Observational studyScientific reports2023
Development of a nomogram model for the early prediction of sepsis-associated acute kidney injury in critically ill patients.
Observational study in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- Prediction models for sepsis-associated acute kidney injury: a systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- Risk factors and nomogram prediction model for prognosis in sepsis with acute kidney injury.BMC nephrology · 2026Article
- Development of a machine learning-based prediction model for acute kidney injury associated with respiratory failure in the intensive care unit.Clinical and experimental medicine · 2025Article
- Bio inspired feature selection and graph learning for sepsis risk stratification.Scientific reports · 2025Article
- Construction and verification of a nomogram model for the risk of death in sepsis patients.Scientific reports · 2025Article
- Construction and verification of a risk factor prediction model for neonatal severe pneumonia.Frontiers in medicine · 2025Article
- Using machine learning to predict the risk of short-term and long-term death in acute kidney injury patients after commencing CRRT.BMC nephrology · 2024Article
- Machine learning for prediction of acute kidney injury in patients diagnosed with sepsis in critical care.PloS one · 2024Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis-associated acute kidney injury is a common complication of sepsis, but it is difficult to predict sepsis-associated acute kidney injury. In this retrospective observational study, adult septic patients were recruited from the MIMIC-III database as the training cohort (n = 4764) and from Xiangya Hospital (n = 1568) and Zhang's database as validation cohorts. We identified eleven predictors with seven independent risk predictors of sepsis-associated acute kidney injury [fluid input_day1 ≥ 3390 ml (HR hazard ratio 1.42), fluid input_day2 ≥ 2734 ml (HR 1.64), platelet_min_day5 ≤ 224.2 × 10
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.