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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]

Purpose: In ICU patients with acute lung injury, the pulse oximetry saturation (Spo 2) to fraction of inspired oxygen (Fio 2) (S/F) ratio is a reliable surrogate measure for the P/F (Pao 2/Fio 2) ratio. Our goal was to determine the correlation of the S/F to the P/F in a large sample of patients undergoing general anesthesia and the influence of positive end-expiratory pressure (PEEP) on this measure. Methods: We studied adult general anesthetics performed with arterial blood gas analysis. Intraoperative data were collected from an anesthesia information system. The S/F ratios corresponding to P/F ratios of 300 were determined. Results: A total of 4439 values were collected. Linear correlation between S/F and P/F was identified (r = 0.46; P < .01) with a P/F of 300 corresponding to an S/F of 206. The correlation was stronger in patients with 5 to 9 cm PEEP (r = 0.52; P < .01), more than 9 cm H2O PEEP (r = 0.68; P < .01), and a P/F ratio of 300 or less (r = 0.61; P < .01). Conclusion: The S/F correlates with the P/F in our cohort of patients undergoing general anesthesia, especially those ventilated with PEEP more than 9 cm H2O and/or with P/F less than 300. It has use as a noninvasive measure to screen for increased pulmonary dysfunction and to trend oxygenation during a general anesthetic. [Copyright &y& Elsevier]