Comorbidities and Subgroups of Patients Surviving Severe Acute Hypercapnic Respiratory Failure in the Intensive Care Unit
Bibliographic record
Abstract
RATIONALE: No methodical assessment of the lung, cardiac, and sleep function of patients surviving an acute hypercapnic respiratory failure episode requiring admission to the intensive care unit (ICU) has been reported in the literature. OBJECTIVES: To prospectively investigate the prevalence and impact of comorbidities in patients treated by mechanical ventilator support (invasive or noninvasive) for acute hypercapnic respiratory failure in the ICU. METHODS: Seventy-eight consecutive patients admitted for an episode of acute hypercapnic respiratory failure underwent an assessment of lung, cardiac, and sleep function by pulmonary function tests, transthoracic echocardiography, and polysomnography 3 months after ICU discharge. MEASUREMENTS AND MAIN RESULTS: Sixty-seven percent (52 of 78) of patients exhibited chronic obstructive pulmonary disease (COPD), although only 19 had been previously diagnosed. Patients without COPD were primarily obese. Prevalence of severe obstructive sleep apnea was 51% (95% confidence interval, 34-69) in patients with COPD and 81% (95% confidence interval, 54-96) in patients without COPD. Previously undiagnosed cardiac dysfunction with preserved ejection fraction was highly prevalent (44%), as was hypertension (67%). More than half of the population demonstrated at least three major comorbidities known to precipitate acute hypercapnic respiratory failure. Multimorbidity was associated with longer time to hospital discharge. Hospital readmission or death occurred in 46% of patients over an average of 3.5 months after discharge. CONCLUSIONS: Severe hypercapnic respiratory failure requiring ICU admission resulted primarily from COPD or obesity. Major comorbidities are highly prevalent in both cases and most often ignored. Surviving acute hypercapnic respiratory failure should be an opportunity to systematically evaluate lung, heart, and sleep functions to improve poor outcomes. Clinical trial registered with www.clinicaltrials.gov (NCT 02111876).
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 | 0.001 | 0.002 |
| 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 | 0.003 |
| 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".