A Comparison of Probabilistic and Deterministic Match Strategies for Linking Prehospital and in-Hospital Stroke Registry Data

Utility of real-world evidence in biosimilar development

Biosimilar development refers to the process of creating a biologic drug that is similar to an existing approved biologic drug, also known as a reference drug. Due to the complex nature of biologics drugs and the inherent variability in their manufacturing process biosimilars are not identical but highly similar to the reference drug in terms of quality, safety, and efficacy. Efficacy and safety trials for biosimilars involve large numbers of patients to confirm comparable clinical performance of the biosimilar and the reference product in appropriately sensitive clinical indications and for appropriate sensitive endpoints. The objective of a biosimilar clinical data is to address slight differences observed at previous steps and to confirm comparable clinical performance of the biosimilar and the reference product. In recent years with advances in big data computing, there has been increasing interest to incorporate the totality of information from different data sources (e.g. Real World data and published literature) in design and conduct of clinical trial to support regulatory objectives. The biosimilar development is an ideal framework for utilization of Real-World Evidence in design of trials as potentially large amount of data are available for the reference dug. Hence there may be an opportunity to use RWD in establishing, improving or validating equivalence margins (EQM) for biosimilar designs, specifically in the case there is no historical published data in the intended sensitive population. In this article, we propose a variation of matching method that seems promising to identify the matched set from a real-world data for which the effect size of targeted endpoint would be comparable to historical data. We believe this is a reasonable approach because in design stage, we can view covariates and secondary endpoints as data feature that can be used in a matching method. This approach was illustrated through a case study which indicated the estimate of the primary endpoint is within 1% of published results and thus RWD may be used to justify or estimate the equivalence margin. To ensure consistent results we recommend using this approach in different indications and endpoint scenarios. Thus utilization of RWD/RWE can provide an important opportunity to increase access to biologic therapies, reducing cost by repurposing existing data.

Emergency Medical Services Compliance With Prehospital Stroke Quality Metrics Is Associated With Faster Stroke Evaluation and Treatment

BACKGROUND

Emergency medical services (EMS) is an important link in the stroke chain of recovery. Various prehospital quality metrics have been proposed for prehospital stroke care, but their individual impact is uncertain. We sought to measure associations between EMS quality metrics and downstream stroke care.

METHODS

This is a retrospective analysis of a cohort of EMS-transported stroke patients assembled through a linkage between Michigan's EMS and stroke registries. We used multivariable regression to quantify the independent associations between EMS quality metric compliance (dispatch within 90 seconds of 911 call, prehospital stroke screen documentation [Prehospital stroke scale], glucose check, last known well time, maintenance of scene times ≤15 minutes, hospital prenotification, and intravenous line placement) and shorter door-to-CT times (door-to-CT ≤25), accounting for EMS recognition, age, sex, race, stroke subtype, severity, and duration of symptoms. We then developed a simple EMS quality score based on metrics associated with early CT and examined its associations with hospital stroke evaluation times, treatment, and patient outcomes.

RESULTS

Five thousand seven hundred seven EMS-transported stroke cases were linked to prehospital records from January 2018 through June 2019. In multivariable analysis, prehospital stroke scale documentation (adjusted odds ratio, 1.4 [1.2-1.6]), glucose check (1.3 [1.1-1.6]), on-scene time ≤15 minutes (1.6 [1.4-1.9]), hospital prenotification ([2.0 [1.4-2.9]), and intravenous line placement (1.8 [1.5-2.1]) were independently associated with a door-to-CT ≤25 minutes. A 5-point quality score (1 point for each element) was therefore developed. In multivariable analysis, a 1-point higher EMS quality score was associated with a shorter time from EMS contact to CT (-9.2 [-10.6 to -7.8] minutes; P<0.001) and thrombolysis (-4.3 [-6.4 to -2.2] minutes; P<0.001), and higher odds of discharge to home (adjusted odds ratio, 1.1 [1.0-1.2]; P=0.002).

CONCLUSIONS

Five EMS actions recommended by national guidelines were associated with rapid CT imaging. A simple quality score derived from these measures was also associated with faster stroke evaluation, greater odds of reperfusion treatment, and discharge to home.

Database quality assessment in research in paramedicine: a scoping review

BACKGROUND

Research in paramedicine faces challenges in developing research capacity, including access to high-quality data. A variety of unique factors in the paramedic work environment influence data quality. In other fields of healthcare, data quality assessment (DQA) frameworks provide common methods of quality assessment as well as standards of transparent reporting. No similar DQA frameworks exist for paramedicine, and practices related to DQA are sporadically reported. This scoping review aims to describe the range, extent, and nature of DQA practices within research in paramedicine.

METHODS

This review followed a registered and published protocol. In consultation with a professional librarian, a search strategy was developed and applied to MEDLINE (National Library of Medicine), EMBASE (Elsevier), Scopus (Elsevier), and CINAHL (EBSCO) to identify studies published from 2011 through 2021 that assess paramedic data quality as a stated goal. Studies that reported quantitative results of DQA using data that relate primarily to the paramedic practice environment were included. Protocols, commentaries, and similar study types were excluded. Title/abstract screening was conducted by two reviewers; full-text screening was conducted by two, with a third participating to resolve disagreements. Data were extracted using a piloted data-charting form.

RESULTS

Searching yielded 10,105 unique articles. After title and abstract screening, 199 remained for full-text review; 97 were included in the analysis. Included studies varied widely in many characteristics. Majorities were conducted in the United States (51%), assessed data containing between 100 and 9,999 records (61%), or assessed one of three topic areas: data, trauma, or out-of-hospital cardiac arrest (61%). All data-quality domains assessed could be grouped under 5 summary domains: completeness, linkage, accuracy, reliability, and representativeness.

CONCLUSIONS

There are few common standards in terms of variables, domains, methods, or quality thresholds for DQA in paramedic research. Terminology used to describe quality domains varied among included studies and frequently overlapped. The included studies showed no evidence of assessing some domains and emerging topics seen in other areas of healthcare. Research in paramedicine would benefit from a standardized framework for DQA that allows for local variation while establishing common methods, terminology, and reporting standards.

Emergency Medical Services Stroke Care Performance Variability in Michigan: Analysis of a Statewide Linked Stroke Registry

Background Emergency medical services (EMS) compliance with recommended prehospital care for patients with acute stroke is inconsistent; however, sources of variability in compliance are not well understood. The current analysis utilizes a linkage between a statewide stroke registry and EMS information system data to explore patient and EMS agency-level contributions to variability in prehospital care. Methods and Results This is a retrospective analysis of a cohort of confirmed stroke cases transported by EMS to hospitals participating in a statewide stroke registry. Using EMS information system data, the authors quantified EMS compliance with 6 performance measures derived from national guidelines for prehospital stroke care: prehospital stroke scale performance, glucose check, stroke recognition, on-scene time ≤15 minutes, time last known well documentation, and hospital prenotification. Multilevel multivariable logistic regression analysis was then used to examine associations between patient-level demographic and clinical characteristics and EMS compliance while accounting for and quantifying the variation attributable to agency of transport and recipient hospital. Over an 18-month period, EMS and stroke registry records were linked for 5707 EMS-transported stroke cases. Compliance ranged from 24% of cases for last known well documentation to 82% for documentation of a glucose check. The other measures were documented in approximately half of cases. Older age, higher National Institutes of Health Stroke Scale, and earlier presentation were associated with more compliant prehospital care. EMS agencies accounted for more than half of the variation in EMS prehospital stroke scale documentation and last known well documentation and 27% of variation in glucose check but <10% of stroke recognition and prenotification variability. Conclusions EMS stroke care remains highly variable across different performance measures and EMS agencies. EMS agency and electronic medical record type are important sources of variability in compliance with key prehospital performance metrics for stroke.

Linking Data From the Australian Stroke Clinical Registry With Ambulance and Emergency Administrative Data in Victoria

Objective: In Australia, approximately 3 in 4 people with acute stroke use an ambulance. Few examples of merging ambulance clinical records, hospital government data, and national registry data for stroke exist. We sought to understand the advantages of using linked datasets for describing the full clinical journey of people with stroke and the possibility of investigating their long-term outcomes based on pre-hospital management of stroke. Method: Patient-level data from the Australian Stroke Clinical Registry (AuSCR) (January 2013-October 2017) were linked with Ambulance Victoria (AV) records and Victorian Emergency Minimum Dataset (VEMD). Probabilistic iterative matching on personal identifiers were used and records merged with a project specific identification number. Results: Of the 7,373 episodes in the AuSCR and 6,001 in the AV dataset; 4,569 (62%) were matched. Unmatched records that were positive for “arrival by ambulance” in the AuSCR and VEMD (no corresponding record in AV) were submitted to AV. AV were able to identify 148/435 additional records related to these episodes. The final cohort included 4,717 records (median age: 73 years, female 42%, ischemic stroke 66%). Conclusion: The results of the data linkage provides greater confidence for use of these data for future research related to pre-hospital management of stroke.

Leveraging National Claims and Hospital Big Data: Cohort Study on a Statin-Drug Interaction Use Case

Background

Linking different sources of medical data is a promising approach to analyze care trajectories. The aim of the INSHARE (Integrating and Sharing Health Big Data for Research) project was to provide the blueprint for a technological platform that facilitates integration, sharing, and reuse of data from 2 sources: the clinical data warehouse (CDW) of the Rennes academic hospital, called eHOP (entrepôt Hôpital), and a data set extracted from the French national claim data warehouse (Système National des Données de Santé [SNDS]).

Objective

This study aims to demonstrate how the INSHARE platform can support big data analytic tasks in the health field using a pharmacovigilance use case based on statin consumption and statin-drug interactions.

Methods

A Spark distributed cluster-computing framework was used for the record linkage procedure and all analyses. A semideterministic record linkage method based on the common variables between the chosen data sources was developed to identify all patients discharged after at least one hospital stay at the Rennes academic hospital between 2015 and 2017. The use-case study focused on a cohort of patients treated with statins prescribed by their general practitioner or during their hospital stay.

Results

The whole process (record linkage procedure and use-case analyses) required 88 minutes. Of the 161,532 and 164,316 patients from the SNDS and eHOP CDW data sets, respectively, 159,495 patients were successfully linked (98.74% and 97.07% of patients from SNDS and eHOP CDW, respectively). Of the 16,806 patients with at least one statin delivery, 8293 patients started the consumption before and continued during the hospital stay, 6382 patients stopped statin consumption at hospital admission, and 2131 patients initiated statins in hospital. Statin-drug interactions occurred more frequently during hospitalization than in the community (3800/10,424, 36.45% and 3253/14,675, 22.17%, respectively; P<.001). Only 121 patients had the most severe level of statin-drug interaction. Hospital stay burden (length of stay and in-hospital mortality) was more severe in patients with statin-drug interactions during hospitalization.

Conclusions

This study demonstrates the added value of combining and reusing clinical and claim data to provide large-scale measures of drug-drug interaction prevalence and care pathways outside hospitals. It builds a path to move the current health care system toward a Learning Health System using knowledge generated from research on real-world health data.

[Linking Health Claims Data and Records of Emergency Medical Services: Building a Bridge via Patient's Health Insurance Number?]

INTRODUCTION

In Germany, Emergency Medical Services (EMS) were involved in a total of 7.3 million emergency cases in 2016/2017. Information on prehospital care is stored in several secondary data sources, yet combined analysis of these data at the level of individual patients or EMS cases happens rarely. Research is needed on which methods and variables are suitable for the linkage of these data sources.

METHODS

We linked EMS records from five Bavarian emergency service districts to health claims data belonging to ten statutory health insurers (data from 2016). Two linkage approaches at the level of individual patient's EMS case/reimbursement case were demonstrated. First, a deterministic linkage was conducted based on the patient's unique identifying health insurance number. The second linkage was probabilistic. As linkage variables, it comprised the only partially available health insurance number plus several non-unique key variables, the latter being a patient's health insurance provider, sex, year of birth and distance travelled. In order to verify the deterministic and the probabilistic linkages' quality, rates of accordance of several variables present in both data sources were calculated.

RESULTS

The starting point for our data linkage were 106,371 EMS records (independent of certain health insurance companies) and 432,693 EMS services reimbursed by health insurers (independent of specific EMS providers). 4,327 EMS records could be linked to health claims data - out of 5,921 EMS records that coded a health insurance company contributing claims data to Inno_RD. With a probabilistic linkage, it was possible to increase this number to a total of 5,379 linked EMS records. All checks carried out indicated a high linkage quality for both the deterministic and the probabilistic approach.

CONCLUSION

A linkage of EMS records with health claims data is possible. In Inno_RD, a probabilistic approach has proven a valuable alternative to deterministic linkage via health insurance number since EMS records can be linked meaningfully even if the health insurance number is unavailable or where a minority of non-unique key variables show non-accordance or missing values.

Automatic Information Exchange in the Early Rescue Chain Using the International Standard Accident Number (ISAN)

Thus far, emergency calls are answered by human operators who interview the calling person in order to obtain all relevant information. In the near future-based on the Internet of (Medical) Things (IoT, IoMT)-accidents, emergencies, or adverse health events will be reported automatically by smart homes, smart vehicles, or smart wearables, without any human in the loop. Several parties are involved in this communication: the alerting system, the rescue service (responding system), and the emergency department in the hospital (curing system). In many countries, these parties use isolated information and communication technology (ICT) systems. Previously, the International Standard Accident Number (ISAN) has been proposed to securely link the data in these systems. In this work, we propose an ISAN-based communication platform that allows semantically interoperable information exchange. Our aims are threefold: (i) to enable data exchange between the isolated systems, (ii) to avoid data misinterpretation, and (iii) to integrate additional data sources. The suggested platform is composed of an alerting, responding, and curing system manager, a workflow manager, and a communication manager. First, the ICT systems of all parties in the early rescue chain register with their according system manager, which tracks the keep-alive. In case of emergency, the alerting system sends an ISAN to the platform. The responsible rescue services and hospitals are determined and interconnected for platform-based communication. Next to the conceptual design of the platform, we evaluate a proof-of-concept implementation according to (1) the registration, (2) channel establishment, (3) data encryption, (4) event alert, and (5) information exchange. Our concept meets the requirements for scalability, error handling, and information security. In the future, it will be used to implement a virtual accident registry.