The Importance of Mehran Score to Predict Acute Kidney Injury in Patients with TAVI: A Large Multicenter Cohort Study.

Mehran Score acute kidney injury transcatheter aortic valve implantation

Journal

Journal of cardiovascular development and disease
ISSN: 2308-3425
Titre abrégé: J Cardiovasc Dev Dis
Pays: Switzerland
ID NLM: 101651414

Informations de publication

Date de publication:
24 May 2023
Historique:
received: 29 03 2023
revised: 16 05 2023
accepted: 20 05 2023
medline: 27 6 2023
pubmed: 27 6 2023
entrez: 27 6 2023
Statut: epublish

Résumé

Transcatheter aortic valve implantation (TAVI) has developed as an alternative to surgery for symptomatic high-risk patients with aortic stenosis (AS). An important complication of TAVI is acute kidney injury. The purpose of the study was to investigate if the Mehran Score (MS) could be used to predict acute kidney injury (AKI) in TAVI patients. This is a multicenter, retrospective, observational study including 1180 patients with severe AS. The MS comprised eight clinical and procedural variables: hypotension, congestive heart failure class, glomerular filtration rate, diabetes, age >75 years, anemia, need for intra-aortic balloon pump, and contrast agent volume use. We assessed the sensitivity and specificity of the MS in predicting AKI following TAVI, as well as the predictive value of MS with each AKI-related characteristic. Patients were categorized into four risk groups based on MS: low (≤5), moderate (6-10), high (11-15), and very high (≥16). Post-procedural AKI was observed in 139 patients (11.8%). MS classes had a higher risk of AKI in the multivariate analysis (HR 1.38, 95% CI, 1.43-1.63, MS was shown to be a predictor of AKI development in TAVI patients.

Sections du résumé

BACKGROUND BACKGROUND
Transcatheter aortic valve implantation (TAVI) has developed as an alternative to surgery for symptomatic high-risk patients with aortic stenosis (AS). An important complication of TAVI is acute kidney injury. The purpose of the study was to investigate if the Mehran Score (MS) could be used to predict acute kidney injury (AKI) in TAVI patients.
METHODS METHODS
This is a multicenter, retrospective, observational study including 1180 patients with severe AS. The MS comprised eight clinical and procedural variables: hypotension, congestive heart failure class, glomerular filtration rate, diabetes, age >75 years, anemia, need for intra-aortic balloon pump, and contrast agent volume use. We assessed the sensitivity and specificity of the MS in predicting AKI following TAVI, as well as the predictive value of MS with each AKI-related characteristic.
RESULTS RESULTS
Patients were categorized into four risk groups based on MS: low (≤5), moderate (6-10), high (11-15), and very high (≥16). Post-procedural AKI was observed in 139 patients (11.8%). MS classes had a higher risk of AKI in the multivariate analysis (HR 1.38, 95% CI, 1.43-1.63,
CONCLUSIONS CONCLUSIONS
MS was shown to be a predictor of AKI development in TAVI patients.

Identifiants

pubmed: 37367393
pii: jcdd10060228
doi: 10.3390/jcdd10060228
pmc: PMC10298873
pii:
doi:

Types de publication

Journal Article

Langues

eng

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Auteurs

Salvatore Arrotti (S)

Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, 41124 Modena, Italy.

Fabio Alfredo Sgura (FA)

Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, 41124 Modena, Italy.

Daniel Enrique Monopoli (DE)

Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, 41124 Modena, Italy.

Valerio Siena (V)

Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, 41124 Modena, Italy.

Giulio Leo (G)

Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, 41124 Modena, Italy.

Vernizia Morgante (V)

Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, 41124 Modena, Italy.

Paolo Cataldo (P)

Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, 41124 Modena, Italy.

Paolo Magnavacchi (P)

Cardiology Division, Baggiovara Hospital, 41100 Modena, Italy.

Davide Gabbieri (D)

Cardiac Surgery Division, Hesperia Hospital, 41125 Modena, Italy.

Vincenzo Guiducci (V)

Division of Cardiology, AUSL-IRCCS Reggio Emilia, 42121 Reggio Emilia, Italy.

Giorgio Benatti (G)

Cardiology Division, Parma University Hospital, 44129 Parma, Italy.

Luigi Vignali (L)

Cardiology Division, Parma University Hospital, 44129 Parma, Italy.

Giuseppe Boriani (G)

Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, 41124 Modena, Italy.

Rosario Rossi (R)

Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, 41124 Modena, Italy.

Classifications MeSH