Evidence map›Paper›PMID 40512357›Full record

ArticleCurrent medical science2025

Malperfusion in Acute Type A Aortic Dissection: Development of a Predictive Diagnostic Model.

Kan-Paatib Barnabo Nampoukime, Adeoumi Esperance Monteiro Igwenandji, You-Min Pan, Lud Merveil Norbely Nouani, Djessica Fortes Gomes, Mustafa Abbas Farhood Sultani, Hai-Hao Wang

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Article in Current medical science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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7 authors.

Kan-Paatib Barnabo NampoukimeDivision of Cardiothoracic and Vascular Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China. barnabo06@gmail.com.
Adeoumi Esperance Monteiro IgwenandjiDivision of Cardiothoracic and Vascular Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
You-Min PanDivision of Cardiothoracic and Vascular Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China. panyoumin@126.com.
Lud Merveil Norbely NouaniTongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Djessica Fortes GomesTongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Mustafa Abbas Farhood SultaniTongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Hai-Hao WangDivision of Cardiothoracic and Vascular Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo investigate the clinical predictors of malperfusion in patients with acute type A aortic dissection (ATAAD) and to construct a diagnostic model to identify high-risk individuals.

methodsA retrospective analysis of 553 ATAAD patients from Tongji Hospital divided into malperfusion and non-malperfusion groups was conducted. Logistic regression was used to identify independent predictors of the outcome. Model performance via the Hosmer-Lemeshow test, decision curve analysis (DCA), the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and predictive values.

resultsMalperfusion was observed in 28.4% of ATAAD patients. Significant predictors included elevated lactate dehydrogenase (LDH) (OR: 1.0019, 95% CI: 1.0002-1.0036, P = 0.027), alanine aminotransferase (ALT) (OR: 0.9936, 95% CI: 0.987-1.000, P = 0.046) and estimated glomerular filtration rate (eGFR) (OR: 0.9877, 95% CI: 0.977-0.998, P = 0.021), suggesting roles for tissue ischemia and impaired renal or hepatic function. Other variables, such as D-dimer, uric acid, creatinine, and NT-proBNP, showed trends toward significance but did not reach the 0.05 threshold. The model demonstrated good calibration (Hosmer-Lemeshow P = 0.318), moderate discriminatory power (AUC = 0.725), high specificity (93.62%), and low sensitivity (26.75%).

conclusionThe model based on routine biochemical markers provides a practical approach for the early identification of malperfusion in ATAAD patients. It shows strong specificity and clinical utility, although its limited sensitivity highlights the need for further refinement. Future improvements should focus on incorporating additional clinical or imaging data to increase diagnostic accuracy.

Indexed as

Aortic AneurysmAortic DissectionAcute DiseaseAdultAgedAlanine TransaminaseBiomarkersFemaleFibrin Fibrinogen Degradation ProductsGlomerular Filtration RateHumansL-Lactate DehydrogenaseMaleMiddle AgedNatriuretic Peptide, BrainPeptide FragmentsAlanine TransaminaseBiomarkersFibrin Fibrinogen Degradation Productsfibrin fragment DL-Lactate DehydrogenaseNatriuretic Peptide, BrainPeptide FragmentsUric AcidAcute type A aortic dissectionBiomarkersDiagnosisMalperfusion

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PMID40512357

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