Immune Reconstitution Kinetics as an Early Predictor for Mortality using Various Hematopoietic Stem Cell Sources in Children

AbstractThe 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

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

COVID-19 vaccine; Adverse drug reaction; MyocarditisVacuna contra el COVID-19; Reacció adversa a fàrmacs; MiocarditisVacuna contra el COVID-19; Reacción adversa a medicamentos; MiocarditisBackground: 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.The project received support from the European Medicines Agency (EMA/2018/23/PE)

Comparison of different pain scoring systems in critically ill patients in a general ICU

Background: Pain in critically ill patients in the intensive care unit (ICU) is common. However, pain assessment in critically ill patients often is complicated because these patients are unable to communicate effectively. Therefore, we designed a study (a) to determine the inter-rater reliability of the Numerical Rating Scale (NRS) and the Behavioral Pain Scale (BPS), (b) to compare pain scores of different observers and the patient, and (c) to compare NRS, BPS, and the Visual Analog Scale (VAS) for measuring pain in patients in the ICU. Methods: We performed a prospective observational study in 113 non-paralyzed critically ill patients. The attending nurses, two researchers, and the patient (when possible) obtained 371 independent observation series of NRS, BPS, and VAS. Data analyses were performed on the sample size of patients (n = 113). Results: Inter-rater reliability of the NRS and BPS proved to be adequate (kappa = 0.71 and 0.67, respectively). The level of agreement within one scale point between NRS rated by the patient and NRS scored by attending nurses was 73%. However, high patient scores (NRS ≥4) were underestimated by nurses (patients 33% versus nurses 18%). In responsive patients, a high correlation between NRS and VAS was found (rs= 0.84, P < 0.001). In ventilated patients, a moderate positive correlation was found between the NRS and the BPS (rs= 0.55, P < 0.001). However, whereas 6% of the observations were NRS of greater than or equal to 4, BPS scores were all very low (median 3.0, range 3.0 to 5.0). Conclusion: The different scales show a high reliability, but observer-based evaluation often underestimates the pain, particularly in the case of high NRS values (≥4) rated by the patient. Therefore, whenever this is possible, ICU patients should rate their pain. In unresponsive patients, primarily the attending nurse involved in daily care should score the patient's pain. In ventilated patients, the BPS should be used only in conjunction with the NRS nurse to measure pain levels in the absence of painful stimuli

A novel method for predicting the budget impact of innovative medicines:validation study for oncolytics

Background High budget impact (BI) estimates of new drugs have led to decision-making challenges potentially resulting in restrictions in patient access. However, current BI predictions are rather inaccurate and short term. We therefore developed a new approach for BI prediction. Here, we describe the validation of our BI prediction approach using oncology drugs as a case study. Methods We used Dutch population-level data to estimate BI where BI is defined as list price multiplied by volume. We included drugs in the antineoplastic agents ATC category which the European Medicines Agency (EMA) considered a New Active Substance and received EMA marketing authorization (MA) between 2000 and 2017. A mixed-effects model was used for prediction and included tumor site, orphan, first in class or conditional approval designation as covariates. Data from 2000 to 2012 were the training set. BI was predicted monthly from 0 to 45 months after MA. Cross-validation was performed using a rolling forecasting origin with e|Ln(observed BI/predicted BI)| as outcome. Results The training set and validation set included 25 and 44 products, respectively. Mean error, composed of all validation outcomes, was 2.94 (median 1.57). Errors are higher with less available data and at more future predictions. Highest errors occur without any prior data. From 10 months onward, error remains constant. Conclusions The validation shows that the method can relatively accurately predict BI. For payers or policymakers, this approach can yield a valuable addition to current BI predictions due to its ease of use, independence of indications and ability to update predictions to the most recent data

Reporting of covariate selection and balance assessment in propensity score analysis is suboptimal: a systematic review

Abstract Objectives: To assess the current practice of propensity score (PS) analysis in the medical literature, particularly the assessment and reporting of balance on confounders. Study Design and Setting: A PubMed search identified studies using PS methods from December 2011 through May 2012. For each article included in the review, information was extracted on important aspects of the PS such as the type of PS method used, variable selection for PS model, and assessment of balance. Results: Among 296 articles that were included in the review, variable selection for PS model was explicitly reported in 102 studies (34.4%). Covariate balance was checked and reported in 177 studies (59.8%). P-values were the most commonly used statistical tools to report balance (125 of 177, 70.6%). The standardized difference and graphical displays were reported in 45 (25.4%) and 11 (6.2%) articles, respectively. Matching on the PS was the most commonly used approach to control for confounding (68.9%), followed by PS adjustment (20.9%), PS stratification (13.9%), and inverse probability of treatment weighting (IPTW, 7.1%). Balance was more often checked in articles using PS matching and IPTW, 70.6% and 71.4%, respectively. Conclusion: The execution and reporting of covariate selection and assessment of balance is far from optimal. Recommendations on reporting of PS analysis are provided to allow better appraisal of the validity of PS-based studies.

Distinguishing patterns in the dynamics of long-term medication use by Markov analysis: beyond persistence

<p>Abstract</p> <p>Background</p> <p>In order to accurately distinguish gaps of varying length in drug treatment for chronic conditions from discontinuation without resuming therapy, short-term observation does not suffice. Thus, the use of inhalation corticosteroids (ICS) in the long-term, during a ten-year period is investigated. To describe medication use as a continuum, taking into account the timeliness and consistency of refilling, a Markov model is proposed.</p> <p>Methods</p> <p>Patients, that filled at least one prescription in 1993, were selected from the PHARMO medical record linkage system (RLS) containing >95% prescription dispensings per patient originating from community pharmacy records of 6 medium-sized cities in the Netherlands.</p> <p>The probabilities of continuous use, the refilling of at least one ICS prescription in each year of follow-up, and medication free periods were assessed by Markov analysis. Stratified analysis according to new use was performed.</p> <p>Results</p> <p>The transition probabilities of the refilling of at least one ICS prescription in the subsequent year of follow-up, were assessed for each year of follow-up and for the total study period.</p> <p>The change of transition probabilities in time was evaluated, e.g. the probability of continuing ICS use of starters in the first two years (51%) of follow-up increased to more than 70% in the following years. The probabilities of different patterns of medication use were assessed: continuous use (7.7%), cumulative medication gaps (1–8 years 69.1%) and discontinuing (23.2%) during ten-year follow-up for new users. New users had lower probability of continuous use (7.7%) and more variability in ICS refill patterns than previous users (56%).</p> <p>Conclusion</p> <p>In addition to well-established methods in epidemiology to ascertain compliance and persistence, a Markov model could be useful to further specify the variety of possible patterns of medication use within the continuum of adherence. This Markov model describes variation in behaviour and patterns of ICS use and could also be useful to investigate continuous use of other drugs applied in chronic diseases.</p

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

Effects of Telephone Counseling Intervention by Pharmacists (TelCIP) on Medication Adherence : Results of a Cluster Randomized Trial

OBJECTIVES: To assess the effect of a pharmacist telephone counseling intervention on patients' medication adherence. DESIGN: Pragmatic cluster randomized controlled trial. SETTING: 53 Community pharmacies in The Netherlands. PARTICIPANTS: Patients ≥18 years initiating treatment with antidepressants, bisphosphonates, Renin-Angiotensin System (RAS)-inhibitors, or statins (lipid lowering drugs). Pharmacies in arm A provided the intervention for antidepressants and bisphosphonates and usual care for RAS-inhibitors and statins. Pharmacies in arm B provided the intervention for RAS-inhibitors and statins and usual care for antidepressants and bisphosphonates. INTERVENTION: INTERVENTION consisted of a telephone counseling intervention 7-21 days after the start of therapy. Counseling included assessment of practical and perceptual barriers and provision of information and motivation. MAIN OUTCOME MEASURE: Primary outcome was refill adherence measured over 1 year expressed as continuous outcome and dichotomous (refill rate≥80%). Secondary outcome was discontinuation within 1 year. RESULTS: In the control arms 3627 patients were eligible and in the intervention arms 3094 patients. Of the latter, 1054 patients (34%) received the intervention. Intention to treat analysis showed no difference in adherence rates between the intervention and the usual care arm (74.7%, SD 37.5 respectively 74.5%, 37.9). More patients starting with RAS-inhibitors had a refill ratio ≥80% in the intervention arm compared to usual care (81.4 vs. 74.9% with odds ratio (OR) 1.43, 95%CI 1.11-1.99). Comparing patients with counseling to patients with usual care (per protocol analysis), adherence was statistically significant higher for patients starting with RAS-inhibitors, statins and bisphosphonates. Patients initiating antidepressants did not benefit from the intervention. CONCLUSIONS: Telephone counseling at start of therapy improved adherence in patients initiating RAS-inhibitors. The per protocol analysis indicated an improvement for lipid lowering drugs and bisphosphonates. No effect for on adherence in patients initiating antidepressants was found. The trial was registered at www.trialregister.nl under the identifier NTR3237

Effects of Telephone Counseling Intervention by Pharmacists (TelCIP) on Medication Adherence: Results of a Cluster Randomized Trial

OBJECTIVES: To assess the effect of a pharmacist telephone counseling intervention on patients' medication adherence. DESIGN: Pragmatic cluster randomized controlled trial. SETTING: 53 Community pharmacies in The Netherlands. PARTICIPANTS: Patients ≥18 years initiating treatment with antidepressants, bisphosphonates, Renin-Angiotensin System (RAS)-inhibitors, or statins (lipid lowering drugs). Pharmacies in arm A provided the intervention for antidepressants and bisphosphonates and usual care for RAS-inhibitors and statins. Pharmacies in arm B provided the intervention for RAS-inhibitors and statins and usual care for antidepressants and bisphosphonates. INTERVENTION: INTERVENTION consisted of a telephone counseling intervention 7-21 days after the start of therapy. Counseling included assessment of practical and perceptual barriers and provision of information and motivation. MAIN OUTCOME MEASURE: Primary outcome was refill adherence measured over 1 year expressed as continuous outcome and dichotomous (refill rate≥80%). Secondary outcome was discontinuation within 1 year. RESULTS: In the control arms 3627 patients were eligible and in the intervention arms 3094 patients. Of the latter, 1054 patients (34%) received the intervention. Intention to treat analysis showed no difference in adherence rates between the intervention and the usual care arm (74.7%, SD 37.5 respectively 74.5%, 37.9). More patients starting with RAS-inhibitors had a refill ratio ≥80% in the intervention arm compared to usual care (81.4 vs. 74.9% with odds ratio (OR) 1.43, 95%CI 1.11-1.99). Comparing patients with counseling to patients with usual care (per protocol analysis), adherence was statistically significant higher for patients starting with RAS-inhibitors, statins and bisphosphonates. Patients initiating antidepressants did not benefit from the intervention. CONCLUSIONS: Telephone counseling at start of therapy improved adherence in patients initiating RAS-inhibitors. The per protocol analysis indicated an improvement for lipid lowering drugs and bisphosphonates. No effect for on adherence in patients initiating antidepressants was found. The trial was registered at www.trialregister.nl under the identifier NTR3237

Propensity score matching and unmeasured covariate imbalance: A simulation study

Background: Selecting covariates for adjustment or inclusion in propensity score (PS) analysis is a trade-off between reducing confounding bias and a risk of amplifying residual bias by unmeasured confounders. Objectives: To assess the covariate balancing properties of PS matching with respect to unmeasured covariates and its impact on bias. Methods: Simulation studies were conducted in binary covariates, treatment and outcome data. In different scenarios, instrumental variables (IV, i.e., variables related to treatment but not to the outcome or other covariates), risk factors (variables related only to the outcome), unmeasured covariates, and confounders with various associations among each other were considered.Treatment effects estimates (risk ratio) were derived after PS matching using Poisson models; balance for each covariate was checked before and after matching using the absolute standardized difference.The choice of covariates for the PS model was compared with respect to bias in the treatment-outcome relation and balance of (unobserved) covariates. Results: PS matching improved balance of measured covariates included in the PS model but exacerbated the imbalance of the unmeasured covariate that was unrelated to measured covariates compared to the full unmatched sample. Inclusion of instrumental variables, independent of unmeasured covariates, exacerbated the imbalance in unmeasured covariates and amplified the residual bias. However, including instrumental variables that were associated with unmeasured covariates improved the balance of unmeasured covariates and reduced bias. When the PS model included variables related to the outcome, exclusion of instrumental variables that were related to unmeasured covariates exacerbated the balance of unmeasured covariates and increased the bias. Conclusions: In choosing covariates for a PS model, the pattern of association among covariates has substantial impact on other covariates' balance and the bias of the treatment effect.Investigators should not rely only on covariate association with treatment or outcome but should take into account possible associations among covariates and explore the balance of other covariates after PS matching