Applying two approaches to detect unmeasured confounding due to time-varying variables in a self-controlled risk interval design evaluating COVID-19 vaccine safety signals, using myocarditis as a case example.

We test the robustness of the self-controlled risk interval (SCRI) design in a setting where time between doses may introduce time-varying confounding, using both negative control outcomes (NCOs) and quantitative bias analysis (QBA). All vaccinated cases identified from 5 European databases between 1 September 2020 and end of data availability were included. Exposures were doses 1-3 of the Pfizer, Moderna, AstraZeneca, and Janssen COVID-19 vaccines; outcomes were myocarditis and otitis externa (NCO). The SCRI used a 60-day control window and dose-specific 28-day risk windows, stratified by vaccine brand and adjusted for calendar time. The QBA included two scenarios: (i) baseline probability of the confounder was higher in the control window and (ii) vice versa. The NCO was not associated with any of the COVID-19 vaccine types or doses except Moderna dose 1 (IRR = 1.09, 95%CI 1.01-1.09). The QBA suggested even the strongest literature-reported confounder (COVID-19; RRmyocarditis = 18.3) could only explain away part of the observed effect from IRR = 3 to IRR = 1.40. The SCRI seems robust to unmeasured confounding in the COVID-19 setting, although a strong unmeasured confounder could bias the observed effect upward. Replication of our findings for other safety signals would strengthen this conclusion.

Impact of vomiting on P2Y12 platelet inhibition in patients with ST-elevation myocardial infarction: A prespecified subanalysis of the ON-TIME 3 trial.

Vomiting is associated with lower levels of ticagrelor concentration and higher platelet reactivity in the early hours of ST-elevation myocardial infarction. These results support reloading with a ticagrelor loading dose and/or treatment with intravenous platelet inhibitors when patients vomit.

Greedy caliper propensity score matching can yield variable estimates of the treatment-outcome association-A simulation study.

PURPOSE: Greedy caliper propensity score (PS) matching is dependent on randomness, which can ultimately affect causal estimates. We sought to investigate the variation introduced by this randomness. METHODS: Based on a literature search to define the simulation parameters, we simulated 36 cohorts of different sizes, treatment prevalence, outcome prevalence, treatment-outcome-association. We performed 1:1 caliper and nearest neighbor (NN) caliper PS-matching and repeated this 1000 times in the same cohort, before calculating the treatment-outcome association. RESULTS: Repeating caliper and NN caliper matching in the same cohort yielded large variations in effect estimates, in all 36 scenarios, with both types of matching. The largest variation was found in smaller cohorts, where the odds ratio (OR) ranged from 0.53 to 10.00 (IQR of ORs: 1.11-1.67). The 95% confidence interval was not consistently overlapping a neutral association after repeating the matching with both algorithms. We confirmed these findings in a noninterventional example study. CONCLUSION: Caliper PS-matching can yield highly variable estimates of the treatment-outcome association if the analysis is repeated.

Efficacy gap between phase II and subsequent phase III studies in oncology.

AIMS: There is a trend for more flexibility in timing of evidence generation in relation to marketing authorization, including the option to complete phase III trials after authorization or not at all. This paper investigated the relation between phase II and III clinical trial efficacy in oncology. METHODS: All oncology drugs approved by the European Medicines Agency (2007-2016) were included. Phase II and phase III trials were matched based on indication and treatment and patient characteristics. Reported objective response rates (ORR), median progression-free survival (PFS) and median overall survival (OS) were analysed through weighted mixed-effects regression with previous treatment, treatment regimen, blinding, randomization, marketing authorization type and cancer type as covariates. RESULTS: A total of 81 phase II-III matches were identified including 252 trials. Mean (standard deviation) weighted difference (phase III minus II) was -4.2% (17.4) for ORR, 2.1 (6.7) months for PFS and -0.3 (5.1) months for OS, indicating very small average differences between phases. Differences varied substantially between individual indications: from -46.6% to 47.3% for ORR, from -5.3 to 35.9 months for PFS and from -13.3 to 10.8 months for OS. All covariates except blinding were associated with differences in effect sizes for at least 1 outcome. CONCLUSIONS: The lack of marked average differences between phases may encourage decision-makers to regard the quality of design and total body of evidence instead of differentiating between phases of clinical development. The large variability emphasizes that replication of study findings remains essential to confirm efficacy of oncology drugs and discern variables associated with demonstrated effects.

Diabetes and Glucose Dysregulation and Transition to Delirium in ICU Patients.

OBJECTIVES: To investigate whether diabetes and glucose dysregulation (hyperglycemia and/or hypoglycemia) are associated with ICU delirium. DESIGN: Prospective cohort study. SETTING: Thirty-two-bed mixed intensive care in a tertiary care center. PATIENTS: Critically ill patients admitted to the ICU with transitions of mental status from awake and nondelirious to delirious or remaining awake and nondelirious on the next day. Patients admitted because of a neurologic illness were excluded. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The study population consisted of 2,745 patients with 1,720 transitions from awake and nondelirious to delirious and 11,421 nontransitions remaining awake and nondelirious. Generalized mixed effects models with logit link function were performed to study the association between diabetes mellitus, glucose dysregulation, and delirium, adjusting for potential confounders. Diabetes was not associated with delirium (odds ratio adjusted, 0.93; 95% CI, 0.73-1.18). In all patients, the occurrence of hyperglycemia (odds ratio adjusted, 1.35; 95% CI, 1.15-1.59) and the occurrence of both hyperglycemia and hypoglycemia on the same day (odds ratio adjusted, 1.65; 95% CI, 1.12-2.28) compared with normoglycemia were associated with transition to delirium. Hypoglycemia was not associated with transition to delirium (odds ratio adjusted, 1.86; 95% CI, 0.73-3.71). In patients without diabetes, the occurrence of hyperglycemia (odds ratio adjusted, 1.41; 95% CI, 1.16-1.68) and the occurrence of both hyperglycemia and hypoglycemia on the same day (odds ratio adjusted, 1.87; 95% CI, 1.07-2.89) were associated with transition to delirium. In patients with diabetes, glucose dysregulation was not associated with ICU delirium. CONCLUSIONS: Diabetes mellitus was not associated with the development of ICU delirium. For hypoglycemia, only a nonsignificant odds ratio for ICU delirium could be noted. Hyperglycemia and the occurrence of hyperglycemia and hypoglycemia on the same day were associated with ICU delirium but only in patients without diabetes. Our study supports the institution of measures to prevent glucose dysregulation in nondiabetic ICU patients and contributes to the understanding of the determinants of delirium.

Methodological comparison of marginal structural model, time-varying Cox regression, and propensity score methods: the example of antidepressant use and the risk of hip fracture.

BACKGROUND: Observational studies including time-varying treatments are prone to confounding. We compared time-varying Cox regression analysis, propensity score (PS) methods, and marginal structural models (MSMs) in a study of antidepressant [selective serotonin reuptake inhibitors (SSRIs)] use and the risk of hip fracture. METHODS: A cohort of patients with a first prescription for antidepressants (SSRI or tricyclic antidepressants) was extracted from the Dutch Mondriaan and Spanish Base de datos para la Investigación Farmacoepidemiológica en Atención Primaria (BIFAP) general practice databases for the period 2001-2009. The net (total) effect of SSRI versus no SSRI on the risk of hip fracture was estimated using time-varying Cox regression, stratification and covariate adjustment using the PS, and MSM. In MSM, censoring was accounted for by inverse probability of censoring weights. RESULTS: The crude hazard ratio (HR) of SSRI use versus no SSRI use on hip fracture was 1.75 (95%CI: 1.12, 2.72) in Mondriaan and 2.09 (1.89, 2.32) in BIFAP. After confounding adjustment using time-varying Cox regression, stratification, and covariate adjustment using the PS, HRs increased in Mondriaan [2.59 (1.63, 4.12), 2.64 (1.63, 4.25), and 2.82 (1.63, 4.25), respectively] and decreased in BIFAP [1.56 (1.40, 1.73), 1.54 (1.39, 1.71), and 1.61 (1.45, 1.78), respectively]. MSMs with stabilized weights yielded HR 2.15 (1.30, 3.55) in Mondriaan and 1.63 (1.28, 2.07) in BIFAP when accounting for censoring and 2.13 (1.32, 3.45) in Mondriaan and 1.66 (1.30, 2.12) in BIFAP without accounting for censoring. CONCLUSIONS: In this empirical study, differences between the different methods to control for time-dependent confounding were small. The observed differences in treatment effect estimates between the databases are likely attributable to different confounding information in the datasets, illustrating that adequate information on (time-varying) confounding is crucial to prevent bias.

Instrumental variables analysis using multiple databases: an example of antidepressant use and risk of hip fracture.

PURPOSE: Instrumental variable (IV) analysis can control for unmeasured confounding, yet it has not been widely used in pharmacoepidemiology. We aimed to assess the performance of IV analysis using different IVs in multiple databases in a study of antidepressant use and hip fracture. METHODS: Information on adults with at least one prescription of a selective serotonin reuptake inhibitor (SSRI) or tricyclic antidepressant (TCA) during 2001-2009 was extracted from the THIN (UK), BIFAP (Spain), and Mondriaan (Netherlands) databases. IVs were created using the proportion of SSRI prescriptions per practice or using the one, five, or ten previous prescriptions by a physician. Data were analysed using conventional Cox regression and two-stage IV models. RESULTS: In the conventional analysis, SSRI (vs. TCA) was associated with an increased risk of hip fracture, which was consistently found across databases: the adjusted hazard ratio (HR) was approximately 1.35 for time-fixed and 1.50 to 2.49 for time-varying SSRI use, while the IV analysis based on the IVs that appeared to satisfy the IV assumptions showed conflicting results, e.g. the adjusted HRs ranged from 0.55 to 2.75 for time-fixed exposure. IVs for time-varying exposure violated at least one IV assumption and were therefore invalid. CONCLUSIONS: This multiple database study shows that the performance of IV analysis varied across the databases for time-fixed and time-varying exposures and strongly depends on the definition of IVs. It remains challenging to obtain valid IVs in pharmacoepidemiological studies, particularly for time-varying exposure, and IV analysis should therefore be interpreted cautiously.

Evaluating different physician's prescribing preference based instrumental variables in two primary care databases: a study of inhaled long-acting beta2-agonist use and the risk of myocardial infarction.

PURPOSE: Instrumental variable (IV) analysis with physician's prescribing preference (PPP) as IV is increasingly used in pharmacoepidemiology. However, it is unclear whether this IV performs consistently across databases. We aimed to evaluate the validity of different PPPs in a study of inhaled long-acting beta2-agonist (LABA) use and myocardial infarction (MI). METHODS: Information on adults with asthma and/or COPD and at least one prescription of beta2-agonist, or muscarinic antagonist was extracted from the CPRD (UK) and the Mondriaan (Netherlands) databases. LABA exposure was considered time-fixed or time-varying. We measured PPPs using previous LABA prescriptions of physicians or proportion of LABA prescriptions per practice. Correlation (r) and standardized difference (SDif) were used to assess assumption of IV analysis. RESULTS: For time-fixed LABA, the IV based on 10 previous prescriptions outperformed the other IVs regarding strength of the IV (r ≥ 0.15) and balance of confounders between IV categories (SDif < 0.10). None of the IVs we considered appeared to be valid for time-varying LABA. In CPRD (n = 490,499), which included approximately 18 times more subjects than Mondriaan (n = 27,459), IVs appeared more valid. LABA was not associated with MI; hazard ratios ranged from 0.86 to 1.18 for conventional analysis, and from 0.61 to 1.24 for the IV analyses with apparent valid IVs. CONCLUSIONS: The validity of physician's prescribing preference as IV strongly depends on how this IV is defined and in which database it is applied. Hence, general recommendations cannot be made, other than to generate several plausible IVs, assess their validity, and report the estimate(s) from apparently valid IVs.

Case-only designs for studying the association of antidepressants and hip or femur fracture.

PURPOSE: The purpose of this study is to evaluate the performance and validity of the case-crossover (CCO) and self-controlled case-series (SCCS) designs when studying the association between hip/femur fracture (HF) and antidepressant (AD) use in general practitioner databases. In addition, comparability with cohort and case-control designs is discussed. METHODS: Adult patients with HF and who received an AD prescription during 2001-2009 were identified from UK's The Health Improvement Network (THIN) and the Dutch Mondriaan databases. AD exposure was classified into current, recent and past/non-use (reference). In the CCO, for each patient, a case moment (date of HF) and four prior control moments at -91, -182, -273 and -365 days were defined. In SCCS, incidence of HF was compared between exposure states. Conditional logistic regression was used in the CCO and Poisson regression in the SCCS to compute odds ratios and incidence rate ratios, respectively. In CCO, we adjusted for time-varying co-medication and in SCCS for age. RESULTS: Adjusted estimates for the effect of current AD exposure on HF were higher in the CCO (co-medication-adjusted odds ratio, THIN: 2.24, 95% confidence interval [CI]: 2.04-2.47; Mondriaan: 2.57, 95%CI [1.50, 4.43]) than in the SCCS (age-adjusted incidence rate ratio, THIN: 1.41, 95%CI [1.32, 1.49]; Mondriaan: 2.14, 95%CI [1.51, 3.03]). The latter were comparable with the traditional designs. CONCLUSION: Case-only designs confirmed the association between AD and HF. The CCO design violated assumptions in this study with regard to exchangeability and length of exposure, and transient effects on outcome. The SCCS seems to be an appropriate design for assessing AD-HF association.

Performance of prior event rate ratio adjustment method in pharmacoepidemiology: a simulation study.

PURPOSE: Prior event rate ratio (PERR) adjustment method has been proposed to control for unmeasured confounding. We aimed to assess the performance of the PERR method in realistic pharmacoepidemiological settings. METHODS: Simulation studies were performed with varying effects of prior events on the probability of subsequent exposure and post-events, incidence rates, effects of confounders, and rate of mortality/dropout. Exposure effects were estimated using conventional rate ratio (RR) and PERR adjustment method (i.e. ratio of RR post-exposure initiation and RR prior to initiation of exposure). RESULTS: In the presence of unmeasured confounding, both conventional and the PERR method may yield biased estimates, but PERR estimates appear generally less biased estimates than the conventional method. However, when prior events strongly influence the probability of subsequent exposure, the exposure effect from the PERR method was more biased than the conventional method. For instance, when the effect of prior events on the exposure was RR = 1.60, the effect estimate from the PERR method was RR = 1.13 and from the conventional method was RR = 2.48 (true exposure effect, RR = 2). In all settings, the variation of the estimates was larger for the PERR method than for the conventional method. CONCLUSION: The PERR adjustment method can be applied to reduce bias as a result of unmeasured confounding. However, only in particular situations, it can completely remove the bias as a result of unmeasured confounding. When applying this method, theoretical justification using available clinical knowledge for assumptions of the PERR method should be provided.

Performance of instrumental variable methods in cohort and nested case-control studies: a simulation study.

PURPOSE: Instrumental variable (IV) analysis is becoming increasingly popular to adjust for confounding in observational pharmacoepidemiologic research. One of the prerequisites of an IV is that it is strongly associated with exposure; if it is weakly associated with exposure, IV estimates are reported to be biased. We aimed to assess the performance of IV estimates in various (pharmaco-)epidemiologic settings. METHODS: Data were simulated for continuous/binary exposure, outcome and IV in cohort and nested case-control (NCC) designs with different incidences of the outcome. Pearson's correlation, point bi-serial correlation, odds ratio (OR), and F-statistic were used to assess the IV-exposure association. Two-stage analysis was performed to estimate the exposure effect. RESULTS: For all types of IV and exposure in the cohort and NCC designs, IV estimates were extremely unstable and biased when the IV was very weakly associated with exposure (e.g. Pearson's correlation < 0.15 for continuous or OR < 2.0 for binary IV and exposure; although specific cut-off values depend on simulation settings). For stronger IVs, estimates were unbiased and become less variable compared with weaker IVs in the case of continuous and binary (risk difference scale) outcomes. For a similar IV-exposure association (e.g. OR = 1.4 and 5% incidence of the outcome), the variability of the estimates was more pronounced in the NCC (standard deviation = 2.37, case : control = 1:5) compared with the cohort design (standard deviation = 1.14). The variability was even more pronounced for rare (≤1%) outcomes. However, IV estimates from the NCC design became less variable with an increasing number of controls per case. Moreover, estimates were biased when the IV was related to confounders even with strong IVs. CONCLUSIONS: Instrumental variable analysis performs poorly when the IV-exposure association is extremely weak, especially in the NCC design. IV estimates in the NCC design become less variable when the number of control increases. As NCC does not use the entire cohort, in order to achieve stable estimates, this design requires a stronger IV-exposure association than the cohort design.

Propensity score balance measures in pharmacoepidemiology: a simulation study.

BACKGROUND: Conditional on the propensity score (PS), treated and untreated subjects have similar distribution of observed baseline characteristics when the PS model is appropriately specified. The performance of several PS balance measures in assessing the balance of covariates achieved by a specific PS model and selecting the optimal PS model was evaluated in simulation studies. However, these studies involved only normally distributed covariates. Comparisons in binary or mixed covariate distributions with rare outcomes, typical of pharmacoepidemiologic settings, are scarce. METHODS: Monte Carlo simulations were performed to examine the performance of different balance measures in terms of selecting an optimal PS model, thus reduction in bias. The balance of covariates between treatment groups was assessed using the absolute standardized difference, the Kolmogorov-Smirnov distance, the Lévy distance, and the overlapping coefficient. Spearman's correlation coefficient (r) between each of these balance measures and bias were calculated. RESULTS: In large sample sizes (n ≥ 1000), all balance measures were similarly correlated with bias (r ranging between 0.50 and 0.68) irrespective of the treatment effect's strength and frequency of the outcome. In smaller sample sizes with mixed binary and continuous covariate distributions, these correlations were low for all balance measures (r ranging between 0.11 and 0.43), except for the absolute standardized difference (r = 0.51). CONCLUSIONS: The absolute standardized difference, which is an easy-to-calculate balance measure, displayed consistently better performance across different simulation scenarios. Therefore, it should be the balance measure of choice for measuring and reporting the amount of balance reached, and for selecting the final PS model.

The association between medication use and health-related quality of life in multimorbid older patients with polypharmacy.

PURPOSE: To explore the association between medication use-related factors and health-related quality of life (HRQoL) in older hospitalised multimorbid patients with polypharmacy. METHODS: This cross-sectional study used the intervention arm data of the OPERAM trial (hospitalised patients ≥ 70 years with polypharmacy). HRQoL was assessed using the visual analogue scale (EQ-VAS) and the EQ-5D index score of the EuroQol questionnaire (EQ-5D-5L). Lower or higher EQ-VAS/EQ-5D was based on the median of the study population. Medication use-related factors included hyperpolypharmacy (≥ 10 medications), anticholinergic and sedative burden, appropriateness of medication (STOPP/START criteria), high-risk medication for hospital (re)admission, medication complexity and adherence. Multivariable logistic regression analysis was used to assess the association between medication use-related factors and HRQoL. RESULTS: A total of 955 patients were included (mean age 79 years, 46% female, median EQ-VAS of 60, median EQ-5D of 0.60). Opioids use was associated with lower EQ-5D and EQ-VAS (aOR EQ-5D: 2.10; 95% CI 1.34-3.32, EQ-VAS: 1.59; 1.11-2.30). Hyperpolypharmacy (aOR 1.37; 1.05-1.80), antibiotics (aOR 1.64; 1.01-2.68) and high medication complexity (aOR 1.53; 1.10-2.15) were associated with lower EQ-VAS. A high anticholinergic and sedative burden (aOR 1.73; 1.11-2.69), presence of multiple prescribing omissions (aOR 1.94; 1.19-3.17) and benzodiazepine use (aOR 2.01; 1.22-3.35) were associated with lower EQ-5D. Especially in hyperpolypharmacy patients, high anticholinergic and sedative burden and medication complexity were associated with a lower HRQoL. CONCLUSION: Several medication use-related factors are significantly associated with a lower HRQoL in hospitalised older patients. Medication complexity is a novel factor, which should be considered when evaluating medication use of older patients with hyperpolypharmacy.

A comparison of four self-controlled study designs in an analysis of COVID-19 vaccines and myocarditis using five European databases.

INTRODUCTION: The aim of this study was to assess the possible extent of bias due to violation of a core assumption (event-dependent exposures) when using self-controlled designs to analyse the association between COVID-19 vaccines and myocarditis. METHODS: We used data from five European databases (Spain: BIFAP, FISABIO VID, and SIDIAP; Italy: ARS-Tuscany; England: CPRD Aurum) converted to the ConcePTION Common Data Model. Individuals who experienced both myocarditis and were vaccinated against COVID-19 between 1 September 2020 and the end of data availability in each country were included. We compared a self-controlled risk interval study (SCRI) using a pre-vaccination control window, an SCRI using a post-vaccination control window, a standard SCCS and an extension of the SCCS designed to handle violations of the assumption of event-dependent exposures. RESULTS: We included 1,757 cases of myocarditis. For analyses of the first dose of the Pfizer vaccine, to which all databases contributed information, we found results consistent with a null effect in both of the SCRI and extended SCCS, but some indication of a harmful effect in a standard SCCS. For the second dose, we found evidence of a harmful association for all study designs, with relatively similar effect sizes (SCRI pre = 1.99, 1.40 - 2.82; SCRI post 2.13, 95 %CI - 1.43, 3.18; standard SCCS 1.79, 95 %CI 1.31 - 2.44, extended SCCS 1.52, 95 %CI = 1.08 - 2.15). Adjustment for calendar time did not change these conclusions. Findings using all designs were also consistent with a harmful effect following a second dose of the Moderna vaccine. CONCLUSIONS: In the context of the known association between COVID-19 vaccines and myocarditis, we have demonstrated that two forms of SCRI and two forms of SCCS led to largely comparable results, possibly because of limited violation of the assumption of event-dependent exposures.

Frequency and timing of adverse reactions to COVID-19 vaccines; A multi-country cohort event monitoring study.

INTRODUCTION: During the COVID-19 pandemic, EMA set-up a large-scale cohort event monitoring (CEM) system to estimate incidence rates of patient-reported adverse drug reactions (ADRs) of different COVID-19 vaccines across the participating countries. This study aims to give an up to date and in-depth analysis of the frequency of patient-reported ADRs after the 1st, 2nd, and booster vaccination, to identify potential predictors in developing ADRs and to describe time-to-onset (TTO) and time-to-recovery (TTR) of ADRs. METHODS: A CEM study was rolled out in a period ranging from February 2021 to February 2023 across multiple European countries; The Netherlands, Belgium, France, the United Kingdom, Italy, Portugal, Romania, Slovakia and Spain. Analysis consisted of a descriptive analyses of frequencies of COVID-19 vaccine-related ADRs for 1st, 2nd and booster vaccination, analysis of potential predictors in developing ADRs with a generalized linear mixed-effects model, analysis of TTO and TTR of ADRs and a sensitivity analysis for loss to follow-up (L2FU). RESULTS: A total of 29,837 participants completed at least the baseline and the first follow-up questionnaire for 1st and 2nd vaccination and 7,250 participants for the booster. The percentage of participants who reported at least one ADR is 74.32% (95%CI 73.82-74.81). Solicited ADRs, including injection site reactions, are very common across vaccination moments. Potential predictors for these reactions are the brand of vaccine used, the patient's age, sex and prior SARS-CoV-2 infection. The percentage of serious ADRs in the study is low for 1st and 2nd vaccination (0.24%, 95%CI 0.19--0.31) and booster (0.26%, 95%CI 0.15, 0.41). The TTO was 14 h (median) for dose 1 and slightly longer for dose 2 and booster dose. TTR is generally also within a few days. The effect of L2FU on estimations of frequency is limited. CONCLUSION: Despite some limitations due to study design and study-roll out, CEM studies can allow prompt and almost real-time observations of the safety of medications directly from a patient-centered perspective, which can play a crucial role for regulatory bodies during an emergency setting such as the COVID-19 pandemic.

Immune reconstitution kinetics as an early predictor for mortality using various hematopoietic stem cell sources in children.

The severity of complications of allogeneic hematopoietic stem cell transplantation (HSCT) is governed mainly by the status of immune reconstitution. In this study, we investigated differences in immune reconstitution with different cell sources and the association between the kinetics of immune reconstitution and mortality. Immunophenotyping was performed every 2 weeks in children who had undergone HSCT between 2004 and 2008 at University Medical Center Utrecht. Lymphocyte reconstitution in the first 90 days after HSCT was studied in relation to mortality in 3 HSCT groups: matched sibling bone marrow (BM) recipients (35 patients), unrelated BM recipients (32 patients), and unrelated cord blood recipients (36 patients). The median age of recipients was 5.9 years (range, 0.1-21 years). The nature and speed of T cell, B cell, and natural killer (NK) cell reconstitution were highly dependent on the cell source. In the first 90 days after HSCT, faster B cell and NK cell reconstitution and delayed T cell reconstitution were shown in unrelated cord blood recipients compared with matched sibling BM and unrelated BM recipients. Of the lymphocyte subsets investigated, a large number of NK cells and a more rapid CD4(+) immune reconstitution over time, resulting in sustained higher CD4(+) counts, were the only predictors of a lower mortality risk in all cell sources. The final model showed that during the first 90 days, patients with an area under the CD4(+) cell receiver- operating curve of >4,300 cells/day and no peak in CD4(+) cell counts had the highest likelihood of survival (hazard ratio for mortality, 0.2; 95% confidence interval, 0.06-0.5). Our data indicate that CD4(+) kinetics may be used to identify patients at greatest risk for mortality early after HSCT.

Time-dependent propensity score and collider-stratification bias: an example of beta2-agonist use and the risk of coronary heart disease.

Stratification and conditioning on time-varying cofounders which are also intermediates can induce collider-stratification bias and adjust-away the (indirect) effect of exposure. Similar bias could be expected when one conditions on time-dependent PS. We explored collider-stratification and confounding bias due to conditioning or stratifying on time-dependent PS using a clinical example on the effect of inhaled short- and long-acting beta2-agonist use (SABA and LABA, respectively) on coronary heart disease (CHD). In an electronic general practice database we selected a cohort of patients with an indication for SABA and/or LABA use and ascertained potential confounders and SABA/LABA use per three month intervals. Hazard ratios (HR) were estimated using PS stratification as well as covariate adjustment and compared with those of Marginal Structural Models (MSMs) in both SABA and LABA use separately. In MSMs, censoring was accounted for by including inverse probability of censoring weights.The crude HR of CHD was 0.90 [95 % CI: 0.63, 1.28] and 1.55 [95 % CI: 1.06, 2.62] in SABA and LABA users respectively. When PS stratification, covariate adjustment using PS, and MSMs were used, the HRs were 1.09 [95 % CI: 0.74, 1.61], 1.07 [95 % CI: 0.72, 1.60], and 0.86 [95 % CI: 0.55, 1.34] for SABA, and 1.09 [95 % CI: 0.74, 1.62], 1.13 [95 % CI: 0.76, 1.67], 0.77 [95 % CI: 0.45, 1.33] for LABA, respectively. Results were similar for different PS methods, but higher than those of MSMs. When treatment and confounders vary during follow-up, conditioning or stratification on time-dependent PS could induce substantial collider-stratification or confounding bias; hence, other methods such as MSMs are recommended.

Impact of opioids on P2Y12 receptor inhibition in patients with ST-elevation myocardial infarction who are pre-treated with crushed ticagrelor: Opioids aNd crushed Ticagrelor In Myocardial infarction Evaluation (ON-TIME 3) trial.

AIMS: Platelet inhibition induced by P2Y12 receptor antagonists in patients with ST-elevation myocardial infarction (STEMI) can be affected by concomitant use of opioids. The aim of this trial was to examine the effect of intravenous (iv) acetaminophen compared with iv fentanyl on P2Y12 receptor inhibition in patients with STEMI. METHODS AND RESULTS: The Opioids aNd crushed Ticagrelor In Myocardial infarction Evaluation (ON-TIME 3) trial randomized 195 STEMI patients who were scheduled to undergo primary percutaneous coronary intervention (PCI) and were pre-treated with crushed ticagrelor to iv acetaminophen (N = 98) or iv fentanyl (N = 97) in the ambulance. The primary endpoint, consisting of the level of platelet reactivity units (PRU) measured immediately after primary PCI, was not significantly different between the study arms [median PRU 104 (IQR 37-215) vs. 175 (63-228), P = 0.18]. However, systemic levels of ticagrelor were significantly higher in the acetaminophen arm at the start of primary PCI [151 ng/mL (32-509) vs. 60 ng/mL (13-206), P = 0.007], immediately after primary PCI [326 ng/mL (94-791) vs. 115 ng/mL (38-326), P = 0.002], and at 1 h after primary PCI [488 ng/mL (281-974) vs. 372 ng/mL (95-635), P = 0.002]. Acetaminophen resulted in the same extent of pain relief when compared with fentanyl [reduction of 3 points on 10-step-pain scale before primary PCI (IQR 1-5)] in both study arms (P = 0.67) and immediately after PCI [reduction of 5 points (3-7); P = 0.96]. CONCLUSION: The iv acetaminophen in comparison with iv fentanyl was not associated with significantly lower platelet reactivity in STEMI patients but resulted in significantly higher ticagrelor plasma levels and was effective in pain relief.

Myocarditis and pericarditis associated with SARS-CoV-2 vaccines: A population-based descriptive cohort and a nested self-controlled risk interval study using electronic health care data from four European countries.

Background: Estimates of the association between COVID-19 vaccines and myo-/pericarditis risk vary widely across studies due to scarcity of events, especially in age- and sex-stratified analyses. Methods: Population-based cohort study with nested self-controlled risk interval (SCRI) using healthcare data from five European databases. Individuals were followed from 01/01/2020 until end of data availability (31/12/2021 latest). Outcome was first myo-/pericarditis diagnosis. Exposures were first and second dose of Pfizer, AstraZeneca, Moderna, and Janssen COVID-19 vaccines. Baseline incidence rates (IRs), and vaccine- and dose-specific IRs and rate differences were calculated from the cohort The SCRI calculated calendar time-adjusted IR ratios (IRR), using a 60-day pre-vaccination control period and dose-specific 28-day risk windows. IRRs were pooled using random effects meta-analysis. Findings: Over 35 million individuals (49·2% women, median age 39-49 years) were included, of which 57·4% received at least one COVID-19 vaccine dose. Baseline incidence of myocarditis was low. Myocarditis IRRs were elevated after vaccination in those aged < 30 years, after both Pfizer vaccine doses (IRR = 3·3, 95%CI 1·2-9.4; 7·8, 95%CI 2·6-23·5, respectively) and Moderna vaccine dose 2 (IRR = 6·1, 95%CI 1·1-33·5). An effect of AstraZeneca vaccine dose 2 could not be excluded (IRR = 2·42, 95%CI 0·96-6·07). Pericarditis was not associated with vaccination. Interpretation: mRNA-based COVID-19 vaccines and potentially AstraZeneca are associated with increased myocarditis risk in younger individuals, although absolute incidence remains low. More data on children (≤ 11 years) are needed.

Measuring balance and model selection in propensity score methods.

PURPOSE: Propensity score (PS) methods focus on balancing confounders between groups to estimate an unbiased treatment or exposure effect. However, there is lack of attention in actually measuring, reporting and using the information on balance, for instance for model selection. We propose to use a measure for balance in PS methods and describe several of such measures: the overlapping coefficient, the Kolmogorov-Smirnov distance, and the Lévy distance. METHODS: We performed simulation studies to estimate the association between these three and several mean based measures for balance and bias (i.e., discrepancy between the true and the estimated treatment effect). RESULTS: For large sample sizes (n = 2000) the average Pearson's correlation coefficients between bias and Kolmogorov-Smirnov distance (r = 0.89), the Lévy distance (r = 0.89) and the absolute standardized mean difference (r = 0.90) were similar, whereas this was lower for the overlapping coefficient (r = -0.42). When sample size decreased to 400, mean based measures of balance had stronger correlations with bias. Models including all confounding variables, their squares and interaction terms resulted in smaller bias than models that included only main terms for confounding variables. CONCLUSIONS: We conclude that measures for balance are useful for reporting the amount of balance reached in propensity score analysis and can be helpful in selecting the final PS model.