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Metabolic Syndrome Predicts Worse Perioperative Outcomes in Patients Treated With Partial Nephrectomy for Renal Cell Carcinoma

S. Luzzago, C. Palumbo, G. Rosiello, A. Pecoraro, M. Deuker, F. Stolzenbach, FA. Mistretta, Z. Tian, G. Musi, E. Montanari, SF. Shariat, F. Saad, A. Briganti, O. de Cobelli, PI. Karakiewicz

. 2020 ; 140 (-) : 91-97. [pub] 20200306

Language English Country United States

Document type Journal Article

Persistent link   https://www.medvik.cz/link/bmc22012599

OBJECTIVE: To test the association between metabolic syndrome (MetS) and its components (high blood pressure, body mass index [BMI] ≥ 30, altered fasting glucose, low high-density lipoprotein cholesterol and high triglycerides) on perioperative outcomes after partial nephrectomy (PN). METHODS: Within the National Inpatient Sample database (2000-2015) we identified all PN patients. First, temporal trends of MetS were reported. Second, the effect of MetS components was tested in multivariable logistic regression models predicting overall and specific perioperative complications. Third, we tested for dose-response from the concomitant effect of multiple MetS components. All models were weighted and adjusted for clustering, as well as all available patient and hospital characteristics. RESULTS: Of 25,875 patients: (1) 59.3% had high blood pressure, (2) 14.7% had BMI ≥ 30, (3) 21.7% had altered fasting glucose, (4) 20.2% had high triglycerides, and (5) <0.01% had low high-density lipoprotein cholesterol. One vs 2 vs 3 vs 4 MetS components were recorded in 34.9% vs 22.9% vs 8.9% vs 2.2% patients. Of all, 11.1% exhibited ≥ 3 components and qualified for MetS. The rates of MetS increased over time (estimated annual percentage changes: +12.0%;P <.001). The 4 tested MetS components (high blood pressure, BMI ≥ 30, altered fasting glucose, and high triglycerides) achieved independent predictor status in multivariable models predicting overall, cardiac, miscellaneous medical, vascular, and respiratory complications, as well as transfusions. Moreover, a statistically significant dose-response was confirmed for the same endpoints. CONCLUSION: MetS and its components consistently and strongly predict perioperative complications after PN. Moreover, the strength of the effect was directly proportional to the number of MetS components exhibited by each individual patient, even if formal MetS diagnosis of ≥ 3 components has not been met.

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$a Luzzago, Stefano $u Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montreal Health Center, Montreal, Quebec, Canada; Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy. Electronic address: stefanoluzzago@gmail.com
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$a Metabolic Syndrome Predicts Worse Perioperative Outcomes in Patients Treated With Partial Nephrectomy for Renal Cell Carcinoma / $c S. Luzzago, C. Palumbo, G. Rosiello, A. Pecoraro, M. Deuker, F. Stolzenbach, FA. Mistretta, Z. Tian, G. Musi, E. Montanari, SF. Shariat, F. Saad, A. Briganti, O. de Cobelli, PI. Karakiewicz
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$a OBJECTIVE: To test the association between metabolic syndrome (MetS) and its components (high blood pressure, body mass index [BMI] ≥ 30, altered fasting glucose, low high-density lipoprotein cholesterol and high triglycerides) on perioperative outcomes after partial nephrectomy (PN). METHODS: Within the National Inpatient Sample database (2000-2015) we identified all PN patients. First, temporal trends of MetS were reported. Second, the effect of MetS components was tested in multivariable logistic regression models predicting overall and specific perioperative complications. Third, we tested for dose-response from the concomitant effect of multiple MetS components. All models were weighted and adjusted for clustering, as well as all available patient and hospital characteristics. RESULTS: Of 25,875 patients: (1) 59.3% had high blood pressure, (2) 14.7% had BMI ≥ 30, (3) 21.7% had altered fasting glucose, (4) 20.2% had high triglycerides, and (5) <0.01% had low high-density lipoprotein cholesterol. One vs 2 vs 3 vs 4 MetS components were recorded in 34.9% vs 22.9% vs 8.9% vs 2.2% patients. Of all, 11.1% exhibited ≥ 3 components and qualified for MetS. The rates of MetS increased over time (estimated annual percentage changes: +12.0%;P <.001). The 4 tested MetS components (high blood pressure, BMI ≥ 30, altered fasting glucose, and high triglycerides) achieved independent predictor status in multivariable models predicting overall, cardiac, miscellaneous medical, vascular, and respiratory complications, as well as transfusions. Moreover, a statistically significant dose-response was confirmed for the same endpoints. CONCLUSION: MetS and its components consistently and strongly predict perioperative complications after PN. Moreover, the strength of the effect was directly proportional to the number of MetS components exhibited by each individual patient, even if formal MetS diagnosis of ≥ 3 components has not been met.
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$a Palumbo, Carlotta $u Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montreal Health Center, Montreal, Quebec, Canada; Urology Unit, ASST Spedali Civili of Brescia. Department of Medical and Surgical Specialties, Radiological Science and Public Health, University of Brescia, Italy
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$a Rosiello, Giuseppe $u Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montreal Health Center, Montreal, Quebec, Canada; Department of Urology and Division of Experimental Oncology, URI, Urological Research Institute, IRCCS San Raffaele Scientific Institute, Milan, Italy
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$a Pecoraro, Angela $u Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montreal Health Center, Montreal, Quebec, Canada; Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Turin, Italy
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$a Deuker, Marina $u Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montreal Health Center, Montreal, Quebec, Canada; Department of Urology, University Hospital Frankfurt, Frankfurt am Main, Germany
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$a Stolzenbach, Franziska $u Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montreal Health Center, Montreal, Quebec, Canada; Martini Klinik, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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$a Mistretta, Francesco Alessandro $u Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montreal Health Center, Montreal, Quebec, Canada; Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
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$a Tian, Zhe $u Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montreal Health Center, Montreal, Quebec, Canada
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$a Musi, Gennaro $u Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
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$a Montanari, Emanuele $u Department of Urology, IRCCS Fondazione Ca' Granda-Ospedale Maggiore Policlinico, University of Milan, Milan, Italy
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$a Shariat, Shahrokh F $u Department of Urology, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria; Departments of Urology, Weill Cornell Medical College, New York, NY; Department of Urology, University of Texas Southwestern, Dallas, TX; Department of Urology, Second Faculty of Medicine, Charles University, Prag, Czech Republic; Institute for Urology and Reproductive Health, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
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$a Saad, Fred $u Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montreal Health Center, Montreal, Quebec, Canada
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$a Briganti, Alberto $u Department of Urology and Division of Experimental Oncology, URI, Urological Research Institute, IRCCS San Raffaele Scientific Institute, Milan, Italy
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$a de Cobelli, Ottavio $u Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
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