Overlooked Shortcomings of Observational Studies of Interventions in Coronavirus Disease 2019: An Illustrated Review for the Clinician

Lessons learned: avoiding bias via multi-state analysis of patients' trajectories in real-time

Objectives

Many studies have attempted to determine the disease severity and patterns of COVID-19. However, at the beginning of the pandemic, the complex patients' trajectories were only descriptively reported, and many analyses were worryingly prone to time-dependent-, selection-, and competing risk biases. Multi-state models avoid these biases by jointly analysing multiple clinical outcomes while taking into account their time dependency, including current cases, and modelling competing events. This paper uses a publicly available data set from the first wave in Israel as an example to demonstrate the benefits of analysing hospital data via multi-state methodology.

Methods

We compared the outcome of the data analysis using multi-state models with the outcome obtained when various forms of bias are ignored. Furthermore, we used Cox regression to model the transitions among the states in a multi-state model. This allowed for the comparison of the covariates' influence on transition rates between the two states. Lastly, we calculated expected lengths of stay and state probabilities based on the multi-state model and visualised it using stacked probability plots.

Results

Compared to standard methods, multi-state models avoid many biases in the analysis of real-time disease developments. The utility of multi-state models is further highlighted through the use of stacked probability plots, which visualise the results. In addition, by stratification of disease patterns by subgroups and visualisation of the distribution of possible outcomes, these models bring the data into an interpretable form.

Conclusion

To accurately guide the provision of medical resources, this paper recommends the real-time collection of hospital data and its analysis using multi-state models, as this method eliminates many potential biases. By applying multi-state models to real-time data, the gained knowledge allows rapid detection of altered disease courses when new variants arise, which is essential when informing medical and political decision-makers as well as the general population.

Decoding Immuno-Competence: A Novel Analysis of Complete Blood Cell Count Data in COVID-19 Outcomes

BACKGROUND

While 'immuno-competence' is a well-known term, it lacks an operational definition. To address this omission, this study explored whether the temporal and structured data of the complete blood cell count (CBC) can rapidly estimate immuno-competence. To this end, one or more ratios that included data on all monocytes, lymphocytes and neutrophils were investigated.

MATERIALS AND METHODS

Longitudinal CBC data collected from 101 COVID-19 patients (291 observations) were analyzed. Dynamics were estimated with several approaches, which included non-structured (the classic CBC format) and structured data. Structured data were assessed as complex ratios that capture multicellular interactions among leukocytes. In comparing survivors with non-survivors, the hypothesis that immuno-competence may exhibit feedback-like (oscillatory or cyclic) responses was tested.

RESULTS

While non-structured data did not distinguish survivors from non-survivors, structured data revealed immunological and statistical differences between outcomes: while survivors exhibited oscillatory data patterns, non-survivors did not. In survivors, many variables (including IL-6, hemoglobin and several complex indicators) showed values above or below the levels observed on day 1 of the hospitalization period, displaying L-shaped data distributions (positive kurtosis). In contrast, non-survivors did not exhibit kurtosis. Three immunologically defined data subsets included only survivors. Because information was based on visual patterns generated in real time, this method can, potentially, provide information rapidly.

DISCUSSION

The hypothesis that immuno-competence expresses feedback-like loops when immunological data are structured was not rejected. This function seemed to be impaired in immuno-suppressed individuals. While this method rapidly informs, it is only a guide that, to be confirmed, requires additional tests. Despite this limitation, the fact that three protective (survival-associated) immunological data subsets were observed since day 1 supports many clinical decisions, including the early and personalized prognosis and identification of targets that immunomodulatory therapies could pursue. Because it extracts more information from the same data, structured data may replace the century-old format of the CBC.

Causal links between sedentary behavior, physical activity, and psychiatric disorders: a Mendelian randomization study

BACKGROUND

Studies suggest a correlation between excessive sedentary behavior, insufficient physical activity, and an elevated likelihood of experiencing psychiatric disorder. Nonetheless, the precise influence of sedentary behavior and physical activity on psychiatric disorder remains uncertain. Hence, the objective of this research was to investigate the possible causal relationship between sedentary behavior, physical activity, and the susceptibility to psychiatric disorder (depression, schizophrenia and bipolar disorder), utilizing a two-sample Mendelian randomization (MR) approach.

METHODS

Potential genetic instruments related to sedentary leisure behaviors were identified from the UK Biobank database, specifically a summary-level genome-wide association study (GWAS) involving 422,218 individuals of European descent. The UK Biobank database also provided the GWAS data for physical activity. Primary analysis was performed using inverse variance weighting (IVW) to assess the causal relationship between sedentary behavior, physical activity, and the risk of psychiatric disorder (depression, schizophrenia and bipolar disorder). Sensitivity analysis was conducted using Cochran's Q test, the MR-Egger intercept test, the MR-pleiotropy RESidual sum and outlier test, leave-one-out analysis, and funnel plot analysis.

RESULTS

According to the IVW analysis, there was a significant association between genetically predicted leisure television watching and an increased risk of depression (odds ratio [OR] = 1.027, 95% confidence interval [CI]: 1.001-1.053; P = 0.04). The IVW analysis also indicated that there was a decreased risk of depression associated with fraction accelerations of > 425 milligravities, as measured by accelerometers (OR = 0.951, 95%CI: 0.914-0.989; P = 0.013). The other MR methods obtained consistent but non-significant results in the same direction. However, there was no evidence of a causal association between genetic liability for moderate-to-vigorous physical activity, accelerometer-assessed physical activity, computer use, or driving and the risk of depression. Furthermore, IVW analysis has also found that driving has a slight effect in reducing the risk of schizophrenia (OR = 0.092, 95%CI: 0.010-0.827; P = 0.033), while leisure television viewing has a significant protective effect against the onset of bipolar disorder (OR = 0.719, 95%CI: 0.567-0.912; P = 0.006).

CONCLUSION

The study provides compelling evidence of a link between depression, bipolar disorder, and excessive TV watching. Furthermore, it suggests that higher accelerometer-assessed fraction accelerations of > 425 milligravities can serve as a genetic protective factor against depression. To mitigate the risk of developing depression, it is advisable to reduce sedentary activities, particularly television watching, and prioritize engaging in vigorous physical exercise.

Current Challenges With the Use of Test-Negative Designs for Modeling COVID-19 Vaccination and Outcomes

The widespread testing for severe acute respiratory syndrome coronavirus 2 infection has facilitated the use of test-negative designs (TNDs) for modeling coronavirus disease 2019 (COVID-19) vaccination and outcomes. Despite the comprehensive literature on TND, the use of TND in COVID-19 studies is relatively new and calls for robust design and analysis to adapt to a rapidly changing and dynamically evolving pandemic and to account for changes in testing and reporting practices. In this commentary, we aim to draw the attention of researchers to COVID-specific challenges in using TND as we are analyzing data amassed over more than two years of the pandemic. We first review when and why TND works and general challenges in TND studies presented in the literature. We then discuss COVID-specific challenges which have not received adequate acknowledgment but may add to the risk of invalid conclusions in TND studies of COVID-19.

The Effect of Heparin Full-Dose Anticoagulation on Survival of Hospitalized, Non-critically Ill COVID-19 Patients: A Meta-analysis of High Quality Studies

BACKGROUND

International COVID-19 guidelines recommend thromboprophylaxis for non-critically ill inpatients to prevent thrombotic complications. It is still debated whether full-dose thromboprophylaxis reduces all-cause mortality. The main aim of this updated systematic review and meta-analysis is to evaluate the effect of full-dose heparin-based thromboprophylaxis on survival in hospitalized non-critically ill COVID-19 patients.

METHODS

A systematic review was performed across Pubmed/Medline, EMBASE, Cochrane Central Register of clinical trials, Clinicaltrials.gov, and medRxiv.org from inception to November 2022. We conducted a meta-analysis of randomized clinical trials (RCTs) comparing full-dose heparin-based anticoagulation to prophylactic or intermediate dose anticoagulation or standard treatment in hospitalized non-critically ill COVID-19 patients. The risk of bias was assessed using the Cochrane risk-of-bias tool for randomized trials and Grading of Recommendations Assessment, Development and Evaluation was applied. The primary outcome was all-cause mortality at the longest follow-up available.

RESULTS

We identified 6 multicenter RCTs involving 3297 patients from 13 countries across 4 continents. The rate of all-cause mortality was 6.2% (103/1662) in the full-dose group vs 7.7% (126/1635) in the prophylactic or intermediate dose group (Risk Ratio [RR] = 0.76; 95% confidence interval [CI] = 0.59-0.98; P = 0.037). The probabilities of any mortality difference and of NNT ≤ 100 were estimated at 98.2% and 84.5%, respectively. The risk of bias was low for all included RCTs and the strength of the evidence was "moderate."

CONCLUSION

Our meta-analysis of high-quality multicenter RCTs suggests that full-dose anticoagulation with heparin or low molecular weight heparin reduces all-cause mortality in hospitalized non-critically ill COVID-19 patients.

STUDY REGISTRATION

PROSPERO, review no. CRD42022348993.

Risk Model Development and Validation in Clinical Oncology: Lessons Learned

Reliable risk models can greatly facilitate patient-centered inferences and decisions. Herein we summarize key considerations related to risk modeling in clinical oncology. Often overlooked challenges include data quality, missing data, effective sample size estimation, and selecting the variables to be included in the risk model. The stability and quality of the model should be carefully interrogated with particular emphasis on rigorous internal validation.

Safety and Efficacy of a MEURI Program for the Use of High Dose Ivermectin in COVID-19 Patients

Background

In the absence of antiviral alternatives, interventions under research for COVID-19 might be offered following guidelines from WHO for monitored emergency use of unregistered and experimental interventions (MEURI). Ivermectin is among several drugs explored for its role against SARS-CoV-2, with a well-known safety profile but conflicting data regarding clinical utility for COVID-19. The aim of this report is to inform on the results of a MEURI Program of high-dose ivermectin in COVID-19 carried out by the Ministry of Health of the Province of La Pampa, Argentina.

Methods

COVID-19 subjects, within 5 days of symptoms onset were invited to participate in the program, which consisted in the administration of ivermectin 0.6 mg/kg/day for 5 days plus standard of care. Active pharmacosurveillance was performed for 21 days, and hepatic laboratory assessments were performed in a subset of patients. Frequency of Intensive Care Unit (ICU) admission and COVID-19-related mortality of subjects in the ivermectin intention to treat group were compared with that observed in inhabitants of the same province during the same period not participating in the program.

Results

From 21,232 subjects with COVID-19, 3,266 were offered and agreed to participate in the ivermectin program and 17,966 did not and were considered as controls. A total of 567 participants reported 819 adverse events (AEs); 3.13% discontinued ivermectin due to adverse events. ICU admission was significantly lower in the ivermectin group compared to controls among participants ≥40 year-old (1.2 vs. 2.0%, odds ratio 0.608; p = 0.024). Similarly, mortality was lower in the ivermectin group in the full group analysis (1.5 vs. 2.1%, odds ratio 0.720; p = 0.029), as well as in subjects ≥ 40 year- old (2.7 vs. 4.1%, odds ratio 0.655; p = 0.005).

Conclusions

This report highlights the safety and possible efficacy of high dose ivermectin as a potentially useful intervention deserving public health-based consideration for COVID-19 patients.

Real-Life Effectiveness and Safety of Baricitinib as Adjunctive to Standard-of-Care Treatment in Hospitalized Patients With Severe Coronavirus Disease 2019

Background

Therapeutic options for hospitalized patients with severe coronavirus disease 2019 (sCOVID-19) are limited. Preliminary data have shown promising results with baricitinib, but real-life experience is lacking. We assessed the safety and effectiveness of add-on baricitinib to standard-of-care (SOC) including dexamethasone in hospitalized patients with sCOVID-19.

Methods

This study is a 2-center, observational, retrospective cohort study of patients with sCOVID-19, comparing outcomes and serious events between patients treated with SOC versus those treated with SOC and baricitinib combination.

Results

We included 369 patients with sCOVID-19 (males 66.1%; mean age 65.2 years; median symptom duration 6 days). The SOC was administered in 47.7% and combination in 52.3%. Patients treated with the combination reached the composite outcome (intensive care unit [ICU] admission or death) less frequently compared with SOC (22.3% vs 36.9%, P = .002). Mortality rate was lower with the combination in the total cohort (14.7% vs 26.6%, P = .005), and ICU admission was lower in patients with severe acute respiratory distress syndrome (29.7% vs 44.8%, P = .03). By multivariable analysis, age (odds ratio [OR] = 1.82, 95% confidence interval [CI] = 1.36–2.44, per 10-year increase), partial pressure of oxygen/fraction of inspired oxygen ratio (OR = 0.60, 95% CI = .52–0.68, per 10 units increase), and use of high-flow nasal cannula (OR = 0.34; 95% CI, .16–0.74) were associated with the composite outcome, whereas baricitinib use was marginally not associated with the composite outcome (OR = 0.52; 95% CI, .26–1.03). However, baricitinib use was found to be significant after inverse-probability weighted regression (OR = 0.93; 95% CI, .87–0.99). No difference in serious events was noted between treatment groups.

Conclusions

In real-life settings, addition of baricitinib to SOC in patients hospitalized with sCOVID-19 is associated with decreased mortality without concerning safety signals.

The Use of Tocilizumab in Patients with COVID-19: A Systematic Review, Meta-Analysis and Trial Sequential Analysis of Randomized Controlled Studies

Background: Among the several therapeutic options assessed for the treatment of coronavirus disease 2019 (COVID-19), tocilizumab (TCZ), an antagonist of the interleukine-6 receptor, has emerged as a promising therapeutic choice, especially for the severe form of the disease. Proper synthesis of the available randomized clinical trials (RCTs) is needed to inform clinical practice. Methods: A systematic review with a meta-analysis of RCTs investigating the efficacy of TCZ in COVID-19 patients was conducted. PubMed, EMBASE, and the Cochrane COVID-19 Study Register were searched up until 30 April 2021. Results: The database search yielded 2885 records; 11 studies were considered eligible for full-text review, and nine met the inclusion criteria. Overall, 3358 patients composed the TCZ arm, and 3131 the comparator group. The main outcome was all-cause mortality at 28–30 days. Subgroup analyses according to trials’ and patients’ features were performed. A trial sequential analysis (TSA) was also carried out to minimize type I and type II errors. According to the fixed-effect model approach, TCZ was associated with a better survival odds ratio (OR) (0.84; 95% confidence interval (CI): 0.75–0.94; I²: 24% (low heterogeneity)). The result was consistent in the subgroup of severe disease (OR: 0.83; 95% CI: 0.74–0.93; I²: 53% (moderate heterogeneity)). However, the TSA illustrated that the required information size was not met unless the study that was the major source of heterogeneity was omitted. Conclusions: TCZ may represent an important weapon against severe COVID-19. Further studies are needed to consolidate this finding.