Abstract Objective To determine the mortality, survival, and causes of death in patients with systemic sclerosis (SSc) through a meta-analysis of the observational studies published up to 2013. ...Methods We performed a systematic review and meta-analysis of the observational studies in patients with SSc and mortality data from entire cohorts published in MEDLINE and SCOPUS up to July 2013. Results A total of 17 studies were included in the mortality meta-analysis from 1964 to 2005 (mid-cohort years), with data from 9239 patients. The overall SMR was 2.72 (95% CI: 1.93–3.83). A total of 43 studies have been included in the survival meta-analysis, reporting data from 13,529 patients. Cumulative survival from onset (first Raynaud׳s symptom) has been estimated at 87.6% at 5 years and 74.2% at 10 years, from onset (non-Raynaud׳s first symptom) 84.1% at 5 years and 75.5% at 10 years, and from diagnosis 74.9% at 5 years and 62.5% at 10 years. Pulmonary involvement represented the main cause of death. Conclusions SSc presents a larger mortality than general population (SMR = 2.72). Cumulative survival from diagnosis has been estimated at 74.9% at 5 years and 62.5% at 10 years. Pulmonary involvement represented the main cause of death.
The etiology of systemic sclerosis (SSc) remains unknown; however, several occupational and environmental factors have been implicated. Our objective was to perform a meta-analysis of all studies ...published on SSc associated with occupational and environmental exposure. The review was undertaken by means of MEDLINE and SCOPUS from 1960 to 2014 and using the terms: “systemic,” “scleroderma,” or “systemic sclerosis/chemically induced” MesH. The Newcastle-Ottawa Scale was used for the qualifying assessment. The inverse variance-weighted method was performed. The meta-analysis of silica exposure included 15 case-control studies overall OR 2.81 (95%CI 1.86–4.23;
p
< 0.001) and 4 cohort studies overall RR 17.52 (95%CI 5.98–51.37;
p
< 0.001); the meta-analysis of solvents exposure included 13 case-control studies (overall OR 2.00 95%CI 1.32–3.02;
p
= 0.001); the meta-analysis of breast implants exposure included 4 case-control studies (overall OR 1.68 (95%CI 1.65–1.71;
p
< 0.001)) and 6 cohort studies (overall RR 2.13 (95%CI 0.86–5.27;
p
= 0.10)); the meta-analysis of epoxy resins exposure included 4 case-control studies (overall OR 2.97 (95%CI 2.31–3.83;
p
< 0.001)), the meta-analysis of pesticides exposure included 3 case-control studies (overall OR 1.02 (95%CI 0.78–1.32;
p
= 0.90)) and, finally, the meta-analysis of welding fumes exposure included 4 studies (overall OR 1.29 (95%CI 0.44–3.74;
p
= 0.64)). Not enough studies citing risks related to hair dyes have been published to perform an accurate meta-analysis. Silica and solvents were the two most likely substances related to the pathogenesis of SSc. While silica is involved in particular jobs, solvents are widespread and more people are at risk of having incidental contact with them.
Limited evidence exists on the role of glucose-lowering drugs in patients with COVID-19. Our main objective was to examine the association between in-hospital death and each routine at-home ...glucose-lowering drug both individually and in combination with metformin in patients with type 2 diabetes mellitus admitted for COVID-19. We also evaluated their association with the composite outcome of the need for ICU admission, invasive and non-invasive mechanical ventilation, or in-hospital death as well as on the development of in-hospital complications and a long-time hospital stay.
We selected all patients with type 2 diabetes mellitus in the Spanish Society of Internal Medicine's registry of COVID-19 patients (SEMI-COVID-19 Registry). It is an ongoing, observational, multicenter, nationwide cohort of patients admitted for COVID-19 in Spain from March 1, 2020. Each glucose-lowering drug user was matched with a user of other glucose-lowering drugs in a 1:1 manner by propensity scores. In order to assess the adequacy of propensity score matching, we used the standardized mean difference found in patient characteristics after matching. There was considered to be a significant imbalance in the group if a standardized mean difference > 10% was found. To evaluate the association between treatment and study outcomes, both conditional logit and mixed effect logistic regressions were used when the sample size was ≥ 100.
A total of 2666 patients were found in the SEMI-COVID-19 Registry, 1297 on glucose-lowering drugs in monotherapy and 465 in combination with metformin. After propensity matching, 249 patients on metformin, 105 on dipeptidyl peptidase-4 inhibitors, 129 on insulin, 127 on metformin/dipeptidyl peptidase-4 inhibitors, 34 on metformin/sodium-glucose cotransporter 2 inhibitor, and 67 on metformin/insulin were selected. No at-home glucose-lowering drugs showed a significant association with in-hospital death; the composite outcome of the need of intensive care unit admission, mechanical ventilation, or in-hospital death; in-hospital complications; or long-time hospital stays.
In patients with type 2 diabetes mellitus admitted for COVID-19, at-home glucose-lowering drugs showed no significant association with mortality and adverse outcomes. Given the close relationship between diabetes and COVID-19 and the limited evidence on the role of glucose-lowering drugs, prospective studies are needed.
Since December 2019, the COVID-19 pandemic has changed the concept of medicine. This work aims to analyze the use of antibiotics in patients admitted to the hospital due to SARS-CoV-2 infection. This ...work analyzes the use and effectiveness of antibiotics in hospitalized patients with COVID-19 based on data from the SEMI-COVID-19 registry, an initiative to generate knowledge about this disease using data from electronic medical records. Our primary endpoint was all-cause in-hospital mortality according to antibiotic use. The secondary endpoint was the effect of macrolides on mortality. Of 13,932 patients, antibiotics were used in 12,238. The overall death rate was 20.7% and higher among those taking antibiotics (87.8%). Higher mortality was observed with use of all antibiotics (OR 1.40, 95% CI 1.21-1.62; p < .001) except macrolides, which had a higher survival rate (OR 0.70, 95% CI 0.64-0.76; p < .001). The decision to start antibiotics was influenced by presence of increased inflammatory markers and any kind of infiltrate on an x-ray. Patients receiving antibiotics required respiratory support and were transferred to intensive care units more often. Bacterial co-infection was uncommon among COVID-19 patients, yet use of antibiotics was high. There is insufficient evidence to support widespread use of empiric antibiotics in these patients. Most may not require empiric treatment and if they do, there is promising evidence regarding azithromycin as a potential COVID-19 treatment.
Real-world observational data are an important source of evidence on the treatment effectiveness for patients hospitalized with coronavirus disease 2019 (COVID-19). However, observational studies ...evaluating treatment effectiveness based on longitudinal data are often prone to methodological biases such as immortal time bias, confounding bias, and competing risks.
For exemplary target trial emulation, we used a cohort of patients hospitalized with COVID-19 (n = 501) in a single centre. We described the methodology for evaluating the effectiveness of a single-dose treatment, emulated a trial using real-world data, and drafted a hypothetical study protocol describing the main components. To avoid immortal time and time-fixed confounding biases, we applied the clone-censor-weight technique. We set a 5-day grace period as a period of time when treatment could be initiated. We used the inverse probability of censoring weights to account for the selection bias introduced by artificial censoring. To estimate the treatment effects, we took the multi-state model approach. We considered a multi-state model with five states. The primary endpoint was defined as clinical severity status, assessed by a 5-point ordinal scale on day 30. Differences between the treatment group and standard of care treatment group were calculated using a proportional odds model and shown as odds ratios. Additionally, the weighted cause-specific hazards and transition probabilities for each treatment arm were presented.
Our study demonstrates that trial emulation with a multi-state model analysis is a suitable approach to address observational data limitations, evaluate treatment effects on clinically heterogeneous in-hospital death and discharge alive endpoints, and consider the intermediate state of admission to ICU. The multi-state model analysis allows us to summarize results using stacked probability plots that make it easier to interpret results.
Extending the emulated target trial approach to multi-state model analysis complements treatment effectiveness analysis by gaining information on competing events. Combining two methodologies offers an option to address immortal time bias, confounding bias, and competing risk events. This methodological approach can provide additional insight for decision-making, particularly when data from randomized controlled trials (RCTs) are unavailable.
Abstract
To determine the proportion of patients with COVID-19 who were readmitted to the hospital and the most common causes and the factors associated with readmission. Multicenter nationwide ...cohort study in Spain. Patients included in the study were admitted to 147 hospitals from March 1 to April 30, 2020. Readmission was defined as a new hospital admission during the 30 days after discharge. Emergency department visits after discharge were not considered readmission. During the study period 8392 patients were admitted to hospitals participating in the SEMI-COVID-19 network. 298 patients (4.2%) out of 7137 patients were readmitted after being discharged. 1541 (17.7%) died during the index admission and 35 died during hospital readmission (11.7%, p = 0.007). The median time from discharge to readmission was 7 days (IQR 3–15 days). The most frequent causes of hospital readmission were worsening of previous pneumonia (54%), bacterial infection (13%), venous thromboembolism (5%), and heart failure (5%). Age odds ratio (OR): 1.02; 95% confident interval (95% CI): 1.01–1.03, age-adjusted Charlson comorbidity index score (OR: 1.13; 95% CI: 1.06–1.21), chronic obstructive pulmonary disease (OR: 1.84; 95% CI: 1.26–2.69), asthma (OR: 1.52; 95% CI: 1.04–2.22), hemoglobin level at admission (OR: 0.92; 95% CI: 0.86–0.99), ground-glass opacification at admission (OR: 0.86; 95% CI:0.76–0.98) and glucocorticoid treatment (OR: 1.29; 95% CI: 1.00–1.66) were independently associated with hospital readmission. The rate of readmission after hospital discharge for COVID-19 was low. Advanced age and comorbidity were associated with increased risk of readmission.
•Chronic IS therapies entail different risk profiles and clinical outcomes in COVID-19 patients.•Chronic corticosteroid use before admission confers higher mortality and risk of ...complications.•Chronic calcineurin inhibitor treatment does not appear to have an effect on mortality.
The aim of this study was to analyze whether subgroups of immunosuppressive (IS) medications conferred different outcomes in COVID-19.
The study involved a multicenter retrospective cohort of consecutive immunosuppressed patients (ISPs) hospitalized with COVID-19 from March to July, 2020. The primary outcome was in-hospital mortality. A propensity score-matched (PSM) model comparing ISP and non-ISP was planned, as well as specific PSM models comparing individual IS medications associated with mortality.
Out of 16 647 patients, 868 (5.2%) were on chronic IS therapy prior to admission and were considered ISPs. In the PSM model, ISPs had greater in-hospital mortality (OR 1.25, 95% CI 0.99–1.62), which was related to a worse outcome associated with chronic corticoids (OR 1.89, 95% CI 1.43–2.49). Other IS drugs had no repercussions with regard to mortality risk (including calcineurin inhibitors (CNI); OR 1.19, 95% CI 0.65–2.20). In the pre-planned specific PSM model involving patients on chronic IS treatment before admission, corticosteroids were associated with an increased risk of mortality (OR 2.34, 95% CI 1.43–3.82).
Chronic IS therapies comprise a heterogeneous group of drugs with different risk profiles for severe COVID-19 and death. Chronic systemic corticosteroid therapy is associated with increased mortality. On the contrary, CNI and other IS treatments prior to admission do not seem to convey different outcomes.
•Dexamethasone or alternative steroids are recommended in severe COVID-19 cases.•The use of tocilizumab in COVID-19 cases, with or without steroids, is still controversial.•Risk for mortality was ...assessed in 186 COVID-19 patients receiving tocilizumab.•Mortality was associated with older age, chronic heart failure, and liver disease.•In tocilizumab-treated patients, the additional use of steroids was beneficial.
To assess the characteristics and risk factors for mortality in patients with severe coronavirus disease-2019 (COVID-19) treated with tocilizumab (TCZ), alone or in combination with corticosteroids (CS).
From March 17 to April 7, 2020, a real-world observational retrospective analysis of consecutive hospitalized adult patients receiving TCZ to treat severe COVID-19 was conducted at our 750-bed university hospital. The main outcome was all-cause in-hospital mortality.
A total of 1,092 patients with COVID-19 were admitted during the study period. Of them, 186 (17%) were treated with TCZ, of which 129 (87.8%) in combination with CS. Of the total 186 patients, 155 (83.3 %) patients were receiving noninvasive ventilation when TCZ was initiated. Mean time from symptoms onset and hospital admission to TCZ use was 12 (±4.3) and 4.3 days (±3.4), respectively. Overall, 147 (79%) survived and 39 (21%) died. By multivariate analysis, mortality was associated with older age (HR = 1.09, p < 0.001), chronic heart failure (HR = 4.4, p = 0.003), and chronic liver disease (HR = 4.69, p = 0.004). The use of CS, in combination with TCZ, was identified as a protective factor against mortality (HR = 0.26, p < 0.001) in such severe COVID-19 patients receiving TCZ. No serious superinfections were observed after a 30-day follow-up.
In patients with severe COVID-19 receiving TCZ due to systemic host-immune inflammatory response syndrome, the use of CS in addition to TCZ therapy, showed a beneficial effect in preventing in-hospital mortality.
•We identified six phenotype subgroups•Löfgren's syndrome comprised 3 clusters (C1, C2, and C3)•C4 cluster comprised patients with pulmonary sarcoidosis•C5 cluster comprised patients with abdominal ...and pulmonary sarcoidosis•C6 cluster comprised patients with stage I plus extrapulmonary sarcoidosis
Sarcoidosis is a heterogeneous disease with high variability in natural history and clinical spectrum. The study aimed to reveal different clinical phenotypes of patients with similar characteristics and prognosis.
Cluster analysis including 26 phenotypic variables was performed in a large cohort of 694 sarcoidosis patients, collected and followed-up from 1976 to 2018 at Bellvitge University Hospital, Barcelona, Spain.
Six homogeneous groups were identified after cluster analysis: C1 (n=47; 6.8%), C2 (n=85; 12.2%), C3 (n=153; 22%), C4 (n=29; 4.2%), C5 (n=168; 24.2%), and C6 (n=212; 30.5%). Presence of bilateral hilar lymphadenopathy (BHL) ranged from 65.5% (C4) to 97.9% (C1). Patients with Löfgren syndrome (LS) were distributed across 3 phenotypes (C1, C2, and C3). In contrast, phenotypes with pulmonary (PS) and/or extrapulmonary sarcoidosis (EPS) were represented by groups C4 (PS 100% with no EPS), C5 (PS 88.7% plus EPS), and C6 (EPS). EPS was concentrated in groups C5 (skin lesions, peripheral and abdominal lymph nodes, and hepatosplenic involvement) and C6 (skin lesions, peripheral lymph nodes, and neurological and ocular involvement). Unlike patients from LS groups, most patients with PS and/or EPS were treated with immunosuppressive therapy, and evolved to chronicity in higher proportion. Finally, the cluster model worked moderately well as a predictive model of chronicity (AUC=0.705).
Cluster analysis identified 6 different clinical patterns with similar phenotypic variables and predicted chronicity in our large cohort of patients with sarcoidosis. Classification of sarcoidosis into phenotypes with prognostic value may help physicians to improve the efficacy of clinical decisions.
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