Recent advances in pulse oximetry

Conventional pulse oximetry uses two wavelengths of light (red and infrared) transmitted through a finger and a photodetector to analyze arterial hemoglobin oxygen saturation and pulse rate. Recent advances in pulse oximetry include: extended analysis of the photo plethysmographic waveform; use of multiple wavelengths of light to quantify methemoglobin, carboxyhemoglobin and total hemoglobin content in blood; and use of electronic processes to improve pulse oximeter signal processing during conditions of low signal-to-noise ratio. These advances have opened new clinical applications for pulse oximeters that will have an impact on patient monitoring and management

Relation between respiratory variations in pulse oximetry plethysmographic waveform amplitude and arterial pulse pressure in ventilated patients.

IntroductionRespiratory variation in arterial pulse pressure is a reliable predictor of fluid responsiveness in mechanically ventilated patients with circulatory failure. The main limitation of this method is that it requires an invasive arterial catheter. Both arterial and pulse oximetry plethysmographic waveforms depend on stroke volume. We conducted a prospective study to evaluate the relationship between respiratory variation in arterial pulse pressure and respiratory variation in pulse oximetry plethysmographic (POP) waveform amplitude.MethodThis prospective clinical investigation was conducted in 22 mechanically ventilated patients. Respiratory variation in arterial pulse pressure and respiratory variation in POP waveform amplitude were recorded simultaneously in a beat-to-beat evaluation, and were compared using a Spearman correlation test and a Bland-Altman analysis.ResultsThere was a strong correlation (r2 = 0.83; P < 0.001) and a good agreement (bias = 0.8 +/- 3.5%) between respiratory variation in arterial pulse pressure and respiratory variation in POP waveform amplitude. A respiratory variation in POP waveform amplitude value above 15% allowed discrimination between patients with respiratory variation in arterial pulse pressure above 13% and those with variation of 13% or less (positive predictive value 100%).ConclusionRespiratory variation in arterial pulse pressure above 13% can be accurately predicted by a respiratory variation in POP waveform amplitude above 15%. This index has potential applications in patients who are not instrumented with an intra-arterial catheter

Prediction of fluid responsiveness using respiratory variations in left ventricular stroke area by transoesophageal echocardiographic automated border detection in mechanically ventilated patients.

BackgroundLeft ventricular stroke area by transoesophageal echocardiographic automated border detection has been shown to be strongly correlated to left ventricular stroke volume. Respiratory variations in left ventricular stroke volume or its surrogates are good predictors of fluid responsiveness in mechanically ventilated patients. We hypothesised that respiratory variations in left ventricular stroke area (DeltaSA) can predict fluid responsiveness.MethodsEighteen mechanically ventilated patients undergoing coronary artery bypass grafting were studied immediately after induction of anaesthesia. Stroke area was measured on a beat-to-beat basis using transoesophageal echocardiographic automated border detection. Haemodynamic and echocardiographic data were measured at baseline and after volume expansion induced by a passive leg raising manoeuvre. Responders to passive leg raising manoeuvre were defined as patients presenting a more than 15% increase in cardiac output.ResultsCardiac output increased significantly in response to volume expansion induced by passive leg raising (from 2.16 +/- 0.79 litres per minute to 2.78 +/- 1.08 litres per minute; p < 0.01). DeltaSA decreased significantly in response to volume expansion (from 17% +/- 7% to 8% +/- 6%; p < 0.01). DeltaSA was higher in responders than in non-responders (20% +/- 5% versus 10% +/- 5%; p < 0.01). A cutoff DeltaSA value of 16% allowed fluid responsiveness prediction with a sensitivity of 92% and a specificity of 83%. DeltaSA at baseline was related to the percentage increase in cardiac output in response to volume expansion (r = 0.53, p < 0.01).ConclusionDeltaSA by transoesophageal echocardiographic automated border detection is sensitive to changes in preload, can predict fluid responsiveness, and can quantify the effects of volume expansion on cardiac output. It has potential clinical applications

Recent advances in pulse oximetry

Abstract Conventional pulse oximetry uses two wavelengths of light (red and infrared) transmitted through a finger and a photodetector to analyze arterial hemoglobin oxygen saturation and pulse rate. Recent advances in pulse oximetry include: extended analysis of the photo plethysmographic waveform; use of multiple wavelengths of light to quantify methemoglobin, carboxyhemoglobin and total hemoglobin content in blood; and use of electronic processes to improve pulse oximeter signal processing during conditions of low signal-to-noise ratio. These advances have opened new clinical applications for pulse oximeters that will have an impact on patient monitoring and management

Hemodynamic monitoring and management in patients undergoing high risk surgery: a survey among North American and European anesthesiologists

Abstract Introduction Several studies have demonstrated that perioperative hemodynamic optimization has the ability to improve postoperative outcome in high-risk surgical patients. All of these studies aimed at optimizing cardiac output and/or oxygen delivery in the perioperative period. We conducted a survey with the American Society of Anesthesiologists (ASA) and the European Society of Anaesthesiology (ESA) to assess current hemodynamic management practices in patients undergoing high-risk surgery in Europe and in the United States. Methods A survey including 33 specific questions was emailed to 2,500 randomly selected active members of the ASA and to active ESA members. Results Overall, 368 questionnaires were completed, 57.1% from ASA and 42.9% from ESA members. Cardiac output is monitored by only 34% of ASA and ESA respondents (P = 0.49) while central venous pressure is monitored by 73% of ASA respondents and 84% of ESA respondents (P < 0.01). Specifically, the pulmonary artery catheter is being used much more frequently in the US than in Europe in the setup of high-risk surgery (85.1% vs. 55.3% respectively, P < 0.001). Clinical experience, blood pressure, central venous pressure, and urine output are the most widely indicators of volume expansion. Finally, 86.5% of ASA respondents and 98.1% of ESA respondents believe that their current hemodynamic management could be improved. Conclusions In conclusion, these results point to a considerable gap between the accumulating evidence about the benefits of perioperative hemodynamic optimization and the available technologies that may facilitate its clinical implementation, and clinical practices in both Europe and the United States

Ability of pleth variability index to detect hemodynamic changes induced by passive leg raising in spontaneously breathing volunteers

IntroductionPleth Variability Index (PVI) is a new algorithm that allows continuous and automatic estimation of respiratory variations in the pulse oximeter waveform amplitude. Our aim was to test its ability to detect changes in preload induced by passive leg raising (PLR) in spontaneously breathing volunteers.MethodsWe conducted a prospective observational study. Twenty-five spontaneously breathing volunteers were enrolled. PVI, heart rate and noninvasive arterial pressure were recorded. Cardiac output was assessed using transthoracic echocardiography. Volunteers were studied in three successive positions: baseline (semirecumbent position); after PLR of 45 degrees with the trunk lowered in the supine position; and back in the semirecubent position.ResultsWe observed significant changes in cardiac output and PVI during changes in body position. In particular, PVI decreased significantly from baseline to PLR (from 21.5 +/- 8.0% to 18.3 +/- 9.4%; P < 0.05) and increased significantly from PLR to the semirecumbent position (from 18.3 +/- 9.4% to 25.4 +/- 10.6 %; P < 0.05). A threshold PVI value above 19% was a weak but significant predictor of response to PLR (sensitivity 82%, specificity 57%, area under the receiver operating characteristic curve 0.734 +/- 0.101).ConclusionPVI can detect haemodynamic changes induced by PLR in spontaneously breathing volunteers. However, we found that PVI was a weak predictor of fluid responsiveness in this setting

Effect of cardiopulmonary bypass on activated partial thromboplastin time waveform analysis, serum procalcitonin and C-reactive protein concentrations

Abstract Introduction Systemic inflammatory response syndrome (SIRS) is a frequent condition after cardiopulmonary bypass (CPB) and makes conventional biological tests fail to detect postoperative sepsis. Biphasic waveform (BPW) analysis is a new biological test derived from activated partial thromboplastin time that has recently been proposed for sepsis diagnosis. The aim of this study was to investigate the accuracy of BPW to detect sepsis after cardiac surgery under CPB. Methods We conducted a prospective study in American Society of Anesthesiologists' (ASA) physical status III and IV patients referred for cardiac surgery under CPB. Procalcitonin (PCT) and BPW were recorded before surgery and every day during the first week following surgery. Patients were then divided into three groups: patients presenting no SIRS, patients presenting with non-septic SIRS and patients presenting with sepsis. Results Thirty two patients were included. SIRS occurred in 16 patients (50%) including 5 sepsis (16%) and 11 (34%) non-septic SIRS. PCT and BPW were significantly increased in SIRS patients compared to no SIRS patients (0.9 [0.5-2.2] vs. 8.1 [2.0-21.3] ng/l for PCT and 0.10 [0.09-0.14] vs. 0.29 [0.16-0.56] %T/s for BPW; P < 0.05 for both). We observed no difference in peak PCT value between the sepsis group and the non-septic SIRS group (8.4 [7.5-32.2] vs. 7.8 [1.9-17.5] ng/l; P = 0.67). On the other hand, we found that BPW was significantly higher in the sepsis group compared to the non-septic SIRS group (0.57 [0.54-0.78] vs. 0.19 [0.14-0.29] %T/s; P < 0.01). We found that a BPW threshold value of 0.465%T/s was able to discriminate between sepsis and non-septic SIRS groups with a sensitivity of 100% and a specificity of 93% (area under the curve: 0.948 +/- 0.039; P < 0.01). Applying the previously published threshold of 0.25%T/s, we found a sensitivity of 100% and a specificity of 72% to discriminate between these two groups. Neither C-reactive protein (CRP) nor PCT had significant predictive value (area under the curve for CRP was 0.659 +/- 0.142; P = 0.26 and area under the curve for PCT was 0.704 +/- 0.133; P = 0.15). Conclusions BPW has potential clinical applications for sepsis diagnosis in the postoperative period following cardiac surgery under CPB

Preoperative predictions of in-hospital mortality using electronic medical record data

Background: Predicting preoperative in-hospital mortality using readily-available electronic medical record (EMR) data can aid clinicians in accurately and rapidly determining surgical risk. While previous work has shown that the American Society of Anesthesiologists (ASA) Physical Status Classification is a useful, though subjective, feature for predicting surgical outcomes, obtaining this classification requires a clinician to review the patient's medical records. Our goal here is to create an improved risk score using electronic medical records and demonstrate its utility in predicting in-hospital mortality without requiring clinician-derived ASA scores. Methods: Data from 49,513 surgical patients were used to train logistic regression, random forest, and gradient boosted tree classifiers for predicting in-hospital mortality. The features used are readily available before surgery from EMR databases. A gradient boosted tree regression model was trained to impute the ASA Physical Status Classification, and this new, imputed score was included as an additional feature to preoperatively predict in-hospital post-surgical mortality. The preoperative risk prediction was then used as an input feature to a deep neural network (DNN), along with intraoperative features, to predict postoperative in-hospital mortality risk. Performance was measured using the area under the receiver operating characteristic (ROC) curve (AUC). Results: We found that the random forest classifier (AUC 0.921, 95%CI 0.908-0.934) outperforms logistic regression (AUC 0.871, 95%CI 0.841-0.900) and gradient boosted trees (AUC 0.897, 95%CI 0.881-0.912) in predicting in-hospital post-surgical mortality. Using logistic regression, the ASA Physical Status Classification score alone had an AUC of 0.865 (95%CI 0.848-0.882). Adding preoperative features to the ASA Physical Status Classification improved the random forest AUC to 0.929 (95%CI 0.915-0.943). Using only automatically obtained preoperative features with no clinician intervention, we found that the random forest model achieved an AUC of 0.921 (95%CI 0.908-0.934). Integrating the preoperative risk prediction into the DNN for postoperative risk prediction results in an AUC of 0.924 (95%CI 0.905-0.941), and with both a preoperative and postoperative risk score for each patient, we were able to show that the mortality risk changes over time. Conclusions: Features easily extracted from EMR data can be used to preoperatively predict the risk of in-hospital post-surgical mortality in a fully automated fashion, with accuracy comparable to models trained on features that require clinical expertise. This preoperative risk score can then be compared to the postoperative risk score to show that the risk changes, and therefore should be monitored longitudinally over time