1236-P: Patients with Diabetes Mellitus and Chronic Kidney Disease Affected by Nonalcoholic Fatty Liver Disease Have Greater Risk of Mortality and Worse Clinical Outcomes
Bibliographic record
Abstract
Background: DM & CKD are affecting million patients worldwide. Nonalcoholic Fatty Liver Disease (NAFLD) is emerging disease predisposing to adverse outcomes. There is limited evidence of clinical outcomes of NAFLD in patients with DM&CKD. We sought to investigate this population. Methods: We queried NIS 2017-2020 for adults hospitalized with DM&CKD & NAFLD. The primary outcome was inpatient mortality. The secondary outcomes were cardiogenic shock, cardiac arrest, GIB, intubation, LOS & total hospital charge. Multivariable logistic regression analysis was used to estimate clinical outcomes. Results: There were 11,254,722 hospitalizations with DM&CKD and 542,140 (4.8%) had NAFLD. NAFLD & non-NAFLD cohorts were with mean age of 53.3 vs. 54.0 yrs; males 53.3% vs 54.0%; Caucasians 58.7% vs 57.7%; HF 49.4% vs 49.5%; PH 11.4% vs 9.0%; AKI 51.9% vs 43.7%; obesity 24.9% vs 27.0%; HLD 47.0% vs 61.1%; anemia 23.6% vs 20.1%; ACS 10.6% vs 10.3%; stroke 1.8% vs 3.0%, respectively. Conclusion: DM&CKD cohort showed significantly higher mortality, worse clinical outcomes & resource utilization (Table 1). They were younger, obese, female, Caucasian, & with more PH, AKI, anemia, ACS. NAFLD is associated with greater risk for MACE, GIB, & ICU care. NALFD is an important risk factor among patients with DM&CKD that predicts adverse outcomes. Disclosure L. Khokhlov: None. U. Thapa: None. L. Pereira: None. M. Fatuyi: None. M. Ali: None. F. Hussain: None. A. Aboelnasr: None. A.C. Brown: None. M. Eerhart: None. M. Eshghabadi: None. C. Contreras: None. K. Shemisa: Speaker's Bureau; Pfizer Inc., Boehringer Ingelheim (Canada) Ltd., Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Amarin Corporation, Bayer Inc.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".