Invasive Mechanical Ventilation Duration Prediction using Survival Analysis
Bibliographic record
Abstract
Abstract Invasive mechanical ventilation is one of the leading life support machines in the intensive care unit (ICU). By identifying the predictors of ventilation time upon arrival, important information can be gathered to improve decisions regarding capacity planning. In this study, first-day ventilated patients’ ventilation time was analyzed using survival analysis. The probabilistic behaviour of ventilation time duration was analyzed and the predictors of ventilation time duration were determined based on available first-day covariates. A retrospective analysis of ICU ventilation time in Ontario was performed with data from ICU patients obtained from the Critical Care Information System (CCIS) in Ontario between July 2015 and December 2016. As part of the protocol for inclusion, a patient must have been connected to an invasive ventilator upon arrival to the ICU. Parametric survival methods were used to characterize ventilation time and to determine associated covariates. Parametric and non-parametric methods were used to determine predictors of ventilation duration for first-day ventilated patients. A total of 128,030 patients visited the ICUs between July 2015 and December 2016. 51,966 (40.59%) patients received invasive mechanical ventilation on arrival. Analysis of ventilation duration suggested that the log-normal distribution provided the best fit to ventilation time, whereas the log-logistic Accelerated Failure Time model best describes the association between the covariates and ventilation duration. ICU site, admission source, admission diagnosis, scheduled admission, scheduled surgery, referring physician, central venous line treatment, arterial line treatment, intracranial pressure monitor treatment, extra-corporeal membrane oxygen treatment, intraaortic balloon pump treatment, other interventions, age group, pre-ICU LOS, and MODS score were significant predictors of the ICU ventilation time. The results show substantial variability in ICU ventilation duration for different ICUs, patient’s demographics, and underlying conditions, and highlight mechanical ventilation as an important driver of ICU costs. The predictive performance of the proposed model showed that both the model and the data can be used to predict an individual patient’s ventilation time and to provide insight into predictors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".