#229 Health inequalities and outcomes following acute kidney injury: a systematic review & meta-analyses of observational studies
Bibliographic record
Abstract
Abstract Background and Aims Inequalities in health describe the uneven distribution of health outcomes that result from genetic or environmental factors. The social determinants of health (SDOH) represent the socioeconomic context within which people are born, grow, work, live and age. Inequalities have been described in relation to AKI incidence and aetiology, including by sex/gender and race/ethnicity, but the extent to which inequalities and the SDOH impact on AKI outcomes is uncertain. The aim of this systematic review and meta-analysis was to determine the impact of health inequalities on AKI outcomes. Method This review has been registered on PROSPERO (CRD42023422307). We included observational studies of adults who experienced at least one episode of AKI that reported outcomes with stratification or subgroup comparison by sex/gender, race/ethnicity, socioeconomic status, income, education, employment, housing, smoking, mental health conditions, geography or insurance status. The primary outcome was all-cause mortality at any time post-AKI. Secondary outcomes were: progression to acute kidney disease; incident chronic kidney disease (CKD); progressive CKD; AKI recovery; cardiovascular events; hospitalisations; intensive care unit admission and hospital length of stay. The search was conducted in MEDLINE, Embase and Web of Science from inception to 10th January 2024. Study selection, extraction and risk of bias via the Newcastle-Ottawa scale were performed independently and studies meta-analysed where possible. Results 7,312 titles/abstracts were screened, and 36 studies included (n = 2,038,441 patients with AKI). Most studies were from high-income countries (n = 31) based on the World Bank classification. No studies contained data from low-income settings and few included data from lower-middle income countries (n = 3) (Fig. 1). Evidence predominantly related to sex/gender (n = 25), race/ethnicity (n = 14) and socioeconomic status (n = 11). Table 1 summarises the results for the primary outcome. In random-effects meta-analyses of relevant studies, no sex/gender differences in all-cause mortality (OR 1.02 [95% CI 0.83–1.25], I2 99%, n = 14) or AKI recovery (OR 0.88 [95% CI 0.69–1.11], I2 67%, n = 7) were seen. Of twelve studies reporting mortality by race/ethnicity, six found no variation by racial/ethnic group. Nine studies reported mortality by socioeconomic status, six of which showed an increase in all-cause mortality among the most deprived sub-populations compared to the most affluent with relative effect sizes varying from minimal (i.e. HR 0.999 least vs most deprived) to modest (i.e. HR 1.20 most vs least deprived). Few studies assessed the impact of mental health (n = 3), insurance (n = 1), housing (n = 2), geography (n = 1) or smoking status (n = 3) and no reports quantified the impact of income, education, employment or substance use. Heterogeneity and level of evidence were not formally assessed. Conclusion This systematic review highlights a paucity of evidence related to health inequalities and AKI, specifically from low-income settings and pertaining to the impact of mental health conditions, substance use, income, education, employment, insurance access, housing and geography. No sex/gender differences in AKI mortality or recovery were identified. Our results support the need for increased resource allocation for patients with AKI who live in socioeconomic deprivation due to increased mortality. There is a need for further evidence to inform policy and target interventions to achieve equitable kidney care.
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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 | 0.019 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".