Outcomes of Telestroke Inter-Hospital Transfers Among Intervention and Non-Intervention Patients
Bibliographic record
Abstract
Background: Telestroke is an established telemedicine method of delivering emergency stroke care. However, not all neurological patients utilizing telestroke service require emergency interventions or transfer to a comprehensive stroke center. To develop an understanding of the appropriateness of inter-hospital neurological transfers utilizing the telemedicine, our study aimed to assess the differences in outcomes of inter-hospital transfers utilizing the service in relation to the need for neurological interventions. Methods: The pragmatic, retrospective analysis included 181 consecutive patients, who were emergently transferred from telestroke-affiliated regional medical centers between October 3, 2021, and May 3, 2022. In this exploratory study investigating the outcomes of telestroke-referred patients, patients receiving interventions were compared to those that did not following transfer to our tertiary center. Neurological interventions included mechanical thrombectomy (MT) and/or tissue plasminogen activator (tPA), craniectomy, electroencephalography (EEG), or external ventricular drain (EVD). Transfer mortality rate, discharge functional status defined by modified Rankin scale (mRS), neurological status defined by National Institutes of Health Stroke Scale (NIHSS), 30-day unpreventable readmission rate, 90-day clinical major adverse cardiovascular events (MACE), and 90-day mRS, and NIHSS were studied. We used X 2 or Fisher exact tests to evaluate the association between the intervention and categorical or dichotomous variables. Continuous or ordinal measures were compared using Wilcoxon rank-sum tests. All tests of statistical significance were considered to be significant at P < 0.05. Results: Among the 181 transferred patients, 114 (63%) received neuro-intervention and 67 (37%) did not. The death rate during the index admission was not statistically significant between the intervention and non-intervention groups (P = 0.196). The discharge NIHSS and mRS were worse in the intervention compared to the non-intervention (P < 0.05 each, respectively). The 90-day mortality and cardiovascular event rates were similar between intervention and non-intervention groups (P > 0.05 each, respectively). The 30-day readmission rates were also similar between the two groups (14% intervention vs. 13.4% non-intervention, P = 0.910). The 90-day mRS were not significantly different between intervention and non-intervention groups (median 3 (IQR: 1 - 6) vs. 2 (IQR: 0 - 6), P = 0.109). However, 90-day NIHSS was worse in the intervention compared to non-intervention group (median 2 (IQR: 0 - 11) vs. 0 (IQR: 0 - 3), P = 0.004). Conclusions: Telestroke is a valuable resource that expedites emergent neurological care via referral to a stroke center. However, not all transferred patients benefit from the transfer process. Future multicenter studies are warranted to study the effects or appropriateness of telestroke networks, and to better understand the patient characteristics, resources allocation, and transferring institutions to improve telestroke care. J Clin Med Res. 2023;15(6):292-299 doi: https://doi.org/10.14740/jocmr4945
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".