Survival of Hospitalized COVID-19 Patients in Northern Italy: A Population-Based Cohort Study by the ITA-COVID-19 Network

Effect of COVID-19 on cardiac surgery volumes in Sweden

Objectives. The coronavirus disease 2019 (COVID-19) pandemic, which commenced in 2020, is known to frequently cause respiratory failure requiring intensive care, with occasional fatal outcomes. In this study, we aimed to conduct a retrospective nationwide observational study on the influence of the pandemic on cardiac surgery volumes in Sweden. Results. In 2020, 9.4% (n = 539) fewer patients underwent open-heart operations in Sweden (n = 5169) than during 2019 (n = 5708), followed by a 5.8% (n = 302) increase during 2021 (n = 5471). The reduction was greater than 15% in three of the eight hospitals in Sweden performing open-heart operations. Compared to 2019, in 2020, the waiting times for surgery were longer, and the patients were slightly younger, had better renal function, and a lower European System for Cardiac Operative Risk Evaluation; moreover, few patients had a history of myocardial infarction. However, more patients had insulin-treated diabetes mellitus, hypertension, peripheral vascular disease, reduced left ventricular function, and elevated pulmonary artery pressure. Urgent procedures were more common, but acute surgery was less common in 2020 than in 2019. Early mortality and postoperative complications were low and did not differ during the three years. Conclusion. The 9.4% decrease in the number of heart surgeries performed in Sweden during the 2020 COVID-19 pandemic, compared to 2019, partially recovered during 2021; however, there was no backlog of patients awaiting heart surgery.

COVID-19 incidence in women of reproductive age: a population-based study in Reggio Emilia, northern Italy

BACKGROUND

Despite being at higher risk of severe disease and pregnancy complications, evidence on susceptibility to SARS-CoV-2 infection in pregnancy is still limited. The aim of the study is to compare the likelihood of undergoing a SARS-CoV-2 test and testing positive for COVID-19 in pregnancy and puerperium with that of the general female population of reproductive age.

METHODS

This is a retrospective population-based cohort study including 117,606 women of reproductive age (March 2020-September 2021) with 6608 (5.6%) women having ≥ 1 pregnancy. Women were linked to the pregnancy registry to be classified as "non-pregnant", "pregnant", and "puerperium"; then, according to the national case-based integrated COVID-19 surveillance system, all women undergoing a SARS-CoV-2 test during the study period were identified. The Incidence Rate Ratio was calculated to compare the likelihood of being tested for SARS-CoV-2 in pregnant, puerperium and non-pregnant women among all women included. The likelihood of having a COVID-19 diagnosis was calculated using two comparators (not-pregnant women and the person-time before/after pregnancy) by means of Cox proportional hazards models, adjusting for age and with the cluster option to control standard error calculation in repeated pregnancies. Only first infection and swabs before the first one positive were included.

RESULTS

The probability of being tested for SARS-CoV-2 was 4.9 (95% CI: 4.8-5.1) and 3.6 times higher (95%CI: 3.4-3.9) in pregnancy (including spontaneous miscarriages) and in the puerperium, respectively. The Hazard Ratio (HR) of covid-19 diagnosis during pregnancy vs. non-pregnancy was 1.17 (95% CI 1.03-1.33) with similar results when comparing the risk during pregnancy with that of the same women outside pregnancy (puerperium excluded), with an HR of 1.13 (95% CI 0.96-1.33); the excess decreased when excluding the test performed at admission for delivery (HR 1.08 (95%CI 0.90-1.30). In the puerperium, the HR was 0.62 (95% CI 0.41-0.92) comparing women with ≥ 1childbirth with all other women and excluding the first two weeks of puerperium.

CONCLUSIONS

Women during pregnancy showed a small increase in the risk of infection, compatible with a higher likelihood of being tested. A lower probability of infection during the puerperium was observed during the entire pandemic period, suggesting likely protective behaviors which were effective in reducing their probability of infection.

COVID-19 fatality rates in hospitalized patients: A new systematic review and meta-analysis

BACKGROUND

SARS-COV2 or COVID-19 disease is an infectious illness that emerged for the first time at the end of 2019, in Wuhan, China and rapidly turned out to be an international pandemic with deleterious effects all over the world. In March 2021, A. Macedo et al., has published the first meta-analysis of hospital mortality, so the authors decided to update those data at a time of emergence of new therapies and increasing vaccination rates.

METHODS

As the outcome of interest was the mortality in hospitalized general patients, the authors looked for articles evaluating the clinical characteristics of those patients, consulting PUBMED (The US National Library of Medicine) and EMBASE (Medical database) in an independent selection using predefined terms of search. A meta-analysis random-effect model was estimated using Mantel-Haenszel method. Heterogeneity among studies was tested using Tau2 statistics and Chi2 statistics.

RESULTS

In a first instance 25 articles were included for final analysis with a total of 103,840 patients, but as the goal was to update the anterior data, these studies were analysed together with the 21 studies of the previous meta-analysis, with a total of 114609 patients. The mortality rate of COVID-19 general patients admitted to the hospital was 16% (95% CI 12; 21, I2 =100%).

CONCLUSION

Global hospital mortality of COVID-19 of general patients was 16%, with quite different rates according to the different geographic areas analysed.

SARS-CoV-2 Infection, Vaccination and Risk of Death in People with An Oncological Disease in Northeast Italy

People with a history of cancer have a higher risk of death when infected with SARS-CoV-2. COVID-19 vaccines in cancer patients proved safe and effective, even if efficacy may be lower than in the general population. In this population-based study, we compare the risk of dying of cancer patients diagnosed with COVID-19 in 2021, vaccinated or non-vaccinated against SARS-CoV-2 and residing in Friuli Venezia Giulia or in the province of Reggio Emilia. An amount of 800 deaths occurred among 6583 patients; the risk of death was more than three times higher among unvaccinated compared to vaccinated ones [HR 3.4; 95% CI 2.9-4.1]. The excess risk of death was stronger in those aged 70-79 years [HR 4.6; 95% CI 3.2-6.8], in patients with diagnosis made <1 year [HR 8.5; 95% CI 7.3-10.5] and in all cancer sites, including hematological malignancies. The study results indicate that vaccination against SARS-CoV-2 infection is a necessary tool to be included in the complex of oncological therapies aimed at reducing the risk of death.

Investigating the role of obstructive pulmonary diseases and eosinophil count at admission on all-cause mortality in SARS-CoV-2 patients : A single center registry-based retrospective cohort study

INTRODUCTION

The impact of asthma and chronic obstructive pulmonary disease (COPD) in the setting of severe acute respiratory syndrome coronavirus 2 (SARS-CoV‑2) infection is not clearly defined. Blood eosinophil count is a standard diagnostic test which, according to the previously published literature, might have a potential prognostic role on mortality in patients with SARS-CoV‑2 infection.

AIM

To investigate the potential prognostic value of peripheral blood eosinophil count on all-cause mortality of patients hospitalized with SARS-CoV‑2 infection, as well as to assess the impact of asthma or COPD premorbidity on all-cause mortality.

MATERIAL AND METHODS

We conducted a retrospective registry-based cohort study. Survival analysis was performed by employing the Cox proportional hazards regression model at 30 days of follow-up. Prognostic value of eosinophil count on all-cause mortality was assessed using receiver-operating characteristic (ROC) curve analysis.

RESULTS

A total of 5653 participants were included in the study. Our model did not reveal that pre-existing asthma or COPD is a statistically significant covariate for all-cause mortality but, indicated that higher eosinophil count at admission might have a protective effect (hazard ratio, HR 0.13 (95% confidence interval, CI 0.06-0.27), p = 0.0001). ROC curve analysis indicates cut-off value of 20 cells/mm³ (81% specificity; 30.9% sensitivity).

CONCLUSION

Our results indicate that eosinophil count at hospital admission might have a potential prognostic role for all-cause mortality at 30 days of follow-up; however this was not demonstrated for pre-existing obstructive lung diseases.

A survival analysis of socio-demographic and clinical predictors among hospitalized COVID-19 patients in Southern Iran

BACKGROUND

This study aimed to evaluate the socio-demographic, clinical, and laboratory risk factors in hospitalized COVID-19 patients during the first 6 months of the SARS-CoV-2 epidemic.

METHOD

This retrospective hospital-based cross-sectional study included all laboratory-confirmed cases of the COVID-19 virus that were admitted to the Shohadaye-Khalije-Fars Hospital in Bushehr, Iran, from February 22, 2020 to September 21, 2020. The patients' records were reviewed during the hospitalization period. The global COVID-19 clinical platform, i.e., the World Health Organization Rapid Case Report Form was used as the data collection tool. We conducted the survival analysis using the Kaplan-Meier and the Stepwise Cox regression analyses.

RESULTS

The analysis included 2108 confirmed cases of COVID-19 with a mean age of 47.81 years (SD 17.78); 56.8% men, 43.2% women and 6.3% (n = 133) deaths. After adjustment, it was found that factors associated with an increased risk of death consisted of chronic kidney disease, intensive care unit admission, cancer, and hemoptysis. The 7-day survival rate was 95.8%, which decreased to 95.1%, 94.0%, and 93.8% on days 14, 21, and 28 of hospitalization, respectively.

DISCUSSION AND CONCLUSION

Older COVID-19 patients with manifestation of hemoptysis and a past medical history of chronic kidney disease and cancer, should be closely monitored to prevent disease deterioration and death, and also should be admitted to the intensive care unit.

The effect of diabetes on COVID-19 incidence and mortality: Differences between highly-developed-country and high-migratory-pressure-country populations

The objective of this study was to compare the effect of diabetes and pathologies potentially related to diabetes on the risk of infection and death from COVID-19 among people from Highly-Developed-Country (HDC), including Italians, and immigrants from the High-Migratory-Pressure-Countries (HMPC). Among the population with diabetes, whose prevalence is known to be higher among immigrants, we compared the effect of body mass index among HDC and HMPC populations. A population-based cohort study was conducted, using population registries and routinely collected surveillance data. The population was stratified into HDC and HMPC, according to the place of birth; moreover, a focus was set on the South Asiatic population. Analyses restricted to the population with type-2 diabetes were performed. We reported incidence (IRR) and mortality rate ratios (MRR) and hazard ratios (HR) with 95% confidence interval (CI) to estimate the effect of diabetes on SARS-CoV-2 infection and COVID-19 mortality. Overall, IRR of infection and MRR from COVID-19 comparing HMPC with HDC group were 0.84 (95% CI 0.82-0.87) and 0.67 (95% CI 0.46-0.99), respectively. The effect of diabetes on the risk of infection and death from COVID-19 was slightly higher in the HMPC population than in the HDC population (HRs for infection: 1.37 95% CI 1.22-1.53 vs. 1.20 95% CI 1.14-1.25; HRs for mortality: 3.96 95% CI 1.82-8.60 vs. 1.71 95% CI 1.50-1.95, respectively). No substantial difference in the strength of the association was observed between obesity or other comorbidities and SARS-CoV-2 infection. Similarly for COVID-19 mortality, HRs for obesity (HRs: 18.92 95% CI 4.48-79.87 vs. 3.91 95% CI 2.69-5.69) were larger in HMPC than in the HDC population, but differences could be due to chance. Among the population with diabetes, the HMPC group showed similar incidence (IRR: 0.99 95% CI: 0.88-1.12) and mortality (MRR: 0.89 95% CI: 0.49-1.61) to that of HDC individuals. The effect of obesity on incidence was similar in both HDC and HMPC populations (HRs: 1.73 95% CI 1.41-2.11 among HDC vs. 1.41 95% CI 0.63-3.17 among HMPC), although the estimates were very imprecise. Despite a higher prevalence of diabetes and a stronger effect of diabetes on COVID-19 mortality in HMPC than in the HDC population, our cohort did not show an overall excess risk of COVID-19 mortality in immigrants.

An Italian multicentre distributed data research network to study the use, effectiveness, and safety of immunosuppressive drugs in transplant patients: Framework and perspectives of the CESIT project

The goal of post-transplant immunosuppressive drug therapy is to prevent organ rejection while minimizing drug toxicities. In clinical practice, a multidrug approach is commonly used and involves drugs with different mechanisms of action, including calcineurin inhibitors (CNI) (tacrolimus or cyclosporine), antimetabolite (antimet) (mycophenolate or azathioprine), inhibitors of mechanistic target of rapamycin (mTOR) (sirolimus or everolimus), and/or steroids. Although evidence based on several randomized clinical trials is available, the optimal immunosuppressive therapy has not been established and may vary among organ transplant settings. To improve the knowledge on this topic, a multiregional research network to Compare the Effectiveness and Safety of Immunosuppressive drugs in Transplant patients (CESIT) has been created with the financial support of the Italian Medicines Agency. In this article, we describe the development of this network, the framework that was designed to perform observational studies, and we also give an overview of the preliminary results that we have obtained. A multi-database transplant cohort was enrolled using a common data model based on healthcare claims data of four Italian regions (Lombardy, Veneto, Lazio, and Sardinia). Analytical datasets were created using an open-source tool for distributed analysis. To link the National Transplant Information System to the regional transplant cohorts, a semi-deterministic record linkage procedure was performed. Overall, 6,914 transplant patients from 2009–19 were identified: 4,029 (58.3%) for kidney, 2,219 (32.1%) for liver, 434 (6.3%) for heart, and 215 (3.1%) for lung. As expected, demographic and clinical characteristics showed considerable variability among organ settings. Although the triple therapy in terms of CNI + antimet/mTOR + steroids was widely dispensed for all settings (63.7% for kidney, 33.5% for liver, 53.3% for heart, and 63.7% for lung), differences in the active agents involved were detected. The CESIT network represents a great opportunity to study several aspects related to the use, safety, and effectiveness of post-transplant maintenance immunosuppressive therapy in real practice.

COVID-19 and arrhythmia: The factors associated and the role of myocardial electrical impulse propagation. An observational study based on cardiac telemetric monitoring

Background

The heart is commonly involved in COVID-19, and rhythm disorders have been largely reported.

Objective

To evaluate the association of some non-cardiac and cardiac comorbidities and QT dispersion with arrhythmias and their impact on outcomes in hospitalized patients with COVID-19.

Methods

Each patient underwent cardiac telemetry monitoring through the entire hospitalization period, laboratory analyses, 12-lead ECG, and lung imaging examination. Patients with arrhythmia were divided into three groups (bradyarrhythmias, tachyarrhythmias, and tachy- and bradyarrhythmias).

Results

Two-hundred patients completed the study (males, 123; mean age, 70.1 years); of these, 80 patients (40%) exhibited rhythm disorders on telemetry. Patients with arrhythmia were older (p &lt; 0.0001), had a greater number of comorbidities (p &lt; 0.0001), higher values of creatinine (p = 0.007), B-type natriuretic peptide (p &lt; 0.0001), troponin (p &lt; 0.0001), C-reactive protein (p = 0.01), ferritin (p = 0.001), D-dimer (p &lt; 0.0001), procalcitonin (p = 0.0008), QT interval (p = 0.002), QTc interval (p = 0.04), and QTc dispersion (p = 0.01), and lower values of sodium (p = 0.03), magnesium (p = 0.04), glomerular filtration rate (p &lt; 0.0001), and hemoglobin (p = 0.008) as compared to patients without arrhythmia. By comparing the three subgroups of patients, no significant differences were found. At multivariate analysis, age [odds ratio (OR) = 1.14 (95% CI: 1.07–1.22); p = 0.0004], coronary artery disease [OR = 12.7 (95% CI: 2.38–68.01); p = 0.005], and circulating troponin [OR = 1.05 (95% CI: 1.003–1.10); p = 0.04] represented risk factors independently associated with arrhythmia. All-cause in-hospital mortality was ∼40-fold higher among patients with arrhythmia [OR = 39.66 (95% CI: 5.20–302.51); p = 0.0004].

Conclusion

Arrhythmias are associated with aging, coronary artery disease, subtle myocardial injury, hyperinflammatory status, coagulative unbalance, and prolonged QTc dispersion in patients with COVID-19, and confer a worse in-hospital prognosis. Given its usefulness, routinary use of cardiac telemetry should be encouraged in COVID wards.

Is There a Doctors' Effect on Patients' Physical Health, Beyond the Intervention and All Known Factors? A Systematic Review

Purpose

Despite billions of doctor visits worldwide each year, little is known on whether doctors themselves affect patients’ physical health after accounting for intervention and confounders such as patients’ and doctors’ data, hospital effects, nor how strong that doctors’ effect is. Knowledge of surgeons’ and psychotherapists’ effects exists, but not for 102 other medical specialties notwithstanding the importance of such knowledge.

Methods

Eligibility Criteria: Randomized controlled trials (RCTs), case-control, and cohort studies including medical doctors except surgeons for any intervention, reporting the proportion of variance in patients’ outcomes owing to the doctors (random effects), or the fixed effects of grading doctors by outcomes, after multivariate adjustment. Exclusions: studies of <15 doctors or solely reporting doctors’ effects for known variables.

Sources

Medline, Embase, PsycINFO, inception to June 2020. Manual search for papers referring/referred to by resulting studies.

Risk of Bias

Using Newcastle–Ottawa scale.

Results

Despite all medical interventions bar surgery being eligible, only thirty cohort papers were found, covering 36,239 doctors, with 10 specialties, 21 interventions, 60 outcomes (17 unique). Studies reported doctors’ effects by grading doctors from best to worst, or by diversely calculating the doctor-attributed percentage of patients’ outcome variation, ie the intra-class correlation coefficient (ICC). Sixteen studies presented fixed effects, 18 random effects, and 3 another approach. No RCTs found. Thirteen studies reported exceptionally good and/or poor performers with confidence intervals wholly outside the average performance. ICC range 0 to 33%, mean 3.9%. Highly diverse reporting, meta-analysis therefore not applicable.

Conclusion

Doctors, on their own, can affect patients’ physical health for many interventions and outcomes. Effects range from negligible to substantial, even after accounting for all known variables. Many published cohorts may reveal valuable information by reanalyzing their data for doctors’ effects. Positive and negative doctor outliers appear regularly. Therefore, it can matter which doctor is chosen.

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Designing social distancing policies for the COVID-19 pandemic: A probabilistic model predictive control approach

The effective control of the COVID-19 pandemic is one the most challenging issues of recent years. The design of optimal control policies is challenging due to a variety of social, political, economical and epidemiological factors. Here, based on epidemiological data reported in recent studies for the Italian region of Lombardy, which experienced one of the largest and most devastating outbreaks in Europe during the first wave of the pandemic, we present a probabilistic model predictive control (PMPC) approach for the systematic study of what if scenarios of social distancing in a retrospective analysis for the first wave of the pandemic in Lombardy. The performance of the proposed PMPC was assessed based on simulations of a compartmental model that was developed to quantify the uncertainty in the level of the asymptomatic cases in the population, and the synergistic effect of social distancing during various activities, and public awareness campaign prompting people to adopt cautious behaviors to reduce the risk of disease transmission. The PMPC takes into account the social mixing effect, i.e. the effect of the various activities in the potential transmission of the disease. The proposed approach demonstrates the utility of a PMPC approach in addressing COVID-19 transmission and implementing public relaxation policies.

Mortality in SARS-CoV-2 Hospitalized Patients Treated with Remdesivir: A Nationwide, Registry-Based Study in Italy

Remdesivir is the first drug approved for treatment of COVID-19 but current evidence for recommending its use for the treatment of moderate-to-severe disease is still controversial among clinical guidelines. We performed a nationwide, registry-based study including all Italian hospitalized patients with COVID-19 treated with remdesivir to assess the impact of major confounders on crude 15-day and 29-day mortality. Mortality was calculated using the Kaplan–Meier estimator and the Cox proportional-hazards model was applied to analyze the risks by patient’s baseline features. In total, 16,462 patients treated with remdesivir from 29 October 2020 to 17 December 2020 were entered in the study. Crude 15-day and 29-day mortality were 7.1% (95% CI, 6.7–7.5%) and 11.7% (95% CI, 11.2–12.2%), respectively. Being treated within two days of admission reduced the risk of death by about 40% (HR 1.4, 95% CI, 1.2–1.6). Results from the largest cohort of remdesivir-treated patients suggests that mortality in SARS-CoV-2 hospitalized patients is substantially influenced by the days between SARS-CoV-2 diagnosis and drug prescription. Current recommendations and future clinical trials for remdesivir alone or in combination should carefully consider the target population and timing for best efficacy of treatment.

Clinical and imaging characteristics of patients with COVID-19 predicting hospital readmission after emergency department discharge: a single-centre cohort study in Italy

OBJECTIVE

We aimed at identifying baseline predictive factors for emergency department (ED) readmission, with hospitalisation/death, in patients with COVID-19 previously discharged from the ED. We also developed a disease progression velocity index.

DESIGN AND SETTING

Retrospective cohort study of prospectively collected data. The charts of consecutive patients with COVID-19 discharged from the Reggio Emilia (Italy) ED (2 March 2 to 31 March 2020) were retrospectively examined. Clinical, laboratory and CT findings at first ED admission were tested as predictive factors using multivariable logistic models. We divided CT extension by days from symptom onset to build a synthetic velocity index.

PARTICIPANTS

450 patients discharged from the ED with diagnosis of COVID-19.

MAIN OUTCOME MEASURE

ED readmission within 14 days, followed by hospitalisation/death.

RESULTS

Of the discharged patients, 84 (18.7%) were readmitted to the ED, 61 (13.6%) were hospitalised and 10 (2.2%) died. Age (OR=1.05; 95% CI 1.03 to 1.08), Charlson Comorbidity Index 3 versus 0 (OR=11.61; 95% CI 1.76 to 76.58), days from symptom onset (OR for 1-day increase=0.81; 95% CI 0.73 to 0.90) and CT extension (OR for 1% increase=1.03; 95% CI 1.01 to 1.06) were associated in a multivariable model for readmission with hospitalisation/death. A 2-day lag velocity index was a strong predictor (OR for unit increase=1.21, 95% CI 1.08 to 1.36); the model including this index resulted in less information loss.

CONCLUSIONS

A velocity index combining CT extension and days from symptom onset predicts disease progression in patients with COVID-19. For example, a 20% CT extension 3 days after symptom onset has the same risk as does 50% after 10 days.

Evaluation of the Potential Risk of Mortality from SARS-CoV-2 Infection in Hospitalized Patients According to the Charlson Comorbidity Index

Background: The pandemic of COVID-19 has represented a major threat to global public health in the last century and therefore to identify predictors of mortality among COVID-19 hospitalized patients is widely justified. The aim of this study was to evaluate the possible usefulness of Charlson Comorbidity Index (CCI) as mortality predictor in patients hospitalized because COVID-19. Methods: This study was carried out in Zacatecas, Mexico, and it included 705 hospitalized patients with suspected of SARS-CoV-2 infection. Clinical data were collected, and the CCI score was calculated online using the calculator from the Sociedad Andaluza de Medicina Intensiva y Unidades Coronarias; the result was evaluated as mortality predictor among the patients with COVID-19. Results: 377 patients were positive for SARS-COV-2. Obesity increased the risk of intubation among the study population (odds ratio (OR) = 2.59; 95 CI: 1.36–4.92; p = 0.003). The CCI values were higher in patients who died because of COVID-19 complications than those observed in patients who survived (p < 0.001). Considering a CCI cutoff > 31.69, the area under the ROC curve was 0.75, with a sensitivity and a specificity of 63.6% and 87.7%, respectively. Having a CCI value > 31.69 increased the odds of death by 12.5 times among the study population (95% CI: 7.3–21.4; p < 0.001). Conclusions: The CCI is a suitable tool for the prediction of mortality in patients hospitalized for COVID-19. The presence of comorbidities in hospitalized patients with COVID-19 reflected as CCI > 31.69 increased the risk of death among the study population, so it is important to take precautionary measures in patients due to their condition and their increased vulnerability to SARS-CoV-2 infection.

Comparing Covid-19 pandemic waves in hospitalized patients - a retrospective, multicenter, cohort study

BACKGROUND

Covid-19 disease was first diagnosed in Israel at the end of February 2020. Until the end of June 2021 842,536 confirmed cases and 6428 deaths were accumulated. The aim of our multicenter retrospective cohort study is to describe the demographic and clinical characteristics of hospitalized patients and to compare the pandemic waves before immunization.

METHODS

Out of 22302 patients hospitalized in general medical centers we randomly selected 6329 admissions for the study. Of these, 3582 and 1106 were eligible for the study in the first period (1 ^st & 2 ^nd waves), and in the second period (3 ^rd wave), respectively.

RESULTS

Thirty-day mortality was higher in the 2nd period than in the 1st period, 25.20% versus 13.68% (P<0.001). Invasive mechanical ventilation supported 9.19% and 14.21% of the patients in the 1st period and 2nd period, respectively. Extracorporeal Membrane Oxygenation (ECMO) was used more than twice as often on the 2nd period .

CONCLUSIONS

Invasive ventilation, use of ECMO and mortality rate were 1.5 to 2 times higher on the 2 ^nd period than in the 1 ^st period. Patients of the 2 ^nd period had a more severe presentation and higher mortality than those of the 1 ^st period.

Predictive determinants of overall survival among re-infected COVID-19 patients using the elastic-net regularized Cox proportional hazards model: a machine-learning algorithm

BACKGROUND

Narrowing a large set of features to a smaller one can improve our understanding of the main risk factors for in-hospital mortality in patients with COVID-19. This study aimed to derive a parsimonious model for predicting overall survival (OS) among re-infected COVID-19 patients using machine-learning algorithms.

METHODS

The retrospective data of 283 re-infected COVID-19 patients admitted to twenty-six medical centers (affiliated with Shiraz University of Medical Sciences) from 10 June to 26 December 2020 were reviewed and analyzed. An elastic-net regularized Cox proportional hazards (PH) regression and model approximation via backward elimination were utilized to optimize a predictive model of time to in-hospital death. The model was further reduced to its core features to maximize simplicity and generalizability.

RESULTS

The empirical in-hospital mortality rate among the re-infected COVID-19 patients was 9.5%. In addition, the mortality rate among the intubated patients was 83.5%. Using the Kaplan-Meier approach, the OS (95% CI) rates for days 7, 14, and 21 were 87.5% (81.6-91.6%), 78.3% (65.0-87.0%), and 52.2% (20.3-76.7%), respectively. The elastic-net Cox PH regression retained 8 out of 35 candidate features of death. Transfer by Emergency Medical Services (EMS) (HR=3.90, 95% CI: 1.63-9.48), SpO2≤85% (HR=8.10, 95% CI: 2.97-22.00), increased serum creatinine (HR=1.85, 95% CI: 1.48-2.30), and increased white blood cells (WBC) count (HR=1.10, 95% CI: 1.03-1.15) were associated with higher in-hospital mortality rates in the re-infected COVID-19 patients.

CONCLUSION

The results of the machine-learning analysis demonstrated that transfer by EMS, profound hypoxemia (SpO2≤85%), increased serum creatinine (more than 1.6 mg/dL), and increased WBC count (more than 8.5 (×109 cells/L)) reduced the OS of the re-infected COVID-19 patients. We recommend that future machine-learning studies should further investigate these relationships and the associated factors in these patients for a better prediction of OS.

Utility of Age-adjusted Charlson Comorbidity Index as a Predictor of Need for Invasive Mechanical Ventilation, Length of Hospital Stay, and Survival in COVID-19 Patients

Background

Multiple parameters may be used to prognosticate coronavirus disease-2019 (COVID-19) patients, which are often expensive laboratory or radiological investigations. We evaluated the utility of age-adjusted Charlson comorbidity index (CCI) as a predictor of outcome in COVID-19 patients treated with remdesivir.

Materials and methods

This was a single-center, retrospective study on 126 COVID-19 patients treated with remdesivir. The age-adjusted CCI, length of hospital stay (LOS), need for invasive mechanical ventilation (IMV), and survival were recorded.

Results

The mean and standard deviation (SD) of age-adjusted CCI were 3.37 and 2.186, respectively. Eighty-six patients (70.5%) had age-adjusted CCI ≤4, and 36 (29.5%) had age-adjusted CCI >4. Among patients with age-adjusted CCI ≤4, 20 (23.3%) required IMV, whereas in those with age-adjusted CCI >4, 19 (52.8%) required IMV (p <0.05, Pearson's chi-square test). In those with age-adjusted CCI ≤4, the mortality was 18.6%, whereas it was 41.7% in patients with age-adjusted CCI >4 (p <0.05, Pearson's chi-square test). The receiver operating curve (ROC) of age-adjusted CCI for predicting the mortality had an area under the curve (AUC) of 0.709, p = 0.001, and sensitivity 68%, specificity 62%, and 95% confidence interval (CI) [0.608, 0.810], for a cutoff score >4. The ROC for age-adjusted CCI for predicting the need for IMV had an AUC of 0.696, p = 0.001, and sensitivity 67%, specificity 63%, and 95% CI [0.594, 0.797], for a cutoff score >4. ROC for age-adjusted CCI as a predictor of prolonged LOS (≥14 days) was insignificant.

Conclusion

In COVID-19 patients, the age-adjusted CCI is an independent predictor of the need for IMV (score >4) and mortality (score >4) but is not useful to predict LOS (CTRI/2020/11/029266).

How to cite this article

Shanbhag V, Arjun NR, Chaudhuri S, Pandey AK. Utility of Age-adjusted Charlson Comorbidity Index as a Predictor of Need for Invasive Mechanical Ventilation, Length of Hospital Stay, and Survival in COVID-19 Patients. Indian J Crit Care Med 2021;25(9):987-991.

Lower incidence of COVID-19 in patients with inflammatory bowel disease treated with non-gut selective biologic therapy

BACKGROUND AND AIM

Since the outbreak of COVID-19, concerns have been raised as to whether inflammatory bowel disease (IBD) patients under biologic therapy may be more susceptible to the disease. This study aimed to determine the incidence and outcomes of COVID-19 in a large cohort of IBD patients on biologic therapy.

METHODS

This observational retrospective multicenter study collected data about COVID-19 in IBD patients on biologic therapy in Italy, between February and May 2020. The main end-points were (i) to assess both the cumulative incidence and clinical outcome of COVID-19, according to different biologic agents and (ii) to compare them with the general population and a cohort IBD patients undergoing non-biologic therapies.

RESULTS

Among 1816 IBD patients, the cumulative incidence of COVID-19 was 3.9 per 1000 (7/1816) with a 57% hospitalization rate and a 29% case-fatality rate. The class of biologic agents was the only risk factor of developing COVID-19 (P = 0.01). Non-gut selective agents were associated with a lower incidence of COVID-19 cases, related symptoms, and hospitalization (P < 0.05). Compared with the general population of Lombardy, an overall lower incidence of COVID-19 was observed (3.9 vs 8.5 per 1000, P = 0.03). Compared with 565 IBD patients on non-biologic therapies, a lower rate of COVID-19 symptoms was observed in our cohort (7.5% vs 18%, P < 0.001).

CONCLUSIONS

Compared with the general population, IBD patients on biologic therapy are not exposed to a higher risk of COVID-19. Non-gut selective agents are associated with a lower incidence of symptomatic disease, supporting the decision of maintaining the ongoing treatment.

Prevalence, determinants, and outcomes of SARS-COV-2 infection among cancer patients. A population-based study in northern Italy

BACKGROUND

It is well established that cancer patients infected with SARS-CoV-2 are at particularly elevated risk of adverse outcomes, but the comparison of SARS-CoV-2 infection risk between cancer patients and cancer-free individuals has been poorly investigated on a population-basis.

METHODS

A population-based study was thus conducted in Friuli Venezia Giulia region, northeastern Italy, to estimate prevalence and determinants of SARS-CoV-2 infection among cancer patients, as compared to cancer-free individuals, and to evaluate adverse outcomes of SARS-CoV-2 infection. The study included 263,042 individuals tested for SARS-CoV-2 in February-December 2020 with cancer history retrieved through the regional cancer registry. Odds ratios (ORs) of SARS-CoV-2 positivity, with corresponding 95% confidence intervals (CIs), were calculated using multivariable logistic regression models, adjusted for sex and age. Hazard ratios (HRs) adjusted for sex and age for intensive care unit (ICU) admission and all-cause death were estimated using Cox models.

RESULTS

Among 26,394 cancer patients tested for SARS-CoV-2, the prevalence of infection was 11.7% versus 16.2% among 236,648 cancer-free individuals, with a corresponding OR = 0.59 (95% CI: 0.57-0.62). The prevalence was much higher (29% in both groups) during the second pandemic wave (October-December 2020). Among cancer patients, age ≥80 years and cancer diagnosis ≥13 months before SARS-CoV-2 testing were the major risk factors of infection. Among 3098 infected cancer patients, the fatality rate was 17.4% versus 15.8% among 23,296 negative ones (HR = 1.63, 95% CI: 1.49-1.78), and versus 5.0% among 38,268 infected cancer-free individuals (HR = 1.23, 95% CI: 1.12-1.36). No significant differences emerged when considering ICU admission risk.

CONCLUSION

Albeit cancer patients reported reduced SARS-CoV-2 infection risk, those infected showed higher mortality than uninfected ones and infected cancer-free population. Study findings claim for continuing to protect cancer patients from SARS-CoV-2, without reducing the level of oncologic care.

Survival of COVID-19 with Multimorbidity Patients

BACKGROUND

The outbreak of SARS-CoV-2 abruptly disseminated in early 2020, overcoming the capacity of health systems to respond the pandemic. It was not until the vaccines were launched worldwide that an increase in survival was observed. The objectives of this study were to analyse the characteristics of survivors and their relationship with comorbidities. We had access to a database containing information on 16,747 hospitalized patients from Mexico, all infected with SARS-CoV-2, as part of a regular follow-up. The descriptive analysis looked for clusters of either success or failure. We categorized the samples into no comorbidities, or one and up to five coexisting with the infection. We performed a logistic regression test to ascertain what factors were more influential in survival. The main variable of interest was survival associated with multimorbidity factors. The database hosted information on hospitalized patients from Mexico between March 2020 through to April 2021. Categories 2 and 3 had the largest number of patients. Survival rates were higher in categories 0 (64.8%), 1 (57.5%) and 2 (51.6%). In total, 1741 (10.5%) patients were allocated to an ICU unit. Mechanical ventilators were used on 1415 patients, corresponding to 8.76%. Survival was recorded in 9575 patients, accounting for 57.2% of the sample population. Patients without comorbidities, younger people and women were more likely to survive.

Large-Scale Postmarketing Surveillance of Biological Drugs for Immune-Mediated Inflammatory Diseases Through an Italian Distributed Multi-Database Healthcare Network: The VALORE Project

BACKGROUND

Biological drugs have improved the management of immune-mediated inflammatory diseases (IMIDs) despite being associated with important safety issues such as immunogenicity, infections, and malignancies in real-world settings.

OBJECTIVE

The aim of this study was to explore the potential of a large Italian multi-database distributed network for use in the postmarketing surveillance of biological drugs, including biosimilars, in patients with IMID.

METHODS

A retrospective cohort study was conducted using 13 Italian regional claims databases during 2010-2019. A tailor-made R-based tool developed for distributed analysis of claims data using a study-specific common data model was customized for this study. We measured the yearly prevalence of biological drug users and the frequency of switches between originator and biosimilars for infliximab, etanercept, and adalimumab separately and stratified them by calendar year and region. We then calculated the cumulative number of users and person-years (PYs) of exposure to individual biological drugs approved for IMIDs. For a number of safety outcomes (e.g., severe acute respiratory syndrome coronavirus 2 [SARS-COV-2] infection), we conducted a sample power calculation to estimate the PYs of exposure required to investigate their association with individual biological drugs approved for IMIDs, considering different strengths of association.

RESULTS

From a total underlying population of almost 50 million inhabitants from 13 Italian regions, we identified 143,602 (0.3%) biological drug users, with a cumulative exposure of 507,745 PYs during the entire follow-up. The mean age ± standard deviation of biological drug users was 49.3 ± 16.3, with a female-to-male ratio of 1.2. The age-adjusted yearly prevalence of biological drug users increased threefold from 0.7 per 1000 in 2010 to 2.1 per 1000 in 2019. Overall, we identified 40,996 users of biosimilars of tumor necrosis factor (TNF)-α inhibitors (i.e., etanercept, adalimumab, and infliximab) in the years 2015-2019. Of these, 46% (N = 18,845) switched at any time between originator and biosimilars or vice versa. To investigate a moderate association (incidence rate ratio 2) between biological drugs approved for IMIDs and safety events of interest, such as optic neuritis (lowest background incidence rate 10.4/100,000 PYs) or severe infection (highest background incidence rate 4312/100,000 PYs), a total of 43,311 PYs and 104 PYs of exposure to individual biological drugs, respectively, would be required. As such, using this network, of 15 individual biological drugs approved for IMIDs, the association with those adverse events could be investigated for four (27%) and 14 (93%), respectively.

CONCLUSION

The VALORE project multi-database network has access to data on more than 140,000 biological drug users (and > 0.5 million PYs) from 13 Italian regions during the years 2010-2019, which will be further expanded with the inclusion of data from other regions and more recent calendar years. Overall, the cumulated amount of person-time of exposure to biological drugs approved for IMIDs provides enough statistical power to investigate weak/moderate associations of almost all individual compounds and the most relevant safety outcomes. Moreover, this network may offer the opportunity to investigate the interchangeability of originator and biosimilars of several TNFα inhibitors in different therapeutic areas in real-world settings.

Chronic Diseases as a Predictor for Severity and Mortality of COVID-19: A Systematic Review With Cumulative Meta-Analysis

Introduction: Given the ongoing coronavirus disease 2019 (COVID-19) pandemic and the consequent global healthcare crisis, there is an urgent need to better understand risk factors for symptom deterioration and mortality among patients with COVID-19. This systematic review aimed to meet the need by determining the predictive value of chronic diseases for COVID-19 severity and mortality.

Methods: We searched PubMed, Embase, Web of Science, and Cumulative Index to Nursing and Allied Health Complete to identify studies published between December 1, 2019, and December 31, 2020. Two hundred and seventeen observational studies from 26 countries involving 624,986 patients were included. We assessed the risk of bias of the included studies and performed a cumulative meta-analysis.

Results: We found that among COVID-19 patients, hypertension was a very common condition and was associated with higher severity, intensive care unit (ICU) admission, acute respiratory distress syndrome, and mortality. Chronic obstructive pulmonary disease was the strongest predictor for COVID-19 severity, admission to ICU, and mortality, while asthma was associated with a reduced risk of COVID-19 mortality. Patients with obesity were at a higher risk of experiencing severe symptoms of COVID-19 rather than mortality. Patients with cerebrovascular disease, chronic liver disease, chronic renal disease, or cancer were more likely to become severe COVID-19 cases and had a greater probability of mortality.

Conclusions: COVID-19 patients with chronic diseases were more likely to experience severe symptoms and ICU admission and faced a higher risk of mortality. Aggressive strategies to combat the COVID-19 pandemic should target patients with chronic diseases as a priority.

Lack of effect on in-hospital mortality of drugs used during COVID-19 pandemic: Findings of the retrospective multicenter COVOCA study

Introduction

During COVID-19 pandemic, the use of several drugs has represented the worldwide clinical practice. However, though the current increase of knowledge about the disease, there is still no effective treatment for the usage of drugs. Thus, we retrospectively assessed use and effects of therapeutic regimens in hospitalized patients on in-hospital mortality.

Methods

COVOCA is a retrospective observational cohort study on 18 COVID centres throughout Campania Region Hospitals. We included adult patients with confirmed SARS-CoV-2 infection, discharged/dead between March/June 2020.

Results

618 patients were included, with an overall in-hospital cumulative mortality incidence of 23.1%. Most prescribed early treatments were antivirals (72%), antibiotics (65%) and hydroxychloroquine/anticoagulants (≈50%). Tocilizumab, indeed, was largely prescribed late during hospitalization. Multivariable models, with a cut-off at day 2 for early COVID-19 therapy administration, did not disclose any significant association of a single drug administration on the clinical outcome.

Discussion

COVOCA represents the first multicenter database in Campania region. None drug class used during the pandemic significantly modified the outcome, regardless of therapy beginning, both overall and net of those already in non-invasive ventilation (NIV)/ orotracheal intubation (OTI) at hospitalization. Our cumulative incidence of mortality seems lower than other described during the same period, particularly in Northern Italy.

The impact of chest CT body composition parameters on clinical outcomes in COVID-19 patients

We assessed the impact of chest CT body composition parameters on outcomes and disease severity at hospital presentation of COVID-19 patients, focusing also on the possible mediation of body composition in the relationship between age and death in these patients. Chest CT scans performed at hospital presentation by consecutive COVID-19 patients (02/27/2020-03/13/2020) were retrospectively reviewed to obtain pectoralis muscle density and total, visceral, and intermuscular adipose tissue areas (TAT, VAT, IMAT) at the level of T7-T8 vertebrae. Primary outcomes were: hospitalization, mechanical ventilation (MV) and/or death, death alone. Secondary outcomes were: C-reactive protein (CRP), oxygen saturation (SO2), CT disease extension at hospital presentation. The mediation of body composition in the effect of age on death was explored. Of the 318 patients included in the study (median age 65.7 years, females 37.7%), 205 (64.5%) were hospitalized, 68 (21.4%) needed MV, and 58 (18.2%) died. Increased muscle density was a protective factor while increased TAT, VAT, and IMAT were risk factors for hospitalization and MV/death. All these parameters except TAT had borderline effects on death alone. All parameters were associated with SO2 and extension of lung parenchymal involvement at CT; VAT was associated with CRP. Approximately 3% of the effect of age on death was mediated by decreased muscle density. In conclusion, low muscle quality and ectopic fat accumulation were associated with COVID-19 outcomes, VAT was associated with baseline inflammation. Low muscle quality partly mediated the effect of age on mortality.

The value of computed tomography in assessing the risk of death in COVID-19 patients presenting to the emergency room

Objective

The aims of this study were to develop a multiparametric prognostic model for death in COVID-19 patients and to assess the incremental value of CT disease extension over clinical parameters.

Methods

Consecutive patients who presented to all five of the emergency rooms of the Reggio Emilia province between February 27 and March 23, 2020, for suspected COVID-19, underwent chest CT, and had a positive swab within 10 days were included in this retrospective study. Age, sex, comorbidities, days from symptom onset, and laboratory data were retrieved from institutional information systems. CT disease extension was visually graded as < 20%, 20–39%, 40–59%, or ≥ 60%. The association between clinical and CT variables with death was estimated with univariable and multivariable Cox proportional hazards models; model performance was assessed using k-fold cross-validation for the area under the ROC curve (cvAUC).

Results

Of the 866 included patients (median age 59.8, women 39.2%), 93 (10.74%) died. Clinical variables significantly associated with death in multivariable model were age, male sex, HDL cholesterol, dementia, heart failure, vascular diseases, time from symptom onset, neutrophils, LDH, and oxygen saturation level. CT disease extension was also independently associated with death (HR = 7.56, 95% CI = 3.49; 16.38 for ≥ 60% extension). cvAUCs were 0.927 (bootstrap bias-corrected 95% CI = 0.899–0.947) for the clinical model and 0.936 (bootstrap bias-corrected 95% CI = 0.912–0.953) when adding CT extension.

Conclusions

A prognostic model based on clinical variables is highly accurate in predicting death in COVID-19 patients. Adding CT disease extension to the model scarcely improves its accuracy.

Key Points

• Early identification of COVID-19 patients at higher risk of disease progression and death is crucial; the role of CT scan in defining prognosis is unclear.

• A clinical model based on age, sex, comorbidities, days from symptom onset, and laboratory results was highly accurate in predicting death in COVID-19 patients presenting to the emergency room.

• Disease extension assessed with CT was independently associated with death when added to the model but did not produce a valuable increase in accuracy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-07993-9.

Awareness of influenza and pneumococcal vaccines in diabetic patients

Purpose

In our study, it was aimed to evaluate the awareness of diabetic patients about vaccination status and vaccines.

Methods

This cross-sectional study was conducted between January 2019 and February 2019. A survey questioning the level of knowledge about and vaccination status for influenza and pneumonia vaccines was applied by face-to-face interviews with patients with diabetes mellitus who admitted to the diabetes outpatient clinic. All results were evaluated with SPSS-20.0.

Results

A total of 202 patients [66 male (32.7%) and 136 female (67.3%) patients; with a mean age of 57.7 ± 11.3 years and mean duration of diabetes 10.7 ± 7.9 years] were recruited in the study. Majority of the patients (92.6%) were type 2 DM patients. 59.4% of the patients had never been vaccinated. The rate of those who had pneumonia vaccine was very low, only 14.7%. The vast majority of the patients had knowledge about vaccines and their most common source of information was nurses. 53% of patients believed that diabetic patients should be vaccinated regularly. 16.8% of the patients were reluctant to have the recommended vaccine. The factor with greatest impact on this was that they did not consider the vaccine necessary. 52.5% of the patients recommended to be vaccinated had the recommended vaccine. 26.4% of the patients who were not enthusiastic about the recommended vaccine had pneumococcal vaccine after being informed about the vaccine.

Conclusion

It was observed that the information given about vaccines positively affected the vaccination rate. The main barrier to vaccination was the lack of information about the need for influenza vaccination. Designing strategies and training programs for healthcare professionals and patients should be the main goal to improve vaccination coverage and vaccination rates.

Cumulative COVID-19 incidence, mortality and prognosis in cancer survivors: A population-based study in Reggio Emilia, Northern Italy

The aim of this population-based study was to evaluate the impact of being a cancer survivor (CS) on COVID-19 risk and prognosis during the first wave of the pandemic (27 February 2020 to 13 May 2020) in Reggio Emilia Province. Prevalent cancer cases diagnosed between 1996 and 2019 were linked with the provincial COVID-19 surveillance system. We compared CS' cumulative incidence of being tested, testing positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), being hospitalized and dying of COVID-19 with that of the general population; we compared COVID-19 prognosis in CS and in patients without cancer. During the study period, 15 391 people (1527 CS) underwent real-time polymerase chain reaction for SARS-CoV-2, of whom 4541 (447 CS) tested positive; 541 (113 CS) died of COVID-19. CS had higher age- and sex-adjusted incidence rate ratios (IRR) of testing (1.28 [95% confidence interval, CI = 1.21-1.35]), of positive test (IRR 1.06 [95% CI = 0.96-1.18]) and of hospitalization and death (IRR 1.27 [95% CI = 1.09-1.48] and 1.39 [95%CI = 1.12-1.71], respectively). CS had worse prognosis when diagnosed with COVID-19, particularly those below age 70 (adjusted odds ratio [OR] of death 5.03; [95% CI = 2.59-9.75]), while the OR decreased after age 70. The OR of death was higher for CS with a recent diagnosis, that is, <2 years (OR = 2.92; 95% CI = 1.64-5.21), or metastases (OR = 2.09; 95% CI = 0.88-4.93). CS showed the same probability of being infected, despite a slightly higher probability of being tested than the general population. Nevertheless, CS were at higher risk of death once infected.

Risk of COVID-19 hospitalization and mortality in rheumatic patients treated with hydroxychloroquine or other conventional DMARDs in Italy

Objectives

To ascertain if hydroxychloroquine (HCQ)/chloroquine (CLQ) and other conventional disease-modifying anti-rheumatic drugs (cDMARDs) use, and rheumatic diseases per se, may be associated with COVID-19-related risk of hospitalization and mortality.

Methods

This case-control study nested within a cohort of cDMARD users was conducted in the Lombardy, Veneto, Tuscany and Lazio regions and Reggio Emilia province. Claims databases were linked to COVID-19 surveillance registries. Risk of COVID-19-related outcomes was estimated using a multivariate conditional logistic regression analysis, comparing HCQ/CLQ vs methotrexate, vs other cDMARDs and vs non-use of these drugs. Presence of rheumatic diseases vs their absence in a non-nested population was investigated.

Results

1275 cases hospitalized due to COVID-19 were matched to 12 734 controls. Compared with recent use of methotrexate, no association between HCQ/CLQ monotherapy and COVID-19 hospitalization (OR 0.83 [95%CI, 0.69–1.00]) or mortality (OR 1.19 [95%CI, 0.85–1.67]) was observed. A lower risk was found when comparing HCQ/CLQ use to the concomitant use of other cDMARDs and glucocorticoids. HCQ/CLQ was not associated with COVID-19 hospitalization as compared with non-use. An increased risk for recent use of either methotrexate monotherapy (OR 1.19 [95% CI, 1.05–1.34]) or other cDMARDs (OR 1.21 [95% CI, 1.08–1.36]) vs non-use was found. Rheumatic diseases were not associated with COVID-19-related outcomes.

Conclusion

HCQ/CLQ use in rheumatic patients was not associated with a protective effect against COVID-19-related outcomes. Use of other cDMARDs was associated with an increased risk when compared with non-use, and, if concomitantly used with glucocorticoids, also vs HCQ/CLQ, probably to be ascribed to immunosuppressive action.

Unexpectedly lower mortality rates in COVID-19 patients with and without type 2 diabetes in Istanbul

AIMS

Type 2 diabetes mellitus (T2DM) is a risk factor for severe COVID-19. Our aim was to compare the clinical outcomes of patients with and without T2DM during the first hit of COVID-19 in Istanbul.

METHODS

A retrospective population-based study was conducted including all consecutive adult symptomatic COVID-19 cases. Patients were confirmed with rt-PCR; treated and monitored in accordance with standard protocols. The primary endpoints were hospitalization and 30-day mortality.

RESULTS

Of the 93,571 patients, 22.6% had T2DM, with older age and higher BMI. Propensity Score matched evaluation resulted in significantly higher rates of hospitalization (1.5-fold), 30-day mortality (1.6-fold), and pneumonia (1.4-fold). They revealed more severe laboratory deviations, comorbidities, and frequent drug usage than the Non-DM group. In T2DM age, pneumonia, hypertension, obesity, and insulin-based therapies were associated with an increased likelihood of hospitalization; whereas age, male gender, lymphopenia, obesity, and insulin treatment were considerably associated with higher odds of death.

CONCLUSIONS

COVID-19 patients with T2DM had worse clinical outcomes with higher hospitalization and 30-day mortality rates than those without diabetes. Compared to most territories of the world, COVID-19 mortality was much lower in Istanbul, which may be associated with accessible healthcare provision and the younger structure of the population.

Risk factors for poor outcomes in hospitalised COVID-19 patients: A systematic review and meta-analysis

BACKGROUND

Understanding the risk factors for poor outcomes among COVID-19 patients could help identify vulnerable populations who would need prioritisation in prevention and treatment for COVID-19. We aimed to critically appraise and synthesise published evidence on the risk factors for poor outcomes in hospitalised COVID-19 patients.

METHODS

We searched PubMed, medRxiv and the WHO COVID-19 literature database for studies that reported characteristics of COVID-19 patients who required hospitalisation. We included studies published between January and May 2020 that reported adjusted effect size of any demographic and/or clinical factors for any of the three poor outcomes: mortality, intensive care unit (ICU) admission, and invasive mechanical ventilation. We appraised the quality of the included studies using Joanna Briggs Institute appraisal tools and quantitatively synthesised the evidence through a series of random-effect meta-analyses. To aid data interpretation, we further developed an interpretation framework that indicated strength of the evidence, informed by both quantity and quality of the evidence.

RESULTS

We included a total of 40 studies in our review. Most of the included studies (29/40, 73%) were assessed as "good quality", with assessment scores of 80 or more. We found that male sex (pooled odds ratio (OR) = 1.32 (95% confidence interval (CI) = 1.18-1.48; 20 studies), older age (OR = 1.05, 95% CI = 1.04-1.07, per one year of age increase; 10 studies), obesity (OR = 1.59, 95% CI = 1.02-2.48; 4 studies), diabetes (OR = 1.25, 95% CI = 1.11-1.40; 11 studies) and chronic kidney diseases (6 studies; OR = 1.57, 95% CI = 1.27-1.93) were associated with increased risks for mortality with the greatest strength of evidence based on our interpretation framework. We did not find increased risk of mortality for several factors including chronic obstructive pulmonary diseases (5 studies), cancer (4 studies), or current smoker (5 studies); however, this does not indicate absence of risk due to limited data on each of these factors.

CONCLUSION

Male sex, older age, obesity, diabetes and chronic kidney diseases are important risk factors of COVID-19 poor outcomes. Our review provides not only an appraisal and synthesis of evidence on the risk factors of COVID-19 poor outcomes, but also a data interpretation framework that could be adopted by relevant future research.

Charlson comorbidity index and a composite of poor outcomes in COVID-19 patients: A systematic review and meta-analysis

BACKGROUND AND AIMS

The ongoing COVID-19 pandemic is disproportionately affecting patients with comorbidities. Therefore, thorough comorbidities assessment can help establish risk stratification of patients with COVID-19, upon hospital admission. Charlson Comorbidity Index (CCI) is a validated, simple, and readily applicable method of estimating the risk of death from comorbid disease and has been widely used as a predictor of long-term prognosis and survival.

METHODS

We performed a systematic review and meta-analysis of CCI score and a composite of poor outcomes through several databases.

RESULTS

Compared to a CCI score of 0, a CCI score of 1-2 and CCI score of ≥3 was prognostically associated with mortality and associated with a composite of poor outcomes. Per point increase of CCI score also increased mortality risk by 16%. Moreover, a higher mean CCI score also significantly associated with mortality and disease severity.

CONCLUSION

CCI score should be utilized for risk stratifications of hospitalized COVID-19 patients.