Source Data Verification Research Articles

A low molecular weight dextran sulphate, ILB®, for the treatment of amyotrophic lateral sclerosis (ALS): An open-label, single-arm, single-centre, phase II trial.

Amyotrophic lateral sclerosis (ALS), also known as Lou Gehrig´s disease, is a rare neurological condition and is the most common motor neurone disease. It is a fatal disease with specific loss of motor neurons in the spinal cord, brain stem, and motor cortex leading to progressive paralysis and usually death within five years of diagnosis. There remains no cure for ALS, and management is focused on a combination of neuroprotective medication, respiratory support, and management by multidisciplinary clinics. This prospective, single-arm, open-label phase II clinical trial of sustained weekly administration of 2 mg/kg ILB® (a low-molecular weight dextran sulphate) was conducted in a single UK hospital. Eligible patients were at least 18 years and had a definite diagnosis of ALS according to El Escorial Criteria. The co-primary outcomes were safety, tolerability, and quantity of ILB® administered. EudraCT number. 2018-000668-28. Between 18-Apr-2019 and 27-Mar-2020, 11 patients were recruited and treated for up to 38 weeks. There were no treatment terminations or withdrawals. One serious adverse event was reported, which was not related to ILB® and resolved without sequalae. 270 mild/moderate adverse events were reported with no intolerable events occurring during the trial. The total number of ILB® treatments administered per patient ranged from 4 to 38, with a cumulative dose ranging from 745 to 6668 mg. As a result of the COVID-19 pandemic and the high-risk status of study participants, recruitment and treatment was suspended early in Mar-2020. At the long-term follow-up, three patients had died after the trial was halted, between 53 and 62 weeks after their final ILB® injection. Long-term weekly ILB® injections of 2 mg/kg was well tolerated and had an acceptable safety profile in patients with ALS. EudraCT: 2018-000668-28. clinicaltrials.gov: NCT03705390. This trial adheres to the principles of GCP in the design, conduct, recording and reporting of clinical trials as listed in part 2, "Conditions and Principles which apply to all Clinical Trials" under the header "Principles based on Articles 2 to 5 of the EU GCP Directive" in the Medicines for Human Use Clinical Trials Regulations (as amended in SI 2006/1928). For clarity, the study did not conform to all aspects of the International Conference on Harmonisation (ICH) E6 R2 Guidelines for GCP (also known as 'ICH GCP'). Of note, we did not use an external database, perform 100% source data verification, and only primary outcome data were analysed in parallel by a second, independent statistician.

Efficiency of eSource Direct Data Capture in Investigator-Initiated Clinical Trials in Oncology

BackgroundClinical trials have become larger and more complex. Thus, eSource should be used to enhance efficiency. This study aimed to evaluate the impact of the multisite implementation of eSource direct data capture (DDC), which we define as eCRFs for direct data entry in this study, on efficiency by analyzing data from a single investigator-initiated clinical trial in oncology.MethodsOperational data associated with the targeted study conducted in Japan was used to analyze time from data occurrence to data entry and data finalization, and number of visits to the site and time spent at the site by clinical research associates (CRAs). Additionally, simulations were performed on the change in hours at the clinical sites during the implementation of eSource DDC.ResultsNo difference in time from data occurrence to data entry was observed between the DDC and the transcribed data fields. However, the DDC fields could be finalized 4 days earlier than the non-DDC fields. Additionally, although no difference was observed in the number of visits for source data verification (SDV) by CRAs, a comparison among sites that introduced eSource DDC and those that did not showed that the time spent at the site for SDV was reduced. Furthermore, the simulation results indicated that even a small amount of data to be collected or a small percentage of DDC-capable items may lead to greater efficiency when the number of subjects per site is significant.ConclusionsThe implementation of eSource DDC may enhance efficiency depending on the study framework and type and number of items to be collected.

P122 The quality of the data in the European Cystic Fibrosis Society Patient Registry as assessed through source data verification from 2018 to 2024

P122 The quality of the data in the European Cystic Fibrosis Society Patient Registry as assessed through source data verification from 2018 to 2024

Source Data Verification (SDV) Quality in Clinical Research: A Scoping Review

Source Data Verification (SDV) Quality in Clinical Research: A Scoping Review

Assessing the impact of risk-based data monitoring on outcomes for a paediatric multicentre randomised controlled trial.

Regulatory guidelines recommend that sponsors develop a risk-based approach to monitoring clinical trials. However, there is a lack of evidence to guide the effective implementation of monitoring activities encompassed in this approach. The aim of this study was to assess the efficiency and impact of the risk-based monitoring approach used for a multicentre randomised controlled trial comparing treatments in paediatric patients undergoing cardiac bypass surgery. This is a secondary analysis of data from a randomised controlled trial that implemented targeted source data verification as part of the risk-based monitoring approach. Monitoring duration and source to database error rates were calculated across the monitored trial dataset. The monitored and unmonitored trial dataset, and simulated trial datasets with differing degrees of source data verification and cohort sizes were compared for their effect on trial outcomes. In total, 106,749 critical data points across 1,282 participants were verified from source data either remotely or on-site during the trial. The total time spent monitoring was 365 hours, with a median (interquartile range) of 10 (7, 16) minutes per participant. An overall source to database error rate of 3.1% was found, and this did not differ between treatment groups. A low rate of error was found for all outcomes undergoing 100% source data verification, with the exception of two secondary outcomes with error rates >10%. Minimal variation in trial outcomes were found between the unmonitored and monitored datasets. Reduced degrees of source data verification and reduced cohort sizes assessed using simulated trial datasets had minimal impact on trial outcomes. Targeted source data verification of data critical to trial outcomes, which carried with it a substantial time investment, did not have an impact on study outcomes in this trial. This evaluation of the cost-effectiveness of targeted source data verification contributes to the evidence-base regarding the context where reduced emphasis should be placed on source data verification as the foremost monitoring activity.

A code for clinical trials centralized monitoring, sharing open-science solutions to high-quality data.

Monitoring of clinical trials is critical to the protection of human subjects and the conduct of high-quality research. Even though the adoption of risk-based monitoring (RBM) has been suggested for many years, the RBM approach has been less widespread than expected. Centralized monitoring is one of the RMB pillars, together with remote-site monitoring visits, reduced Source Data Verification (SDV) and Source Document Reviews (SDR). The COVID-19 pandemic promoted disruptions in the conduction of clinical trials, as on-site monitoring visits were adjourned. In this context, the transition to RBM by all actors involved in clinical trials has been encouraged. In order to ensure the highest quality of data within a COVID-19 clinical trial, a centralized monitoring tool alongside Case Report Forms (CRFs) and synchronous automated routines were developed at the clinical research platform, Fiocruz, Brazilian Ministry of Health. This paper describes how these tools were developed, their features, advantages, and limitations. The software codes, and the CRFs are available at the Fiocruz Data Repository for Research-Arca Dados, reaffirming Fiocruz's commitment to Open Science practices.

Blockchain based trusted execution environment architecture analysis for multi - source data fusion scenario

Multi-source data fusion techniques are widely applied in dynamic target detection scenarios, such as target situational awareness, radar signal resolution, and feature fusion labeling. Currently, techniques including clustering, neural networks, Bayesian analysis, and machine learning have been applied to improve the success rate of multi-source data fusion in terms of interference data noise reduction. The research on data tampering prevention of multiple data sources is mainly based on the data distributed authentication technology. The research on performing data fusion process in a trusted execution environment is mainly based on cryptography and codec technology. This paper focuses on the technical application architecture that can effectively improve the comprehensive efficiency of multi-source data fusion processing under the constraints of business scenarios. Accordingly, this paper proposes a trusted execution environment architecture based on blockchain technology for multi-source data fusion scenarios. It integrates the strategy of trusted data source data verification in blockchain smart contracts into the typical multi-source data fusion application architecture. After comparison tests in a simulation environment, the trusted execution environment architecture based on blockchain technology has shown considerable improvements in fusion success rate with limited performance cost.

Application of Statistical Methods for Central Statistical Monitoring and Implementations on the German Multiple Sclerosis Registry

Monitoring of clinical trials is a fundamental process required by regulatory agencies. It assures the compliance of a center to the required regulations and the trial protocol. Traditionally, monitoring teams relied on extensive on-site visits and source data verification. However, this is costly, and the outcome is limited. Thus, central statistical monitoring (CSM) is an additional approach recently embraced by the International Council for Harmonisation (ICH) to detect problematic or erroneous data by using visualizations and statistical control measures. Existing implementations have been primarily focused on detecting inlier and outlier data. Other approaches include principal component analysis and distribution of the data. Here we focus on the utilization of comparisons of centers to the Grand mean for different model types and assumptions for common data types, such as binomial, ordinal, and continuous response variables. We implement the usage of multiple comparisons of single centers to the Grand mean of all centers. This approach is also available for various non-normal data types that are abundant in clinical trials. Further, using confidence intervals, an assessment of equivalence to the Grand mean can be applied. In a Monte Carlo simulation study, the applied statistical approaches have been investigated for their ability to control type I error and the assessment of their respective power for balanced and unbalanced designs which are common in registry data and clinical trials. Data from the German Multiple Sclerosis Registry (GMSR) including proportions of missing data, adverse events and disease severity scores were used to verify the results on Real-World-Data (RWD).

Big data in oncology: Challenges and solutions.

6513 Background: Oncology care is complex and often multimodal. With recent technological advances, only a fraction of data is structured feasibly for research. Here we present a step-by-step method of building a novel comprehensive pan-cancer oncology data model using standard data definitions and industry-standard benchmarks. Methods: A team of 133 members was assembled including a project manager, bioinformatic engineers, business analysts, biostatisticians, data stewardship experts, clinical curators, and quality assurance (QA) managers. We first identified data domains that capture a comprehensive patient journey, leveraging existing oncology data models as a starting point, including NAACCR, PRISSMM (Deb Schrag & Eva Lepisto), and mCODE (ASCO). A common data model was developed using standard terms plus 5-10 disease specific elements (DSE). REDCap was used as the database platform, as it is HIPAA compliant and allows customizations. The data was stored in AuroraDB using an architecture and products that provide scalability from both an integration and consumption perspective. Results: We identified 10 data domains, including 186 distinct elements: demographics (20), comorbidities (2); cancer diagnosis & staging (27), pathology (45), imaging (18), medications (11), oncology responses (11), radiation treatments (14), cancer surgeries (11), cancer genomic (19), tumor markers (8), and vitals (8). Standard ontologies were used, including ICD-0-3 histology codes, ICD-10 comorbidities codes, CPT cancer surgeries codes, and CTCAE 5.0 for toxicities. We identified a data steward for each tumor type across medical oncology, surgery, pathology, radiology, and radiation oncology domains who aided curator training and the identification of DSE. QA managers and analysts performed 20% source data verification. In addition, we built REDCap rules (applicable across a form), and complex queries (applicable across multiple forms). To support QA and clinical engagement, interactive Tableau dashboards were constructed. In addition, timing and quality errors were monitored via Tableau dashboards at the individual curator level to provide timely feedback, leading to improved data quality and curation efficiency in real time. The Medical oncology and radiology domains were the most time-consuming, whereas Cancer diagnosis was the most difficult to curate. Conclusions: We collected genomic and phenomic data for 15,579 patients across six tumor types to date. Collecting comprehensive oncology data across tumor types is possible but requires institutional support, collaboration between clinical & informatics teams, and a dedicated QA team. [Table: see text]

A clinical site workload prediction model with machine learning lifecycle

In clinical trial monitoring, substantial resources are allocated to perform source data verification (SDV). SDV ensures accurate and reliable transcription of trial participant information. Clinical site visits are typically scheduled at a fixed frequency for SDV, without objectively factoring in individual site workload. This often results in wasted resources and directly influences clinical trial cost. We leveraged historical data from several hundred clinical trials to predict SDV workload at trial sites using machine learning. Specifically, we adopted cross industry standard process for data mining (CRISP-DM) process model and devised a novel deep learning pipeline for longitudinal clinical trial data. The deep learning pipeline, which comprises recurrent neural network-based encoder and decoder, ingests multivariate sequence data from study sites and predicts SDV workload for future months. We also developed an efficient model enhancement workflow, in a data science platform that facilitates machine learning operations best practices, for timely adaptation of new features and changes. Several enhancement iterations have been performed since the launch of first SDV workload prediction model, resulting in a more accurate latest model compared to previous versions. We discuss these enhancements in the context of CRISP-DM phases. In conclusion, the SDV workload prediction model has enabled informed planning and optimization of resources within clinical trial operations.

We Know What You Agreed To, Don't We?-Evaluating the Quality of Paper-Based Consents Forms and Their Digitalized Equivalent Using the Example of the Baltic Fracture Competence Centre Project.

The informed consent is the legal basis for research with human subjects. Therefore, the consent form (CF) as legally binding document must be valid, that is, be completely filled-in stating the person's decision clearly and signed by the respective person. However, especially paper-based CFs might have quality issues and the transformation into machine-readable information could add to low quality. This paper evaluates the quality and arising quality issues of paper-based CFs using the example of the Baltic Fracture Competence Centre (BFCC) fracture registry. It also evaluates the impact of quality assurance (QA) measures including giving site-specific feedback. Finally, it answers the question whether manual data entry of patients' decisions by clinical staff leads to a significant error rate in digitalized paper-based CFs. Based on defined quality criteria, monthly QA including source data verification was conducted by two individual reviewers since the start of recruitment in December 2017. Basis for the analyses are the CFs collected from December 2017 until February 2019 (first recruitment period). After conducting QA internally, the sudden increase of quality issues in May 2018 led to site-specific feedback reports and follow-up training regarding the CFs' quality starting in June 2018. Specific criteria and descriptions on how to correct the CFs helped in increasing the quality in a timely matter. Most common issues were missing pages, decisions regarding optional modules, and signature(s). Since patients' datasets without valid CFs must be deleted, QA helped in retaining 65 datasets for research so that the final datapool consisted of 840 (99.29%) patients. All quality issues could be assigned to one predefined criterion. Using the example of the BFCC fracture registry, CF-QA proved to significantly increase CF quality and help retain the number of available datasets for research. Consequently, the described quality indicators, criteria, and QA processes can be seen as the best practice approach.

Impact of monitoring approaches on data quality in clinical trials.

Source data verification (SDV) has been reported to account for up to 25% of the budget in clinical trials (CT) and cost-benefit of SDV has been questioned. Guidelines for risk-based monitoring (RBM) were published in 2013 by agencies and in 2016, ICH-GCP-E6-(R2) added a requirement for risk-based approaches. This report will perform a comparison of the impact of RBM vs classic monitoring (CM) on data quality (defined as accuracy of data reporting from source data to final trial data) and expected impact on costs of CTs. Data on residual errors from four, large comparable randomised CTs were examined by post-trial SDV. Observed discrepancies were analysed in the categories of "overall" data, "major efficacy" and "major safety". In each category, the residual error rate was calculated as the number of discrepancies divided by the number of data-fields verified. A total of 1 716 087 data points were verified using CM and 323 174 using RBM. The overall error rate was 0.40% for RBM and 0.37% for CM (P < 0.01). For major efficacy, defined by risk assessment, the error rate was 0.15% and 0.28% (P < 0.0001); in major safety, defined by risk assessment, the error rate was 0.49% and 0.67% (P = 0.15), both in favour of the RBM approach. These empirical data, directly comparing RBM with CM, suggest that RBM improves data quality regarding data-points of major importance to trial outcomes, efficacy and major safety. Overall, the RBM approach showed a correlation to reduced amount of data collection errors with major relevance for interpretation of study results and subject safety as well as reducing on-site monitoring and data cleaning resources.

Identifying the association rules between adverse events and concomitant medicines in clinical trial data management using random forest

Adverse events (AEs) and concomitant medications (CMs) underreporting remains a recurrent issue in clinical trials. This study aimed to build a mapping relationship between the AE and CM using a random forest (RF) model that can be embedded in the current Electronic Data Capture (EDC) system, to enable a reliable detection of underreporting AE or CM. Current data including 524 AEs and 684 CMs for 13,905 individuals, were taken from three cancer clinical trials and six other studies that are non-public. An additional validation dataset was consisted of 448 independent patients with 269 unique AEs and 407 CMs from a cancer clinical trial on Paclitaxel. We found that the machine learning method exhibited in a learning task between AE and CM well for a common AE like hypertension that was caused by a single cause, but it was ineffective when the AE, such as the increased blood alkaline phosphatase, was caused by complex reasons or just an associated symptom of some diseases. This study suggests the potential of automatically detecting the underreported AE and CM in detail, and it will improve further safety and validity inspections from clinical trials. ABBREVIATIONS: AE, adverse event; ATC, anatomical therapeutic chemical; CM, concomitant medication; EDC, electronic data capture; FDA, Food and Drug Administration; GCP, good clinical practice; MedDRA, medical dictionary for regulatory activities; NMPA, National Medical Products Administration; PT, preferred term; RCT, randomized controlled trial; RF, random forests; SDV, source data verification; VIM, variable importance measure

Ten Years of Data Verification: The European Congenital Heart Surgeons Association Congenital Database Audits.

Congenital heart surgery databases are tools for internal programmatic evaluation, benchmarking institutional results to multi-institutional aggregate data, and research. Therefore, it is essential to ensure the completeness and accuracy of data. This study analyzes the results of ten years of on-site source data verification of the European Congenital Heart Surgeons Association Congenital Heart Surgery Database (ECHSA CHSD). All data forms verified between 2009 and 2018 were analyzed. The data form consists of 12 data elements: dates of birth, admission, surgery, discharge, and death; weight; case category; cardiopulmonary bypass time; aortic cross-clamp time; validation rules; diagnoses; and procedures. Descriptive data calculation and rates of completeness and accuracy were determined. The trend of error rate of seven centers with ≥5 visits was analyzed. Sixty-nine on-site verification visits took place at 17 centers. A total of 26,245 cases were verified; 2,841 of these 26,245 cases (10.8%) showed an error. The total mean error rate of centers for all years was 12.3 ± 2.1%. Rates of completeness and accuracy were 99% and 89.2%, respectively. Coded diagnoses and procedure analysis revealed that 716 (2.7%) and 456 (1.7%) datasets were incorrect, respectively. Rates of completeness and accuracy of dates were 100%, and 97.1%, respectively. Validation fields showed no errors. Source data verification is an appropriate tool to determine completeness and accuracy of data. The ECHSA CHSD verification analysis of a ten-year period showed a high level of completeness and accuracy. The verified data of the ECHSA CHSD are well-suited for benchmarking and research.

Establishing a Data Quality Baseline in the AKTIN Emergency Department Data Registry - A Secondary Use Perspective.

Secondary use of clinical data is an increasing application that is affected by the data quality (DQ) of its source systems. Techniques such as audits and risk-based monitoring for controlling DQ often rely on source data verification (SDV). SDV requires access to data generating systems. We present an approach to a targeted SDV based on manual input and synthetic data that is applicable in low resource settings with restricted system access. We deployed the protocol in the DQ management of the AKTIN Emergency Department Data Registry. Our targeted approach has shown to be feasible to form a DQ baseline that can be used for different DQ monitoring processes such as the identification of different error sources.

Evaluating the evaluators - Developing evidence of quality oversight effectiveness for clinical trial monitoring: Source data verification, source data review, statistical monitoring, key risk indicators, and direct measure of high risk errors

BackgroundDetailed information about clinical trial quality typically is not published in primary publications. This is a significant issue for physicians, advisory groups, and regulators. In addition, the efficacy of methods to find critical errors that affect trial integrity has not been tested. For more than 20 years, visits to research sites for source data verification (SDV) have been the gold standard method for oversight by pharmaceutical, biotech, device, and vaccine manufacturers. However, there is no evidence of its effectiveness and significant evidence of its lack of effectiveness. GapsWe consulted documents from the Food and Drug Administration (FDA), European Medicines Association (EMA), and International Council for Harmonization (ICH) to break down components of this pivotal issue into gaps in monitoring, oversight cost, and test validation and effectiveness. RecommendationsWe recommend that the pharma, biotech, academic, and publishing communities develop and provide reference datasets with defined errors for trials similar to the reference standards for analytic tests; systematically test the different monitoring methods using the reference datasets as well as provide sensitivity, specificity, positive and negative predictive values, and time to critical finding for each methodology for different types of trial errors; and publish the details of trial quality in each publication, including the number of major deviations. ConclusionsThis will allow the scientific community to improve measurements of research quality, evaluate new methods, and compare the effectiveness of different oversight approaches in the same environment.

Reliability and feasibility of centralized monitoring approach in source data verification during clinical trials

As per ICH GCP, monitoring can be defined as “The act of overseeing the progress of a clinical trial, and of ensuring that it is conducted, recorded, and reported in accordance with the protocol, Standard Operating Procedures (SOPs), Good Clinical Practice (GCP), and the applicable regulatory requirement(s).” Clinical experiments are important for deeper understanding of how interventions work in humans. The aim of the monitoring of the clinical trial is to avoid errors that may compromise patient safety and results of the trial and enables more frequent monitoring of the integrity of the trial and could improve safety concerns by detecting errors earlier than expected. Risk-based monitoring in clinical trials is the process of identification, assessment, monitoring and mitigation of risks that could affect the quality or safety of a study. Centralized monitoring has a direct impact on the clinical trials. One of the most basic and fundamental need of centralized monitoring is to avoid the repetition in clinical trials, saving both time and Money. Central monitoring greatly influences the Patient safety, data integrity and monitoring costs in a clinical trial. This helps in increasing the effectiveness of the monitoring process reducing the burden on the sponsor. The basic and fundamental priority of a clinical trial is to ensure the safety and well-being of the subject. In this article, we will be focusing on how Centralized monitoring can increase the effectiveness and efficiency of the monitoring process and source data verification (SDV) in clinical trials with a special emphasis on Risk based monitoring (RBM).

FAIR and Quality Assured Data - The Use Case of Trueness.

The FAIR Guiding Principles do not address the quality of data and metadata. Therefore, data collections could be FAIR but useless. In a funding initiative of registries for health services research, trueness of data received special attention. Completeness in the definition of recall was selected to represent this dimension in a cross-registry benchmarking. The first analyses of completeness revealed a diversity of its implementation. No registry was able to present results exactly as requested in a guideline on data quality. Two registries switched to a source data verification as alternative, the three others downsized to the dimension integrity. The experiences underline that the achievement of appropriate data quality is a matter of costs and resources, whereas the current Guiding Principles quote for a transparent culture regarding data and metadata. We propose the extension to FAIR-Q, data collections should not only be findable, accessible, interoperable, and reusable, but also quality assured.

Monitoring strategies for clinical intervention studies.

Monitoring strategies for clinical intervention studies.

The incidence, time distribution and prognostic value of monomorphic ventricular tachycardia in ST-elevation myocardial infarction: the prespecified analysis of VALIDATE SWEADHEART trial

Abstract Background The assessment of prognostic impact of ventricular arrhythmias in ST-elevation myocardial infarction (STEMI) is currently based mainly on their timing with regard to the symptom onset and does not distinguish between monomorphic ventricular tachycardia (VT) and polymorphic VT/ventricular fibrillation (VF). However, recent data indicate long-term hazard of monomorphic VT occurring early in the course of STEMI. Purpose To evaluate the incidence, time distribution and prognostic value of early monomorphic VT compared to polymorphic VT/VF in STEMI patients treated by primary percutaneous coronary interventions (PCI). Methods A prespecified analysis of the multicentre prospective registry-based randomised VALIDATE-SWEDEHEART trial included STEMI patients enrolled at 16 sites in Sweden between June 2014 and September 2016. Source data verification regarding the type and timing of arrhythmia from all patients with VT/VF during STEMI was performed. Survival status was obtained from the Swedish national population registry. Endpoint was total mortality at 180 days. Results In total, 2886 patients were identified. Among them, 97 (3.4%) had VF or polymorphic VT, 16 (0.5%) monomorphic VT, 6 (0.2%) had other undefined shockable rhythm. Total mortality (10.9% vs 2.8%, p≤0.001) was higher among patients with VT/VF. VT/VF was associated with total mortality (HR 3.18 95% CI 1.74–5.8; p≤0.001) after adjustment on age, gender and myocardial infarction localisation. In patients discharged from hospital, VT/VF did not influence the long-term prognosis. Patients with monomorphic VT had similar clinical characteristics as compared to those with polymorphic VT/VF. The time distribution of VT/VF differed with regard to the type of arrhythmia: 63% of monomorphic VT/VF episodes occurred after PCI (n=10) compared to 24% (n=23) of all documented polymorphic VT/VF, p=0.003. Total mortality (12.5% vs 10.3%, p=0.678) did not differ between patients with monomorphic VT and polymorphic VT/VF. In Cox model, total mortality was not associated with the type of arrhythmia (Figure). Conclusion Early VT/VF is a marker of poor short-term outcome in patients with STEMI, which does not affect long-term prognosis in those who are successfully resuscitated and discharged from hospital. The incidence of monomorphic VT in STEMI treated by primary PCI is low, and it occurs mainly after PCI. Though no significant difference in mortality was found between patients with monomorphic VT and polymorphic VT/VF, the observed low incidence hampers drawing conclusions with regard to the prognostic hazard impact of monomorphic VT. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): The Swedish Heart Lung Foundation