A Risk Model of Admitting Patients With Silent SARS-CoV-2 Infection to Surgery and Development of Severe Postoperative Outcomes and Death
Bibliographic record
Abstract
OBJECTIVE: To model the risk of admitting silent COVID-19-infected patients to surgery with subsequent risk of severe pulmonary complications and mortality. SUMMARY BACKGROUND DATA: With millions of operations cancelled during the COVID-19 pandemic, pressure is mounting to reopen and increase surgical activity. The risk of admitting patients who have silent SARS-Cov-2 infection to surgery is not well investigated, but surgery on patients with COVID-19 is associated with poor outcomes. We aimed to model the risk of operating on nonsymptomatic infected individuals and associated risk of perioperative adverse outcomes and death. METHODS: We developed 2 sets of models to evaluate the risk of admitting silent COVID-19-infected patients to surgery. A static model let the underlying infection rate (R rate) and the gross population-rate of surgery vary. In a stochastic model, the dynamics of the COVID-19 prevalence and a fixed population-rate of surgery was considered. We generated uncertainty intervals (UIs) for our estimates by running low and high scenarios using the lower and upper 90% uncertainty limits. The modelling was applied for high-income regions (eg, United Kingdom (UK), USA (US) and European Union without UK (EU27), and for the World (WORLD) based on the WHO standard population. RESULTS: Both models provided concerning rates of perioperative risk over a 24-months period. For the US, the modelled rates were 92,000 (UI 68,000-124,000) pulmonary complications and almost 30,000 deaths (UI 22,000-40·000), respectively; for Europe, some 131,000 patients (UI 97,000-178,000) with pulmonary complications and close to 47,000 deaths (UI 34,000-63,000) were modelled. For the UK, the model suggested a median daily number of operations on silently infected ranging between 25 and 90, accumulating about 18,700 (UI 13,700-25,300) perioperative pulmonary complications and 6400 (UI 4600-8600) deaths. In high-income regions combined, we estimated around 259,000 (UI 191,000-351,000) pulmonary complications and 89,000 deaths (UI 65,000-120,000). For the WORLD, even low surgery rates estimated a global number of 1.2 million pulmonary complications and 350,000 deaths. CONCLUSIONS: The model highlights a considerable risk of admitting patients with silent COVID-19 to surgery with an associated risk for adverse perioperative outcomes and deaths. Strategies to avoid excessive complications and deaths after surgery during the pandemic are needed.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".