Tesi etd-01122022-033733
Link copiato negli appunti
Tipo di tesi
Master di Secondo Livello
Autore
IACOVELLI, FORTUNATO
URN
etd-01122022-033733
Titolo
Acute kidney injury after transcatheter aortic valve implantation: predictive value of currently available risk scores
Struttura
Istituto di Scienze della Vita
Corso di studi
Corsi Alta Formazione - PERCUTANEOUS INTERVENTIONAL TREATMENT OF STRUCTURAL HEART DISEASES
Commissione
relatore Dott. BERTI, SERGIO
Presidente Prof. PASSINO, CLAUDIO
Relatore Dott. Contegiacomo, Gaetano
Presidente Prof. PASSINO, CLAUDIO
Relatore Dott. Contegiacomo, Gaetano
Parole chiave
- acute kidney injury
- aortic stenosis
- risk scores
- transcatheter aortic valve implantation
Data inizio appello
21/01/2022;
Disponibilità
completa
Riassunto analitico
Aim of this study was to compare four different acute kidney injury (AKI) risk scores (Mehran score, ACEF score, WBH score and CR4EATME3AD3 score) and to evaluate their predictive performance after TAVI in a huge population.
No significant differences were observed between patients with and without AKI in term of demographic characteristics. Patients developing AKI were 235 they were older and presented more frequently chronic kidney disease (defined as an estimated glomerular filtration rate <60 ml/min/1.73m2) and high mean pulmonary arterial systolic pressure (41.95±14.34 mmHg, p <0.001). There were significant differences between AKI e no-AKI patients in terms of AKI risk scores. For each risk score we analyzed the predictive value of a cut-off, found in literature, and we observed that there were significant differences between the two groups. At the univariate analysis age (OR 1.04, 95% CI 1.01 – 1.07, p=0,007), peripheral arterial disease (PAD) (OR 1.56, 95% CI 1.16 - 2.10, p=0.003 ), chronic kidney disease (OR 1.79, 95% CI 1.35 – 2.36, p <0.001), chronic or persistent atrial fibrillation (OR 1.44, 95% CI 1.01 – 2.04, p=0.043), Mehran score (OR 1.08, 95% CI 1.03 – 1.12, p <0.001), WBH score (OR 1.32, 95% CI 1.18 – 1.47, p <0,001), ACEF score (OR 1.37, 95% CI 1.08 – 1.74, p=0.010), CR4EATME3AD3 score (OR 1.10, 95% CI 1.06 – 1.15, p <0.001), low osmolar contrast medium (LOCM) (OR 2.37, 95% CI 1.62 – 3.48, p <0,001), contrast medium volume/eGFR ≥3.9 (OR 2.15 95% CI 1.49 – 3.11, p <0.001), any bleeding (OR 2 95% CI 1.50 – 2.66, p <0.001), any transfusion (OR 2.57, 95% CI 1.84 - 3.58, p <0,001) were predictor of AKI. Moreover, at a multivariate analysis only PAD, chronic or persistent atrial fibrillation, contrast medium volume/eGFR ≥3.9, LOCM and any bleeding and transfusion were significant independent predictors of AKI.
Our ROC curve analysis showed a correlation between AKI and all the risk score with these cut-off: Mehran score 11.5 (AUC 0.576 ± 0.020, p <0.001), WBH 3.5 (AUC 0.604 ± 0.020, p <0,001), ACEF score 1.39 (AUC 0.551 ± 0.021, p=0.012), CR4EATME3AD3 score 5.5 (AUC 0.597 ± 0.020 p<0.001).
This was the first study that compared four different AKI risk score in a huge population; the sensitivity and specificity of these scores are far from what we expected: would not be the case to analyze the data and create a more performant and predictive AKI risk score?
No significant differences were observed between patients with and without AKI in term of demographic characteristics. Patients developing AKI were 235 they were older and presented more frequently chronic kidney disease (defined as an estimated glomerular filtration rate <60 ml/min/1.73m2) and high mean pulmonary arterial systolic pressure (41.95±14.34 mmHg, p <0.001). There were significant differences between AKI e no-AKI patients in terms of AKI risk scores. For each risk score we analyzed the predictive value of a cut-off, found in literature, and we observed that there were significant differences between the two groups. At the univariate analysis age (OR 1.04, 95% CI 1.01 – 1.07, p=0,007), peripheral arterial disease (PAD) (OR 1.56, 95% CI 1.16 - 2.10, p=0.003 ), chronic kidney disease (OR 1.79, 95% CI 1.35 – 2.36, p <0.001), chronic or persistent atrial fibrillation (OR 1.44, 95% CI 1.01 – 2.04, p=0.043), Mehran score (OR 1.08, 95% CI 1.03 – 1.12, p <0.001), WBH score (OR 1.32, 95% CI 1.18 – 1.47, p <0,001), ACEF score (OR 1.37, 95% CI 1.08 – 1.74, p=0.010), CR4EATME3AD3 score (OR 1.10, 95% CI 1.06 – 1.15, p <0.001), low osmolar contrast medium (LOCM) (OR 2.37, 95% CI 1.62 – 3.48, p <0,001), contrast medium volume/eGFR ≥3.9 (OR 2.15 95% CI 1.49 – 3.11, p <0.001), any bleeding (OR 2 95% CI 1.50 – 2.66, p <0.001), any transfusion (OR 2.57, 95% CI 1.84 - 3.58, p <0,001) were predictor of AKI. Moreover, at a multivariate analysis only PAD, chronic or persistent atrial fibrillation, contrast medium volume/eGFR ≥3.9, LOCM and any bleeding and transfusion were significant independent predictors of AKI.
Our ROC curve analysis showed a correlation between AKI and all the risk score with these cut-off: Mehran score 11.5 (AUC 0.576 ± 0.020, p <0.001), WBH 3.5 (AUC 0.604 ± 0.020, p <0,001), ACEF score 1.39 (AUC 0.551 ± 0.021, p=0.012), CR4EATME3AD3 score 5.5 (AUC 0.597 ± 0.020 p<0.001).
This was the first study that compared four different AKI risk score in a huge population; the sensitivity and specificity of these scores are far from what we expected: would not be the case to analyze the data and create a more performant and predictive AKI risk score?
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