![]() ![]() Multivariable models adjusted for covariates in table 1. (A) Estimated probability of AKI from the univariable moving-window with a bin width of 10% of the data (B) multivariable logistic regression smoothed by restricted cubic spline with 3 knots at 10th, 50th, and 90th percentiles of given blood pressure component. Univariable and multivariable relationship between AKI and lowest blood pressure for a cumulative 5 min for each of four blood pressure components. ![]() Relationship between lowest blood pressure values and acute kidney injury (AKI). MINS, myocardial injury after noncardiac surgery SBP, systolic arterial pressure. The blue lines in (A) and smoothed lines with 95% confidence bands in (B) indicate estimated probability of myocardial injury as a function of the lowest 5 min of each component. Histogram at the bottom of each graph shows the fraction of patients at each lowest blood pressure value. Based mainly on the multivariable plots, blood pressure component thresholds of 90 mmHg for systolic blood pressure, 65 mmHg for mean arterial pressure (MAP), 50 mmHg for diastolic blood pressure (DBP), and 35 mmHg for pulse pressure (PP) were visual change-points associated with increasing odds of myocardial injury. ( A) Estimated probability of myocardial injury from a univariable moving-window with a bin width of 10% of the data (B) Multivariable logistic regression smoothed by restricted cubic spline with 3 knots at 10th, 50th, and 90th percentiles of given blood pressure component. Univariable and multivariable relationship between myocardial injury and lowest blood pressure for a cumulative 5 min for each of four blood pressure components. Relationship between lowest blood pressure values and myocardial injury. We then used multivariable logistic regression to model the relationships while adjusting for confounding a linearity test between each blood pressure component and response was modeled by a restricted cubic spline function with three knots, located at the 10th, 50th, and 90th percentiles, and Wald chi-square test. Starting from the lowest values of the predictor variable, the proportion with the outcome was calculated and plotted for a fixed number of subjects the bin was repeatedly moved to the right by a fixed number of subjects to create multiple overlapping bins until the proportion with outcome was estimated and plotted for the entire range of the predictor. Univariable moving average plots were constructed for each (binary) outcome variable as a way to display the relationship between a binary outcome and continuous predictor. ![]() ![]() Specifically, we first assessed the univariable relationship between myocardial and kidney injury and the lowest cumulative 5 min for each blood pressure component using moving-average smoothing plots. ![]()
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