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Intraoperative hypotension is associated with myocardial injury, acute kidney injury, and death. In routine practice, specific causes of intraoperative hypotension are often unclear. A more detailed understanding of underlying haemodynamic alterations of intraoperative hypotension may identify specific treatments. We thus aimed to use machine learning - specifically, hierarchical clustering - to identify underlying haemodynamic alterations causing intraoperative hypotension in major abdominal surgery patients. Specifically, we tested the hypothesis that there are distinct endotypes of intraoperative hypotension, which may help refine therapeutic interventions.We conducted a secondary analysis of intraoperative haemodynamic measurements from a prospective observational study in 100 patients who had major abdominal surgery under general anaesthesia. We used stroke volume index, heart rate, cardiac index, systemic vascular resistance index, and pulse pressure variation measurements. Intraoperative hypotension was defined as any mean arterial pressure ≤65 mm Hg or a mean arterial pressure between 66 and 75 mm Hg requiring a norepinephrine infusion rate exceeding 0.1 μg kgA total of 615 episodes of intraoperative hypotension occurred in 82 patients (46 [56%] female; median age: 64 [57, 73] yr) who had surgery of a median duration of 270 (195, 335) min. Hierarchical clustering revealed six distinct intraoperative hypotension endotypes. Based on their clinical characteristics, we labelled these endotypes as (1) myocardial depression, (2) bradycardia, (3) vasodilation with cardiac index increase, (4) vasodilation without cardiac index increase, (5) hypovolaemia, and (6) mixed type.Hierarchical clustering identified six endotypes of intraoperative hypotension. If validated, considering these intraoperative hypotension endotypes may enable causal treatment of intraoperative hypotension.

The new noninvasive finger sensor system NICCI (Getinge; Gothenburg, Sweden) allows continuous cardiac output monitoring. We aimed to investigate its cardiac output measurement performance.To investigate the NICCI system's cardiac output measurement performance.Prospective method comparison study.University Medical Center Hamburg-Eppendorf, Hamburg, Germany.Fifty-one patients after cardiac surgery.We performed a method comparison study in 51 patients after cardiac surgery to compare NICCI cardiac output (CO NICCI ) and NICCI cardiac output calibrated to pulmonary artery thermodilution cardiac output measurement (CO NICCI-CAL ) with pulmonary artery thermodilution cardiac output (CO PAT ). As a secondary analysis we also compared CNAP cardiac output (CO CNAP ) and externally calibrated CNAP cardiac output (CO CNAP-CAL ) with CO PAT .We analysed 299 cardiac output measurement pairs. The mean of the differences (95% limits of agreement) between CO NICCI and CO PAT was 0.6 (-1.8 to 3.1) l min -1 with a percentage error of 48%. The mean of the differences between CO NICCI-CAL and CO PAT was -0.4 (-1.9 to 1.1) l min -1 with a percentage error of 29%. The mean of the differences between CO CNAP and CO PAT was 1.0 (-1.8 to 3.8) l min -1 with a percentage error of 53%. The mean of the differences between CO CNAP-CAL and CO PAT was -0.2 (-2.0 to 1.6) l min -1 with a percentage error of 35%.The agreement between CO NICCI and CO PAT is not clinically acceptable.The study was registered in the German Clinical Trial Register (DRKS00023189) after inclusion of the first patient on October 2, 2020.

Perioperative hemodynamic management aims to optimize organ perfusion pressure and blood flow-assuming this ensures that oxygen delivery meets cellular metabolic needs. Cellular metabolic needs are reflected by energy expenditure. A better understanding of energy expenditure under general anesthesia could help tailor perioperative hemodynamic management to actual demands. We thus sought to assess energy expenditure under general anesthesia. Our primary hypothesis was that energy expenditure under general anesthesia is lower than preoperative awake resting energy expenditure.We conducted an observational study on patients having elective noncardiac surgery at the University Medical Center Hamburg-Eppendorf (Germany) between September 2019 and March 2020. We assessed preoperative awake resting energy expenditure, energy expenditure under general anesthesia, and energy expenditure after surgery using indirect calorimetry. We compared energy expenditure under general anesthesia at incision to preoperative awake resting energy expenditure using a Wilcoxon signed-rank test for paired measurements.We analyzed 60 patients. Median (95% confidence interval [CI]) preoperative awake resting energy expenditure was 953 (95% CI, 906-962) kcal d-1 m-2. Median energy expenditure under general anesthesia was 680 (95% CI, 642-711) kcal d-1 m-2-and thus 263 (95% CI, 223-307) kcal d-1 m-2 or 27% (95% CI, 23%-30%) lower than preoperative awake resting energy expenditure (P.001).Median energy expenditure under general anesthesia is about one-quarter lower than preoperative awake resting energy expenditure in patients having noncardiac surgery.

Acute myocardial injury is common after noncardiac surgery and associated with mortality. Impaired intraoperative cardiovascular dynamics are a risk factor for acute myocardial injury. Optimizing intraoperative cardiovascular dynamics may thus reduce acute myocardial injury. We aimed to investigate the effect of intraoperative personalized goal-directed hemodynamic management on the incidence of acute myocardial injury. We hypothesized that personalized goal-directed hemodynamic management reduces the incidence of acute myocardial injury compared to routine hemodynamic management in high-risk patients having major abdominal surgery. We performed a post-hoc secondary analysis of a randomized clinical trial including 180 high-risk major abdominal surgery patients that were randomized to personalized goal-directed hemodynamic management or routine hemodynamic management. We compared the incidences of acute myocardial injury—defined according to the Fourth Universal Definition of Myocardial Infarction (2018)—between patients randomized to personalized goal-directed hemodynamic management or routine hemodynamic management by calculating the relative and absolute risk reduction together with 95% Wald confidence intervals and P values. Acute myocardial injury occurred in 4 of 90 patients (4%) in the personalized goal-directed hemodynamic management group and in 12 of 90 patients (13%) in the routine hemodynamic management group (relative risk: 0.33, 95% confidence interval: 0.11 to 0.99, P = 0.036; absolute risk reduction: − 9%, 95% confidence interval: − 17% to − 0.68%, P = 0.034). In this post-hoc secondary analysis, intraoperative personalized goal-directed hemodynamic management reduced the incidence of acute myocardial injury compared to routine hemodynamic management in high-risk patients having major abdominal surgery. This needs to be confirmed in larger prospective trials.

We compared blood pressure (BP) values obtained with a new optical smartphone application (OptiBP™) with BP values obtained using a non-invasive automatic oscillometric brachial cuff (reference method) during the first 2 h of surveillance in a post-anesthesia care unit in patients after non-cardiac surgery. Three simultaneous BP measurements of both methods were recorded every 30 min over a 2-h period. The agreement between measurements was investigated using Bland-Altman and error grid analyses. We also evaluated the performance of the OptiBP™ using ISO81060-2:2018 standards which requires the mean of the differences ± standard deviation (SD) between both methods to be less than 5 mmHg ± 8 mmHg. Of 120 patients enrolled, 101 patients were included in the statistical analysis. The Bland-Altman analysis demonstrated a mean of the differences ± SD between the test and reference methods of + 1 mmHg ± 7 mmHg for mean arterial pressure (MAP), + 2 mmHg ± 11 mmHg for systolic arterial pressure (SAP), and + 1 mmHg ± 8 mmHg for diastolic arterial pressure (DAP). Error grid analysis showed that the proportions of measurement pairs in risk zones A to E were 90.3% (no risk), 9.7% (low risk), 0% (moderate risk), 0% (significant risk), 0% (dangerous risk) for MAP and 89.9%, 9.1%, 1%, 0%, 0% for SAP. We observed a good agreement between BP values obtained by the OptiBP™ system and BP values obtained with the reference method. The OptiBP™ system fulfilled the AAMI validation requirements for MAP and DAP and error grid analysis indicated that the vast majority of measurement pairs (≥ 99%) were in risk zones A and B.Trial Registration ClinicalTrials.gov Identifier: NCT04262323.

The NICCI system (Getinge, Gothenburg, Sweden) is a new noninvasive haemodynamic monitoring system using a finger sensor.We aimed to investigate the performance of the NICCI system to measure blood pressure and pulse pressure variation compared with intra-arterial measurements.A prospective method comparison study.University Medical Center Hamburg-Eppendorf, Hamburg, Germany.Forty-seven neurosurgery patients.We performed a method comparison study in 47 neurosurgery patients to compare NICCI blood pressure measurements (BP NICCI ) with intra-arterial blood pressure measurements (BP ART ) (Bland-Altman analysis, four-quadrant plot, error grid analysis) and NICCI pulse pressure variation measurements (PPV NICCI ) with pulse pressure variation calculated manually from the intra-arterial blood pressure waveform (PPV ART ) (Bland-Altman analysis, predictive agreement, Cohen's kappa).The mean of the differences ± standard deviation (95% limits of agreement) between BP NICCI and BP ART was 11 ± 10 mmHg (-8 to 30 mmHg) for mean blood pressure (MBP), 3 ± 12 mmHg (-21 to 26 mmHg) for systolic blood pressure (SBP) and 12 ± 10 mmHg (-8 to 31 mmHg) for diastolic blood pressure (DBP). In error grid analysis, 54% of BP NICCI and BP ART MBP measurement pairs were classified as 'no risk', 43% as 'low risk', 3% as 'moderate risk' and 0% as 'significant risk' or 'dangerous risk'. The mean of the differences between PPV NICCI and PPV ART was 1 ± 3% (-4 to 6%). The predictive agreement between PPV NICCI and PPV ART was 80% and Cohen's kappa was 0.55.The absolute agreement between BP NICCI and BP ART was not clinically acceptable. We recommend not using the current version of the NICCI system for blood pressure monitoring during surgery. The absolute agreement between PPV NICCI and PPV ART was clinically acceptable with moderate predictive agreement regarding pulse pressure variation categories. The NICCI system needs to be further developed and re-evaluated when an improved version is available.The study was registered in the German Clinical Trials Register (DRKS00023188) on 2 October 2020.

Hypotension during induction of anaesthesia is associated with organ injury. Continuous arterial pressure monitoring might help reduce hypotension. We tested the hypothesis that continuous intra-arterial compared with intermittent oscillometric arterial pressure monitoring reduces hypotension during induction of anaesthesia in noncardiac surgery patients.In this single-centre randomised trial, 242 noncardiac surgery patients in whom intra-arterial arterial pressure monitoring was planned were randomised to unblinded continuous intra-arterial or to intermittent oscillometric arterial pressure monitoring (with blinded intra-arterial arterial pressure monitoring) during induction of anaesthesia. The primary endpoint was the area under a mean arterial pressure (MAP) of 65 mm Hg within the first 15 min of induction of anaesthesia. Secondary endpoints included areas under MAP values of 60, 50, and 40 mm Hg and durations of MAP values65,60,50, and40 mm Hg.There were 224 subjects available for analysis. The median (25th-75th percentile) area under a MAP of 65 mm Hg was 15 (2-36) mm Hg • min in subjects assigned to continuous intra-arterial monitoring and 46 (7-111) mm Hg • min in subjects assigned to intermittent oscillometric monitoring (P0.001). Subjects assigned to continuous intra-arterial monitoring had smaller areas under MAP values of 60, 50, and 40 mm Hg and shorter durations of MAP values65,60,50, and40 mm Hg than subjects assigned to intermittent oscillometric monitoring.Continuous intra-arterial arterial pressure monitoring reduces hypotension during induction of anaesthesia compared with intermittent oscillometric arterial pressure monitoring in noncardiac surgery patients. In patients for whom an arterial catheter is planned, clinicians might therefore consider inserting the arterial catheter before rather than after induction of anaesthesia.NCT04894019.

The finger-cuff system CNAP (CNSystems Medizintechnik, Graz, Austria) allows non-invasive automated measurement of pulse pressure variation (PPVCNAP). We sought to validate the PPVCNAP-algorithm and investigate the agreement between PPVCNAP and arterial catheter-derived manually calculated pulse pressure variation (PPVINV). This was a prospective method comparison study in patients having neurosurgery. PPVINV was the reference method. We applied the PPVCNAP-algorithm to arterial catheter-derived blood pressure waveforms (PPVINV−CNAP) and to CNAP finger-cuff-derived blood pressure waveforms (PPVCNAP). To validate the PPVCNAP-algorithm, we compared PPVINV−CNAP to PPVINV. To investigate the clinical performance of PPVCNAP, we compared PPVCNAP to PPVINV. We used Bland–Altman analysis (absolute agreement), Deming regression, concordance, and Cohen's kappa (predictive agreement for three pulse pressure variation categories). We analyzed 360 measurements from 36 patients. The mean of the differences between PPVINV−CNAP and PPVINV was −0.1% (95% limits of agreement (95%-LoA) −2.5 to 2.3%). Deming regression showed a slope of 0.99 (95% confidence interval (95%-CI) 0.91 to 1.06) and intercept of −0.02 (95%-CI −0.52 to 0.47). The predictive agreement between PPVINV−CNAP and PPVINV was 92% and Cohen’s kappa was 0.79. The mean of the differences between PPVCNAP and PPVINV was −1.0% (95%-LoA−6.3 to 4.3%). Deming regression showed a slope of 0.85 (95%-CI 0.78 to 0.91) and intercept of 0.10 (95%-CI −0.34 to 0.55). The predictive agreement between PPVCNAP and PPVINV was 82% and Cohen’s kappa was 0.48. The PPVCNAP-algorithm reliably calculates pulse pressure variation compared to manual offline pulse pressure variation calculation when applied on the same arterial blood pressure waveform. The absolute and predictive agreement between PPVCNAP and PPVINV are moderate.

BACKGROUND Cardiac output is an important hemodynamic variable and determines oxygen delivery. In contrast to blood pressure, cardiac output is rarely measured even in high-risk surgical patients, suggesting that clinicians consider blood pressure to be a reasonable indicator of systemic blood flow. However, the relationship depends on constant vascular tone and volume, both of which routinely vary during anesthesia and surgery. We therefore tested the hypothesis that there is no clinically meaningful correlation between mean arterial pressure and cardiac index in major abdominal surgery patients. METHODS In this prospective observational study, we assessed the relationship between mean arterial pressure and cardiac index in 100 patients having major abdominal surgery under general anesthesia. RESULTS The pooled within-patient correlation coefficient calculated using meta-analysis methods was r = 0.34 (95% confidence interval, 0.28-0.40). Linear regression using a linear mixed effects model of cardiac index on mean arterial pressure revealed that cardiac index increases by 0.014 L·min-1·m-2 for each 1 mm Hg increase in mean arterial pressure. The 95% Wald confidence interval of this slope was 0.011 to 0.018 L·min-1·m-2·mm Hg-1 and thus within predefined equivalence margins of -0.03 and 0.03 L·min-1·m-2·mm Hg-1, thereby demonstrating lack of clinically meaningful association between mean arterial pressure and cardiac index. CONCLUSIONS There is no clinically meaningful correlation between mean arterial pressure and cardiac index in patients having major abdominal surgery. Intraoperative blood pressure is thus a poor surrogate for cardiac index.

Predicting fluid responsiveness is essential when treating surgical or critically ill patients. When using a pulmonary artery catheter, pulse pressure variation and systolic pressure variation can be calculated from right ventricular and pulmonary artery pressure waveforms.We conducted a prospective interventional study investigating the ability of right ventricular pulse pressure variation (PPVThirty-three patients were included in the final analysis. Thirteen patients (39%) were fluid-responders with a mean increase in cardiac index of 25.3%. The AUROC was 0.60 (95%-CI 0.38 to 0.81) for PPVRight ventricular and pulmonary artery pulse pressure variation and systolic pressure variation seem to be weak predictors of fluid responsiveness in CABG surgery patients.

Postinduction and intraoperative hypotension are associated with organ injury in non-cardiac surgery patients. Automated ambulatory blood pressure monitoring can identify chronic arterial hypertension and nocturnal blood pressure non-dipping. We tested the hypotheses that: a) chronic arterial hypertension and nocturnal non-dipping are independent risk factors for postinduction and intraoperative hypotension; and b) adding information on chronic arterial hypertension and nocturnal non-dipping improves hypotension prediction models based on readily available preoperative clinical information.Prediction model development based on a secondary analysis of a prospective observational study.German university medical center.366 non-cardiac surgery patients who had preoperative automated ambulatory blood pressure monitoring.Multivariable analyses to identify risk factors for postinduction and intraoperative hypotension. Area under receiver operating characteristics curves (AUROC) and likelihood-ratio tests to test whether adding information on chronic arterial hypertension and nocturnal non-dipping improves hypotension prediction models based on readily available preoperative clinical information.Risk factors for postinduction hypotension were age in years (odds ratio: 1.06 (95% confidence interval: 1.03 to 1.10), P = 0.001), American Society of Anesthesiologists physical status class (1.85 (1.02 to 3.35), P = 0.043), preoperative use of angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers (15.19 (1.76 to 131.46), P = 0.013), chronic arterial hypertension (2.54 (1.49 to 4.34), P = 0.001), and nocturnal non-dipping (3.61 (2.09 to 6.23), P 0.001). The model's AUROC was 0.76 (95% confidence interval: 0.71 to 0.81) with and 0.67 (0.62 to 0.73) without information on chronic arterial hypertension and nocturnal non-dipping (P 0.001). Risk factors for intraoperative hypotension were male sex (1.73 (1.07 to 2.80), P = 0.025), chronic arterial hypertension (4.35 (2.33 to 8.14), P 0.001), and nocturnal non-dipping (3.56 (2.07 to 6.11), P 0.001). The model's AUROC was 0.76 (0.70 to 0.81) with and 0.63 (0.57 to 0.69) without information on chronic arterial hypertension and nocturnal non-dipping (P 0.001).Chronic arterial hypertension and nocturnal non-dipping are independent risk factors for postinduction and intraoperative hypotension in non-cardiac surgery patients. Adding information on chronic arterial hypertension and nocturnal non-dipping moderately improved hypotension prediction models based on preoperative clinical information.

It remains unknown what constitutes physiologically relevant intraoperative bradycardia. Intraoperative bradycardia is usually defined using absolute heart rate thresholds, ignoring preoperative baseline heart rates. In contrast, we considered defining intraoperative bradycardia relative to preoperative ambulatory nighttime heart rate. Specifically, we hypothesized that the individual mean intraoperative heart rate is lower than the mean preoperative ambulatory nighttime heart rate. We, therefore, sought to investigate the relationship between the intraoperative and preoperative ambulatory nighttime heart rates in adults having noncardiac surgery with general anesthesia. Additionally, we sought to investigate the incidence of intraoperative bradycardia using relative versus absolute heart rate thresholds.We conducted a secondary analysis of a database from a prospective study including preoperative ambulatory and intraoperative heart rates in 363 patients having noncardiac surgery with general anesthesia.The mean intraoperative heart rate was lower than the mean nighttime heart rate (mean difference, -9 bpm; 95% confidence interval [CI], -10 to -8 bpm; P.001). The mean intraoperative heart rate was lower than the mean nighttime heart rate in 319 of 363 patients (88%; 95% CI, 84%-91%). The incidence of intraoperative bradycardia was 42% (95% CI, 38%-47%) when it was defined as intraoperative heart rate30% lower than mean nighttime heart rate and 43% (95% CI, 38%-49%) when it was defined as intraoperative heart rate45 bpm.The mean intraoperative heart rate is lower than the mean nighttime heart rate in about 9 of 10 patients. Intraoperative bradycardia might thus be physiologically and clinically important. Future research needs to investigate whether there is an association between intraoperative bradycardia and postoperative outcomes.

Blood pressure is the main determinant of organ perfusion. Hypotension is common in patients having surgery and in critically ill patients. The severity and duration of hypotension are associated with hypoperfusion and organ dysfunction. Hypotension is mostly treated reactively after low blood pressure values have already occurred. However, prediction of hypotension before it becomes clinically apparent would allow the clinician to treat hypotension pre-emptively, thereby reducing the severity and duration of hypotension. Hypotension cannowbepredictedminutes before it actually occurs from the blood pressure waveform using machine-learning algorithms that can be trained to detect subtle changes in cardiovascular dynamics preceding clinically apparent hypotension. However, analyzing the complex cardiovascular system is a challenge because cardiovascular physiology is highly interdependent, works within complicated networks, and is influenced by compensatory mechanisms. Improved hemodynamic data collection and integration will be a key to improve current models and develop new hypotension prediction models. (C) 2019 Elsevier Ltd. All rights reserved.

Background Pulmonary artery thermodilution is the clinical reference method for cardiac output monitoring. Because both continuous and intermittent pulmonary artery thermodilution are used in clinical practice it is important to know whether cardiac output measurements by the two methods are clinically interchangeable. Methods We performed a systematic review and meta-analysis of clinical studies comparing cardiac output measurements assessed using continuous and intermittent pulmonary artery thermodilution in adult surgical and critically ill patients. 54 studies with 1522 patients were included in the analysis. Results The heterogeneity across the studies was high. The overall random effects model-derived pooled estimate of the mean of the differences was 0.08 (95%-confidence interval 0.01 to 0.16) L/min with pooled 95%-limits of agreement of − 1.68 to 1.85 L/min and a pooled percentage error of 29.7 (95%-confidence interval 20.5 to 38.9)%. Conclusion The heterogeneity across clinical studies comparing continuous and intermittent pulmonary artery thermodilution in adult surgical and critically ill patients is high. The overall trueness/accuracy of continuous pulmonary artery thermodilution in comparison with intermittent pulmonary artery thermodilution is good (indicated by a pooled mean of the differences PROSPERO registration number CRD42020159730.

Background The optimal method for blood pressure monitoring in obese surgical patients remains unknown. Arterial catheters can cause potential complications, and noninvasive oscillometry provides only intermittent values. Finger cuff methods allow continuous noninvasive monitoring. The authors tested the hypothesis that the agreement between finger cuff and intraarterial measurements is better than the agreement between oscillometric and intraarterial measurements. Methods This prospective study compared intraarterial (reference method), finger cuff, and oscillometric (upper arm, forearm, and lower leg) blood pressure measurements in 90 obese patients having bariatric surgery using Bland–Altman analysis, four-quadrant plot and concordance analysis (to assess the ability of monitoring methods to follow blood pressure changes), and error grid analysis (to describe the clinical relevance of measurement differences). Results The difference (mean ± SD) between finger cuff and intraarterial measurements was −1 mmHg (± 11 mmHg) for mean arterial pressure, −7 mmHg (± 14 mmHg) for systolic blood pressure, and 0 mmHg (± 11 mmHg) for diastolic blood pressure. Concordance between changes in finger cuff and intraarterial measurements was 88% (mean arterial pressure), 85% (systolic blood pressure), and 81% (diastolic blood pressure). In error grid analysis comparing finger cuff and intraarterial measurements, the proportions of measurements in risk zones A to E were 77.1%, 21.6%, 0.9%, 0.4%, and 0.0% for mean arterial pressure, respectively, and 89.5%, 9.8%, 0.2%, 0.4%, and 0.2%, respectively, for systolic blood pressure. For mean arterial pressure and diastolic blood pressure, absolute agreement and trending agreement between finger cuff and intraarterial measurements were better than between oscillometric (at each of the three measurement sites) and intraarterial measurements. Forearm performed better than upper arm and lower leg monitoring with regard to absolute agreement and trending agreement with intraarterial monitoring. Conclusions The agreement between finger cuff and intraarterial measurements was better than the agreement between oscillometric and intraarterial measurements for mean arterial pressure and diastolic blood pressure in obese patients during surgery. Forearm oscillometry exhibits better measurement performance than upper arm or lower leg oscillometry. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

Pulse wave analysis enables cardiac output to be estimated continuously and in real time. Pulse wave analysis methods can be classified into invasive, minimally invasive, and noninvasive and into externally calibrated, internally calibrated, and uncalibrated methods.

We recently proposed continuous error grid analysis to describe the clinical relevance of measurement differences between a test and a reference method for arterial blood pressure (AP) measurement. Here, we present instructions on how to perform continuous error grid analysis in AP method comparison studies and provide a freely accessible computer program for automated computing of continuous error grids and calculation of the proportion of measurement pairs in the different risk zones.

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 I2 (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 –14.0 to 22.5 mm Hg for mean AP (Q=230.4 [P

Purpose:Noonan syndrome (NS) is an autosomal-dominant disorder characterized by craniofacial dysmorphism, growth retardation, cardiac abnormalities, and learning difficulties. It belongs to the RASopathies, which are caused by germ-line mutations in genes encoding components of the RAS mitogen-activated protein kinase (MAPK) pathway. RIT1 was recently reported as a disease gene for NS, but the number of published cases is still limited.Methods:We sequenced RIT1 in 310 mutation-negative individuals with a suspected RASopathy and prospectively in individuals who underwent genetic testing for NS. Using a standardized form, we recorded clinical features of all RIT1 mutation-positive patients. Clinical and genotype data from 36 individuals with RIT1 mutation reported previously were reviewed.Results:Eleven different RIT1 missense mutations, three of which were novel, were identified in 33 subjects from 28 families; codons 57, 82, and 95 represent mutation hotspots. In relation to NS of other genetic etiologies, prenatal abnormalities, cardiovascular disease, and lymphatic abnormalities were common in individuals with RIT1 mutation, whereas short stature, intellectual problems, pectus anomalies, and ectodermal findings were less frequent.Conclusion:RIT1 is one of the major genes for NS. The RIT1-associated phenotype differs gradually from other NS subtypes, with a high prevalence of cardiovascular manifestations, especially hypertrophic cardiomyopathy, and lymphatic problems. © 2016 American College of Medical Genetics and Genomics.

An amendment to this paper has been published and can be accessed via the original article.

Intraoperative hypotension (IOH) i.e., low arterial blood pressure (AP) during surgery is common in patients having non-cardiac surgery under general anaesthesia. It has a multifactorial aetiology, and is associated with major postoperative complications including acute kidney injury, myocardial injury and death. Therefore, IOH may be a modifiable risk factor for postoperative complications. However, there is no uniform definition for IOH. IOH not only occurs during surgery but also after the induction of general anaesthesia before surgical incision. However, the optimal therapeutic approach to IOH remains elusive. There is evidence from one small randomised controlled trial that individualising AP targets may reduce the risk of postoperative organ dysfunction compared with standard care. More research is needed to define individual AP harm thresholds, to develop therapeutic strategies to treat and avoid IOH, and to integrate new technologies for continuous AP monitoring.

Postoperative mortality is a major healthcare problem. In order to decrease postoperative mortality, avoiding postoperative complications is key. In turn, to avoid postoperative complications, modifiable risk factors independently associated with these complications need to be identified and avoided. One of the modifiable risk factors for postoperative complications may be perioperative hypotension— i.e., low blood pressure in the perioperative period. In this review, we discuss perioperative hypotension that includes intraoperative hypotension (IOH) and postoperative hypotension (POH), and its impact on postoperative patient outcomes, challenges related to its diagnosis, and potential therapeutic approaches. IOH is common in patients having non-cardiac surgery under general anesthesia and is associated with acute kidney injury, myocardial injury, and death. The relationship between IOH and serious postoperative complications is supported by many observational analyses and one randomized trial that suggests that individualized blood pressure management reduces the risk of postoperative organ dysfunction compared with usual care. More randomized controlled trials are needed before recommendations can be given on how to individualize intraoperative blood pressure targets in clinical routine. POH refers to hypotension occurring on the remaining day of surgery and during the first days after surgery. Available data suggest that POH after non-cardiac surgery is common, profound, and largely undetected by current vital sign monitoring on the general care ward. There is evidence that POH is associated with adverse postoperative outcomes, especially myocardial injury, acute kidney injury, and death. However, there is a need for more data on the pathophysiology, impact, and management of POH. Continuous ward monitoring might enable POH to be detected and treated in a timely manner. However, strategies to prevent or treat POH based on continuous ward monitoring need to be tested for their effectiveness to improve quality of care or patient- centered outcomes in large-scale interventional trials.