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19. Cause and preventability of in-hospital mortality after PCI: A statewide root-cause analysis of 1,163 deaths.

Author

Moroni, Francesco, Seth, Milan, Changezi, Hameem U., Karve, Milind, Arora, Dilip S., Sharma, Manoj, Pielsticker, Elizabeth, Berman, Aaron D., Lee, Daniel, Qureshi, M. Imran, Azzalini, Lorenzo, Sukul, Devraj, and Gurm, Hitinder S.

Subjects
HOSPITAL mortality, PERCUTANEOUS coronary intervention, DEATH rate, VASOMOTOR conditioning, CAUSES of death
Abstract

Background: Mortality is the most devastating complication of percutaneous coronary interventions (PCI). Identifying the most common causes and mechanisms of death after PCI in contemporary practice is an important step in further reducing periprocedural mortality. Objectives: To systematically analyze the cause and circumstances of in-hospital mortality in a large, multi-center, statewide cohort. Methods: In-hospital deaths after PCI occurring at 39 hospitals included in the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) between 2012 and 2014 were retrospectively reviewed using validated methods. A priori PCI-related mortality risk was estimated using the validated BMC2 model. Results: A total of 1,163 deaths after PCI were included in the study. Mean age was 71±13 years, and 507 (44%) were women. Left ventricular failure was the most common cause of death (52% of cases). The circumstance of death was most commonly related to prior acute cardiovascular condition (61% of cases). Procedural complications were considered contributing to mortality in 235 (20%) cases. Death was rated as not preventable or slightly preventable in 1,045 (89.9%) cases. The majority of the deaths occurred in intermediate or high-risk patients, but 328 (28.2%) deaths occurred in low-risk patients (<5% predicted risk of mortality). PCI was considered rarely appropriate in 30% of preventable deaths. Conclusions: In-hospital mortality after PCI is rare, and primarily related to pre-existing critical acute cardiovascular condition. However, approximately 10% of deaths were preventable. Further research is needed to characterize preventable deaths, in order to develop strategies to improve procedural safety. [ABSTRACT FROM AUTHOR]

Published
2024
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20. Merging machine learning and patient preference: a novel tool for risk prediction of percutaneous coronary interventions.

Author

Hamilton, David E, Albright, Jeremy, Seth, Milan, Painter, Ian, Maynard, Charles, Hira, Ravi S, Sukul, Devraj, and Gurm, Hitinder S

Subjects
PERCUTANEOUS coronary intervention, MACHINE learning, PATIENT preferences, ACUTE kidney failure, DATABASES
Abstract

Background and Aims Predicting personalized risk for adverse events following percutaneous coronary intervention (PCI) remains critical in weighing treatment options, employing risk mitigation strategies, and enhancing shared decision-making. This study aimed to employ machine learning models using pre-procedural variables to accurately predict common post-PCI complications. Methods A group of 66 adults underwent a semiquantitative survey assessing a preferred list of outcomes and model display. The machine learning cohort included 107 793 patients undergoing PCI procedures performed at 48 hospitals in Michigan between 1 April 2018 and 31 December 2021 in the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) registry separated into training and validation cohorts. External validation was conducted in the Cardiac Care Outcomes Assessment Program database of 56 583 procedures in 33 hospitals in Washington. Results Overall rate of in-hospital mortality was 1.85% (n = 1999), acute kidney injury 2.51% (n = 2519), new-onset dialysis 0.44% (n = 462), stroke 0.41% (n = 447), major bleeding 0.89% (n = 942), and transfusion 2.41% (n = 2592). The model demonstrated robust discrimination and calibration for mortality {area under the receiver-operating characteristic curve [AUC]: 0.930 [95% confidence interval (CI) 0.920–0.940]}, acute kidney injury [AUC: 0.893 (95% CI 0.883–0.903)], dialysis [AUC: 0.951 (95% CI 0.939–0.964)], stroke [AUC: 0.751 (95%CI 0.714–0.787)], transfusion [AUC: 0.917 (95% CI 0.907–0.925)], and major bleeding [AUC: 0.887 (95% CI 0.870–0.905)]. Similar discrimination was noted in the external validation population. Survey subjects preferred a comprehensive list of individually reported post-procedure outcomes. Conclusions Using common pre-procedural risk factors, the BMC2 machine learning models accurately predict post-PCI outcomes. Utilizing patient feedback, the BMC2 models employ a patient-centred tool to clearly display risks to patients and providers (https://shiny.bmc2.org/pci-prediction/). Enhanced risk prediction prior to PCI could help inform treatment selection and shared decision-making discussions. [ABSTRACT FROM AUTHOR]

Published
2024
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