Exploring the Impact of Age, Frailty, and Multimorbidity on the Effect of ICU Interventions: A Systematic Review of Randomized Controlled Trials
Bibliographic record
Abstract
OBJECTIVES: To date, age, frailty, and multimorbidity have been used primarily to inform prognosis in older adults. It remains uncertain, however, whether these patient factors may also predict response to critical care interventions or treatment outcomes. DATA SOURCES: We conducted a systematic search of top general medicine and critical care journals for randomized controlled trials (RCTs) examining critical care interventions published between January 1, 2011, and December 31, 2021. STUDY SELECTION: We included RCTs of critical care interventions that examined any one of three subgroups-age, frailty, or multimorbidity. We excluded cluster RCTs, studies that did not report interventions in an ICU, and studies that did not report data examining subgroups of age, frailty, or multimorbidity. DATA EXTRACTION: We collected study characteristics (single vs. multicountry enrollment, single vs. multicenter enrollment, funding, sample size, intervention, comparator, primary outcome and secondary outcomes, length of follow-up), study population (inclusion and exclusion criteria, average age in intervention and comparator groups), and subgroup data. We used the Instrument for assessing the Credibility of Effect Modification Analyses instrument to evaluate the credibility of subgroup findings. DATA SYNTHESIS: Of 2037 unique citations, we included 48 RCTs comprising 50,779 total participants. Seven (14.6%) RCTs found evidence of statistically significant effect modification based on age, whereas none of the multimorbidity or frailty subgroups found evidence of statistically significant subgroup effect. Subgroup credibility ranged from very low to moderate. CONCLUSIONS: Most critical care RCTs do not examine for subgroup effects by frailty or multimorbidity. Although age is more commonly considered, the cut-point is variable, and relative effect modification is rare. Although interventional effects are likely similar across age groups, shared decision-making based on individual patient preferences must remain a priority. RCTs focused specifically on critically ill older adults or those living with frailty and/or multimorbidity are crucial to further address this research question.
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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 | 0.062 | 0.241 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.022 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".