Pronostic des patients de plus de 75 ans avec insuffisance rénale aiguë lors d'un recours aux urgences
Bibliographic record
Abstract
Acute kidney injury (AKI) encounters multiple definitions, making its epidemiology hard to determine, with an heterogenous incidence as well as a high morbidity and mortality even with advanced renal replacement therapy and intensive care protocols. Kellum et al. (2021) showed us that in high-income countries, AKI incident rate was around 31.7% with a 33% mortality in the intensive care units. In France, AKI represents 25% of inpatients and more than 4 million of patients every year, highlighting its position as a real public health outcome. Because of its heterogeneity, AKI is hard to characterize, identify and study in the emergency department (ED), Foxwell et al. (2020, UK) and Scheuermeyer et al. (2017, Canada), respectively, shown a 12.2% AKI-related mortality at 30 days and a 10% AKI-related mortality at 90 days for all adult patients visiting the emergency department. The population of patients visiting the ED being more and more elderly (>75 years) with numerous comorbidities and frailty factors, one of the aim of this retrospective study would be to evaluate AKI-related mortality and identify its potential predictive factors in an older (>75 years) population of patients, 60 days after an ED visit. Our study focused on including 75 years old and above patients attending the ED of Louis-Mourier hospital (Paris) during a 7 months’ time period (May 2022 to December 2022). Upon the 2 652 visits, 331 patients were identified as presenting AKI criteria; 180 had reached AKI stage 1, 114 stage 2 while 37 had reached stage 3 of AKI. 60 (18%) of these 331 included patients died 60 days after attending the ED. For these patients, the serum creatinine (SCr) was significantly higher than in the surviving group (185 μmol/l vs 160 μmol/l); similar results were found for Frailty criteria were (42% vs 20%). Furthermore, stage 1 AKI patients seemed to show significantly higher survival probability, compared to stage 2 and 3 (p = 0.008). After regression multivariate analysis, age (OR 1.08 [1.02-1.14]), SCr (OR 1.004 [1.00-1.009]), heart frequency (OR 1.027 [1.01-1.04]), hypernatremia (OR 1.09 [1.03-1.16]), pulmonary ED diagnosis (OR 2.28 [1.01-5.16]) and cancer (OR 9.72 [2.52-37.45]), in association with ED-AKI, are independent factors of 60 days-mortality. 75 years old patients, visiting the ED with a biologically identified ED-AKI, have a 60 days mortality of 18% upon ED-admission. The predictive factors of such 60 days mortality are advanced age, high SCr, pulmonary disease, cancer, high heart frequency and hypernatremia.
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".