Seasonal Effects of High-Altitude Forest Travel on Cardiovascular Function: An Overlooked Cardiovascular Risk of Forest Activity

Cardiovascular physiological responses involving hypoxemia in low temperature environments at high altitude have yet to be adequately investigated. This study aims to demonstrate the health effects of hypoxemia and temperature changes in cardiovascular functions (CVFs) by comparing intra-individual differences as participants ascend from low (298 m, 21.9 °C) to high altitude (2729 m, 9.5 °C). CVFs were assessed by measuring the arterial pressure waveform according to cuff sphygmomanometer of an oscillometric blood pressure (BP) device. The mean ages of participants in winter and summer were 43.6 and 41.2 years, respectively. The intra-individual brachial systolic, diastolic BP, heart rate, and cardiac output of participants significantly increased, as participants climbed uphill from low to high altitude forest. Following the altitude increase from 298 m to 2729 m, with the atmosphere gradually reducing by 0.24 atm, the measured average SpO2 of participants showed a significant reduction from 98.1% to 81.2%. Using mixed effects model, it is evident that in winter, the differences in altitude affects CVFs by significantly increases the systolic BP, heart rate, left ventricular dP/dt max and cardiac output. This study provides evidence that cardiovascular workload increased significantly among acute high-altitude travelers as they ascend from low to high altitude, particularly in winter.

Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis

Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background With appropriate algorithms, computers can learn to detect patterns and associations in large data sets. The authors’ goal was to apply machine learning to arterial pressure waveforms and create an algorithm to predict hypotension. The algorithm detects early alteration in waveforms that can herald the weakening of cardiovascular compensatory mechanisms affecting preload, afterload, and contractility. Methods The algorithm was developed with two different data sources: (1) a retrospective cohort, used for training, consisting of 1,334 patients’ records with 545,959 min of arterial waveform recording and 25,461 episodes of hypotension; and (2) a prospective, local hospital cohort used for external validation, consisting of 204 patients’ records with 33,236 min of arterial waveform recording and 1,923 episodes of hypotension. The algorithm relates a large set of features calculated from the high-fidelity arterial pressure waveform to the prediction of an upcoming hypotensive event (mean arterial pressure < 65 mmHg). Receiver-operating characteristic curve analysis evaluated the algorithm’s success in predicting hypotension, defined as mean arterial pressure less than 65 mmHg. Results Using 3,022 individual features per cardiac cycle, the algorithm predicted arterial hypotension with a sensitivity and specificity of 88% (85 to 90%) and 87% (85 to 90%) 15 min before a hypotensive event (area under the curve, 0.95 [0.94 to 0.95]); 89% (87 to 91%) and 90% (87 to 92%) 10 min before (area under the curve, 0.95 [0.95 to 0.96]); 92% (90 to 94%) and 92% (90 to 94%) 5 min before (area under the curve, 0.97 [0.97 to 0.98]). Conclusions The results demonstrate that a machine-learning algorithm can be trained, with large data sets of high-fidelity arterial waveforms, to predict hypotension in surgical patients’ records.