Increased Mortality and Costs Associated with Adverse Events in Intensive Care Unit Patients
Bibliographic record
Abstract
Background: Adverse events (AEs) are defined as unintended complications occurring to patients as a result of medical care. AEs are especially prevalent in the intensive care unit (ICU) setting and may lead to negative patient outcomes. Although many studies have examined the impact of AEs on patient outcomes, few have investigated their associated costs. Methods: The study population consisted of 17 173 adult patients (≥18 years of age) who were admitted to the ICU at The Ottawa Hospital (TOH) between 2011 and 2016. AEs were categorized using an established International Classification of Diseases 10th revision (ICD-10) patient safety indicators (PSI) system for AE detection. Logistic regression was performed to determine the association between AEs and in-hospital outcomes, including mortality. In addition, we constructed a generalized linear model to assess the independent association between AEs and total hospital costs. Results: Patients who experienced an AE had longer total hospital and ICU lengths of stay, required more invasive ICU interventions, had more complex discharge plans, and experienced higher rates of in-hospital mortality compared to those who did not experience an AE. Average total hospital costs and ICU-specific costs were higher among patients who experienced an AE ($72 718; $46 715) relative to their counterparts ($20 543; $16 217), but the per day cost was comparable in both groups. After controlling for age, sex, patient comorbidities, and illness severity, AEs were significantly associated with an increased odds of mortality (OR = 1.13, 95% CIs = 1.04, 1.22) and total average costs (Cost Ratio = 1.04, 95% CIs = 1.06, 1.08). The most impactful AE subtypes from a cost- and patient-perspective were hospital-acquired infections (HAI) and cardiac-related AEs. Conclusion: Incidence of AEs among ICU patients is associated with higher patient mortality and elevated costs. Specific causes of these AEs should be investigated, with further protocols and interventions developed to reduce their occurrence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".