SGLT2 Inhibition in Patients With Type 2 Diabetes Mellitus Post-Nephrectomy: A Single-Center Case Series
Notice bibliographique
Résumé
Background: Nephrectomy is the mainstay of treatment for many kidney cancers, but has been correlated with increased incidence of acute kidney injury (AKI) and chronic kidney disease (CKD). Recently, sodium-glucose cotransporter-2 (SGLT2) inhibition has been shown to decrease the incidence of end-stage kidney disease and death in people with type 2 diabetes mellitus (T2D). However, at present, there has been no description of the use of SGLT2 inhibition in patients with T2D and solitary kidney despite the high risk of CKD progression. Objective: To characterize the use of SGLT2 inhibition and kidney function in a series of patients with T2D with prior nephrectomy for renal cell carcinoma (RCC). Design: Retrospective case series. Setting: University hospital outpatient onco-nephrology clinic. Patients: Patients post-nephrectomy for RCC with T2D who were prescribed an SGLT2 inhibitor. Measurements: Serum creatinine, albumin to creatinine ratio (ACR), HgA1c, and blood pressure measurements. Methods: Patients post-nephrectomy with incident use of SGLT2 inhibitor were identified from an existing registry of patients followed in the Onco-Nephrology Clinic at our institution from May 2019 to March 2021. Demographics, medication use, time since nephrectomy, cancer diagnosis, serum creatinine, ACR measurements, and blood pressure measurements were extracted from electronic medical records. Results: Five patients were identified who had initiated SGLT2 inhibition post-nephrectomy. All patients were male, had T2D, and a prior history of hypertension. Renal cell carcinoma was the clinical indication for nephrectomy in all patients. None of patients were prescribed diuretics, and all were receiving renin-angiotensin system (RAS) inhibition therapies. The time from nephrectomy to SGLT2 inhibitor initiation ranged from 5 to 74 months. Baseline mean estimated glomerular filtration rate (eGFR) values were 49 mL/min/1.73 m 2 (95% confidence interval [CI]: 31.5-66.5), and mean ACRs were 8.7 mg/mmol (95% CI: 0.6-16.9). After 6 months of SGLT2 inhibition, the mean eGFR and ACR values were 58 mL/min/1.73 m 2 (95% CI: 29.7-86.2) and 23.8 mg/mmol (95% CI: 0-60), respectively. After 16 to 18 months of follow-up (4 patients), the mean eGFR was 56 mL/min/1.73 m 2 (95% CI: 37.3-74.7), and mean ACR was 10.5 (95% CI: 0-30.5), similar to baseline values before SGTL2i therapy initiation. At baseline, mean systolic blood pressure was 128 mm Hg (95% CI: 118.3-140.9) and remained similar after 12 months of treatment (mean 131 mm Hg [95% CI: 112.3-149.7]). There were no adverse events related to AKI, electrolyte disturbances, ketoacidosis, or genitourinary infections during the 18-month follow-up period. Limitations: Small sample size, lack of a comparison group, and the variable timing of clinical data collection, including eGFR levels following initiation of SGLT2 inhibition. Conclusions: SGLT2 inhibition is becoming a standard component of nephrology care to reduce kidney function decline, cardiovascular risk, and mortality. To our knowledge, our report is the first to provide longitudinal data on SGLT2 inhibitor usage in patients with T2D and solitary kidneys post-nephrectomy. Larger prospective studies are needed to determine the efficacy and safety of SGLT2 inhibition strategies for kidney protection in patients post-nephrectomy.
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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».