Background: Finger cuff technologies allow continuous noninvasive arterial blood pressure (AP) and cardiac output/index (CO/CI) monitoring. Methods: We performed a meta-analysis of studies comparing finger cuff-derived AP and CO/CI measurements with invasive measurements in surgical or critically ill patients. We calculated overall random effects model-derived pooled estimates of the mean of the differences and of the percentage error (PE; CO/CI studies) with 95%-confidence intervals (95%-CI), pooled 95%-limits of agreement (95%-LOA), Cochran's Q and I 2 (for heterogeneity). Results: The pooled mean of the differences (95%-CI) was 4.2 (2.8 to 5.62) mm Hg with pooled 95%-LOA of e14.0 to 22.5 mm Hg for mean AP (Q¼230.4 [P<0.001], I 2 ¼91%). For mean AP, the mean of the differences between finger cuff technologies and the reference method was 5±8 mm Hg in 9/27 data sets (33%). The pooled mean of the differences (95%-CI) was e0.13 (e0.43 to 0.18) L min À1 with pooled 95%-LOA of e2.56 to 2.23 L min À1 for CO (Q¼66.7 [P<0.001], I 2 ¼90%) and 0.07 (0.01 to 0.13) L min À1 m À2 with pooled 95%-LOA of e1.20 to 1.15 L min À1 m À2 for CI (Q¼5.8 [P¼0.326], I 2 ¼0%). The overall random effects model-derived pooled estimate of the PE (95%-CI) was 43 (37 to 49)% (Q¼48.6 [P<0.001], I 2 ¼63%). In 4/19 data sets (21%) the PE was 30%, and in 10/19 data sets (53%) it was 45%. Conclusions: Study heterogeneity was high. Several studies showed interchangeability between AP and CO/CI measurements using finger cuff technologies and reference methods. However, the pooled results of this meta-analysis indicate that AP and CO/CI measurements using finger cuff technologies and reference methods are not interchangeable in surgical or critically ill patients. Clinical trial number: PROSPERO registration number: CRD42019119266.

The measurement of arterial pressure (AP) is a key component of hemodynamic monitoring. A variety of different innovative AP monitoring technologies became recently available. The decision to use these technologies must be based on their measurement performance in validation studies. These studies are AP method comparison studies comparing a new method ("test method") with a reference method. In these studies, different comparative statistical tests are used including correlation analysis, Bland-Altman analysis, and trending analysis. These tests provide information about the statistical agreement without adequately providing information about the clinical relevance of differences between the measurement methods. To overcome this problem, we, in this study, propose an "error grid analysis" for AP method comparison studies that allows illustrating the clinical relevance of measurement differences. We constructed smoothed consensus error grids with calibrated risk zones derived from a survey among 25 specialists in anesthesiology and intensive care medicine. Differences between measurements of the test and the reference method are classified into 5 risk levels ranging from "no risk" to "dangerous risk"; the classification depends on both the differences between the measurements and on the measurements themselves. Based on worked examples and data from the Multiparameter Intelligent Monitoring in Intensive Care II database, we show that the proposed error grids give information about the clinical relevance of AP measurement differences that cannot be obtained from Bland-Altman analysis. Our approach also offers a framework on how to adapt the error grid analysis for different clinical settings and patient populations.

Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background Normal blood pressure varies among individuals and over the circadian cycle. Preinduction blood pressure may not be representative of a patient’s normal blood pressure profile and cannot give an indication of a patient’s usual range of blood pressures. This study therefore aimed to determine the relationship between ambulatory mean arterial pressure and preinduction, postinduction, and intraoperative mean arterial pressures. Methods Ambulatory (automated oscillometric measurements at 30-min intervals) and preinduction, postinduction, and intraoperative mean arterial pressures (1-min intervals) were prospectively measured and compared in 370 American Society of Anesthesiology physical status classification I or II patients aged 40 to 65 yr having elective noncardiac surgery with general anesthesia. Results There was only a weak correlation between the first preinduction and mean daytime mean arterial pressure (r = 0.429, P &lt; 0.001). The difference between the first preinduction and mean daytime mean arterial pressure varied considerably among individuals. In about two thirds of the patients, the lowest postinduction and intraoperative mean arterial pressures were lower than the lowest nighttime mean arterial pressure. The difference between the lowest nighttime mean arterial pressure and a mean arterial pressure of 65 mmHg varied considerably among individuals. The lowest nighttime mean arterial pressure was higher than 65 mmHg in 263 patients (71%). Conclusions Preinduction mean arterial pressure cannot be used as a surrogate for the normal daytime mean arterial pressure. The lowest postinduction and intraoperative mean arterial pressures are lower than the lowest nighttime mean arterial pressure in most patients.