Implementation of a Surveillance System for Severe Acute Respiratory Infections at a Tertiary Care Hospital in Austria: Protocol for a Retrospective Longitudinal Feasibility Study
Bibliographic record
Abstract
BACKGROUND: The risk of a large number of severe acute respiratory infection (SARI) cases emerging is a global concern. SARI can overwhelm the health care capacity and cause several deaths. Therefore, the Austrian Agency for Health and Food Safety will explore the feasibility of implementing an automatic electronically based SARI surveillance system at a tertiary care hospital in Austria as part of the hospital network, initiated by the European Centre for Disease Prevention and Control. OBJECTIVE: We aim to investigate the availability of routinely collected health record data pertaining to respiratory infections and the optimal approach to use such available data for systematic surveillance of SARI in a real-world setting, describe the characteristics of patients with SARI before and after the beginning of the COVID-19 pandemic, and investigate the feasibility of identifying the risk factors for a severe outcome (intensive care unit admission or death) in patients with SARI. METHODS: We will test the feasibility of a surveillance system, as part of a large European network, at a tertiary care hospital in the province of Lower Austria (called Regional Hospital Wiener Neustadt). It will be a cross-sectional study for the inventory of the electronic data records and implementation of automatic data retrieval for the period of January 2019 through the end of December 2022. The analysis will include an exploration of the database structure, descriptive analysis of the general characteristics of the patients with SARI, estimation of the SARI incidence rate, and assessment of the risk factors and different levels of severity of patients with SARI using logistic regression analysis. RESULTS: This will be the first study to assess the feasibility of SARI surveillance at a large 800-bed tertiary care hospital in Austria. It will provide a general overview of the potential for establishing a hospital-based surveillance system for SARI. In addition, if successful, the electronic surveillance will be able to improve the response to early warning signs of new SARI, which will better inform policy makers in strengthening the surveillance system. CONCLUSIONS: The findings will support the expansion of the SARI hospital-based surveillance system to other hospitals in Austria. This network will be of use to Austria in preparing for future pandemics. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/47547.
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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 | 0.080 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".