A risk prediction score for acute kidney injury in the intensive care unit
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Acute kidney injury (AKI) is common in critically ill patients and is associated with high morbidity and mortality. Early identification of high-risk patients provides an opportunity to develop strategies for prevention, early diagnosis and treatment of AKI. METHODS: We undertook this multicenter prospective cohort study to develop and validate a risk score for predicting AKI in patients admitted to an intensive care unit (ICU). Patients were screened for predictor variables within 48 h of ICU admission. Baseline and acute risk factors were recorded at the time of screening and serum creatinine was measured daily for up to 7 days. A risk score model for AKI was developed with multivariate regression analysis combining baseline and acute risk factors in the development cohort (573 patients) and the model was further evaluated on a test cohort (144 patients). Validation was performed on an independent prospective cohort of 1300 patients. The discriminative ability of the risk model was assessed by the area under the receiver operating characteristic curve (AUROC) and model calibration was evaluated by Hosmer-Lemeshow statistic. AKI was defined by the Kidney Disease: Improving Global Outcomes criteria (absolute change of 0.3 mg/dL or relative change of 50% from baseline serum creatinine in 48 h to 7 days, respectively). RESULTS: AKI developed in 754 (37.2%) patients. In the multivariate model, chronic kidney disease, chronic liver disease, congestive heart failure, hypertension, atherosclerotic coronary vascular disease, pH ≤ 7.30, nephrotoxin exposure, sepsis, mechanical ventilation and anemia were identified as independent predictors of AKI and the AUROC for the model in the test cohort was 0.79 [95% confidence interval (CI) 0.70-0.89]. On the external validation cohort, the AUROC value was 0.81 (95% CI 0.78-0.83). The risk model demonstrated good calibration in both cohorts. Positive and negative predictive values for the optimal cutoff value of ≥ 5 points in test and validation cohorts were 22.7 and 96.1% and 31.8 and 95.4%, respectively. CONCLUSIONS: A risk score model integrating chronic comorbidities and acute events at ICU admission can identify patients at high risk to develop AKI. This risk assessment tool could help clinicians to stratify patients for primary prevention, surveillance and early therapeutic intervention to improve care and outcomes of ICU patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it