Background: The Hypotension Prediction Index is designed to predict intraoperative hypotension in a timely manner and is based on arterial waveform analysis using machine learning. It has recently been suggested that this algorithm is highly correlated with the mean arterial pressure itself. Therefore, the aim of this study was to compare the index with mean arterial pressure–based prediction methods, and it is hypothesized that their ability to predict hypotension is comparable. Methods: In this observational study, the Hypotension Prediction Index was used in addition to routine intraoperative monitoring during moderate- to high-risk elective noncardiac surgery. The agreement in time between the default Hypotension Prediction Index alarm (greater than 85) and different concurrent mean arterial pressure thresholds was evaluated. Additionally, the predictive performance of the index and different mean arterial pressure–based methods were assessed within 5, 10, and 15 min before hypotension occurred. Results: A total of 100 patients were included. A mean arterial pressure threshold of 73 mmHg agreed 97% of the time with the default index alarm, whereas a mean arterial pressure threshold of 72 mmHg had the most comparable predictive performance. The areas under the receiver operating characteristic curve of the Hypotension Prediction Index (0.89 [0.88 to 0.89]) and concurrent mean arterial pressure (0.88 [0.88 to 0.89]) were almost identical for predicting hypotension within 5 min, outperforming both linearly extrapolated mean arterial pressure (0.85 [0.84 to 0.85]) and delta mean arterial pressure (0.66 [0.65 to 0.67]). The positive predictive value was 31.9 (31.3 to 32.6)% for the default index alarm and 32.9 (32.2 to 33.6)% for a mean arterial pressure threshold of 72 mmHg. Conclusions: In clinical practice, the Hypotension Prediction Index alarms are highly similar to those derived from mean arterial pressure, which implies that the machine learning algorithm could be substituted by an alarm based on a mean arterial pressure threshold set at 72 or 73 mmHg. Further research on intraoperative hypotension prediction should therefore include comparison with mean arterial pressure–based alarms and related effects on patient outcome. Editor’s Perspective: What We Already Know about This Topic: What This Article Tells Us That Is New:

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Wearable technology is finding its way in clinical practices. Physical Activity (PA) describes patient’s functional status after cardiac surgery and in remote monitoring by use of PA trackers. Objective: The aim of this work is to assess the usability of a wearable fitness tracker, to monitor patients who underwent coronary artery bypass surgery, by either the conventional Off-Pump procedure (OPCAB) or robotically assisted CABG (RA-MIDCAB). We hypothesized a faster recovery of functional status after RA-MIDCAB in the first weeks after discharge. Methods: Patients undergoing RA-MIDCAB or OPCAB were included. Each patient received a Fitbit Charge Heart Rate PA tracker (Fitbit Inc., San Francisco, CA, USA) following discharge. Rehabilitation progress was assessed by measuring the number of steps and physical activity level (PAL) daily. PAL was calculated as energy expenditure divided by basic metabolic rate. Results: 10 RA-MIDCAB patients with a median age of 68 (IQR: [60;76]) and 12 OPCAB patients with a median age of 69 (IQR: [65;76]) were included. Baseline characteristics were comparable except for BMI (RA-MIDCAB: 26 Kg/m² [24;27] versus OPCAB: 29 Kg/m² [27;31]; P<0.001, respectively). Intubation time (P<0.05) was significantly lower in the RA-MIDCAB group. A clear trend, although not statistically significant, was observed towards a higher number of steps in RA-MIDCAB patients in the first week following discharge. Conclusions: Wearable PA trackers can describe functional status in a cardiac rehabilitation setting after surgery. RA-MIDCAB patients have an advantage in recovery in the first weeks of revalidation reflected by the number of steps and PAL, measured by the Fitbit Charge HR, compared to OPCAB patients. However, unsupervised assessment of daily PA varied greatly and this could involve consequences for the use of these trackers as research tools. Clinical Trial: S59757

The physiological response to physical exercise is now recognized as an important tool which can aid the diagnosis and treatment of cardiovascular diseases. This is due to the fact that several mechanisms are needed to accommodate a higher cardiac output and a higher oxygen delivery to tissues. The aim of the present work is to provide a fully closed loop cardiorespiratory simulator reproducing the main physiological mechanisms which arise during aerobic exercise. The simulator also provides a representation of the impairments of these mechanisms in heart failure condition and their effect on limiting exercise capacity. The simulator consists of a cardiovascular model including the left and right heart, pulmonary and systemic circulations. This latter is split into exercising and non-exercising regions and is controlled by the baroreflex and metabolic mechanisms. In addition, the simulator includes a respiratory model reproducing the gas exchange in lungs and tissues, the ventilation control and the effects of its mechanics on the cardiovascular system. The simulator was tested and compared to the data in the literature at three different workloads whilst cycling (24, 48 and 73 watts). The results show that the simulator is able to reproduce the response to exercise in terms of: heart rate (from 67 to 134 bpm), cardiac output (from 5.3 to 10.2 l/min), leg blood flow (from 0.7 to 3.0 l/min), peripheral resistance (from 0.9 to 0.5 mmHg/(cm3/s)), central arteriovenous oxygen difference (from 4.5 to 10.8 ml/dl) and ventilation (6.1 to 25.5 l/min). The simulator was further adapted to reproduce the main impairments observed in heart failure condition, such as reduced sensitivity of baroreflex and metabolic controls, lower perfusion to the exercising regions (from 0.6 to 1.4 l/min) and hyperventilation (from 9.2 to 40.2 l/min). The simulator we developed is a useful tool for the description of the basic physiological mechanisms operating during exercise. It can reproduce how these mechanisms interact and how their impairments could limit exercise performance in heart failure condition. The simulator can thus be used in the future as a test bench for different therapeutic strategies aimed at improving exercise performance in cardiopathic subjects.