What Determines Do-Not-Resuscitate Status in Critically Ill HIV Patients?
Bibliographic record
Abstract
Abstract Background Mortality and morbidity of people living with HIV have declined in the era of combination antiretroviral therapy (cART). However, Intensive Care Unit (ICU) admission rates remain high. In this study, we identified predictors of Do-Not-Resuscitate (DNR) status in critically ill HIV patients. Methods Retrospective cohort study of all first-time admissions of HIV-infected patients to five ICUs in Edmonton, Alberta from 2002 to 2014. Data collected included demographics, comorbidities, markers of HIV disease severity and control, admission diagnoses, severity of illness, organ failure, and DNR status. Multivariable logistic regression analysis was performed to identify factors associated with DNR status. Results During the study period, 282 patients were admitted to the ICU for the first time. Mean (SD) age was 44 (±10) years, 169 (60%) were male, 134 (48%) aboriginal, 153 (55%) co-infected with hepatitis C virus, and 184 (65%) had a history of polysubstance use. Median (IQR) CD4 count and viral load were 125 (30–300) cells/mm3and 28,000 (110–270,000) copies/mL, respectively. Only 98 (35%) patients were receiving cART at the time of admission while 45 (16%) were newly diagnosed in the ICU. Most common admission diagnosis was sepsis 189 (64%), 213 (76%) received mechanical ventilation, 133 (47%) vasopressor support and 35 (12%) renal replacement therapy. Sixty-seven (24%) patients were DNR and support was withdrawn in 42 (15%). In multivariable analysis, APACHE II score (adjusted odds ratio [aOR] 1.13; 95% CI, 1.08–1.19, P < 0.001), coronary artery disease (CAD) (aOR 5.7; 95% CI, 1.2–27.8, P = 0.03), prior opportunistic infection (OI) (aOR 2.6; 95% CI, 1.2–5.6, P = 0.015) and duration of HIV infection (aOR 1.07 per year; 95% CI, 1.01–1.14, P = 0.025) were independently associated with DNR status. Other factors such as ethnicity, HIV risk factor(s), CD4 count and viral load were not associated with DNR status. Conclusion In this relatively young cohort, one in four patients had DNR status during ICU admission. DNR designation was associated with severity of illness, along with CAD, prior OI, and duration of HIV infection. Future work should characterize the timing of patient DNR orders relative to ICU admission and describe patient and provider-specific factors that may influence decision-making towards DNR status. Disclosures All authors: No reported disclosures.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".