Evidence map›Paper›PMID 40488200›Full record

ArticleFrontiers in neurology2025

The Lithuanian Stroke Database: selection of national stroke care performance measures.

Austėja Dapkutė, Justas Trinkūnas, Daiva Rastenytė, Vaidas Matijošaitis, Saulius Taroza, Dalius Jatužis, Sandra Baužaitė-Babušienė, Aleksandras Vilionskis, Andrius Klimašauskas, Julius Juodakis and 2 more

Abstract read
In one paragraph

Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Austėja DapkutėClinic of Neurology and Neurosurgery, Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania.
Justas TrinkūnasDepartment of Information Systems, Faculty of Fundamental Sciences, Vilnius Gediminas Technical University, Vilnius, Lithuania.
Daiva RastenytėDepartment of Neurology, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania.
Vaidas MatijošaitisDepartment of Neurology, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania.
Saulius TarozaClinic of Neurology and Neurosurgery, Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania.
Dalius JatužisClinic of Neurology and Neurosurgery, Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania.
Sandra Baužaitė-BabušienėDepartment of Neurology, Republican Panevėžys Hospital, Panevėžys, Lithuania.
Aleksandras VilionskisClinic of Neurology and Neurosurgery, Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania.
Andrius KlimašauskasClinic of Anaesthesiology and Intensive Care, Faculty of Medicine, Institute of Clinical Medicine, Vilnius University, Vilnius, Lithuania.
Julius JuodakisCentre for Digital Medicine, Translational Health Research Institute, Faculty of Medicine, Vilnius University, Vilnius, Lithuania.
Julius JaramavičiusClinic of Emergency Medicine, Faculty of Medicine, Institute of Clinical Medicine, Vilnius University, Vilnius, Lithuania.
Rytis MasiliūnasClinic of Neurology and Neurosurgery, Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The Lithuanian Stroke Database (StrokeLT) aims to automate data collection and key performance indicator (KPI) monitoring across all stroke-ready hospitals, addressing the limitations of manual processes and facilitating evidence-based improvements in stroke care nationwide. This publication outlines the selection process and target values of the KPIs designed to standardise and enhance stroke care quality in Lithuania. Study population: The database will include all adult patients diagnosed with stroke or transient ischemic attack (TIA), admitted to Lithuanian stroke-ready hospitals, encompassing approximately 9,582 annual stroke and 1,899 TIA admissions based on 2023 data. The database will ensure comprehensive national coverage by integrating data from stroke centres via a centralised electronic health record system. Main variables: A total of 53 KPIs were selected through a multi-stage Delphi process involving national experts and guided by international standards. These KPIs include 44 process metrics, such as timeliness metrics, early rehabilitation, and availability of secondary prevention, as well as 8 outcome metrics, including functional recovery, completion of a patient feedback survey and mortality. This framework enables comprehensive monitoring across all stages of patient care, as well as incorporating valuable patient feedback. Conclusion: The Lithuanian Stroke Database establishes a standardised automated framework for monitoring stroke care using 53 KPIs, selected through a multi-stage Delphi process involving all relevant stakeholders.

Indexed as

performance measuresquality improvementstrokestroke databasestroke key performance indicators

Identifiers

PMID40488200
PMCPMC12141015

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.