Assessing Clinical Impressions of Early Warning Score Integration With the Rapid Response Team: Protocol for a Prospective Cohort Study (Preprint)
Bibliographic record
Abstract
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
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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 | 0.033 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.078 | 0.015 |
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".