could not corroborate these results. Apart from the small number of current smokers in our sample (only 22 patients and 3 deaths among them) which may lead to high variability in the estimations for this subgroup, this fact might have two explanations. First, smoking habits were not systematically collected in all clinical records. Therefore, the existence of a considerable amount of missing data (42 patients) for this variable may have distorted the results. Second, patients with high number of comorbidities (i.e., DM, pulmonary diseases or living in retirement homes) might plausibly show a lower frequency of smoking habits given their condition, whilst their intrinsic risk of mortality is high. Again, this hypothesis should be tested in specific studies. Two findings from our crude analysis stand out: the low frequency of death in our patients with no comorbidities (5.8%), and the high percentage of deaths among patients with dependence for BADL (approaching 80%) and/or who lived in retirement homes. These three variables are closely related with each other, and with older age, as shown by the Cox regression models. Although we can venture no explanation for the reasons behind these relationships, these three variables alone or in combination appear to define a population group at high risk of death, as also suggested in earlier studies [31–36]. We believe this high risk should be taken into account in setting priorities for the indication for hospital admission. In particular, patients who live in retirement homes are likely to have most of the risk factors we identified, and in fact, it is estimated that nearly 12,200 older persons have died during the current pandemic among residents of public, joint public–private, and private retirement facilities in Spain . This makes it imperative to develop specific, co-ordinated actions across all such centres during the epidemic, in order to ensure that the necessary material and human resources are available to care for these especially vulnerable patients. To date, few studies have analysed the causes of higher morbidity and mortality in this population group [38,39], and no such studies have been done in Spain. Our findings suggest the need for a specific approach to care for this group both in hospital emergency services and in residential retirement facilities.
The models we obtained with our adjusted analysis showed that in our series of patients hospitalized for COVID-19, characteristics present before SARS-CoV-2 infection that were independently associated mortality were age (exponential increase in patients 50 years old and older) and DM. A study from China based on a retrospective cohort of 191 patients identified CVD and DM as chronic diseases associated with greater mortality. However, in contrast to our finding for DM, Zhou and colleagues found that this association disappeared in their adjusted analysis . In this study, associations were detected only for patient age (odds ratio 1.10 per 1-year increase in age, 95% confidence interval [95%CI] 1.03–1.17).
At least one other review of comorbidities associated with COVID-19 found that CVD and DM were among the factors most strongly associated with an increased risk of mortality, although this was based only on crude data .
With regard to baseline variables relating to the characteristics of SARS-CoV-2 infection on admission, those that yielded independent associations with mortality were greater need for externally supplied oxygen to maintain adequate saturation (low SatO2/FiO2 ratio as a result of ARDS) and high SOFA and CURB-65 scores. These findings are consistent with factors identified in earlier reports from countries other than Spain [7,16,41–43]. Zhou and colleagues  detected two risk parameters measured on admission: SOFA score (odds ratio 5.65, 95%CI 2.61–12.23) and D-dimer concentration greater than 1 μg/ml (odds ratio 18.42, 95%CI 2.64–128.55). Although D-dimer has been consistently associated with higher mortality [44,45], we could not corroborate these results in our study.
As noted in the Methods section, the main limitation of this study is the selection bias resulting from out inclusion criteria: patients who were either discharged or died, excluding those who were still hospitalized at the end of the recruitment period. Nonetheless, we feel the use of Cox regression models minimized the impact of this bias on the magnitude of our estimated associations, given that the estimates were a function of time to death. Because our patients comprise the first sample of people admitted with the characteristics reported here, the duration of follow-up was brief and this may have led to the selection of “extreme” cases, i.e. those who were the first to be discharged or the first to die in our hospital. This factor makes our findings especially useful for studies of the first patients to be hospitalized in other settings. These considerations, along with the chronology of the COVID-19 pandemic in Spain, make it likely that the patients selected for this study differed from those who were still hospitalized at the end of the recruitment period. In other words, compared to subsequently admitted patients, those analysed here may have had more and/or more severe clinical signs and symptoms as grounds for admission. Another potential source of error is the lack of adjustment for variables we did not measure, either because they were not recorded in the medical records or were not considered relevant in light of our current (incomplete, constantly changing) knowledge of COVID-19. There is another potential bias concerning the selection of patients which are mainly elderly patients coming from a localized region in Spain. Therefore, the results could not be widely generalized. A final consideration is that we designed a monocentric study with a limited number of patients, while a great number of different factors were analysed as independent variables. This limited sample size necessitated a sequential process to develop our models, with a stepwise approach and low number of predictive variables.
In addition to the close association between mortality and comorbidities, dependence for BADL and living in a retirement facility in our crude analysis, the main baseline factors that were independently associated in our adjusted analysis with mortality in patients hospitalized for COVID-19 were older age, DM, low SatO2/FiO2 ratio, and high SOFA and CURB-65 scores. These characteristics define a profile of patients in Spain with an elevated mortality rate for whom a specific approach to clinical management is urgently needed.
We thank K. Shashok for translating the original manuscript into English, and the staff working at the Service of Preventive Medicine and Public Health, San Cecilio Clinical University Hospital, Granada, Spain, for assistance in data collection. We also acknowledge the clinicians and staff of San Cecilio Hospital for their tireless commitment to patient care in the setting of this outbreak.
- 1. Ministerio de Sanidad. Gobierno de España. Situación de COVID-19 en España. Incidencia acumulada en los últimos 14 días. 2020; https://covid19.isciii.es/
- 2. Ministerio de Sanidad. Gobierno de España. Actualización n° 86. Enfermedad por el coronavirus (COVID-19). 2020; https://www.mscbs.gob.es/profesionales/saludPublica/ccayes/alertasActual/nCov-China/documentos/Actualizacion_86_COVID-19.pdf
- 3. Ministerio de Sanidad. Gobierno de España. Actualización n° 85. Enfermedad por el coronavirus (COVID-19). 2020; https://www.mscbs.gob.es/profesionales/saludPublica/ccayes/alertasActual/nCov-China/documentos/Actualizacion_85_COVID-19.pdf
- 4. Guan W, Ni Z, Hu Y, Liang W, Ou C, He J, et al. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med. 2020; 382:1708–1720. pmid:32109013
- 5. Lai CC, Liu YH, Wang CY, Wang YH, Hsueh SC, Yen MY, et al. Asymptomatic carrier state, acute respiratory disease, and pneumonia due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2): Facts and myths. J Microbiol Immunol Infect. 2020; S1684-1182(20)30040-2. pmid:32173241
- 6. Informe sobre la situación de COVID-19 en España. Informe de COVID-19 n° 21. Red Nacional de Vigilancia Epidemiológica. 2020; https://www.isciii.es/QueHacemos/Servicios/VigilanciaSaludPublicaRENAVE/EnfermedadesTransmisibles/Documents/INFORMES/Informes%20COVID19/Informe%20n°%2021.%20Situación%20de%20COVID19%20en%20España%20a%206%20de%20abril%20de%202020.pdf
- 7. World Health Organization. Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). 2020; https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf
- 8. Wu Z, McGoogan JM. Outbreak in China: Summary of a Report of 72314 Cases from the Chinese Center for Disease Control and Prevention. JAMA. 2020. pmid:32091533
- 9. Ming-Yen NG, Elaine LEE, Yang YP, Yang J, LI F, Wang X, et al. Imaging Profile of the COVID-19 Infection: Radiologic Findings and Literature Review. Radiology: Cardiothoracic Imaging, 2020.
- 10. Huang LL, Shen SP, Yu P, Wei YY. Dynamic basic reproduction number-based evaluation for current prevention and control of COVID-19 outbreak in China. Zhonghua Liu Xing Bing Xue Za Zhi Zhonghua Liuxingbingxue Zazhi. 2020;41:466–469. pmid:32113197
- 11. Chung M, Bernheim A, Mei X, Zhang N, Huang M, Xianjun Zeng X, et al. CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV). Radiology. 2020;295:202–207. pmid:32017661
- 12. Yang X, Yu Y, Xu J, Shu H, Xia J, Liu H, et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. Lancet Respir Med. 2020. pmid:32105632
- 13. Ministerio de sanidad. Gobierno de España. Información científico-técnica. Enfermedad por coronavirus, COVID-19. 2020; https://www.mscbs.gob.es/profesionales/saludPublica/ccayes/alertas%20Actual/nCov-China/documentos/20200404_ITCoronavirus.pdf
- 14. Chen G, Wu D, Guo W. Clinical and immunologic features in severe and moderate coronavirus disease 2019. J. Clin. Invest. 2020;130:2620–2029. pmid:32217835
- 15. Zhou Y, Fu B, Zheng X, Wang D, Zhao C, Qi Y, et al. Aberrant pathogenic GM-CSF+ T cells and inflammatory CD14+CD16+ monocytes in severe pulmonary syndrome patients of a new coronavirus. bioRxiv; 2020. Available from: https://www.biorxiv.org/content/10.1101/2020.02.12.945576v1
- 16. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. The Lancet. 2020;395:1054–1062. pmid:32171076
- 17. Morra ME, Van Thanh L, Kamel MG, Ghazy AA, Altibi AMA, Dat LM, et al. Clinical outcomes of current medical approaches for Middle East respiratory syndrome: A systematic review and meta-analysis. Rev Med Virol. 2018;28(3):e1977. pmid:29664167
- 18. Chan JC, Tsui EL, Wong VC, Hospital Authority SARS Collaborative Group. Prognostication in severe acute respiratory syndrome: a retrospective time-course analysis of 1312 laboratory-confirmed patients in Hong Kong. Respirology. 2007;12(4):531–542. pmid:17587420
- 19. StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC.
- 20. Centro Nacional de Epidemiología. Red Nacional de Vigilancia Epidemiológica. Informe sobre la situación de COVID-19 en España. Informe 14. 2020; https://www.isciii.es/QueHacemos/Servicios/VigilanciaSaludPublicaRENAVE/EnfermedadesTransmisibles/Documents/INFORMES/Informes%20COVID19/Informe%20n%C2%BA%2014.%20Situaci%C3%B3n%20de%20COVID19%20en%20Espa%C3%B1a%20a%2024%20marzo%20de%202020.pdf
- 21. Beam Dowd J, Andriano L, Brazel DM, Rotondi V, Block P, Ding X, et al. Demographic science aids in understanding the spread and fatality rates of COVID-19. Proc Natl Acad Sci U S A. 2020. pmid:32300018
- 22. The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team. Vital Surveillances: The Epidemiological Characteristics of an Outbreak of 2019 Novel Coronavirus Diseases (COVID-19). China CDC Wkly, 2020; http://weekly.chinacdc.cn/en/article/id/e53946e2-c6c4-41e9-9a9b-fea8db1a8f51
- 23. Ma Ch, Gu j, Hou P, Zhang L, Bai Y, Guo Z, et al. Incidence, clinical characteristics and prognostic factor of patients with COVID-19: a systematic review and meta-analysis. Medrxiv; 2020.
- 24. Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. The Lancet. 2020. pmid:32007143
- 25. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA. 2020;323:1061–1069. pmid:32031570
- 26. Ministerio de Sanidad. Gobierno de España. Información científico-técnica. Enfermedad por coronavirus, COVID-19. 2020; https://www.mscbs.gob.es/profesionales/saludPublica/ccayes/alertasActual/nCov-China/documentos/20200417_ITCoronavirus.pdf
- 27. Lee Y, Min P, Lee S, Kim SW. Prevalence and duration of acute loss of smell or taste in COVID-19 patients. J Korean Med Sci. 2020;35(18):e174. pmid:32383370
- 28. Speth MM, Singer-Cornelius T, Obere M, Gengler I, Brockmeier SJ, Sedaghat AR. Olfactory Dysfunction and Sinonasal Symptomatology in COVID-19: Prevalence, Severity, Timing, and Associated Characteristics. Otolaryngol Head Neck Surg. 2020:194599820929185. pmid:32423357
- 29. Cattaruzza MS, Zagà V, Gallus S, D’Argenio P, Gorini G. Tobacco smoking and COVID-19 pandemic: old and new issues. A summary of the evidence from the scientific literature. Acta Biomed. 2020;91(2):106–112. pmid:32420934.
- 30. Vardavas CI, Nikitara K. COVID-19 and smoking: A systematic review of the evidence. Tob Induc Dis. 2020;18:20. pmid:32206052
- 31. Rong-Hui D, Li-Rong L, Cheng-Qing Y, Wang W, Tan-Ze C, Ming L, et al. Predictors of Mortality for Patients with COVID-19 Pneumonia Caused by SARS-CoV-2: A Prospective Cohort Study. Eur Respir J. 2020. pmid:32269088
- 32. Abbatecola AM, Antonelli-Incalz R. COVID-19 Spiraling of Frailty in Older Italian Patients. J Nutr Health Aging. 2020;24:453–455. pmid:32346677
- 33. Severe Outcomes Among Patients with Coronavirus Disease 2019 (COVID-19)-United States, February 12–March 16, 2020. MMWR Morb Mortal Wkly Rep. 2020;69:343–346. pmid:32214079
- 34. Ki M. Epidemiologic characteristics of early cases with 2019 novel coronavirus (2019-nCoV) disease in Korea. Epidemiology and Health. 2020;42:e2020007. pmid:32035431
- 35. Yang J, Zheng Y, Gou X, Pu K, Chen Z, Guo Q, et al. Prevalence of comorbidities in the novel Wuhan coronavirus (COVID-19) infection: a systematic review and meta-analysis. Int J Infect Dis; 2020. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1201971220301363
- 36. Onder G, Rezza G, Brusaferro S. Case-Fatality Rate and Characteristics of Patients Dying in Relation to COVID-19 in Italy. JAMA. 2020. pmid:32203977
- 37. Unos 12.190 ancianos muertos en residencias de España con diagnóstico o síntomas de coronavirus. El independiente, 2020. Available from: https://www.elindependiente.com/vida-sana/2020/04/17/cerca-de-12-190-ancianos-han-muerto-en-las-residencias-de-espana-con-diagnostico-o-sintomas-de-covid-19/
- 38. Goh KJ, Choong MC, Cheong EH, Kalimuddin S, Wen SD, Phua GC, et al. Rapid Progression to Acute Respiratory Distress Syndrome: Review of Current Understanding of Critical Illness From COVID-19 Infection. Ann Acad Med Singapore. 2020;49:108–118. pmid:32200400
- 39. Wang L, He W, Yu X, Hu D, Bao M, Liu H, et al. Coronavirus disease 2019 in elderly patients: Characteristics and prognostic factors based on 4-week follow-up. J Infect. 2020; S0163-4453(20)30146-8. pmid:32240670
- 40. Singh AK, Gupta R, Misra A. Comorbidities in COVID-19: Outcomes in hypertensive cohort and controversies with renin angiotensin system blockers. Diabetes Metab Syndr. 2020;14:283–287. pmid:32283499
- 41. Lovato A, de Filippis C. Clinical Presentation of COVID-19: A Systematic Review Focusing on Upper Airway Symptoms. Ear Nose Throat J. 2020; 145561320920762. pmid:32283980
- 42. Riccardo F, Ajelli M, Andrianou X, Bella A, Del Masso M, Fabiani M, et al. Epidemiological characteristics of COVID-19 cases in Italy and estimates of the reproductive numbers one month into the epidemic. MedRxiv; 2020. Available from: https://www.medrxiv.org/content/10.1101/2020.04.08.20056861v1
- 43. World Health Organization (WHO). Clinical management of severe acute respiratory infection (SARI) when COVID-19 disease is suspected. 2020; https://www.who.int/publications-detail/clinical-management-of-severe-acute-respiratory-infection-when-novel-coronavirus-(ncov)-infection-is-suspected
- 44. Zhang L, Yan X, Fan Q, Liu H, Liu X, Liu Z, et al. D-dimer levels on admission to predict in-hospital mortality in patients with Covid-19. J Thromb Haemost. 2020;18(6):1324–1329. pmid:32306492
- 45. Connors JM, Levy JH. COVID-19 and its implications for thrombosis and anticoagulation. Blood. 2020;135(23):2033–2040. pmid:32339221