https://orcid.org/0000-0002-0310-3529
Figures
Abstract
Background
Italy has been the first Western country to be heavily affected by the spread of SARS-COV-2 infection and among the pioneers of the clinical management of pandemic. To improve the outcome, identification of patients at the highest risk seems mandatory.
Objectives
Aim of this study is to identify comorbidities and clinical conditions upon admission associated with in-hospital mortality in several COVID Centers in Campania Region (Italy).
Methods
COVOCA is a multicentre retrospective observational cohort study, which involved 18 COVID Centers throughout Campania Region, Italy. Data were collected from patients who completed their hospitalization between March-June 2020. The endpoint was in-hospital mortality, assessed either from data at discharge or death certificate, whilst all exposure variables were collected at hospital admission.
Results
Among 618 COVID-19 hospitalized patients included in the study, 143 in-hospital mortality events were recorded, with a cumulative incidence of about 23%. At multivariable logistic analysis, male sex (OR 2.63, 95%CI 1.42–4.90; p = 0.001), Chronic Liver Disease (OR 5.88, 95%CI 2.39–14.46; p<0.001) and malignancies (OR 2.62, 95%CI 1.21–5.68; p = 0.015) disclosed an independent association with a poor prognosis, Glasgow Coma Scale (GCS) and Respiratory Severity Scale allowed to identify at higher mortality risk. Sensitivity analysis further enhanced these findings.
Citation: Galiero R, Pafundi PC, Simeon V, Rinaldi L, Perrella A, Vetrano E, et al. (2020) Impact of chronic liver disease upon admission on COVID-19 in-hospital mortality: Findings from COVOCA study. PLoS ONE 15(12): e0243700. https://doi.org/10.1371/journal.pone.0243700
Editor: Francesco Di Gennaro, National Institute for Infectious Diseases Lazzaro Spallanzani-IRCCS, ITALY
Received: October 29, 2020; Accepted: November 27, 2020; Published: December 10, 2020
Copyright: © 2020 Galiero 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: All relevant data are within the manuscript and its Supporting Information files.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
After SARS-CoV-2 disease (COVID-19) outbreak in Wuhan in December 2019, Italy has been the first Western country heavily affected by the spread of the infection. Our country was among the pioneers of the clinical management of pandemic for all other European and non-European National Healthcare Systems (NHS), as undergone to a stress test for both healthcare and economic systems [1].
World reports stated that approximatively 20–51% of patients had at least one pre-existing disease on admission, among which the most prevalent were diabetes (10–20%), hypertension (10–15%), cardiovascular and cerebrovascular diseases (7–40%), liver disease (2–11%) [2,3]. The relationship between pre-existent comorbidities and adverse clinical outcome has been largely investigated. However, data are still partially controversial [2,4–9]. Particularly, several papers have reported a positive association between preexisting chronic liver diseases and either the risk of infection or the hospitalization of COVID-19 patients. The evidence of an association between liver disease and mortality among hospitalized patients is still poor [2,10].
To date, while waiting for a vaccine and specific therapies, and therefore to define the best clinical management, the identification of patients at the highest risk seems mandatory.
Aim of this study is to identify comorbidities associated with in-hospital mortality in several COVID Centers in Campania Region (Italy), with a particular focus on chronic liver disease.
Materials and methods
Study design and participants
COVOCA (observational study on the COVID-19 population hOspitalized in CAmpania Region) is a retrospective observational cohort study, which involved 18 COVID centers throughout Hospitals of Campania Region, Italy. In depth, 11 were sub-intensive COVID-19 Units, 6 low intensive adapted with respiratory devices and 1 Intensive Care Unit.
We included all adult patients (≥ 18 years) with laboratory confirmed SARS-CoV-2 infection, who completed their hospitalization (discharged or dead) in the period between March 13, 2020 and July, 6 2020, of whom clinical records were available. All patients with either incomplete or missing clinical and laboratory data at baseline were excluded from the study. Flow-chart is shown in Fig 1. Data were accessed from July 10, 2020.
Clinical charts and hospital electronic records were used as data sources. All data were fully anonymized by the participating centers before being accessed. The study was approved by the local Ethics Committee (University of Campania Luigi Vanvitelli) and is in accordance with 1976 Declaration of Helsinki and its later amendments.
Variables (Outcome and exposure)
Microbiological diagnosis SARS-CoV-2 infection was defined by Real-Time Polymerase Chain Reaction of nasal-pharyngeal swab specimen.
The endpoint was in-hospital mortality, assessed either from data at discharge or death certificate. Exposure variables were collected at hospital admission, as follows: (a) demographic and anthropometric characteristics (sex, age, rapid COVID-19 serum test, RT-PCR swab specimen); (b) anamnestic data (number of COVID-19 positives in the family and how many days before hospitalization); (c) symptoms (fever, diarrhea, cough, chest and abdominal pain, anosmia, dysgeusia and related days of onset); previous comorbidities (smoking habit, diabetes, hypertension, chronic cardiac disease, chronic kidney disease (CKD), chronic liver disease (CLD), chronic respiratory disease, neurological disorders or malignancies). Pre-existing comorbidities were evaluated both singularly and according to their presence (absent, 1, 2 and ≥3). In depth, diabetes diagnosis was placed according to the anamnestic records and laboratory exams at admission following American Diabetes Association (ADA) most recent guidelines [11]. A similar approach was used also for hypertension diagnosis [12]. Chronic cardiac diseases, which include ischemic cardiopathy, previous acute myocardial infarction (AMI), heart failure, valvulopathy and atrial fibrillation, were instead diagnosed according to anamnestic data and clinical records. Likewise, also CKD (chronic renal failure) [13], chronic respiratory diseases (chronic obstructive pulmonary disease, asthma, lung fibrosis) [14], neurologic disorders and malignancies. Particularly, as for CLD, chronic hepatopathy from HCV and HBV was diagnosed according to anamnestic records, and laboratory exams at hospitalization. As for cirrhosis, diagnosis was placed considering clinical records and both laboratory and instrumental examination. Finally, NAFLD was diagnosed only based on instrumental and laboratory tests at baseline [15].
All preexisting comorbidities were further weighted calculating the Charlson Comorbidity Index adjusted for age, already tested in COVID-19 patients [16].
At physical examination, data on body temperature, heart and respiratory rates and Acute Respiratory Distress Syndrome (ARDS) Scale (defined as absent, mild, moderate and severe) were also collected. As for Glasgow Coma Score (GCS/15), this was categorized into: Mild impaired consciousness (GCS Score >13), Moderate/Severe impaired consciousness (GCS Score ≤13) and missing data.
In addition, data on laboratory exams at admission were obtained, such as blood cells count, liver, cardiac and kidney function, inflammation indexes and coagulation. As for instrumental exams, instead, either chest computerized tomography (CT), X-Ray or ultrasound were performed to characterize COVID-19 typical lesions.
Also drug therapies, either ongoing or introduced during hospitalization were further collected, as well as respiratory supports throughout the entire period of hospitalization (nasal cannula/Venturi Mask, non-invasive ventilation (NIV) and orotracheal intubation (OTI)) and classified in a Respiratory Severity Scale (RSS).
Statistical analysis
Categorical data were expressed as number and percentages, whilst continuous variables either as mean and standard deviation (SD) or median and interquartile range (IQR), according to their distribution, previously tested by the Shapiro-Wilk test. The presence of missing data has been managed as reported: a) for continuous data no imputation method, single or multiple, was used—the presence of a missing data has been used as such (NA); b) for categorical data, missing information was treated creating a specific 'missing' category for each variable under analysis. The data were also described considering the number of comorbidities, and also evaluating the statistical association with a p overall to test for any differences among groups of comorbidities (either ANOVA or Kruskal-Wallis test for continuous data, depending on data distribution; Chi-squared or Fisher's exact tests for dichotomous/categorical data, depending on sample size) and p for trend to look for a linear, increasing or decreasing, association trend (nonparametric test for trend across ordered groups or chi-square statistic for the trend). The association between dichotomous variables, in our case the presence or absence of comorbidity, for each possible crossing, was assessed calculating the phi coefficient [17]. Univariable and multivariable logistic regression models were performed to evaluate association between in-hospital mortality and exposure variables. Odds ratios and 95% confidence intervals (OR—95% CI) have been calculated for all models. Variables were included in the multivariable model according the following rules [18]: (a) significance at the univariable analysis (p < 0.05); (b) lack of co-linearity (explored also with preliminary analysis). Comparison between models, to evaluate the improvement and significance of the model, was performed applying the Likelihood-ratio test (LR test), in the case of nested models, or the Bayesian Information Criterion (BIC), in the case of non-nested models, preferring models with lower BIC values. As sensitivity analysis the Firth's correction proposed by van Smeden M et al. was used to increase the accuracy of beta estimates of the model [19]. In addition, the cumulative incidence function (CIF) was calculated, showing the cumulative failure rates over time due to in-hospital mortality. For this particular condition, discharge was considered as a competing event due to the fact that quick recovery and loss to follow-up of patients who are no longer at risk of death from covid-19 should be considered as ‘informative’ censoring. A p-value <0.05 was considered as statistically significant. All analyses were performed using statistical software STATA v16 (StataCorp. 2019. College Station, TX: StataCorp LLC).
Results
Characteristics of the study population
662 patients positive at Sars-Cov-2 swab specimen, which required hospitalization, were considered eligible for the study and, of these, 44 were excluded due to incomplete clinical records. 618 patients were finally included in the study, mainly males (61.3%), with a mean age of 65 years (SD 15.2) and a median duration of hospitalization of 20 days [IQR 13–29 days]. The median time elapsed between onset of symptoms and hospitalization was of 4.5 days [IQR 2–7]. At the time of hospitalization, 63.6% of patients did not show any symptom of ARDS, while moderate and severe symptoms were observed, respectively, in about the 13.1% and 7.4%.
In addition, as for the RSS, 330 patients (53.4%) received respiratory support, at the time of hospitalization, either with Venturi mask or nasal cannula. Only the 13% needed either NIV or OTI. 46 patients (7.4%) showed moderate to severe impaired consciousness according to Glasgow Coma Scales (GCS/15). All clinical characteristics at admission are reported in Table 1.
Pre-existent comorbidities
In our study population almost half of patients had hypertension, whilst cardiovascular diseases (especially atrial fibrillation, acute myocardial infarction and stroke) were reported in the 26.6%. Less prevalent were instead chronic respiratory diseases, CKD and CLD, as well as malignancies, neurologic disorders, smoking habit and diabetes. Overall, 166 patients (27%) were not affected by any comorbidity, whereas 172 (28%) showed either three or more concomitant comorbidities. As shown in Table 1, a stratification by comorbidity number (0, 1, 2, 3 or more) allowed to deepen the description of the clinical features. As well, comorbidities were further weighted by calculating the Charlson Comorbidity Index (CCI) which, as expected, revealed higher in those with either 2 or >3 pre-existing comorbidities on admission.
Very interesting, and partly expected, are the associations (both p-overall and p-trend) of comorbidity number with age, ARDS scale, GCS/15 and RSS.
In-hospital mortality and clinical prognostic factors
During the observation period, 143 in-hospital mortality events were recorded, with a cumulative incidence of about 23%.
The univariable logistic regression analysis on the association between clinical variables and mortality disclosed a significant association between mortality and age (OR 1.05, 95%CI 1.04–1.07; p<0.001). As well, also Moderate ARDS scale (Moderate vs Absent, OR 2.78, 95%CI 1.52–5.07; p = 0.001) and severe ARDS scale (Severe vs Absent, OR 11.01, 95%CI 5.38–22.53; p<0.001) revealed significantly associated with mortality. As for the respiratory severity scale, also subjects undergone to NIV (NIV vs None, OR 5.81, 95%CI 2.94–11.49; p<0.001) and OTI (OTI vs None, OR 12.90, 95%CI 5.38–30.93; p<0.001) disclosed a significantly higher mortality risk. A moderate-to-severe impaired consciousness at GCS/15 score was associated with a worse clinical outcome (OR 10.12, 95%CI 5.21–19.65; p<0.001).
For what concerns comorbidities already present at admission, several of them were associated with a poor outcome at univariable analysis: Chronic Cardiac Disease (OR 2.05, 95%CI 1.38–3.05; p<0.001), CKD (OR 1.88, 95%CI 1.09–3.23; p = 0.023), CLD (OR 5.67, 95%CI 2.80–11.47; p<0.001), Chronic Respiratory Disease (OR 2.55, 95%CI 1.61–4.04; p<0.001) and Malignant Neoplasm (OR 2.18, 95%CI 1.21–3.93; p<0.010).
Consistently with these findings, a significant association also emerged between a higher mortality rate and the co-presence of at least two comorbidities (OR 2.48, 95%CI 1.35–4.57; p<0.001 for 2 comorbidities and OR 3.70, 95%CI 2.12–6.44; p<0.001 for ≥3). All data are reported in Table 2.
The subsequent multivariable model was based on the following concepts and rules: 1) the variables statistically significant at the univariable model were included; 2) we decided to consider and correct also for sex; 3) as we aimed to evaluate the single comorbidities, we did not include the variable ‘comorbidity number’, after appropriate evaluation, in the preliminary analysis with the statistics phi, that none of the single comorbidities showed high levels of correlation; 4) in the choice between ARDS scale and RSS, given the overlap of clinical nature, we chose the best univariable model evaluated with the BIC score.
The significance emerged at univariable analysis was partially confirmed at the multivariable, with the addition of sex (Male vs Female -ref-; OR 2.63, 95%CI 1.42–4.90; p = 0.001). Of note, CLD (OR 5.88, 95%CI 2.39–14.46; p<0.001) and malignancies (OR 2.62, 95%CI 1.21–5.68; p = 0.015) disclosed an independent association with a poor prognosis. Likewise, also NIV/OTI respiratory supports revealed independent associated with a higher in-hospital mortality (OR 4.63, 95%CI 1.89–11.34; p<0.001 and OR 6.65; 95%CI 1.93–22.99; p = 0.003, respectively), as well as a moderate-to-severe impaired consciousness at GCS/15 (OR 7.26, 95%CI 3.43–15.37; p<0.001).
A sensitivity analysis, excluding comorbidities not statistically significant in the previous model, further confirmed the observed associations. Both multivariable models are shown in Table 3.
As an additional sensitivity analysis the Firth's correction was used, this analysis showed similar results of the reported models.
Cumulative incidence of the study population, both overall and subdivided according to the number of coexisting comorbidities and of those independently associated with a worse prognosis are further described in Fig 2 (Panels A→D).
Cumulative incidence functions (CIFs) of overall comorbidities (Panel A), Chronic Liver Disease (Panel B), malignancies (Panel C) and number of coexisting comorbidities (Panel D).
The significant association between CLD and poor COVID-19 prognosis, confirmed both at multivariable and sensitivity analysis, have prompted us to focus on the subgroup with CLD, mainly characterized by either viral or metabolic hepatopathies. In particular, of these, the 45.7% were affected by Chronic C Hepatitis, 37.1% had Non-alcoholic Fatty Liver Disease (NAFLD), whilst the remaining 17.2% had Chronic B Hepatitis. None of them had hepatocellular carcinoma, acute liver failure, autoimmune liver diseases or other forms of CLD. The major part has a Child–Pugh score A (51.4%) or B (42.9%) score, whilst only two disclosed a Child–Pugh score C (5.7%). All clinical characteristics at baseline are shown in S1 Table.
We further consider modifications of liver function indexes in this subgroup according to the clinical outcome. Both Aspartate and Alanine aminotransferase (AST and ALT), γ-glutamyl transferase (γ-GT) and bilirubin were almost similar at baseline between alive and dead patients, whilst at end of hospitalization, there was a significant increase of all these indexes among dead patients; in particular of total bilirubin (p = 0.0011), AST (p = 0.020), ALT (p = 0.016), and γ-GT (p = 0.002). Whole data are shown in S1 Table.
Consistent with these findings, a significant association between worsening of liver function and mortality was further observed in the entire study population (S2 Table).
Discussion
The still current absence of specific therapies approved by National and International Health Regulatory Authorities, 8 months after the WHO declaration of a pandemic, requires us to identify which clinical conditions affect the outcome of patients. This aspect is mandatory to identify subjects at greatest risk, for whom hospitalization must be early and management very close.
COVOCA, to date the largest multicenter study in terms of sample size in the Campania region involving COVID-19 hospitalized patients, presents several insights.
The association between different comorbidities and mortality in COVID-19 patients has already been partially investigated [20–24].
In particular, in our study cohort chronic comorbidities disclosed a prevalence almost comparable to that from previous evidence [25]. In depth, hypertension was the most prevalent (48.9%), followed by chronic cardiac disease (26.9%), diabetes (18.3%), chronic respiratory disease (15.7%), chronic neurological disorders (15%), CKD (10.8%), malignancies (8.6%) and CLD (5.7%). In acute we considered GCS/15 and RSS.
In our study we observed an independent association between in-hospital mortality and poor clinical conditions on admission. In particular, RSS (in patients either in NIV or OTI, OR 4.63 and 6.65 respectively), CLD (OR 5.88) and malignancies (OR 2.62) revealed strong predictor of mortality. As well, a moderate-to-severe impaired consciousness at GCS/15 revealed a negative prognostic element, thus confirming the reliability of this parameter also in COVID-19, as suggested by other authors [25].
This finding is consistent with previous studies focused on the clinical effects of NIV/OTI on patients’ prognosis [26,27]. However, this topic still remains controversial and thus it deserves further insights [28,29].
Of note, an association emerged between mortality and number of comorbidities, with a 3.7 times higher risk of mortality among those who had at least 3 comorbidities vs those who had none, suggesting that COVID-19 patients with several comorbidities are more frail. This finding seems consistent with data from large studies conducted in Northern Italy [30], as well as with national reports of “Istituto Superiore di Sanità (ISS)” [31]. This finding may be explained by the dysregulation of main physiologic systems (pituitary-hypothalamus-adrenal, immune and sympathetic systems) triggered by the presence of multiple comorbidities. Such hypothesis has been recently proposed as a cause of the increase mortality rate in COVID-19 patients with multiple comorbidities [21,32].
Surprisingly, in our study the significant association of both CKD and cardiovascular diseases came lost on multivariable analysis. Such finding might have been affected by a not excessively low GFR (mean 35.8 ml/min/1.73m2) as well as by the lack of information on the ventricular ejection fraction does not allow us to establish whether it was conserved or not.
The CLD incidence observed in our study population seems comparable to that described in previous reports [33–36]. Worthy of note is the association between mortality and CLD (OR 5.88), finding strongly enhancing data reported in a recent US large observational study which registered almost a 3-times increased mortality risk [37].
Actually, the relationship between CLD and COVID-19 outcome has been investigated in very few studies, from which an association between a worse outcome and chronic hepatopathy emerged [2,10,38].
We observed almost in all CLD patients survived at the end of hospitalization a Child–Pugh score A, while the deceased had either a Child–Pugh score B or C. Moreover, CLD patients died earlier than the overall COVID population, confirming that chronic liver disease rapidly worsens the prognosis of this patients’ setting.
Originally, we observed during hospitalization an increase of hepatic damage (assessed by AST, GGT and bilirubin) both in CLD subjects with poor prognosis, as well as in overall COVID dead patients too.
Currently, the reason for a worse prognosis of COVID-19 patients with a pre-existing hepatopathy still remains unclear, as well as the role of COVID-19 in the exacerbation of liver disease. Based on our findings, we could hypothesize that patients with CLD at an advanced stage are already characterized by a worse clinical condition, which negatively affects their prognosis. However, also a dysregulation of the innate immune response may represent an aspect of liver injury in COVID-19, which would enhance the risk of mortality in this subsetting [39].
As for the innate immune response, several pathophysiologic mechanisms have been assumed, such as the direct cytotoxic effect of the virus on the liver, mediated by the angiotensin converting enzyme 2 (ACE2) expressed in the cytomembrane [40]. It has also been observed that an interaction of the virus with Kupffer cells and intra-hepatic T lymphocytes could stimulate a liver damage [41,42]. A further hypothesis concerns the systemic reaction induced by the virus, which would activate inflammatory molecules such as IL-6 and TNF-alpha, thus triggering a liver damage [34,43–45].
An interesting new hypothesis could involve an imbalance between free radicals and the main antioxidants, in particular the hepatic glutathione (GSH), decreased in chronic liver disease [46].
Due to the observation that several viral infections are characterized by a decrease in GSH, many in vitro studies have been performed, showing that GSH can attack viruses with different replicative mechanisms (HIV and other retroviruses, influenza and parainfluenza viruses, rhinovirus and HSV-1) and inhibit viral replication at different stages [47,48].
Intriguingly, on these bases, low levels of GSH have been recently suggested among the major causes of the excessive inflammatory response linked to severe COVID-19 outcome [48]. This could represent a possible rationale for high mortality in COVID-19 patients with CLD.
Moreover, some studies showed that most of drugs used in COVID-19 treatment, especially antivirals, could either determine a liver damage or re-activate latent viral diseases [49].
This study presents several limitations. Firstly, beyond its retrospective observational design, is the low number of CLD patients. Though the remarkable statistical significance and the support from the previous evidence, the limited sample size of CLD subpopulation did not allow to further assess which liver disease was majorly associated with mortality. As well, we could not assess data on BMI as most of Centers could not supply this information due to clinical conditions of patients. These missing data did not allow the inclusion of BMI among risk factors as well as its impact on a poor outcome [24]. As well, it would been useful to consider previous socio-economic status, as reported in diverse papers [24].
On the other hand, the methodological rigor of the analysis allows us to deem our findings as reliable and generalizable even on larger populations.
Conclusion
Outcome of patients hospitalized for COVID-19 appears strongly affected by both clinical conditions on admission (GCS and RSS) and comorbidities. Notably, their association may reduce patients' ability to overcome the infection and the complications it triggers. Pre-existing chronic liver disease, especially in the moderate-advanced phase, appears relevant in significantly worsening prognosis of hospitalized patients. Actually, this could be due to key role of liver in the biosynthesis of molecules involved in coagulation balance and inflammatory response.
Further research is mandatory to deepen and expand the study of prognostic factors in hospitalized patients [50] and to evaluate at medium/long term the effect of COVID-19 on the progression of comorbidities, in particular CLD, in patients who have overcome the infection.
Supporting information
S1 Table. Characteristics of CLD subpopulation at Baseline and at discharge/death (n = 35).
https://doi.org/10.1371/journal.pone.0243700.s001
(DOCX)
S2 Table. Baseline and discharge liver function indexes modifications in the entire study population (n = 618).
https://doi.org/10.1371/journal.pone.0243700.s002
(DOCX)
Acknowledgments
COVOCA study group
Alfredo Caturano, Erica Vetrano, Maria Alfano, Domenico Beccia (Unit of Internal Medicine, Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli”, Naples), Prof. Nicola Coppola, Dr.ssa Caterina Monari and Giulia De Angelis (Centro COVID A.O.U. Vanvitelli, Department of Mental Health and Public Medicine, Naples); Dr. Roberto Parrella and Dr. Gianfranco Gaglione (Cotugno Hospital—U.O.C. Respiratory Infectious Diseases); Dr. Fiorentino Fraganza (Cotugno Hospital–U.O.C. Anestesia and Intensive Care Unit); Dr. Costanza Sbreglia and Dr. Serena Parente (Cotugno Hospital—U.O.C. Infectious Diseases of the Elderly); Dr. Nicola Maturo and Dr. Paolina Lumino (Cotugno Hospital—U.O.S.D. Infectious Diseases Emergency and Acceptance); Dr. Vincenzo Esposito and Dr. Annamaria Rossomando (Cotugno Hospital–IVth Division of Immunodeficiency and Gender Infectious Diseases,); Dr. Giosuele Calabria, Mario Catalano and Laurenza Paradiso (Cotugno Hospital–IXth Division of Infectious Diseases and Interventional Ultrasound); Dr. Carolina Rescigno and Dr. Alberto Maraolo (Cotugno Hospital–U.O.C. Infectious Diseases and Neurology); Dr. Carolina Bologna (Internal Medicine Unit, Ospedale Del Mare, Naples); Dr. Antonio Pagano and Dr. Fabio Numis (Emergency and Acceptance Unit, "Santa Maria delle Grazie" Hospital, Pozzuoli); Dr. Laura Vocciante, Antonio Asti and Michele Langella (General Medicine Unit—Loreto Mare Hospital, Naples); Dr. Riccardo Nevola (Sant'Ottone Frangipane Hospital, Ariano Irpino (Avellino)); Dr. Maria Amitrano, Sara Mangiacapra and Mariangela Raimondo (U.O.C. Internal Medicine—Moscati Hospital, Avellino); Dr. Annamaria Romano (U.O.C. Pneumology—Moscati Hospital, Avellino); Dr. Carmine Coppola and Ferdinando Scarano (COVID Center "S. Anna e SS. Madonna della Neve" Hospital, Boscotrecase); Dr. Alessandro Perrella, Andrea Del Mastro and Rodolfo Nasti (Cardarelli Hospital, Naples); Dr. Benedetto Polverino ("Giovanni da Procida" Hospital, Salerno); Prof. Paolo Maggi, Dr. Alessio Codella and Paolo Cirillo (U.O.C. Infectious and Tropical diseases, S. Anna e S. Sebastiano Hospital, Caserta); Dr. Pellegrino De Lucia Sposito (Covid Center—Maddaloni Hospital)
The Authors thank all the colleagues, nurses and workers who are facing COVID 19 disease.
References
- 1.
WHO. “Clinical management of COVID-19 disease”. REFERENCE NUMBER: WHO/2019-nCoV/clinical/2020.5. https://apps.who.int/iris/bitstream/handle/10665/332196/WHO-2019-nCoV-clinical-2020.5-eng.pdf?sequence=1&isAllowed=y (accessed 13 August 2020)
- 2. Iavarone M, D'Ambrosio R, Soria A, Triolo M, Pugliese N, Del Poggio P, et al. High rates of 30-day mortality in patients with cirrhosis and COVID-19 [published online ahead of print, 2020 Jun 9]. J Hepatol. 2020;S0168-8278(20)30365-2. pmid:32526252
- 3. Guan WJ, Liang WH, Zhao Y, Liang HR, Chen ZS, Li YM, et al. Comorbidity and its impact on 1590 patients with COVID-19 in China: a nationwide analysis. Eur Respir J. 2020;55(5):2000547. Published 2020 May 14. pmid:32217650
- 4. Luo L, Fu M, Li Y, et al. The potential association between common comorbidities and severity and mortality of coronavirus disease 2019: A pooled analysis [published online ahead of print, 2020 Oct 7]. Clin Cardiol. 2020;10.1002/clc.23465. pmid:33026120
- 5. Rodríguez-Molinero A, Gálvez-Barrón C, Miñarro A, et al. Association between COVID-19 prognosis and disease presentation, comorbidities and chronic treatment of hospitalized patients. PLoS One. 2020;15(10):e0239571. Published 2020 Oct 15. pmid:33057443
- 6. Khateri S, Mohammadi H, Khateri R, Moradi Y. The Prevalence of Underlying Diseases and Comorbidities in COVID-19 Patients; an Updated Systematic Review and Meta-analysis. Arch Acad Emerg Med. 2020 Sep 12;8(1):e72. pmid:33134968; PMCID: PMC7588001.
- 7. Polverino F, Stern DA, Ruocco G, et al. Comorbidities, Cardiovascular Therapies, and COVID-19 Mortality: A Nationwide, Italian Observational Study (ItaliCO). Front Cardiovasc Med. 2020;7:585866. Published 2020 Oct 9. pmid:33195473
- 8. Mi J, Zhong W, Huang C, Zhang W, Tan L, Ding L. Gender, age and comorbidities as the main prognostic factors in patients with COVID-19 pneumonia. Am J Transl Res. 2020 Oct 15;12(10):6537–6548. pmid:33194050; PMCID: PMC7653634.
- 9. Fang H, Liu Q, Xi M, et al. Impact of comorbidities on clinical prognosis in 1280 patients with different types of COVID-19 [published online ahead of print, 2020 Oct 13]. J Investig Med. 2020;jim-2020-001555. pmid:33051358
- 10. Targher G, Mantovani A, Byrne CD, Wang XB, Yan HD, Sun QF, et al. Risk of severe illness from COVID-19 in patients with metabolic dysfunction-associated fatty liver disease and increased fibrosis scores. Gut. 2020;69(8):1545–1547. pmid:32414813
- 11. American Diabetes Association. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2020. Diabetes Care. 2020 Jan;43(Suppl 1):S14–S31. pmid:31862745.
- 12. Williams B, Mancia G, Spiering W, et al. 2018 ESC/ESH Guidelines for the management of arterial hypertension [published correction appears in Eur Heart J. 2019 Feb 1;40(5):475]. Eur Heart J. 2018;39(33):3021–3104. pmid:30165516
- 13. KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int. suppl. 2013; 3 (1)
- 14.
WHO Chronic respiratory diseases. https://www.who.int/health-topics/chronic-respiratory-diseases#tab=tab_1
- 15. Ribeiro RT, Marinho RT, Sanches JM. Classification and staging of chronic liver disease from multimodal data. IEEE Trans Biomed Eng. 2013 May;60(5):1336–44. Epub 2012 Dec 19. pmid:23268381.
- 16. Tuty Kuswardhani RA, Henrina J, Pranata R, Anthonius Lim M, Lawrensia S, Suastika K. Charlson comorbidity index and a composite of poor outcomes in COVID-19 patients: A systematic review and meta-analysis. Diabetes Metab Syndr. 2020 Oct 28;14(6):2103–2109. Epub ahead of print. pmid:33161221; PMCID: PMC7598371.
- 17. Goldstein R. Phi Coefficient (fourfold correlation). Stata Technical Bulletin, 1992, vol. 1, issue 3
- 18.
Harrell F. Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. 2nd ed. 2015, Springer
- 19. van Smeden M, de Groot JA, Moons KG, Collins GS, Altman DG, Eijkemans MJ, et al. No rationale for 1 variable per 10 events criterion for binary logistic regression analysis. BMC Med Res Methodol. 2016 Nov 24;16(1):163. pmid:27881078
- 20. Du RH, Liang LR, Yang CQ, Wang W, Cao TZ, Li M, et al. Predictors of mortality for patients with COVID-19 pneumonia caused by SARS-CoV-2: a prospective cohort study. Eur Respir J. 2020 May 7;55(5):2000524. Erratum in: Eur Respir J. 2020 Sep 24;56(3): pmid:32269088
- 21. Ssentongo P, Ssentongo AE, Heilbrunn ES, Ba DM, Chinchilli VM. Association of cardiovascular disease and 10 other pre-existing comorbidities with COVID-19 mortality: A systematic review and meta-analysis. PLoS One. 2020 Aug 26;15(8):e0238215. pmid:32845926
- 22. Pranata R, Huang I, Lim MA, Wahjoepramono EJ, July J. Impact of cerebrovascular and cardiovascular diseases on mortality and severity of COVID-19-systematic review, meta-analysis, and meta-regression. J Stroke Cerebrovasc Dis. 2020 Aug;29(8):104949. Epub 2020 May 14. pmid:32410807
- 23. Espinosa OA, Zanetti ADS, Antunes EF, Longhi FG, Matos TA, Battaglini PF. Prevalence of comorbidities in patients and mortality cases affected by SARS-CoV2: a systematic review and meta-analysis. Rev Inst Med Trop Sao Paulo. 2020 Jun 22;62:e43. pmid:32578683
- 24. Suleyman G, Fadel RA, Malette KM, Hammond C, Abdulla H, Entz A, et al. Clinical Characteristics and Morbidity Associated With Coronavirus Disease 2019 in a Series of Patients in Metropolitan Detroit. JAMA Netw Open. 2020 Jun 1;3(6):e2012270. pmid:32543702
- 25. Zhang L, Sun W, Wang Y, Wang X, Liu Y, Zhao S, et al. Clinical Course and Mortality of Stroke Patients With Coronavirus Disease 2019 in Wuhan, China. Stroke. 2020 Sep;51(9):2674–2682. Epub 2020 Jul 31. pmid:32755348
- 26. 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 May;8(5):475–481. Epub 2020 Feb 24. Erratum in: Lancet Respir Med. 2020 Apr;8(4):e26. pmid:32105632
- 27. Schünemann HJ, Khabsa J, Solo K, Khamis AM, Brignardello-Petersen R, El-Harakeh A, et al. Ventilation Techniques and Risk for Transmission of Coronavirus Disease, Including COVID-19: A Living Systematic Review of Multiple Streams of Evidence. Ann Intern Med. 2020 Aug 4;173(3):204–216. Epub 2020 May 22. pmid:32442035
- 28. Raoof S, Nava S, Carpati C, Hill NS. High-Flow, Noninvasive Ventilation and Awake (Nonintubation) Proning in Patients With Coronavirus Disease 2019 With Respiratory Failure. Chest. 2020 Jul 15:S0012-3692(20)31910-3. Epub ahead of print. pmid:32681847
- 29. Meng L, Qiu H, Wan L, Ai Y, Xue Z, Guo Q, et al. Intubation and Ventilation amid the COVID-19 Outbreak: Wuhan's Experience. Anesthesiology. 2020 Jun;132(6):1317–1332. pmid:32195705
- 30. Iaccarino G, Grassi G, Borghi C, Ferri C, Salvetti M, Volpe M, et al. Age and Multimorbidity Predict Death Among COVID-19 Patients: Results of the SARS-RAS Study of the Italian Society of Hypertension. Hypertension. 2020 Aug;76(2):366–372. Epub 2020 Jun 22. pmid:32564693.
- 31. Palmieri L, Vanacore N, Donfrancesco C, Lo Noce C, Canevelli M, Punzo O, et al.; Italian National Institute of Health COVID-19 Mortality Group. Clinical Characteristics of Hospitalized Individuals Dying With COVID-19 by Age Group in Italy. J Gerontol A Biol Sci Med Sci. 2020 Sep 16;75(9):1796–1800. pmid:32506122; PMCID: PMC7314182.
- 32.
Schulkin J. (Ed.). (2004). Allostasis, homeostasis, and the costs of physiological adaptation. Cambridge University Press. https://doi.org/10.1017/CBO9781316257081
- 33. Shalimar , Elhence A, Vaishnav M, Kumar R, Pathak P, Soni KD, et al. Poor outcomes in patients with cirrhosis and Corona Virus Disease-19. Indian J Gastroenterol. 2020 Jun;39(3):285–291. Epub 2020 Aug 15. pmid:32803716
- 34. Sun J, Aghemo A, Forner A, Valenti L. COVID-19 and liver disease. Liver Int. 2020 Jun;40(6):1278–1281. pmid:32251539
- 35. Boettler T, Marjot T, Newsome PN, Mondelli MU, Maticic M, Cordero E, et al. Impact of COVID-19 on the care of patients with liver disease: EASL-ESCMID position paper after 6 months of the pandemic. JHEP Rep. 2020 Oct;2(5):100169. Epub 2020 Aug 4. pmid:32835190
- 36. Ji D, Zhang D, Yang T, Mu J, Zhao P, Xu J, et al. Effect of COVID-19 on patients with compensated chronic liver diseases. Hepatol Int. 2020 Jul 30:1–10. Epub ahead of print. pmid:32734407
- 37. Singh S, Khan A. Clinical Characteristics and Outcomes of Coronavirus Disease 2019 Among Patients With Preexisting Liver Disease in the United States: A Multicenter Research Network Study [published online ahead of print, 2020 May 4]. Gastroenterology. 2020;S0016-5085(20)30585-0. pmid:32376408
- 38. Portincasa P, Krawczyk M, Smyk W, Lammert F, Di Ciaula A. COVID-19 and nonalcoholic fatty liver disease: two intersecting pandemics. Eur J Clin Invest. 2020 Jun 26:e13338. Online ahead of print. pmid:32589264
- 39. Ali N. Relationship Between COVID-19 Infection and Liver Injury: A Review of Recent Data. Front Med (Lausanne). 2020 Jul 21;7:458. pmid:32793619
- 40. Huang R, Zhu L, Wang J, Xue L, Liu L, Yan X, et al. Clinical features of COVID-19 patients with non-alcoholic fatty liver disease. Hepatol Commun. 2020 Aug 6:10.1002/hep4.1592. Epub ahead of print. pmid:32838108
- 41. Adams DH, Hubscher SG. Systemic viral infections and collateral damage in the liver. Am J Pathol. 2006 Apr;168(4):1057–9. pmid:16565481
- 42. Labenz C, Toenges G, Wörns MA, Sprinzl MF, Galle PR, Schattenberg JM. Liver injury in patients with severe acute respiratory syndrome coronavirus-2 infection: a systematic review and meta-analysis. Eur J Gastroenterol Hepatol. 2020 Aug 10. Epub ahead of print. pmid:32796355
- 43. Medetalibeyoglu A, Catma Y, Senkal N, Ormeci A, Cavus B, Kose M, et al. The effect of liver test abnormalities on the prognosis of COVID-19. Ann Hepatol. 2020 Sep 10:S1665-2681(20)30165-4. Epub ahead of print. pmid:32920162
- 44. Lei F, Liu YM, Zhou F, Qin JJ, Zhang P, Zhu L, et al. Longitudinal Association Between Markers of Liver Injury and Mortality in COVID-19 in China. Hepatology. 2020 Aug;72(2):389–398. pmid:32359177
- 45. Zhang C, Shi L, Wang FS. Liver injury in COVID-19: management and challenges. Lancet Gastroenterol Hepatol. 2020 May;5(5):428–430. Epub 2020 Mar 4. pmid:32145190
- 46. Guo T, Chang L, Xiao Y, Liu Q. S-Adenosyl-L-Methionine for the Treatment of Chronic Liver Disease: A Systematic Review and Meta-Analysis. PLoS ONE 2015, 10(3): e0122124. pmid:25774783
- 47. Fraternale A, Paoletti MF, Casabianca A, Nencioni L, Garaci E, Palamara AT, et al. GSH and analogs in antiviral therapy. Molecular Aspects of Medicine 30 (2009) 99–110 pmid:18926849
- 48. Silvagno F, Vernone A, Pescarmona GP. The Role of Glutathione in Protecting against the Severe Inflammatory Response Triggered by COVID-19. Antioxidants 2020, 9, 624; pmid:32708578
- 49. Yang RX, Zheng RD, Fan JG. Etiology and management of liver injury in patients with COVID-19. World J Gastroenterol. 2020 Aug 28;26(32):4753–4762. pmid:32921955
- 50. Ciccone G, Deandrea S, Clavenna A, Kirchmayer U, Simeon V, Acampora A, et al. COVID-19 e ricerca clinico-epidemiologica in Italia: proposta di un’agenda di ricerca su temi prioritari da parte dell’Associazione Italiana di Epidemiologia. Epidemiol Prev 2020; 44(5–6) suppl 2: in press