Assessing Clinical Impressions of Early Warning Score Integration With the Rapid Response Team: Protocol for a Prospective Cohort Study (Preprint)
Notice bibliographique
Résumé
BACKGROUND The use of early warning scores (EWSs), which integrate real-time vital sign monitoring, can help rapid response teams (RRTs) proactively identify patients at risk of deterioration. However, existing EWSs demonstrate limited evidence for the reduction of clinically important adverse events. The Visensia Safety Index (VSI) is an EWS that combines heart rate, blood pressure, temperature, oxygen saturation, and respiration rate vital sign information to generate a VSI acuity score ranging from 0, signifying the lowest risk of deterioration, to 5, signifying highest risk of deterioration. Continuous monitoring of the risk of deterioration, with alerts triggered by a score of 3.0 or greater, prompts medical attention. OBJECTIVE This protocol outlines the methodology for assessing the feasibility of combining a portable continuous vital sign monitoring system (Masimo Root Monitor) with VSI monitoring to evaluate patients at high risk of acute deterioration at The Ottawa Hospital (TOH). METHODS This 2-phase, prospective cohort study will be conducted at TOH. Patient participants will include adults (≥18 years) with high-risk conditions, such as those undergoing high-risk elective surgery, malignant hematology or oncology patients, and those admitted with infections. Exclusion criteria include patients receiving comfort care or those in specialized units requiring higher-level monitoring. Eligible patients will be monitored using the VSI with alerts to trigger rapid response team evaluation. In tandem, health care workers including physicians, nurses, and research staff who are involved in patient care and monitoring will be recruited for semistructured interviews. These interviews will explore health care providers’ clinical impressions of the VSI as an EWS to identify barriers and enablers to implementation using the Consolidated Framework for Implementation Research. Interviews will assess impressions of the monitoring technology, clinical workflow challenges, and perceptions of implementation. Data analysis will involve directed content analysis of interview transcripts to identify barriers and drivers for successful implementation. Furthermore, the feasibility of the VSI will be assessed by evaluating the proportion of continuous data successfully collected and transmitted, the timeliness of VSI-triggered alerts and their communication to the RRT team, the regular updating of the predictive tool, the rate of reported VSI-triggered events, and the completion rates of study-related tasks by research and clinical staff. RESULTS As of April 2025, we can report that enrollment is complete, and analysis of the results is ongoing. Interviews are still being transcribed and analyzed. We anticipate submission for publication of the results of the study by the summer of 2025. CONCLUSIONS The execution of this prospective, mixed methods feasibility study requires a multidisciplinary effort. The study uses continuous vital sign monitoring, combined with prediction of deterioration risk, used to activate the RRT. Evaluating the feasibility and clinical impressions of this trial implementation is the first step in exploring monitoring-based predictive decision support. CLINICALTRIAL ClinicalTrials.gov NCT05108376; https://clinicaltrials.gov/study/NCT05108376 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/65360
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,033 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,078 | 0,015 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».