COVID-19 is associated with higher risk of venous thrombosis, but not arterial thrombosis, compared with influenza: Insights from a large US cohort

Introduction Infection with SARS-CoV-2 is typically compared with influenza to contextualize its health risks. SARS-CoV-2 has been linked with coagulation disturbances including arterial thrombosis, leading to considerable interest in antithrombotic therapy for Coronavirus Disease 2019 (COVID-19). However, the independent thromboembolic risk of SARS-CoV-2 infection compared with influenza remains incompletely understood. We evaluated the adjusted risks of thromboembolic events after a diagnosis of COVID-19 compared with influenza in a large retrospective cohort. Methods We used a US-based electronic health record (EHR) dataset linked with insurance claims to identify adults diagnosed with COVID-19 between April 1, 2020 and October 31, 2020. We identified influenza patients diagnosed between October 1, 2018 and April 31, 2019. Primary outcomes [venous composite of pulmonary embolism (PE) and acute deep vein thrombosis (DVT); arterial composite of ischemic stroke and myocardial infarction (MI)] and secondary outcomes were assessed 90 days post-diagnosis. Propensity scores (PS) were calculated using demographic, clinical, and medication variables. PS-adjusted hazard ratios (HRs) were calculated using Cox proportional hazards regression. Results There were 417,975 COVID-19 patients (median age 57y, 61% women), and 345,934 influenza patients (median age 47y, 66% women). Compared with influenza, patients with COVID-19 had higher venous thromboembolic risk (HR 1.53, 95% CI 1.38–1.70), but not arterial thromboembolic risk (HR 1.02, 95% CI 0.95–1.10). Secondary analyses demonstrated similar risk for ischemic stroke (HR 1.11, 95% CI 0.98–1.25) and MI (HR 0.93, 95% CI 0.85–1.03) and higher risk for DVT (HR 1.36, 95% CI 1.19–1.56) and PE (HR 1.82, 95% CI 1.57–2.10) in patients with COVID-19. Conclusion In a large retrospective US cohort, COVID-19 was independently associated with higher 90-day risk for venous thrombosis, but not arterial thrombosis, as compared with influenza. These findings may inform crucial knowledge gaps regarding the specific thromboembolic risks of COVID-19.

Thyroid Hormone Changes in the Northern Area of Tianjin during the COVID-19 Pandemic

Purpose. This study aimed to determine whether and how stress-induced thyroid hormone changes occur during the COVID-19 pandemic in the northern area of Tianjin. Methods. This study comprised two groups of study subjects in Tianjin: before (2019) and during (2020) the COVID-19 outbreak. Subjects were included if they had FT3, FT4, and TSH concentrations and thyroid TPOAb or TgAb information available. People who were pregnant, were lactating, or had mental illness were excluded. We used propensity score matching to form a cohort in which patients had similar baseline characteristics, and their anxiety level was measured by the Hamilton Anxiety Rating Scale (HAMA). Results. Among the 1395 eligible people, 224 in Group A and 224 in Group B had similar propensity scores and were included in the analyses. The detection rate of abnormal thyroid function was decreased in pandemic Group B (69.2% vs. 93.3%, χ2 = 42.725, p < 0.01 ), especially for hypothyroidism (14.29% vs. 35.71%, χ2 = 27.429, p < 0.01 ) and isolated thyroid-related antibodies (25.89% vs. 38.39%, χ2 = 8.023, p < 0.01 ). The level of FT4 (z = −2.821, p < 0.01 ) and HAMA score (7.63 ± 2.07 vs. 5.40 ± 1.65, t = 16.873, p < 0.01 ) went up in Group B; however, TSH (z = −5.238, p < 0.01 ), FT3 (z = −3.089, p = 0.002 ), TgAb (z = −11.814, p < 0.01 ), and TPOAb (z = −9.299, p < 0.01 ) were lower, and HAMA was positive with FT3 (r = 0.208, p < 0.01 ) and FT4 (r = 0.247, p < 0.01 ). Conclusion. People in the northern area of Tianjin during the COVID-19 outbreak were at an increased risk of higher FT4, lower FT3, and lower TSH. The HAMA scores increased in emergency situations and were positively correlated with the levels of FT3 and FT4.

Three-step matching algorithm to enhance between-group comparability and minimize confounding in comparative effectiveness studies

We developed a three-step matching algorithm to enhance the between-group comparability for comparative drug effect studies involving prevalent new-users of the newer study drug versus older comparator drug(s). The three-step matching scheme is to match on: (1) index date of initiating the newer study drug to align the cohort entry time between study groups, (2) medication possession ratio measures that consider prior exposure to all older comparator drugs, and (3) propensity scores estimated from potential confounders. Our approach is illustrated with a comparative cardiovascular safety study of glucagon-like peptide-1 receptor agonist (GLP-1ra) versus sulfonylurea (SU) in type 2 diabetes patients using Taiwan’s National Health Insurance Research Database 2003–2015. 66% of 3195 GLP-1ra users had previously exposed to SU. The between-group comparability was well-achieved after implementing the matching algorithm (i.e., standardized mean difference < 0.2 for all baseline patient characteristics). Compared to SU, the use of GLP-1ra yielded a significantly reduced risk of the primary composite cardiovascular events (hazard ratio [95% confidence interval]: 0.71 [0.54–0.95], p = 0.022). Our matching scheme can enhance the between-group comparability in prevalent new-user cohort designs to minimize time-related bias, improve confounder adjustment, and ensure the reliability and validity of study findings.

The Role of Distributional Overlap on the Precision Gain of Bounds for Generalization

Over the past ten years, propensity score methods have made an important contribution to improving generalizations from studies that do not select samples randomly from a population of inference. However, these methods require assumptions and recent work has considered the role of bounding approaches that provide a range of treatment impact estimates that are consistent with the observable data. An important limitation to bound estimates is that they can be uninformatively wide. This has motivated research on the use of propensity score stratification to narrow bounds. This article assesses the role of distributional overlap in propensity scores on the effectiveness of stratification to tighten bounds. Using the results of two simulation studies and two case studies, I evaluate the relationship between distributional overlap and precision gain and discuss the implications when propensity score stratification is used as a method to improve precision in the bounding framework.

SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids

Tumor-homing peptides (THPs) are small peptides that can recognize and bind cancer cells specifically. To gain a better understanding of THPs’ functional mechanisms, the accurate identification and characterization of THPs is required. Although some computational methods for in silico THP identification have been proposed, a major drawback is their lack of model interpretability. In this study, we propose a new, simple and easily interpretable computational approach (called SCMTHP) for identifying and analyzing tumor-homing activities of peptides via the use of a scoring card method (SCM). To improve the predictability and interpretability of our predictor, we generated propensity scores of 20 amino acids as THPs. Finally, informative physicochemical properties were used for providing insights on characteristics giving rise to the bioactivity of THPs via the use of SCMTHP-derived propensity scores. Benchmarking experiments from independent test indicated that SCMTHP could achieve comparable performance to state-of-the-art method with accuracies of 0.827 and 0.798, respectively, when evaluated on two benchmark datasets consisting of Main and Small datasets. Furthermore, SCMTHP was found to outperform several well-known machine learning-based classifiers (e.g., decision tree, k-nearest neighbor, multi-layer perceptron, naive Bayes and partial least squares regression) as indicated by both 10-fold cross-validation and independent tests. Finally, the SCMTHP web server was established and made freely available online. SCMTHP is expected to be a useful tool for rapid and accurate identification of THPs and for providing better understanding on THP biophysical and biochemical properties.

Worse patient-reported outcomes and higher risk of reoperation and adverse events after total hip replacement in patients with opioid use in the year before surgery: a Swedish register-based study on 80,483 patients

Background and purpose — Recent studies indicate that preoperative use of opioids could be associated with higher rates of complications and worse patient-reported outcomes (PROs) after orthopedic surgery. We investigated the prevalence of preoperative opioid use and analyzed its influence on risk of revision, adverse events (AE), and PROs in patients with total hip replacement (THR). Patients and methods — This observational study included 80,483 patients operated on in 2008–2016 with THRs due to osteoarthritis. Data was obtained from the Swedish Hip Arthroplasty Register, Statistics Sweden’s sociodemographic registers, the Swedish National Patient Register, and the Prescribed Drug Register. We focused on patients with ≥ 4 opioid prescriptions filled 1 year prior to THR. To control for confounding, we used propensity scores to weight subjects in our analyses. Logistic and linear regression was used for outcome variables with adjustments for sociodemographic variables and comorbidities. Results — Patients with ≥ 4 opioid prescriptions in the year before THR (n = 14,720 [18%]) had a higher risk of revision within 2 years (1.8% vs. 1.1% OR 1.4, 95% CI 1.3–1.6) and AE within 90 days (9.4% vs. 6.4% OR 1.2, 95% CI 1.2–1.3) compared with patients without opioid treatment in the preoperative period. Patients with ≥ 4 opioid prescriptions rated 5 points worse on a 0–100 scale of Pain VisualAnalogue Scale (VAS) and 9 points worse on a generalhealth (EQ) VAS 1 year postoperatively. Interpretation — Having ≥ 4 opioid prescriptions filled in the year before surgery is associated with a higher risk of revision, adverse events, and worse PROs after THR. Consequently, preoperative opioid treatment should be addressed in the clinical assessment of patients eligible for THR.

Improving the causal treatment effect estimation with propensity scores by the bootstrap

When observational studies are used to establish the causal effects of treatments, the estimated effect is affected by treatment selection bias. The inverse propensity score weight (IPSW) is often used to deal with such bias. However, IPSW requires strong assumptions whose misspecifications and strategies to correct the misspecifications were rarely studied. We present a bootstrap bias correction of IPSW (BC-IPSW) to improve the performance of propensity score in dealing with treatment selection bias in the presence of failure to the ignorability and overlap assumptions. The approach was motivated by a real observational study to explore the potential of anticoagulant treatment for reducing mortality in patients with end-stage renal disease. The benefit of the treatment to enhance survival was demonstrated; the suggested BC-IPSW method indicated a statistically significant reduction in mortality for patients receiving the treatment. Using extensive simulations, we show that BC-IPSW substantially reduced the bias due to the misspecification of the ignorability and overlap assumptions. Further, we showed that IPSW is still useful to account for the lack of treatment randomization, but its advantages are stringently linked to the satisfaction of ignorability, indicating that the existence of relevant though unmeasured or unused covariates can worsen the selection bias.

Methodological Perspectives on the Study of the Health Effects of Unemployment – Reviewing the Mode of Unemployment, the Statistical Analysis Method and the Role of Confounding Factors

Introduction: Studying the relationship between unemployment and health raises many methodological challenges. In the current study, the aim was to evaluate how different ways of measuring unemployment and the choice of statistical model affects the effect estimate. Methods: The Northern Swedish cohort was used, and two follow-up surveys thereof from 1995 and 2007, as well as register data about unemployment. Self-reported current unemployment, self-reported accumulated unemployment and register-based accumulated unemployment were used to measure unemployment and its effect on self-reported health was evaluated. Analyses were conducted with G-computation, logistic regression and three estimators for the inverse probability weighting propensity scores, and 11 potentially confounding variables were part of the analyses. Results were presented with absolute differences in the proportion with poor self-reported health between unemployed and employed individuals for all estimators but logistic regression. Results: Of the initial 1083 pupils in the cohort, 488–693 individuals were defined as employed and 61–214 individuals were defined as unemployed in our different analyses. In the analyses, the deviation was large between the unemployment measures, with a difference of at least 2.5% in effect size when unemployed was compared with employed for the self-reported and register-based unemployment modes. The choice of statistical method only had a small influence on effect estimates and the deviation was in most cases lower than 1%. When models were compared based on the choice of potential confounders in the analytical model, the deviations were rarely above 0.6% when comparing models with 4 and 11 potential confounders. Our variable for health selection was the only one that strongly affected estimates when it was not part of the statistical model. Conclusions: Misspecifications of the statistical model or choice of analytical method might not matter much for effect estimates of the relationship between unemployment and health except for the inclusion of a variable measuring earlier health status before becoming unemployed. On the other hand, how unemployment is measured is highly important.