23 Pre-surgical visceral adipose tissue may be a novel pre-surgical risk factor for acute kidney injury among clear cell renal cell cancer patients undergoing radical nephrectomy
Notice bibliographique
Résumé
Abstract Background Patients with renal cancer undergoing partial (PN) or radical nephrectomy (RN) are at risk for acute kidney injury (AKI). Established risk factor for AKI include baseline comorbidities (e.g., obesity, hypertension), normal renal parenchymal loss, and ischemia-reperfusion injury. Pre-surgical body composition may be an overlooked modifiable patient characteristic that influences risk of AKI. We hypothesized that patients with higher visceral adipose tissue quantity or lower skeletal muscle quantity would have a higher risk of AKI. Methods The RESOLVE study at Memorial Sloan Kettering Cancer Center is a retrospective study of 1239 patients with stage I-III clear cell renal cell carcinoma (ccRCC) undergoing PN or RN from 2000 to 2020. We excluded from analysis patients with solitary kidney, those without preoperative serum creatinine (sCr) measurements, and those without a sCr measurement within seven days following nephrectomy, yielding a study population of 1,200 ccRCC patients with longitudinal sCr assessment (n=754 PN; n=446 PN). AKI was defined as a binary variable using the Kidney Disease – Improving Global Outcomes (KDIGO) criteria, based on a threshold of either a 1.5x relative or 0.3 mg/dl absolute increase in sCr within seven days. The cross-sectional areas and radiodensities of visceral adipose and skeletal muscle tissues were determined from pre-surgical computed tomography (CT) scans at the third lumbar vertebrae using Automatica software. We used generalized linear models with a binomial family and identity link to estimate seven-day risk differences (RD) and 95% confidence intervals in subgroups defined by surgery type. Results AKI was more frequent among patients undergoing RN (66% vs 26% among PN). Male patients, those with higher eGFR prior to surgery, those with lower stage/smaller tumors, and those with a history of hyperlipidemia more frequently experienced AKI in the post-operative period. No associations were observed with other reported comorbidities (diabetes, hypertension). While no association was observed between BMI and risk of AKI, visceral adipose and skeletal muscle variables were significantly associated with risk of AKI in univariate models. After adjustment for age, sex, comorbidities, and other body composition variables, only higher visceral adipose tissue quantity remained significantly associated with increased risk of AKI [RD per 40 unit increase (95% CI): 5.2 (1.3, 9.2)]. Muscle characteristics were not associated with AKI in multivariable models. Among patients undergoing PN, we observed a higher frequency of AKI among male patients, those with higher stage/larger tumors, and those with longer ischemia times. No significant associations were observed with comorbidity histories. In univariate models, visceral adipose tissue quantity and quality as well as skeletal muscle quantity were associated with AKI risk; however, after adjustment for age, sex, comorbidities, and other body composition variables, we observed no significant associations between any body composition feature and AKI in patients undergoing PN. Conclusions Associations between pre-surgical body composition and risk of AKI vary by surgery type. Visceral adipose tissue quantity was associated with risk of AKI among patients undergoing RN, but not PN. This finding may be relevant for patient counseling, consideration for nephron-sparing surgery, and development of interventions to lower visceral adipose tissue quantity. Future studies should evaluate the impact of both pre-surgical and post-surgical change in body composition in relation on chronic kidney disease after nephrectomy.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».