ArticleRenal failure2025
Preoperative serum Raman spectroscopy and machine learning for predicting postoperative acute kidney injury after acute type A aortic dissection.
Article in Renal failure, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute type A aortic dissection (ATAAD) represents an aggressive cardiovascular condition with substantial morbidity and mortality rates. Postoperative acute kidney injury (AKI) is a complication that contributes significantly to worse prognosis following total aortic arch replacement (TAAR) surgery. Currently, there are neither effective predictive methods nor data regarding the application of preoperative serum Raman spectroscopy for early identification of AKI after TAAR surgery. Our study aimed to develop a predictive model and assess its effectiveness in prediction of AKI after TAAR surgery. Preoperative blood biochemical indicators were investigated while Raman spectra from preoperative serum samples were analyzed by quantifying the peak intensities. Following appropriate feature selection procedures, multiple machine learning classification models were integrated to identify the optimal model for predicting postoperative AKI. To facilitate personalized risk assessment, Shapley Additive exPlanations (SHAP) interpretation was developed. In summary, 396 patients were enrolled in this study and incidence of postoperative AKI was 56.1% (222/396). Out of the 15 biochemical parameters and 5 intensities of specific Raman peaks possessing statistical differences, 6 indictors (creatinine, cystatin C, neutrophil, platelet, glucose, carbon dioxide combining power [CO2cp]) and 3 Raman peak intensities (505, 947 and 1208 cm
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.