Hitinder S Gurm1, Milan Seth, Judith Kooiman, David Share. 1. Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI 48109, USA. hgurm@med.umich.edu
Abstract
OBJECTIVES: The aim of the study was to develop and validate a tool for predicting risk of contrast-induced nephropathy (CIN) in patients undergoing contemporary percutaneous coronary intervention (PCI). BACKGROUND: CIN is a common complication of PCI and is associated with adverse short- and long-term outcomes. Previously described risk scores for predicting CIN either have modest discrimination or include procedural variables and thus cannot be applied for pre-procedural risk stratification. METHODS: Random forest models were developed using 46 pre-procedural clinical and laboratory variables to estimate the risk of CIN in patients undergoing PCI. The 15 most influential variables were selected for inclusion in a reduced model. Model performance estimating risk of CIN and new requirement for dialysis (NRD) was evaluated in an independent validation data set using area under the receiver-operating characteristic curve (AUC), with net reclassification improvement used to compare full and reduced model CIN prediction after grouping in low-, intermediate-, and high-risk categories. RESULTS: Our study cohort comprised 68,573 PCI procedures performed at 46 hospitals between January 2010 and June 2012 in Michigan, of which 48,001 (70%) were randomly selected for training the models and 20,572 (30%) for validation. The models demonstrated excellent calibration and discrimination for both endpoints (CIN AUC for full model 0.85 and for reduced model 0.84, p for difference <0.01; NRD AUC for both models 0.88, p for difference = 0.82; net reclassification improvement for CIN 2.92%, p = 0.06). CONCLUSIONS: The risk of CIN and NRD among patients undergoing PCI can be reliably calculated using a novel easy-to-use computational tool (https://bmc2.org/calculators/cin). This risk prediction algorithm may prove useful for both bedside clinical decision making and risk adjustment for assessment of quality.
OBJECTIVES: The aim of the study was to develop and validate a tool for predicting risk of contrast-induced nephropathy (CIN) in patients undergoing contemporary percutaneous coronary intervention (PCI). BACKGROUND:CIN is a common complication of PCI and is associated with adverse short- and long-term outcomes. Previously described risk scores for predicting CIN either have modest discrimination or include procedural variables and thus cannot be applied for pre-procedural risk stratification. METHODS: Random forest models were developed using 46 pre-procedural clinical and laboratory variables to estimate the risk of CIN in patients undergoing PCI. The 15 most influential variables were selected for inclusion in a reduced model. Model performance estimating risk of CIN and new requirement for dialysis (NRD) was evaluated in an independent validation data set using area under the receiver-operating characteristic curve (AUC), with net reclassification improvement used to compare full and reduced model CIN prediction after grouping in low-, intermediate-, and high-risk categories. RESULTS: Our study cohort comprised 68,573 PCI procedures performed at 46 hospitals between January 2010 and June 2012 in Michigan, of which 48,001 (70%) were randomly selected for training the models and 20,572 (30%) for validation. The models demonstrated excellent calibration and discrimination for both endpoints (CIN AUC for full model 0.85 and for reduced model 0.84, p for difference <0.01; NRD AUC for both models 0.88, p for difference = 0.82; net reclassification improvement for CIN 2.92%, p = 0.06). CONCLUSIONS: The risk of CIN and NRD among patients undergoing PCI can be reliably calculated using a novel easy-to-use computational tool (https://bmc2.org/calculators/cin). This risk prediction algorithm may prove useful for both bedside clinical decision making and risk adjustment for assessment of quality.
Authors: Daniel M Alyesh; Milan Seth; David C Miller; James M Dupree; John Syrjamaki; Devraj Sukul; Simon Dixon; Eve A Kerr; Hitinder S Gurm; Brahmajee K Nallamothu Journal: Circ Cardiovasc Qual Outcomes Date: 2018-06
Authors: Robert M Cronin; Jacob P VanHouten; Edward D Siew; Svetlana K Eden; Stephan D Fihn; Christopher D Nielson; Josh F Peterson; Clifton R Baker; T Alp Ikizler; Theodore Speroff; Michael E Matheny Journal: J Am Med Inform Assoc Date: 2015-06-23 Impact factor: 4.497
Authors: Jennifer N Cooper; Lai Wei; Soledad A Fernandez; Peter C Minneci; Katherine J Deans Journal: Comput Biol Med Date: 2014-12-08 Impact factor: 4.589