Climbing Smarter: A Predictive Model for Acute Mountain Sickness Risk at High Altitudes

Climbing Smarter: A Predictive Model for Acute Mountain Sickness Risk at High Altitudes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Climbing Smarter: A Predictive Model for Acute Mountain Sickness Risk at High Altitudes Jose Cardoso-Landivar, Cristina Aljama, Nathalie Pinos, Maria Jesus Cruz, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6940463/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Acute Mountain Sickness (AMS) is a common and potentially severe condition-affecting individual who ascend to high altitudes. Understanding the predictive factors associated with AMS is essential for prevent health risks in high-altitude environments. Objective : identify key demographic and physiological predictors of AMS and develop a logistic regression model to estimate the likelihood of its occurrence. Methods : A prospective, descriptive field study was conducted at the José Ribas refuge (4,800 m) on Cotopaxi volcano (5,898 m) in the Ecuadorian Andes. Volunteer mountaineers who spent at least eight hours at the refuge before attempting the summit were included. Demo-graphic and physiological variables were collected upon arrival and after 12 hours, at which point AMS was assessed using the Lake Louise AMS Self-Report questionnaire. Logistic regression models were employed with model calibration and discrimination assessed using the Brier score and AUROC, respectively. Internal validation was performed via bootstrap resampling and 10-fold cross-validation. Results : 136 volunteer mountaineers were included. Key predictive factors for AMS included (p < 0.05 for all) low arterial oxygen saturation (OR: 0.92 [95%CI: 0.85-0.99]), low diastolic blood pressure (OR: 0.96 [0.92-0.99]), prior history of AMS (OR: 2.80 [95%CI:1.17 -6.70]), and limited previous high-altitude experience (low OR: 18.25 [95%CI: 4.859 68.56], moderate OR: 5.14 [ 95%CI: 1.78-14.85]).The model demonstrated strong discriminatory performance, (AUROC of 0.83; 95% CI: 0.76-0.90), and a good calibration with a slope of 0.97 (95% CI: 0.77 to 1.17) and a Brier Score of 0.16. Internal validation confirmed model stability and robustness. Conclusion : The developed model effectively predicts AMS risk using readily measurable physiological and demographic variables. Its application could enhance risk assessment and preventive strategies for individuals engaging in high-altitude activities. Earth and environmental sciences/Climate sciences/Atmospheric science Earth and environmental sciences/Environmental sciences Health sciences/Medical research Acute Mountain Sickness AMS high altitude mountaineering predictive modeling physiological variables Figures Figure 1 1. Introduction The exploration of high-altitude regions has fascinated humanity for centuries, offering unique challenges and experiences. In recent years, high mountain sports such as mountaineering have become increasingly popular. More and more residents of sea level areas are venturing to altitudes above 3,000m for pleasure or work. This is reflected in data such as the number of annual hikers on some of the most traveled peaks; in 2019 nearly 1.2 million tourists visited Nepal, 197,786 for pilgrimage and 171,937 for trekking and mountaineering [ 1 ] and the latest figures (2018) reporting the yearly numbers of climbers on the various routes of Kilimanjaro amount to more than 47,000 [ 2 ]. However, exposure to high altitudes carries significant well described health risks [ 3 , 4 ]. The Acute Mountain Sickness (AMS) include a range of symptoms from mild dis-comfort to potentially life-threatening complications; it represents a significant medical concern for those venturing into mountainous environments. AMS, whose symptoms include headaches, fatigue, nausea, and difficulty breathing, is primarily attributed to rap-id exposure to high altitude and the resulting decrease in partial oxygen pressure in the air. The most severe manifestations include High-Altitude Pulmonary Edema (HAPE) and High-Altitude Cerebral Edema (HACE), both of which significantly contribute to high-altitude mortality. HAPE is one of the leading causes of altitude-related deaths, with an estimated mortality rate of up to 50% if left untreated. Similarly, HACE, resulting from swelling of the brain due to hypoxia-induced vascular permeability, has a mortality rate ranging between 15–40%, depending on the severity and timeliness of intervention [ 5 ].. The AMS diagnosis is clinical and the Lake Louise Questionnaire (LLQ) and the Acute Mountain Sickness-Cerebral score (AMS-C) are two assessment tools for identifying acute mountain sickness with LLQ being the most commonly used scoring system used to assess AMS due to its facility [ 6 ]. The incidence of AMS has been documented to be 43% higher above 4,300 m and 34% higher above 3,650 m [ 7 ] and the usefulness of different drugs for its prevention remains controversial [ 8 , 9 ]. The main determinants of AMS described are the speed of ascent, the altitude reached, individual susceptibility, the intensity of physical exercise, and the prior degree of acclimatization [ 10 , 11 , 12 ]. However, the incidence and severity of its onset vary greatly, and we are not yet able to predict the risk of developing AMS. The objective of the present study was to describe and analyze the relationship between demographic and physiological variables and the incidence of AMS and develop a univariate and multivariate logistic regression model to identify predictive factors of AMS. 2. Materials and Methods This was a prospective, descriptive, field study. Data were collected at the José Ribas refuge (4800 m) on the Cotopaxi volcano (5898 m) in the Ecuadorian Andes due to its altitude and its relatively easy access Patient recruitment was performed in the climbing sea-son between June and August 2024 2.1 Participants Patients were included consecutively upon arrival at the refuge. All participants recruited were volunteer mountaineers who spent at least 8 hours in the refuge before ascending to the summit of the Cotopaxi volcano and returning. As exclusion criteria, we did not include any participant with a history of chronic cardiovascular or respiratory diseases or individuals under 18 years of age. The study was approved by the Ethics Committee of the Catholic University of Cuenca- Ecuador (UCACUE-UASB-M-CEISH-020) and all participants provided signed informed consent. 2.2 Measurements and variables First, a baseline visit was carried out at the shelter upon the participants' arrival and initial measurements of demographic and physiological variables After 12 hours, a second visit was conducted, and AMS development was assessed using the Lake Louise AMS Self-Report (LLSR) questionnaire. The LLSR defines AMS as the presence of headaches in addition to three other symptoms, including gastrointestinal symptoms, fatigue/weakness, and dizziness/lightheadedness. Each symptom is appointed a point on a scale from 0 to 3, with 0 being no effect and 3 being severe. A total score of 3 or greater, with the presence of headaches, in a setting of rapid ascent to high altitude, is diagnosed as acute mountain sickness [A0] The following variables were collected from all the participants: Sociodemographic variables: age, sex, comorbidities, acclimatation history, departure altitude, use of drugs to prevent altitude sickness, previous history of AMS and previous high mountain experience were collected using a simple questionnaire. Physiological variables: Heart rate and blood pressure (systolic and diastolic) were measured using an automatic sphygmomanometer (OMRON Model BP5250). To ensure accuracy, three measurements were taken after 30 minutes of relative rest following the participant’s arrival at the refuge. The first measurement was discarded, and the final blood pressure value was calculated as the mean of the second and third measurements. A 1–3 minute interval was maintained between each measurement to minimize variability. Following the blood pressure measurement, arterial oxygen saturation (SpO₂) was assessed using two non-invasive pulse oximeters: RIESTER Ri-Fox N and SleepU Ring Sensor. In cases where discordance between the two devices was observed, the final SpO₂ value was obtained by averaging the readings from both pulse oximeters. 2.3 Statistical Analysis Categorical variables were reported as absolute frequencies and percentages, while quantitative variables were summarized as medians and interquartile ranges (25th–75th percentiles). Comparisons between categorical variables were conducted using Fisher's exact test, and differences in continuous variables were assessed using the Mann-Whitney U test. No missing data imputation was performed, as no missingness was identified for the key variables described in this study. Logistic regression models were employed to select the best subset of predictors for AMS, after evaluating model fitting characteristics. Odds ratios (ORs) with 95% confidence intervals (95% CI) were calculated for the predictors. To assess deviations from linearity, continuous variables were modelled in three ways: as continuous linear variables, as categorical variables (using tertiles), and by incorporating non-linear transformation terms into the models. A stepwise forward selection method was applied to fit the final model, based on performance improvement. Entry and removal thresholds for model variables were set at significance levels of 5% and 10%, respectively. Variables not initially included in the model (p ≥ 0.10) were subsequently tested against the final model to evaluate whether their inclusion improved model fit, as determined by p < 0.10 or a lower Akaike Information Criterion (AIC) value. Calibration, which measures how closely predicted probabilities align with actual outcomes, was assessed using predicted probabilities from the final model and comparing them against observed probabilities. This was done through deciles of predictions and the Brier score (14) A calibration plot was generated to evaluate whether the observed vs. predicted regression slope was equal to 1 and the intercept equal to 0, as would be expected for a perfect fit. Discrimination, defined as the model's ability to correctly distinguish between two outcome classes (e.g., occurrence vs. non-occurrence of AMS), was evaluated using the area under the receiver operating characteristic curve (AUROC) (15). Internal validation of the predictive model was performed using bootstrap analyses with 2,000 simulations and 10-fold cross-validation strategies. All methods were performed in accordance with the relevant guidelines and regulations and the study was approved by the Ethics Committee of the Catholic University of Cuenca- Ecuador (UCACUE-UASB-M-CEISH-020) and all participants provided signed informed consent. All statistical analyses were conducted using SAS software (v9.4; SAS Institute Inc., Cary, NC, USA). This study adheres to the "Transparent Reporting of a Multivariable Pre-diction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement" (16). Statistical significance was set at a two-tailed p-value < 0.05 for all analyses, except in the multivariate models, where variables with p-values between 0.05 and 0.1 were not excluded. 3. Results 3.1. Population A total of 136 volunteer mountaineers participated in the study, all of whom had no prior diagnosis of chronic respiratory or cardiovascular disease. The mean age was 36.9 years, with 36% of participants being female. Acute Mountain Sickness (AMS) was observed in 52 participants (38%). Prior AMS symptoms were reported by 50 mountaineers (36.7%). (Table 1 ) Regarding acclimatization, 118 participants (86.7%) had spent more than five days at altitudes above 2,500 meters in the two months preceding the ascent. A minority (9.5%) used preventive medication for altitude sickness. Table 1 Baseline Characteristics According to AMS status Category / Descriptive No AMS(n = 84) Yes AMS (n = 52) TOTAL(n = 136) p-value Gender Female 26 (31.0%) 23 (44.2%) 49 (36.0%) 0.1432 History of AMS No 62 (73.8%) 24 (46.2%) 86 (63.2%) 0.0016 Mild 13 (15.5%) 11 (21.2%) 24 (17.6%) Moderate 6 (7.1%) 15 (28.8%) 21 (15.4%) Severe 3 (3.6%) 2 (3.8%) 5 ( 3.7%) Previous AMS Any grade (vs rest) 22 (26.2%) 28 (53.8%) 50 (36.8%) 0.0026 Mountain Experience High 38 (45.2%) 5 (9.6%) 43 (31.6%) 5d > 2500m) 77 (91.7%) 41 (78.8%) 118 (86.8%) 0.0356 Profilaxis Yes 5 (6.0%) 8 (15.4%) 13 (9.6%) 0.0781 Previous COVID Yes 0 (0.0%) 4 (7.7%) 4 ( 2.9%) 0.0174 Age (Years) 35 (31–45) 34.50 (28–42.50) 35 (30–43) 0.3460 Start altitude (m a.s.l.) 2800 (2500–2850) 2800 (2100–2850) 2800 (2500–2850) 0.8533 Heart Rate (bpm) 91 (77–103) 100.50 (88–117) 94.50 (80–108) 0.0019 Oxygen Saturation (%) 83 (78.5–86) 79.50 (75–84) 82 (78–85) 0.0019 SBP (mmHg) 127.117.5–139) 124 (111.5–136.5) 126 (116–138) 0.1301 DBP (mmHg) 85 (76.5–92) 82.50 (74–89) 83.50 (75.50–91) 0.1076 MAP (mmHg) 98.7 (91.7–109.0) 96.83 (87.17–102.7) 98 (91.2–106) 0.0886 Lake Louis Score (4800msnm > 6hours 1 (1–2) 5 (4–6) 2 (1–4.5) < .0001 3.2 Variables associated with higher risk of AMS The univariate analysis identified the following variables as significantly associated with a higher risk of AMS: heart rate, arterial oxygen saturation, prior AMS, previous pulmonary or cerebral edema, lack of acclimatization, limited high-altitude experience (low or moderate), and altitude at departure. Diastolic blood pressure (DBP), systolic blood pressure (SBP), and the use of preventive medication approached statistical significance but did not reach the 5% cutoff at the univariate testing (Table 2 ). Table 2 Estimation of risks (odds ratios -OR- and 95%CI) of AMS from Univariable and Multivariable Logistic Regression Models Univariable Multivariable Model Values Bootstrap Values p-value OR [95%CI] p-value OR [95%CI] p-value OR [95%CI] Sex (male) 0.1186 0.56 [ 0.27–1.15] Age (years) 0.3403 0.98 [ 0.94–1.01] HR (BPM) 0.0012 1.03 [ 1.01–1.05] Sat (%) 0.0004 0.88 [ 0.82–0.94] 0.02 0.91 [0.84–0.99] 0.02 0.91 [0.83–0.99] SBP (mmHg) 0.0727 0.98 [ 0.96–1.00] DBP (mmHg) 0.055 0.96 [ 0.93–1.00] 0.02 0.95 [0.92–0.99] 0.03 0.95 [0.91–0.99] Prev AMS 0.0014 3.28 [ 1.58–6.82] 0.02 2.80 [1.17–6.69] 0.02 2.93 [1.18–7.37] Prev Pulm Or Cerebral Edema 0.4084 0.39 [ 0.04–3.60] Acclimatization 0.0377 0.33 [ 0.12–0.94] Mountain Experience (ref = High) < .0001 1 (reference) < .0001 1 (reference) < .0001 1 (reference) Moderate 5.13 [ 1.77–14.84] 4.05 [1.30 -12.61] 4.24[1.27–13.84] Low 18.57 [ 5.52–62.47] 18.25 [4.85–68.56] 21.28[5.11–88.46] Mountain Experience (High) < .0001 0.12 [ 0.04–0.35] Mountain Experience (High-Mod) < .0001 0.16 [ 0.06–0.39] Mountain Experience (Low) < .0001 6.11 [ 2.52–14.78] Drug Prophylaxis 0.078 2.87 [ 0.88–9.31] Previous COVID 0.981 NE Departure altitude (per 100 m increase) 0.011 0.95 [ 0.001–0.904] AUC-ROC [95%CI] AUC-ROC [95%CI] Model 0.83 [0.76–0.90] 10-fold cross-validation 0.79 [0.71–0.87] Boostrap 0.84 [0.77–0.91] Brier Score = 0.1602 Brier Score = 0.1551 3.3 Multivariate predictive model In the final multivariate predictive model, lower arterial oxygen saturation was significantly associated with an increased risk of Acute Mountain Sickness (AMS), with an odds ratio (OR) of 0.92 (95% CI: 0.85–0.99, p = 0.0281). Lower diastolic blood pressure was also identified as a significant factor, with an OR of 0.96 (95% CI: 0.92–0.99, p = 0.0285). A prior history of AMS was associated with a higher risk of AMS in the current ascent (OR: 2.80, 95% CI: 1.17–6.70, p = 0.0207). Limited previous high-altitude experience was also a significant predictor. Participants with low high-altitude experience had an OR of 18.25 (95% CI: 4.86–68.56), while those with moderate experience showed an OR of 5.14 (95% CI: 1.78–14.85, p < 0.0001) (Table 2 ). 3.4 Calibration analysis and Discriminatory ability Calibration analysis showed a good fit between predicted and observed probabilities of AMS. The estimated intercept was 0.01 (95% CI: -0.08 to 0.10), and the slope was 0.97 (95% CI: 0.77–1.17). Statistical tests showed no significant deviation from perfect calibration (intercept: p = 0.7800; slope: p = 0.7314). The Brier score was 0.160 (95% CI: 0.126–0.195), reflecting good predictive accuracy. (Fig. 1 and Table 2 ) Footnote for Fig. 1 : The estimated intercept was 0.0116 (95% CI: -0.081 to 0.104) and the slope was 0.9696 (95% CI: 0.772 to 1.167). Statistical tests showed no significant deviation from perfect calibration, with p = 0.7800 for the intercept (testing whether it differs from 0) and p = 0.7314 for the slope (testing whether it differs from 1). The Brier score was 0.1602 (95% CI: 0.1255 to 0.1950). The model demonstrated strong discriminatory ability, with an AUROC of 0.83 (95% CI: 0.76–0.90) (Table 2 ). Internal validation using bootstrap resampling and 10-fold cross-validation confirmed the stability of the model, yielding consistent OR estimates and AUROC values (Table 2 ). A step-by-step guide for calculating individual AMS risk predictions is provided in the Table 3 . Table 3 Logistic regression coefficients to calculate probability of AMS Variable Category Coefficient Linear predictor Intercept 8.5447 8.5447 DBP -0.0435 -0.0435*[Observed DBP] Oxygen Sat -0.0891 -0.0891 *[Observed Sat (O2)] Mountain experience High 0 select coefficient value associated to the observed category Moderate 1.3988 Low 2.9043 Previous AMS No 0 select coefficient value associated to the observed category Yes 1.0296 Linear predictor for the observed values of the model variables [Sum of column values] From the multivariate model, a linear predictor score can be calculated by summing the intercept (a constant value), the regression coefficients for the qualitative variable categories, and the products of the observed values of continuous variables with their corresponding coefficients (as shown in the table above). And from the linear predictor the probability is calculated according to the logistic model as follows: $$\:Probability=\frac{1}{1+{e}^{-\left[linear\:predictor\right]}}$$ For example, consider a patient with the following characteristics: DBP = 99, Sat(O₂) = 87, moderate mountain experience, and a history of previous AMS. The linear predictor would be calculated as: 8.5447+ (-0.0435*99) + (-0.0891*87) + (1.3988) + (1.0296) = -1.08510 and the probability of AMS: $$\:Probability=\frac{1}{1+{e}^{-\left[linear\:predictor\right]}}=\frac{1}{1+{e}^{-\left[-1.08510\right]}}=0.253\to\:25.3\%$$ For another example, consider a patient with the following characteristics: SBP = 60, Sat(O₂) = 73, low mountain experience, and a history of previous AMS. The linear predictor would be calculated as: 8.5447+ (-0.0435*60) + (-0.0891*73) + (2.9043) + (1.0296) = -1.08510 and the probability of AMS: $$\:Probability=\frac{1}{1+{e}^{-\left[linear\:predictor\right]}}=\frac{1}{1+{e}^{-\left[3.36430\right]}}=0.967\to\:96.7$$ 4. Discussion This study aimed to identify key demographic and physiological predictors associated with Acute Mountain Sickness (AMS) and to develop a logistic regression model for estimating AMS risk among mountaineers. Our findings show that arterial oxygen saturation, heart rate, previous AMS history, and mountain experience significantly predicted AMS occurrence. The logistic regression model demonstrated strong calibration and good discriminatory performance, with an AUROC of 0.83, indicating its strong predictive capacity. Model calibration is an essential step for validating predictive accuracy, assessing how closely the predicted probabilities align with observed outcomes. The calibration results presented in this study were robust, with a Brier score indicating good agreement between observed and predicted AMS incidences. Specifically, the calibration slope and intercept showed no significant deviations from ideal values, suggesting reliable predictive capabilities for the model within the context studied. These findings underscore the potential utility of our model in clinical or practical mountaineering settings, aiding decision-making regarding preventive strategies for AMS. In our cohort, AMS was observed in 52 participants (38%), which is quite consistent with previous studies describing the incidence of AMS [ 7 ]. Hackett and Rennie [ 17 ] found an incidence of AMS of 43% at 4,343 m in trekkers in Nepal. Maggiorini et al [ 18 ] found an incidence in climbers in the Alps of 9% at 2,850 m, 13% at 3,050 m, and 34% at 3,650 m. Honigman et all [T3] reported an incidence of AMS of approximately 22% in Summit County, CO, at moderate altitudes of 2,500 to 2,900 m with no difference between men and women, while only a modestly higher altitude of slightly > 3,000 m resulted in an incidence of 42%. To date, there are no models that can predict the risk of AMS. Many researchers have search for ways to predict AMS, and different tests have been described with controversial results. A low hypoxic ventilatory response (HVR) has been proposed as a marker of susceptibility to AMS [ 20 ], but some field studies have found no connection between HVR and AMS [ 21 ] [ 22 ]. Monitoring heart rate and resting arterial oxygen saturation (R-Spo2) have been proposed as simple indicators of inadequate acclimatization to high altitudes and impending AMS [ 23 ], but there is a large variability in the results [ 24 ] [ 25 ]. Pulse oximetry, the currently recommended test, is a simple indicator of altitude acclimatization, but its predictive value is only moderate for predicting AMS [ 26 ] [ 27 ] [ 28 ], and several subsequent studies present negative results on its usefulness in real life. Overall, it ap-pears that the use of a single physiological measurement is not sufficient to predict the risk of onset or the severity of AMS [ 29 ] [ 30 ]. Some studies also identifies transcriptomic signatures and critical pathways, such as hemoglobin regulation and CREB signaling, associated with severe acute mountain sickness during rapid ascent to high altitude, suggesting potential biomarkers for early diagnosis and prediction of individual susceptibility [ 31 ]. The predictors identified in our study, such as arterial oxygen saturation, heart rate, and previous AMS history, align with existing literature highlighting the importance of physiological and historical factors in AMS risk [ 7 , 11 ]. These findings reinforce the importance of simple and readily measurable clinical markers in identifying individuals at risk. Our results align with previous studies emphasizing that lower oxygen saturation and elevated heart rate are indicative of compromised physiological adaptation to altitude [ 10 ], and that prior experience with AMS significantly increases the likelihood of recurrence [ 32 ] It is also important to highlight that acclimatization history and previous high-altitude experience emerged as relevant predictors, emphasizing the protective role of gradual adaptation to hypoxia [ 33 ]. The practical implications of these findings are significant, as mountaineers and healthcare providers could incorporate acclimatization strategies into their preparation routines, thereby potentially reducing AMS incidence and preventing the mortality associated to more severe forms of AMS like HAPE and HACE. Our study has several limitations. First, all the participants were volunteers, which could lead to selection bias. Second, despite promising internal validation outcomes achieved through bootstrap resampling and 10-fold cross-validation and with the high number of patients included for what is usual in high mountain studies, it is crucial to highlight the absence of external validation. External validation studies are necessary to confirm the model's generalizability and predictive accuracy across different populations and various environmental conditions. Without such external validation, caution must be exercised in applying this predictive tool universally, especially considering demographic diversity and potential physiological variability among mountaineers. Moreover, the data collection was conducted at a single altitude (4,800 meters above sea level at José Ribas refuge on Cotopaxi volcano). While this altitude is representative of typical mountaineering ascents in the Andean region, it limits the extrapolation of findings to other altitude ranges. Physiological responses and the associated risk of AMS may differ significantly at lower altitudes or in more extreme conditions at higher elevations. Therefore, future re-search should validate the predictive model using a broader spectrum of altitudes to ensure the applicability and robustness of the predictive factors identified. Exploring these relationships at various altitudes could also reveal additional or nuanced physiological responses relevant to AMS development. 5. Conclusions Our logistic regression model demonstrates strong internal predictive performance for AMS using easily accessible demographic and physiological measures. While this model provides valuable insights and practical benefits for AMS risk assessment at the studied altitude. Plans are underway to conduct such studies to strengthen the evidence supporting its use in broader clinical settings. Abbreviations The following abbreviations are used in this manuscript: AMS Acute Mountain Sickness OR Odds Ratio POCUS Point of care ultrasound AUROC Area under the curve (ROC) Declarations AUTHOR CONTRIBUTIONS : J.C.L., X.M. C.A. and F.T.; methodology, F.T.; software, J.C.-L. and N.P.; validation, J.C.-L., C.A, F.T and X.M; formal analysis, J.C.L..; investigation, J.C.-L., C.A., and N.P.; resources, M.J.C., X.M. and F.T.; data curation, J.C.-L. and C.A.; writing—original draft prepara-tion, F.T., X.M, J.C.L. and C.A writing—review and editing, J.C.-L., C.A., N.P., M.J.C., X.M., and F.T ; supervision, X.M. and F.T.; project administration, X.M.. All authors have read and agreed to the published version of the manuscript. "This work has been carried out within the frame-work of the Doctoral Program in Medicine at the Universitat Autònoma de Barcelona." FUNDING : This research received no external funding INSTITUTIONAL REVIEW BOARD STATEMENT : The study was approved by the Ethics Committee of the Catholic University of Cuenca- Ecuador ( UCACUE-UASB-M-CEISH-020). INFORMED CONSENT STATEMENT : All participants provided signed informed consent. DATA AVAILABILITY STATEMENT : The data supporting the findings of this study are available upon reasonable request from the corresponding author. Due to ethical and privacy concerns, the data are not publicly accessible. ACKNOWLEDGMENTS : We would like to express our sincere gratitude to the National Parks of Ec-uador for their invaluable collaboration and support during the execution of this study. Their assistance and dedication greatly facilitated our research, enabling successful completion of our objectives. We also want to mention special collaborators in the field, Ma. Gracia Machuca, Daniel Carrion, Sofia Cardoso, the mountain guides and all the people despite extreme conditions of the data collecting have been supporting these study. CONFLICTS OF INTEREST: Jose Cardoso has no conflict of interest to declare . Cristina Aljama has re-ceived speaker fees from FAES farma, Chiesi, AstraZeneca, Zambon, GSK and CSL Behring. Xa-vier Muñoz has received fees as a speaker, scientific advisor or participant of clinical studies of (in alphabetical order): AstraZeneca, Boehringer Ingelheim, Chiesi, Faes, Gebro, Glax-oSmithKline, Menarini, Mundifarma, Novartis, Sanofi,Teva. Ferran Torres has received DSMB fees from Argenx BV, Archivel and Connecta, consultancy fees from Archivel, LEO Pharma, FAES and Boehringer Ingelheim, all outside the submitted work. References Government of Nepal Nepal tourism facts. (2019). https://www.tourism.gov.np//files/statistics/21.pdf The most popular trekking routes on Kilimanjaro - the latest figures | CMK. Climb Mount Kilimanjaro. 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Prevalence of acute mountain sickness in the Swiss Alps. BMJ 301 (6756), 853–855. 10.1136/bmj.301.6756.853 (1990). PMID: 2282425; PMCID: PMC1663993. Honigman, B. et al. Acute mountain sickness in a general tourist population at moderate altitudes. Ann Intern Med. ;118(8):587 – 92. (1993). 10.7326/0003-4819-118-8-199304150-00003 . Erratum in: Ann Intern Med 1994 Apr 15;120(8):698. PMID: 8452324. Moore, L. G. et al. Low acute hypoxic ventilatory response and hypoxic depression in acute altitude sickness. J Appl Physiol (1985). ;60(4):1407-12. (1986). 10.1152/jappl.1986.60.4.1407 . PMID: 3084449. Hohenhaus, E. P. A. et al. Ventilatory and pulmonary vascular response to hypoxia and susceptibility to high altitude pulmonary oedemaEur. Respir. J.81825 – 1833., and (1995). Ventilatory and pulmonary vascular response to hypoxia and susceptibility to high altitude pulmonary oedema. Eur. Respir. J. 8:1825–1833. (1995). Bärtsch, P. S. E., Bärtsch, P., Swenson, E. R., Paul, A. & Jülg, B. Hypoxic ventilatory response, ventilation, gas exchange, and fluid balance in acute mountain sicknessHigh Alt. Med. Biol.3361 – 376., and Hohenhaus E. (2002). Hypoxic ventilatory response, ventilation, gas exchange, and fluid balance in acute mountain sickness. High Alt. Med. Biol. 3:361–376 (2002). https://doi.org/10.1089/ham.2009.1060 Burtscher, M. S. C. F. M., Burtscher, M., Szubski, C. & Faulhaber, M. Prediction of the susceptibility to AMS in simulated altitudeSleep Breathing.12103-108., and (2008). Prediction of the susceptibility to AMS in simulated altitude. Sleep Breathing. 12:103–108. (2008). https://doi.org/10.1089/ham.2009.106 Botella de Maglia, J. & Compte Torrero, L. Saturación arterial de oxígeno a gran altitud. Estudio en montañeros no aclimatados y en habitantes de alta montaña [Arterial oxygen saturation at high altitude. A study on unacclimatised mountaineers and mountain dwellers]. Med. Clin. (Barc) . 124 (5), 172–176 (2005). Spanish. doi: 10.1157/13071480. PMID: 15725367. Heikki, M., Karinen, J. E., Peltonen, M. & Kähönen, O. Prediction of Acute Mountain Sickness by Monitoring Arterial Oxygen Saturation During Ascent Tikkanen High Altitude Med. Biology 2010 11 :4, 325–332 Roach, R. C., Greene, E. R., Schoene, R. B. & Hackett, P. H. Arterial oxygen saturation for prediction of acute mountain sickness. Aviat. Space Environ. Med. 69 , 1182–1185 (1998). Karinen, H. M., Peltonen, J. E., Kähönen, M. & Tikkanen, H. O. Prediction of acute mountain sickness by monitoring arterial oxygen saturation during ascent. High Alt Med Biol. (2010). Winter;11(4):325 – 32 10.1089/ham.2009.1060 . PMID: 21190501. Goves, J. S. L. et al. Pulse oximetry for the prediction of acute mountain sickness: A systematic review. Exp Physiol. ;109(12):2057–2072. doi: 10.1113/EP091875. Epub 2024 Sep 25. PMID: 39323005; PMCID: PMC11607621. (2024). Chen, H. C. et al. Change in oxygen saturation does not predict acute mountain sickness on Jade Mountain. Wilderness Environ Med. ;23(2):122-7. (2012). 10.1016/j.wem.2012.03.014 . PMID: 22656657. Wagner, D. R., Knott, J. R. & Fry, J. P. Oximetry fails to predict acute mountain sickness or summit success during a rapid ascent to 5640 meters. Wilderness Environ Med. ;23(2):114 – 21. (2012). 10.1016/j.wem.2012.02.015 . PMID: 22656656. Yang, R., Gautam, A., Hammamieh, R., Roach, R. C. & Beidleman, B. A. Transcriptomic signatures of severe acute mountain sickness during rapid ascent to 4,300 m. Front. Physiol. 15 , 1477070. 10.3389/fphys.2024.1477070 (2025). PMID: 39944919; PMCID: PMC11813865. Pesce, C. et al. Determinants of acute mountain sickness and success on Mount Aconcagua (6962 m). High. Alt Med. Biol. 6 , 158–166. 10.1089/ham.2005.6.158 (2005). Schneider, M., Bernasch, D., Weymann, J. & Holle, R. Acute mountain sickness: influence of susceptibility, pre-exposure, and ascent rate. Med. Sci. Sports Exerc. 34 , 1886–1891 (2002). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6940463","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":504882457,"identity":"fb897671-b12a-4e88-a86e-b10af3a2eb4f","order_by":0,"name":"Jose Cardoso-Landivar","email":"","orcid":"","institution":"Universitat Autònoma de Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Jose","middleName":"","lastName":"Cardoso-Landivar","suffix":""},{"id":504882459,"identity":"9923054d-5a03-4432-9030-968365c0747a","order_by":1,"name":"Cristina Aljama","email":"","orcid":"","institution":"Pneumology Service, Hospital Universitari Vall d'Hebron","correspondingAuthor":false,"prefix":"","firstName":"Cristina","middleName":"","lastName":"Aljama","suffix":""},{"id":504882462,"identity":"7de71141-c6e8-47f9-acde-68c3d72d0acd","order_by":2,"name":"Nathalie Pinos","email":"","orcid":"","institution":"University of Cuenca","correspondingAuthor":false,"prefix":"","firstName":"Nathalie","middleName":"","lastName":"Pinos","suffix":""},{"id":504882463,"identity":"3331d0aa-601b-4824-9131-088b49312ce7","order_by":3,"name":"Maria Jesus Cruz","email":"","orcid":"","institution":"Universitat Autònoma de Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Jesus","lastName":"Cruz","suffix":""},{"id":504882464,"identity":"5095ccdb-28bc-43b8-905e-1792999b18e7","order_by":4,"name":"Xavier Muñoz","email":"","orcid":"","institution":"Universitat Autònoma de Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Xavier","middleName":"","lastName":"Muñoz","suffix":""},{"id":504882465,"identity":"896334cf-a47a-426c-93a2-0001149ae6e5","order_by":5,"name":"Ferran Torres","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBAC9gYUbgUDgwEhLTwHILQEhDoD1XKAaC2MbcRoYe999uHnDoY6fumzDx/+nHdY3pyB+eHnD/i08Bw3ntl7hkFCsi/d2Jh322HDnQ1sxhL4bLGXSGNm4G1jkDA4w8YmzbjtMOOGAzwMeLXwyD9jZvwL0cL+8+ecw/ZALcw/8GqRYGNmhtnCwNtwOBGohQ2/LTxpzMyybRKSM3vYmKV5jqUn72xmM7M4g08L+zFmxrdtNvz8PGyMH3/UWNtuZ29+fKMCjxYokEBiMxNWPgpGwSgYBaOAAAAAM7NCPoX7iLEAAAAASUVORK5CYII=","orcid":"","institution":"Biostatistics Unit, Medical School, Universitat Autònoma de Barcelona","correspondingAuthor":true,"prefix":"","firstName":"Ferran","middleName":"","lastName":"Torres","suffix":""}],"badges":[],"createdAt":"2025-06-20 16:08:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6940463/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6940463/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90055198,"identity":"7ea3d040-e5e0-42a6-944c-245c833e4baa","added_by":"auto","created_at":"2025-08-28 00:49:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":166095,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCalibration plot comparing observed versus predicted probabilities of AMS by deciles, derived from the logistic regression model.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFootnote for figure 1: The estimated intercept was 0.0116 (95% CI: -0.081 to 0.104) and the slope was 0.9696 (95% CI: 0.772 to 1.167). Statistical tests showed no significant deviation from perfect calibration, with p = 0.7800 for the intercept (testing whether it differs from 0) and p = 0.7314 for the slope (testing whether it differs from 1). The Brier score was 0.1602 (95% CI: 0.1255 to 0.1950).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture1.png","url":"https://assets-eu.researchsquare.com/files/rs-6940463/v1/bc7781d8e4faf1132ee8e8b6.png"},{"id":94597949,"identity":"0da9eb2f-9e8c-43ee-8704-c58c15c0d1d4","added_by":"auto","created_at":"2025-10-28 18:50:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1086319,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6940463/v1/886b690f-1486-47fc-8206-058e4910ae1b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Climbing Smarter: A Predictive Model for Acute Mountain Sickness Risk at High Altitudes","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe exploration of high-altitude regions has fascinated humanity for centuries, offering unique challenges and experiences. In recent years, high mountain sports such as mountaineering have become increasingly popular. More and more residents of sea level areas are venturing to altitudes above 3,000m for pleasure or work. This is reflected in data such as the number of annual hikers on some of the most traveled peaks; in 2019 nearly 1.2\u0026nbsp;million tourists visited Nepal, 197,786 for pilgrimage and 171,937 for trekking and mountaineering [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and the latest figures (2018) reporting the yearly numbers of climbers on the various routes of Kilimanjaro amount to more than 47,000 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, exposure to high altitudes carries significant well described health risks [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe Acute Mountain Sickness (AMS) include a range of symptoms from mild dis-comfort to potentially life-threatening complications; it represents a significant medical concern for those venturing into mountainous environments. AMS, whose symptoms include headaches, fatigue, nausea, and difficulty breathing, is primarily attributed to rap-id exposure to high altitude and the resulting decrease in partial oxygen pressure in the air. The most severe manifestations include High-Altitude Pulmonary Edema (HAPE) and High-Altitude Cerebral Edema (HACE), both of which significantly contribute to high-altitude mortality. HAPE is one of the leading causes of altitude-related deaths, with an estimated mortality rate of up to 50% if left untreated. Similarly, HACE, resulting from swelling of the brain due to hypoxia-induced vascular permeability, has a mortality rate ranging between 15\u0026ndash;40%, depending on the severity and timeliness of intervention [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].. The AMS diagnosis is clinical and the Lake Louise Questionnaire (LLQ) and the Acute Mountain Sickness-Cerebral score (AMS-C) are two assessment tools for identifying acute mountain sickness with LLQ being the most commonly used scoring system used to assess AMS due to its facility [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe incidence of AMS has been documented to be 43% higher above 4,300 m and 34% higher above 3,650 m [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and the usefulness of different drugs for its prevention remains controversial [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The main determinants of AMS described are the speed of ascent, the altitude reached, individual susceptibility, the intensity of physical exercise, and the prior degree of acclimatization [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, the incidence and severity of its onset vary greatly, and we are not yet able to predict the risk of developing AMS.\u003c/p\u003e\u003cp\u003eThe objective of the present study was to describe and analyze the relationship between demographic and physiological variables and the incidence of AMS and develop a univariate and multivariate logistic regression model to identify predictive factors of AMS.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eThis was a prospective, descriptive, field study. Data were collected at the Jos\u0026eacute; Ribas refuge (4800 m) on the Cotopaxi volcano (5898 m) in the Ecuadorian Andes due to its altitude and its relatively easy access Patient recruitment was performed in the climbing sea-son between June and August 2024\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Participants\u003c/h2\u003e\u003cp\u003ePatients were included consecutively upon arrival at the refuge. All participants recruited were volunteer mountaineers who spent at least 8 hours in the refuge before ascending to the summit of the Cotopaxi volcano and returning. As exclusion criteria, we did not include any participant with a history of chronic cardiovascular or respiratory diseases or individuals under 18 years of age.\u003c/p\u003e\u003cp\u003e The study was approved by the Ethics Committee of the Catholic University of Cuenca- Ecuador (UCACUE-UASB-M-CEISH-020) and all participants provided signed informed consent.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Measurements and variables\u003c/h2\u003e\u003cp\u003eFirst, a baseline visit was carried out at the shelter upon the participants' arrival and initial measurements of demographic and physiological variables\u003c/p\u003e\u003cp\u003eAfter 12 hours, a second visit was conducted, and AMS development was assessed using the Lake Louise AMS Self-Report (LLSR) questionnaire. The LLSR defines AMS as the presence of headaches in addition to three other symptoms, including gastrointestinal symptoms, fatigue/weakness, and dizziness/lightheadedness. Each symptom is appointed a point on a scale from 0 to 3, with 0 being no effect and 3 being severe. A total score of 3 or greater, with the presence of headaches, in a setting of rapid ascent to high altitude, is diagnosed as acute mountain sickness [A0]\u003c/p\u003e\u003cp\u003eThe following variables were collected from all the participants:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eSociodemographic variables: age, sex, comorbidities, acclimatation history, departure altitude, use of drugs to prevent altitude sickness, previous history of AMS and previous high mountain experience were collected using a simple questionnaire.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePhysiological variables: Heart rate and blood pressure (systolic and diastolic) were measured using an automatic sphygmomanometer (OMRON Model BP5250). To ensure accuracy, three measurements were taken after 30 minutes of relative rest following the participant\u0026rsquo;s arrival at the refuge. The first measurement was discarded, and the final blood pressure value was calculated as the mean of the second and third measurements. A 1\u0026ndash;3 minute interval was maintained between each measurement to minimize variability.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eFollowing the blood pressure measurement, arterial oxygen saturation (SpO₂) was assessed using two non-invasive pulse oximeters: RIESTER Ri-Fox N and SleepU Ring Sensor. In cases where discordance between the two devices was observed, the final SpO₂ value was obtained by averaging the readings from both pulse oximeters.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e\u003cp\u003eCategorical variables were reported as absolute frequencies and percentages, while quantitative variables were summarized as medians and interquartile ranges (25th\u0026ndash;75th percentiles). Comparisons between categorical variables were conducted using Fisher's exact test, and differences in continuous variables were assessed using the Mann-Whitney U test. No missing data imputation was performed, as no missingness was identified for the key variables described in this study.\u003c/p\u003e\u003cp\u003eLogistic regression models were employed to select the best subset of predictors for AMS, after evaluating model fitting characteristics. Odds ratios (ORs) with 95% confidence intervals (95% CI) were calculated for the predictors. To assess deviations from linearity, continuous variables were modelled in three ways: as continuous linear variables, as categorical variables (using tertiles), and by incorporating non-linear transformation terms into the models. A stepwise forward selection method was applied to fit the final model, based on performance improvement. Entry and removal thresholds for model variables were set at significance levels of 5% and 10%, respectively. Variables not initially included in the model (p\u0026thinsp;\u0026ge;\u0026thinsp;0.10) were subsequently tested against the final model to evaluate whether their inclusion improved model fit, as determined by p\u0026thinsp;\u0026lt;\u0026thinsp;0.10 or a lower Akaike Information Criterion (AIC) value.\u003c/p\u003e\u003cp\u003eCalibration, which measures how closely predicted probabilities align with actual outcomes, was assessed using predicted probabilities from the final model and comparing them against observed probabilities. This was done through deciles of predictions and the Brier score (14) A calibration plot was generated to evaluate whether the observed vs. predicted regression slope was equal to 1 and the intercept equal to 0, as would be expected for a perfect fit.\u003c/p\u003e\u003cp\u003eDiscrimination, defined as the model's ability to correctly distinguish between two outcome classes (e.g., occurrence vs. non-occurrence of AMS), was evaluated using the area under the receiver operating characteristic curve (AUROC) (15). Internal validation of the predictive model was performed using bootstrap analyses with 2,000 simulations and 10-fold cross-validation strategies.\u003c/p\u003e\u003cp\u003e All methods were performed in accordance with the relevant guidelines and regulations and the study was approved by the Ethics Committee of the Catholic University of Cuenca- Ecuador (UCACUE-UASB-M-CEISH-020) and all participants provided signed informed consent.\u003c/p\u003e\u003cp\u003eAll statistical analyses were conducted using SAS software (v9.4; SAS Institute Inc., Cary, NC, USA). This study adheres to the \"Transparent Reporting of a Multivariable Pre-diction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement\" (16).\u003c/p\u003e\u003cp\u003eStatistical significance was set at a two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all analyses, except in the multivariate models, where variables with p-values between 0.05 and 0.1 were not excluded.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Population\u003c/h2\u003e\u003cp\u003eA total of 136 volunteer mountaineers participated in the study, all of whom had no prior diagnosis of chronic respiratory or cardiovascular disease. The mean age was 36.9 years, with 36% of participants being female. Acute Mountain Sickness (AMS) was observed in 52 participants (38%). Prior AMS symptoms were reported by 50 mountaineers (36.7%). (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eRegarding acclimatization, 118 participants (86.7%) had spent more than five days at altitudes above 2,500 meters in the two months preceding the ascent. A minority (9.5%) used preventive medication for altitude sickness.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Characteristics According to AMS status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory / Descriptive\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo AMS(n\u0026thinsp;=\u0026thinsp;84)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes AMS (n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTOTAL(n\u0026thinsp;=\u0026thinsp;136)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (31.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23 (44.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49 (36.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.1432\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory of AMS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62 (73.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24 (46.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e86 (63.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (15.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (21.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24 (17.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (7.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (28.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21 (15.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSevere\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (3.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 ( 3.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious AMS Any grade (vs rest)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (26.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28 (53.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50 (36.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMountain Experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (45.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (9.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43 (31.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37 (44.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25 (48.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e62 (45.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (10.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (42.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31 (22.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory o cerebral or pulmonary edema\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (4.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (1.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 ( 3.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.6527\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcclimatization (last 2m, \u0026gt;5d\u0026thinsp;\u0026gt;\u0026thinsp;2500m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77 (91.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41 (78.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e118 (86.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0356\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProfilaxis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (6.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (15.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13 (9.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0781\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious COVID\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (7.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 ( 2.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0174\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (Years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (31\u0026ndash;45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.50 (28\u0026ndash;42.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35 (30\u0026ndash;43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.3460\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStart altitude (m a.s.l.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2800 (2500\u0026ndash;2850)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2800 (2100\u0026ndash;2850)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2800 (2500\u0026ndash;2850)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.8533\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart Rate (bpm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91 (77\u0026ndash;103)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.50 (88\u0026ndash;117)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e94.50 (80\u0026ndash;108)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxygen Saturation (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83 (78.5\u0026ndash;86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79.50 (75\u0026ndash;84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e82 (78\u0026ndash;85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e127.117.5\u0026ndash;139)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e124 (111.5\u0026ndash;136.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e126 (116\u0026ndash;138)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.1301\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85 (76.5\u0026ndash;92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82.50 (74\u0026ndash;89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e83.50 (75.50\u0026ndash;91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.1076\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMAP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98.7 (91.7\u0026ndash;109.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96.83 (87.17\u0026ndash;102.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e98 (91.2\u0026ndash;106)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0886\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLake Louis Score (4800msnm\u0026thinsp;\u0026gt;\u0026thinsp;6hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (1\u0026ndash;2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (4\u0026ndash;6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (1\u0026ndash;4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Variables associated with higher risk of AMS\u003c/h2\u003e\u003cp\u003eThe univariate analysis identified the following variables as significantly associated with a higher risk of AMS: heart rate, arterial oxygen saturation, prior AMS, previous pulmonary or cerebral edema, lack of acclimatization, limited high-altitude experience (low or moderate), and altitude at departure. Diastolic blood pressure (DBP), systolic blood pressure (SBP), and the use of preventive medication approached statistical significance but did not reach the 5% cutoff at the univariate testing (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimation of risks (odds ratios -OR- and 95%CI) of AMS from Univariable and Multivariable Logistic Regression Models\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c3\" namest=\"c2\" rowspan=\"2\"\u003e\u003cp\u003eUnivariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003eMultivariable\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eModel Values\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eBootstrap Values\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR [95%CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR [95%CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOR [95%CI]\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (male)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.56 [ 0.27\u0026ndash;1.15]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c5\" namest=\"c4\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c7\" namest=\"c6\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.98 [ 0.94\u0026ndash;1.01]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHR (BPM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.03 [ 1.01\u0026ndash;1.05]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSat (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.88 [ 0.82\u0026ndash;0.94]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.91 [0.84\u0026ndash;0.99]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.91 [0.83\u0026ndash;0.99]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.98 [ 0.96\u0026ndash;1.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.96 [ 0.93\u0026ndash;1.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.95 [0.92\u0026ndash;0.99]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.95 [0.91\u0026ndash;0.99]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrev AMS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.28 [ 1.58\u0026ndash;6.82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.80 [1.17\u0026ndash;6.69]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.93 [1.18\u0026ndash;7.37]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrev Pulm Or Cerebral Edema\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.39 [ 0.04\u0026ndash;3.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c5\" namest=\"c4\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c7\" namest=\"c6\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcclimatization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.33 [ 0.12\u0026ndash;0.94]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMountain Experience (ref\u0026thinsp;=\u0026thinsp;High)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1 (reference)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.13 [ 1.77\u0026ndash;14.84]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.05 [1.30 -12.61]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.24[1.27\u0026ndash;13.84]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.57 [ 5.52\u0026ndash;62.47]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.25 [4.85\u0026ndash;68.56]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e21.28[5.11\u0026ndash;88.46]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMountain Experience (High)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.12 [ 0.04\u0026ndash;0.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"5\" nameend=\"c5\" namest=\"c4\" rowspan=\"6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"5\" nameend=\"c7\" namest=\"c6\" rowspan=\"6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMountain Experience (High-Mod)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.16 [ 0.06\u0026ndash;0.39]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMountain Experience (Low)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.11 [ 2.52\u0026ndash;14.78]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrug Prophylaxis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.87 [ 0.88\u0026ndash;9.31]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious COVID\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeparture altitude (per 100 m increase)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.95 [ 0.001\u0026ndash;0.904]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eAUC-ROC [95%CI]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eAUC-ROC [95%CI]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eModel\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c3\" namest=\"c2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.83 [0.76\u0026ndash;0.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e10-fold cross-validation\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.79 [0.71\u0026ndash;0.87]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBoostrap\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.84 [0.77\u0026ndash;0.91]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eBrier Score\u0026thinsp;=\u0026thinsp;0.1602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eBrier Score\u0026thinsp;=\u0026thinsp;0.1551\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Multivariate predictive model\u003c/h2\u003e\u003cp\u003eIn the final multivariate predictive model, lower arterial oxygen saturation was significantly associated with an increased risk of Acute Mountain Sickness (AMS), with an odds ratio (OR) of 0.92 (95% CI: 0.85\u0026ndash;0.99, p\u0026thinsp;=\u0026thinsp;0.0281). Lower diastolic blood pressure was also identified as a significant factor, with an OR of 0.96 (95% CI: 0.92\u0026ndash;0.99, p\u0026thinsp;=\u0026thinsp;0.0285). A prior history of AMS was associated with a higher risk of AMS in the current ascent (OR: 2.80, 95% CI: 1.17\u0026ndash;6.70, p\u0026thinsp;=\u0026thinsp;0.0207). Limited previous high-altitude experience was also a significant predictor. Participants with low high-altitude experience had an OR of 18.25 (95% CI: 4.86\u0026ndash;68.56), while those with moderate experience showed an OR of 5.14 (95% CI: 1.78\u0026ndash;14.85, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Calibration analysis and Discriminatory ability\u003c/h2\u003e\u003cp\u003eCalibration analysis showed a good fit between predicted and observed probabilities of AMS. The estimated intercept was 0.01 (95% CI: -0.08 to 0.10), and the slope was 0.97 (95% CI: 0.77\u0026ndash;1.17). Statistical tests showed no significant deviation from perfect calibration (intercept: p\u0026thinsp;=\u0026thinsp;0.7800; slope: p\u0026thinsp;=\u0026thinsp;0.7314). The Brier score was 0.160 (95% CI: 0.126\u0026ndash;0.195), reflecting good predictive accuracy. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eFootnote for\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: \u003cem\u003eThe estimated intercept was 0.0116 (95% CI: -0.081 to 0.104) and the slope was 0.9696 (95% CI: 0.772 to 1.167). Statistical tests showed no significant deviation from perfect calibration, with p\u0026thinsp;=\u0026thinsp;0.7800 for the intercept (testing whether it differs from 0) and p\u0026thinsp;=\u0026thinsp;0.7314 for the slope (testing whether it differs from 1). The Brier score was 0.1602 (95% CI: 0.1255 to 0.1950).\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe model demonstrated strong discriminatory ability, with an AUROC of 0.83 (95% CI: 0.76\u0026ndash;0.90) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Internal validation using bootstrap resampling and 10-fold cross-validation confirmed the stability of the model, yielding consistent OR estimates and AUROC values (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A step-by-step guide for calculating individual AMS risk predictions is provided in the Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic regression coefficients to calculate probability of AMS\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLinear predictor\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.5447\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.5447\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0435*[Observed DBP]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxygen Sat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0891 *[Observed Sat (O2)]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eMountain experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eselect coefficient value associated to the observed category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.3988\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.9043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePrevious AMS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eselect coefficient value associated to the observed category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0296\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eLinear predictor for the observed values of the model variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[Sum of column values]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFrom the multivariate model, a linear predictor score can be calculated by summing the intercept (a constant value), the regression coefficients for the qualitative variable categories, and the products of the observed values of continuous variables with their corresponding coefficients (as shown in the table above). And from the linear predictor the probability is calculated according to the logistic model as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Probability=\\frac{1}{1+{e}^{-\\left[linear\\:predictor\\right]}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFor example, consider a patient with the following characteristics: DBP\u0026thinsp;=\u0026thinsp;99, Sat(O₂)\u0026thinsp;=\u0026thinsp;87, moderate mountain experience, and a history of previous AMS. The linear predictor would be calculated as:\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e8.5447+ (-0.0435*99) + (-0.0891*87) + (1.3988) + (1.0296) = -1.08510\u003c/h2\u003e\u003cp\u003eand the probability of AMS:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:Probability=\\frac{1}{1+{e}^{-\\left[linear\\:predictor\\right]}}=\\frac{1}{1+{e}^{-\\left[-1.08510\\right]}}=0.253\\to\\:25.3\\%$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFor another example, consider a patient with the following characteristics: SBP\u0026thinsp;=\u0026thinsp;60, Sat(O₂)\u0026thinsp;=\u0026thinsp;73, low mountain experience, and a history of previous AMS. The linear predictor would be calculated as:\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e8.5447+ (-0.0435*60) + (-0.0891*73) + (2.9043) + (1.0296) = -1.08510\u003c/h2\u003e\u003cp\u003eand the probability of AMS:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:Probability=\\frac{1}{1+{e}^{-\\left[linear\\:predictor\\right]}}=\\frac{1}{1+{e}^{-\\left[3.36430\\right]}}=0.967\\to\\:96.7$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study aimed to identify key demographic and physiological predictors associated with Acute Mountain Sickness (AMS) and to develop a logistic regression model for estimating AMS risk among mountaineers. Our findings show that arterial oxygen saturation, heart rate, previous AMS history, and mountain experience significantly predicted AMS occurrence. The logistic regression model demonstrated strong calibration and good discriminatory performance, with an AUROC of 0.83, indicating its strong predictive capacity. Model calibration is an essential step for validating predictive accuracy, assessing how closely the predicted probabilities align with observed outcomes. The calibration results presented in this study were robust, with a Brier score indicating good agreement between observed and predicted AMS incidences. Specifically, the calibration slope and intercept showed no significant deviations from ideal values, suggesting reliable predictive capabilities for the model within the context studied. These findings underscore the potential utility of our model in clinical or practical mountaineering settings, aiding decision-making regarding preventive strategies for AMS.\u003c/p\u003e\u003cp\u003eIn our cohort, AMS was observed in 52 participants (38%), which is quite consistent with previous studies describing the incidence of AMS [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Hackett and Rennie [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] found an incidence of AMS of 43% at 4,343 m in trekkers in Nepal. Maggiorini et al [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] found an incidence in climbers in the Alps of 9% at 2,850 m, 13% at 3,050 m, and 34% at 3,650 m. Honigman et all [T3] reported an incidence of AMS of approximately 22% in Summit County, CO, at moderate altitudes of 2,500 to 2,900 m with no difference between men and women, while only a modestly higher altitude of slightly\u0026thinsp;\u0026gt;\u0026thinsp;3,000 m resulted in an incidence of 42%.\u003c/p\u003e\u003cp\u003eTo date, there are no models that can predict the risk of AMS. Many researchers have search for ways to predict AMS, and different tests have been described with controversial results. A low hypoxic ventilatory response (HVR) has been proposed as a marker of susceptibility to AMS [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], but some field studies have found no connection between HVR and AMS [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Monitoring heart rate and resting arterial oxygen saturation (R-Spo2) have been proposed as simple indicators of inadequate acclimatization to high altitudes and impending AMS [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], but there is a large variability in the results [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Pulse oximetry, the currently recommended test, is a simple indicator of altitude acclimatization, but its predictive value is only moderate for predicting AMS [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and several subsequent studies present negative results on its usefulness in real life. Overall, it ap-pears that the use of a single physiological measurement is not sufficient to predict the risk of onset or the severity of AMS [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Some studies also identifies transcriptomic signatures and critical pathways, such as hemoglobin regulation and CREB signaling, associated with severe acute mountain sickness during rapid ascent to high altitude, suggesting potential biomarkers for early diagnosis and prediction of individual susceptibility [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe predictors identified in our study, such as arterial oxygen saturation, heart rate, and previous AMS history, align with existing literature highlighting the importance of physiological and historical factors in AMS risk [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These findings reinforce the importance of simple and readily measurable clinical markers in identifying individuals at risk. Our results align with previous studies emphasizing that lower oxygen saturation and elevated heart rate are indicative of compromised physiological adaptation to altitude [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and that prior experience with AMS significantly increases the likelihood of recurrence [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] It is also important to highlight that acclimatization history and previous high-altitude experience emerged as relevant predictors, emphasizing the protective role of gradual adaptation to hypoxia [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The practical implications of these findings are significant, as mountaineers and healthcare providers could incorporate acclimatization strategies into their preparation routines, thereby potentially reducing AMS incidence and preventing the mortality associated to more severe forms of AMS like HAPE and HACE.\u003c/p\u003e\u003cp\u003eOur study has several limitations. First, all the participants were volunteers, which could lead to selection bias. Second, despite promising internal validation outcomes achieved through bootstrap resampling and 10-fold cross-validation and with the high number of patients included for what is usual in high mountain studies, it is crucial to highlight the absence of external validation. External validation studies are necessary to confirm the model's generalizability and predictive accuracy across different populations and various environmental conditions. Without such external validation, caution must be exercised in applying this predictive tool universally, especially considering demographic diversity and potential physiological variability among mountaineers. Moreover, the data collection was conducted at a single altitude (4,800 meters above sea level at Jos\u0026eacute; Ribas refuge on Cotopaxi volcano). While this altitude is representative of typical mountaineering ascents in the Andean region, it limits the extrapolation of findings to other altitude ranges. Physiological responses and the associated risk of AMS may differ significantly at lower altitudes or in more extreme conditions at higher elevations. Therefore, future re-search should validate the predictive model using a broader spectrum of altitudes to ensure the applicability and robustness of the predictive factors identified. Exploring these relationships at various altitudes could also reveal additional or nuanced physiological responses relevant to AMS development.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eOur logistic regression model demonstrates strong internal predictive performance for AMS using easily accessible demographic and physiological measures. While this model provides valuable insights and practical benefits for AMS risk assessment at the studied altitude. Plans are underway to conduct such studies to strengthen the evidence supporting its use in broader clinical settings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eThe following abbreviations are used in this manuscript:\u003c/p\u003e\n\u003cp\u003eAMS\u0026nbsp; Acute Mountain Sickness\u003c/p\u003e\n\u003cp\u003eOR\u0026nbsp; Odds Ratio\u003c/p\u003e\n\u003cp\u003ePOCUS\u0026nbsp;\u0026nbsp;Point of care ultrasound\u003c/p\u003e\n\u003cp\u003eAUROC Area under the curve (ROC)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e: J.C.L., X.M. C.A. and F.T.; methodology, F.T.; software, J.C.-L. and N.P.; validation, J.C.-L., C.A, F.T and X.M; formal analysis, J.C.L..; investigation, J.C.-L., C.A., and N.P.; resources, M.J.C., X.M. and F.T.; data curation, J.C.-L. and C.A.; writing\u0026mdash;original draft prepara-tion, F.T., X.M, J.C.L. and C.A \u0026nbsp;writing\u0026mdash;review and editing, J.C.-L., C.A., N.P., M.J.C., X.M., and F.T ; supervision, X.M. and F.T.; project administration, X.M.. All authors have read and agreed to the published version of the manuscript. \u0026quot;This work has been carried out within the frame-work of the Doctoral Program in Medicine at the Universitat Aut\u0026ograve;noma de Barcelona.\u0026quot;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e: This research received no external funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eINSTITUTIONAL REVIEW BOARD STATEMENT\u003c/strong\u003e: The study was approved by the Ethics Committee of the Catholic University of Cuenca- Ecuador ( UCACUE-UASB-M-CEISH-020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eINFORMED CONSENT STATEMENT\u003c/strong\u003e: \u0026nbsp;All participants provided signed informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e: The data supporting the findings of this study are available upon reasonable request from the corresponding author. Due to ethical and privacy concerns, the data are not publicly accessible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e: We would like to express our sincere gratitude to the National Parks of Ec-uador for their invaluable collaboration and support during the execution of this study. Their assistance and dedication greatly facilitated our research, enabling successful completion of our objectives. We also want to mention special collaborators in the field, Ma. Gracia Machuca, Daniel Carrion, Sofia Cardoso, the mountain guides and all the people despite extreme conditions of the data collecting have been supporting these study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICTS OF INTEREST:\u003c/strong\u003e Jose Cardoso has no conflict of interest to declare . Cristina Aljama has re-ceived speaker fees from FAES farma, Chiesi, AstraZeneca, Zambon, GSK and CSL Behring. Xa-vier Mu\u0026ntilde;oz has received fees as a speaker, scientific advisor or participant of clinical studies of (in alphabetical order): AstraZeneca, Boehringer Ingelheim, Chiesi, Faes, Gebro, Glax-oSmithKline, Menarini, Mundifarma, Novartis, Sanofi,Teva. Ferran Torres has received DSMB fees from Argenx BV, Archivel and Connecta, consultancy fees from Archivel, LEO Pharma, FAES and Boehringer Ingelheim, all outside the submitted work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGovernment of Nepal Nepal tourism facts. 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Biol.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 158\u0026ndash;166. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/ham.2005.6.158\u003c/span\u003e\u003cspan address=\"10.1089/ham.2005.6.158\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchneider, M., Bernasch, D., Weymann, J. \u0026amp; Holle, R. Acute mountain sickness: influence of susceptibility, pre-exposure, and ascent rate. \u003cem\u003eMed. Sci. Sports Exerc.\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e, 1886\u0026ndash;1891 (2002).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acute Mountain Sickness, AMS, high altitude, mountaineering, predictive modeling, physiological variables","lastPublishedDoi":"10.21203/rs.3.rs-6940463/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6940463/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAcute Mountain Sickness (AMS) is a common and potentially severe condition-affecting individual who ascend to high altitudes. Understanding the predictive factors associated with AMS is essential for prevent health risks in high-altitude environments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: identify key demographic and physiological predictors of AMS and develop a logistic regression model to estimate the likelihood of its occurrence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A prospective, descriptive field study was conducted at the José Ribas refuge (4,800 m) on Cotopaxi volcano (5,898 m) in the Ecuadorian Andes. Volunteer mountaineers who spent at least eight hours at the refuge before attempting the summit were included. Demo-graphic and physiological variables were collected upon arrival and after 12 hours, at which point AMS was assessed using the Lake Louise AMS Self-Report questionnaire. Logistic regression models were employed with model calibration and discrimination assessed using the Brier score and AUROC, respectively. Internal validation was performed via bootstrap resampling and 10-fold cross-validation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: 136 volunteer mountaineers were included. Key predictive factors for AMS included (p \u0026lt; 0.05 for all) low arterial oxygen saturation (OR: 0.92 [95%CI: 0.85-0.99]), low diastolic blood pressure (OR: 0.96 [0.92-0.99]), prior history of AMS (OR: 2.80 [95%CI:1.17 -6.70]), and limited previous high-altitude experience (low OR: 18.25 [95%CI: 4.859 68.56], moderate OR: 5.14 [ 95%CI: 1.78-14.85]).The model demonstrated strong discriminatory performance, (AUROC of 0.83; 95% CI: 0.76-0.90), and a good calibration with a slope of 0.97 (95% CI: 0.77 to 1.17) and a Brier Score of 0.16. Internal validation confirmed model stability and robustness.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: The developed model effectively predicts AMS risk using readily measurable physiological and demographic variables. Its application could enhance risk assessment and preventive strategies for individuals engaging in high-altitude activities.\u003c/p\u003e","manuscriptTitle":"Climbing Smarter: A Predictive Model for Acute Mountain Sickness Risk at High Altitudes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-28 00:49:50","doi":"10.21203/rs.3.rs-6940463/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d252580d-db97-4e79-9ce3-464af2575baa","owner":[],"postedDate":"August 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53629405,"name":"Earth and environmental sciences/Climate sciences/Atmospheric science"},{"id":53629406,"name":"Earth and environmental sciences/Environmental sciences"},{"id":53629407,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-10-28T18:07:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-28 00:49:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6940463","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6940463","identity":"rs-6940463","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}