Not All Critically Ill Obese Patients Are the Same: The Influence of Prior Comorbidities
Bibliographic record
Abstract
Purpose. Data suggest that obesity in critical illness is associated with improved outcomes. We postulate that these findings may be influenced by preillness comorbidities. We sought to determine if critically ill obese patients without significant comorbidity had improved mortality compared to obese patients with multiple comorbidities. Materials and Methods. We analyzed data from a prospective observational study conducted in 3 tertiary ICUs. Severely obese (body mass index ≥30) adults in the ICU for ≥24 hours were identified and classified into limited comorbid illnesses (0-1) or multiple comorbidities (≥2). The primary outcome was the odds ratio (OR) of mortality at day 28. Important secondary outcomes were ICU length of stay and ICU free days in the first 28 days. Results. 598 patients were enrolled; 183 had BMI ≥30. Of these, 38 had limited comorbidities and 145 had multiple comorbidities. In unadjusted analyses, obese patients with multiple comorbidities were 4.70 times (95% CI 1.07-20.6) as likely to die by day 28 compared to patients with limited comorbidities (P = 0.04). After stratifying by admission diagnosis and adjusting for APACHE II score, the influence of comorbidities remained large and trended toward significance (OR 4.28, 95% CI 0.92-20.02, P = 0.06). In adjusted analyses, obese patients with multiple comorbidities tended to have longer ICU duration (3.06 days, SE 2.28, P = 0.18) and had significantly fewer ICU free days in the first 28 days (-3.92 days, SE 1.83, P = 0.03). Conclusions. Not all critically ill obese patients are the same. Those with less comorbidity may have better outcomes than those with multiple comorbidities. This may be important when considering prognosis and discussing care with patients and families.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".