{"paper_id":"4ca2115a-9ccd-4de5-80d0-e2b12d2ac09a","body_text":"Predictability of Adult Patient Medical Emergency Condition from Triage Vital Signs and Comorbidities: A Single-Center, Observational Study | 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 Research Article Predictability of Adult Patient Medical Emergency Condition from Triage Vital Signs and Comorbidities: A Single-Center, Observational Study Maral YAZICI, Ahmet Sefa YETER, Sinan GENÇ, Ayça KOCA, Ahmet Burak OĞUZ, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4913657/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Oct, 2024 Read the published version in BMC Emergency Medicine → Version 1 posted 13 You are reading this latest preprint version Abstract Background Vital signs and comorbid diseases are the first information evaluated in patients admitted to the emergency department (ED). This study aims to assess the relationship between initial vital signs, comorbid diseases, and medical emergency conditions (MEC) in patients admitted to the ED. Methods This prospective study was designed as a single-center observational study, including patients admitted to a tertiary ED between 16.06.2022 and 09.09.2022. Patients younger than 18, readmitted to the ED within 24 hours, or absence of vital signs due to cardiac arrest were excluded from the study. Vital signs and comorbid diseases of all patients were recorded. The mortality within 24 hours, the need for intensive care unit admission, emergency surgery, and life-saving procedures were considered “medical emergency conditions”. The role of vital signs and comorbid diseases in predicting emergencies was analyzed by binary logistic regression. Results A total of 10022 patients were included in the study; 5056 (50.4%) were female, and 4966 (49.6%) were male. The median age of patients was 46 (min-max: 18–104). Six hundred four patients presented with a MEC. 3480 (34.7%) patients had at least one comorbidity, while 5031 (50.2%) patients had at least one abnormal vital sign. Hypoxia (Odd’s Ratio [OR]: 1.73), diastolic hypotension (OR: 3.71), tachypnea (OR: 8.09), and tachycardia (OR: 1.61) were associated with MECs. Hemiplegia (OR: 5.7), leukemia (OR: 4.23), and moderate-severe liver disease (OR: 2.99) were the most associated comorbidities with MECs. In our study, a MEC was detected in 3.6% (186 patients) of the patients with no abnormal vital signs and without any comorbidities. Conclusion Among the vital signs, hypoxia, diastolic hypotension, tachypnea, and tachycardia should be considered indicators of a MEC. Hemiplegia, leukemia, and moderate-severe liver disease are the most relevant comorbidities that may accompany the MECs. Vital Signs Comorbidity Mortality Medical Emergency Condition Life-Saving Procedure Figures Figure 1 BACKGROUND Over the years, overcrowding in emergency departments (ED) and high patient density have increased the importance of triage in the ED ( 1 ). Triage is the primary tool to assess patients’ severity of illness or injury upon arrival to the ED ( 2 ). The purpose of triage is to determine the patient’s emergency status and prioritize emergent patients ( 3 ). The critical step of triage is a dynamic process related to various components. An initial clinical assessment is essential to distinguish the patients and recognize those needing immediate care. According to established international triage scores used worldwide, the parameters of these scores may vary ( 4 ). Common characteristics components are initial vital signs, chief complaint and discriminators, and prediction of resource requirements. However, the joint entity of these scoring systems is vital signs. Vital signs, with their dynamic and instantaneous variability, are among the simplest and most important information obtained about admitted patients. Vital signs are measurements of the body's most basic functions; they help detect or monitor medical problems or patients’ progress. The four classic vital signs are body temperature, heart rate (HR), and blood pressure ( 5 ), respiratory rate (RR). These vital signs may significantly impact patient triage scores and clinical outcomes. For example, a patient admitted with a headache may have a different trajectory if their blood pressure is elevated. Similarly, the number of critical vital signs is associated with mortality; as the number of abnormal vital signs increases, mortality rates increase ( 6 ). Besides vital signs, another entity that may be relevant to illness or injury severity and clinical outcome is the comorbid illness of a patient. Likewise, a patient admitted with a headache should be categorized differently if they have a history of a previous brain aneurysm. Although initially developed for breast cancer patients, the Charlson comorbidity index (CCI) is a widely used index accepted as valid for predicting disease burden and mortality ( 7 ). This study aims to determine the effect of vital signs and comorbidities on predicting medical emergency conditions (MEC) within 24 hours METHODS Study Type and Design This was a prospective study conducted in an academic ED environment. The study was approved by our institution's Health Research Ethics Board (number: İ04-211-22). Study Setting and Population The prospective observational study was conducted in a large tertiary care hospital with an approximate annual of 50,000 ED admissions. All adult patients admitted to the ED with any complaint between 16.06.2022 and 09.09.2022 were eligible for inclusion. Patients younger than 18 years, re-admitted to the ED within 24 hours, and presented with no vital signs due to cardiac arrest were excluded from the study. Informed consent was obtained from patients or legal guardians. The vital signs recorded from patients were body temperature, heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), respiratory rate (RR), and oxygen saturation (SaO 2 ). Mindray Beneheart D6 was used for RR, HR, SaO 2, and blood pressure, and Unaan YNA-800 was used for body temperature. These data were recorded in the study form. According to the modified Charlson comorbid index, comorbidities were recorded in the study form as yes/no ( 7 ). Information regarding medical history was obtained from patients’ first-degree relatives or informal caregivers when patients were incompetent to give it themselves. A medical emergency condition was defined as mortality within 24 hours, need for intensive care unit admission, need for emergency surgery, and life-saving procedures. The MEC, as mentioned earlier, was reviewed through the “Hospital Information Management System” and if one of these four criteria was positive, the patient's status was recorded as in a \"medical emergency condition\" ( 3 , 8 ). Assisted ventilation, intubation, surgical airway, emergent non-invasive positive pressure ventilation, defibrillation, emergent cardioversion, external pacing, chest needle decompression, pericardiocentesis, open thoracotomy, bronchoscopy, intraosseous access, significant intravenous fluid resuscitation, blood administration, control of major bleeding, hemodialysis, and use of medications like naloxone-dextrose-dopamine-dobutamine-noradrenaline-adrenalin-atropine-adenosine were considered as life-saving procedures. Patients who will benefit from the treatments to be applied in the intensive care unit, who cannot be followed and treated outside the intensive care unit, who need mechanical ventilation, who need non-invasive mechanical ventilation, who need continuous invasive hemodynamic monitoring, and who require vasoactive drug therapy were considered as patients who need for intensive care unit admission. Abnormal vital signs were defined according to the following cutoff values: body temperature > 37.8 C° - body temperature < 35 C°, HR < 60 beats/minute–HR > 100 beats/minute, SBP < 90 mmHg – SBP > 140 mmHg, DBP < 60 mmHg – DBP > 90 mmHg, RR > 24 breaths/minute, and SaO 2 < 94%, RR < 12/minute – RR > 24/minute ( 9 – 13 ). Statistical Analysis All statistical analyses were performed using SPSS 26.0 (IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp. 2019) package program. The mean and standard deviation with frequency and percentage were used as descriptive statistics for normally distributed data. The median and minimum-maximum values were used for non-normally distributed data. A comparison of categorical variables between groups was carried out with a Chi-square test or Fisher's exact test. The relationship between initial vital signs, comorbidities, and MECs was determined using binary logistic regression analysis, and the odds ratio was calculated to determine the factors affecting MEC. A p-value less than 0.05 was considered significant. RESULTS The study evaluated 10577 patients admitted to the ED between 16.06.2022 and 09.09.2022 (Figure.1). Five hundred fifty-five patients were excluded because of re-admission, age under 18, and cardiac arrest. Of 10022 patients participating in the study, 5056 (50.4%) were female, and 4966 (49.6%) were male. The median age of the patients was 46 (min-max: 18–104). Hypoxia was detected in 992 (9.9%) patients. 236 (2.4%) patients had diastolic hypotension, while 1691 (16.9%) had diastolic hypertension. Systolic hypotension was observed in 50 (0.5%) patients. 1986 (19.8%) patients had systolic hypertension. Tachypnea and high body temperature were detected in 170 (1.7%) and 593 (5.9%) patients. Pulse counts were outside the normal range in 2077 (20.7%) patients. In 6360 patients without any comorbidity or MEC, mean DBP and SBP were 80 ± 11.7 mmHg and 127 ± 18.9 mmHg. Median RR, HR, and SaO 2 were 18/min (min-max: 10–29/min), 88/min (min-max: 49–170/min), and 97% (min-max: 75–100%) relatively. The mean body temperature was 36.4 ± 0.6°C. At least one MEC was detected in 604 (6%) of all patients (Table.1). At least one of the life-saving procedures was applied to 320 (3.2%) patients. Non-invasive mechanical ventilation, endotracheal intubation, and surgical airway were applied in 44, 58, and 2 patients. Defibrillation, medical/electrical cardioversion, and external pacing were required in 2, 22, and 4 patients, respectively. Fluid resuscitation was performed in 60 patients, blood replacement was performed in 92 patients, vasopressor agents were performed in 66 patients, and hemodialysis was performed in 38 patients. Adrenaline was administered in 64 patients for anaphylaxis. Six patients needed needle/tube thoracostomy, 48 needed bleeding control surgery, and ten needed bronchoscopy. Of the 604 patients with one of the MEC, 206 (34.1%) had hypoxia, and 97 (16.1%) had diastolic hypotension. Elevated DBP was recorded in 112 (18.5%) patients. Tachypnea, bradycardia, and tachycardia were detected in 101 (59.4%), 91 (14.3%), and 188 (31.1%) patients, respectively. Female gender and increasing age were associated with MECs. Fever and SBP were not statistically significant in predicting the MEC. Table 2 shows the frequencies and the odds ratios of vital signs and comorbidities regarding MEC. Hemiplegia, leukemia, moderate-severe liver disease, and any tumor presence had the most significant relationship with MECs. Other comorbidities in the modified Charlson comorbidity index did not significantly predict the MEC at admission. Table 1 Frequency of Medical Emergency Conditions Medical Emergency Conditions n * Percentage Percentage in all patients (n = 10022) Mortality within 24 hours 40 6.6 0.4 ICU requirement 410 67.9 4.1 Emergency Surgery requirement 86 14.2 0.9 Life-Saving procedures 320 52.9 3.2 *: Some of the patients have a coexistence of medical emergency conditions. ICU: intensive care unit Table 2 Risk Factors in Patients with the MEC Significant Risks Non-MEC n (%) Any MEC n (%) OR 95% CI p-value Lower upper CAD 775 (83.6%) 152 (16.4%) 1,566 1,231 1,993 0.000 PVD 234 (85.4%) 40 (14.6%) 1,530 1,023 2,288 0.038 CTD 288 (89.4%) 34 (10.6%) 1,619 1,066 2,459 0.024 Diabetes Mellitus 1146 (87.3%) 166 (12.7%) 1,318 1,052 1,651 0.016 Hemiplegia 20 (62.5%) 12 (37.5%) 5,378 2,248 12,870 0,000 Moderate-Severe Kidney Disease 276 (80.7%) 66 (19.3%) 1,583 1,139 2,201 0.006 Any Tumor Presence 361 (84.9%) 64 (15.1%) 2,268 1,666 3,088 0,000 Leukemia 46 (85.2%) 8 (14.8%) 4,239 1,935 9,284 0,000 Moderate-Severe Liver Disease 90 (75%) 30 (25%) 2,990 1,857 4,814 0,000 Metastatic Solid Tumor 228 (78.4%) 58 (21.6%) 2,360 1,647 3,382 0,000 Hypoxia 786 (79.2%) 206 (20.8%) 1,739 1,366 2,215 0,000 Diastolic Hypotension 236 (70.1%) 97 (29.1%) 3,718 2,711 5,100 0,000 Diastolic Hypertension 1691 (93.8%) 112 (6.2%) 0.945 0.748 1,195 0.638 Tachypnea 69 (40.6%) 101 (59.4%) 8,090 5,528 11,840 0,000 Bradycardia 78 (85.7%) 13 (14.3%) 1,291 0.662 2,519 0.453 Tachycardia 1798 (90.5%) 188 (9.5%) 1,611 1,309 1,983 0,000 MEC: Medical Emergency Condition, CAD: Coronary Artery Disease, PVD: Peripheral Vascular Disease, CTD: Connective Tissue Disease, OR: Odd's ratio, CI: Confidence interval, p-value < 0.05 was considered significant. At least one MEC was detected in 3.6% (n = 186) of the patients with no abnormal vital signs or comorbidities. As the number of abnormal vital signs increased, the rate of MEC increased. The relationship between the number of abnormal vital signs, including the mean arterial pressure and the presence of MEC, is shown in Table 3 . Table 3 Abnormal Vital Findings and Medical Emergency Conditions Frequencies Total Abnormal Vital Findings Medical Emergency Conditions n = 10022 None Any 0 4991 (96.4%) 186 (3.6%) 5177 1 661 (92.1%) 57 (7.9%) 718 2 1653 (93.9%) 108 (6.1%) 1761 3 373 (88%) 51 (12%) 424 4 398 (91.1%) 39 (8.9%) 437 5 73 (73%) 27 (27%) 100 6 767 (92.4%) 63 (7.6%) 830 7 122 (84.1%) 23 (15.9%) 145 8 298 (90%) 33 (10%) 331 9 77 (88.5%) 10 (11.5%) 87 10 5 (41.7%) 7 (58.3%) 12 DISCUSSION The measurement, recording, and reporting of vital signs are integral to patient management. These findings vary in disease and provide immediate information for the experienced healthcare professional about the underlying pathology. In addition, comorbidities give information on the patient’s prognosis and lead to different ways of patient management. The study showed the importance of vital signs such as DBP and comorbidities in determining a patient’s emergency status, such as MECs. The oxygen saturation expresses the percentage of hemoglobin molecules saturated with oxygen. This indicates the state of hypoxemia. As in our study, hypoxia carries a risk for mortality, and mortality rates increase as hypoxia deepens ( 6 , 14 ). It is a marker for hospitalized patients' intensive care unit (ICU) admission ( 15 ). The low oxygen saturation value before discharge is even significant for re-admission to the ED ( 9 , 12 ). Diastolic blood pressure is the resting pressure on the arteries between each cardiac contraction. Low DBP causes high pulse pressure, and increased pulse pressure may indicate arterial stiffness, often due to aging or cardiovascular disease. High diastolic blood pressure causes low pulse pressure, which may be a marker of poor heart function with decreased cardiac output. Although not mentioned in the literature, which is similar to our study ( 9 , 12 , 14 ), low DBP can predict mortality ( 6 ); it has even been shown that low DBP is a better predictor of cardiac arrest ( 16 ). Even though high DBP was not found significant in predicting the emergency status in the study, Bleyer et al. showed an increase in mortality at values of 120–130 mmHg and above for DBP, unlike our study ( 6 ). Systolic blood pressure is the maximum pressure on the arteries during left ventricular contraction. Low SBP causes low pulse pressure, a marker of poor heart function, and reduced cardiac output. On the other hand, high SBP may be due to renal, endocrine, intracranial, pregnancy-related, and cardiovascular causes, as well as essential. In the study, low and high SBP was not statistically significant in predicting the MEC. Contrary to the survey, low SBP was a risk factor for mortality, and mortality rates increase as the low SBP deepens ( 6 , 14 ). The low SBP before discharge resulted in re-admission to the ED and mortality ( 9 , 12 ). An increase in mortality was found at values of 200 mmHg and above for SBP ( 14 ). Respiratory rates vary with age. The average resting RR for adults is 10–20 breaths per minute. An increased number of breaths carries a significant risk for mortality, and mortality rates increase as tachypnea worsens ( 6 , 14 ). A high RR before discharge is a good indicator for re-admission to the ED. As the number of breaths increases, the transfer rates of hospitalized patients to the ICU and mortality increase ( 12 , 16 ). Heart rate is an important variable that determines cardiovascular risk. Tachycardia is a resting HR above 100 beats/min in adults. Tachycardias may occur due to physiological processes such as effort, anemia, pain, and anxiety. It may also arise for compensation in pathological processes such as hypoxia, fever, acidosis, hyperthyroidism, shock, and coronary ischemia. A heart rate below 60 beats/minute is bradycardia in adults except for athletes ( 17 ). A high HR is significant for mortality; the mortality rates increase with an increase in heart rate ( 6 , 14 ). Tachycardia is a good indicator of in-hospital cardiac arrest, and a high heart rate before discharge increases the re-admission to the ED ( 9 , 12 , 16 ). While low heart rate was not statistically significant in predicting emergencies in the study, an increase in mortality was found in low heart rates ( 14 ). Body temperature is a vital sign affected by many internal and external sources. A healthy person's body temperature ranges from 36.5 to 37.8° C. Like the study, Nguyen et al. did not find an increase in re-admission to the ED and 30-day mortality with high body temperature before discharge ( 12 ). However, there are also studies showing an increase in mortality as the body temperature increases, and there is an increase in re-admissions to the ED in patients with high body temperature before discharge ( 6 , 9 , 14 ). Current findings have not clarified the role of body temperature in predicting an emergency status. Bleyer et al. classified the comorbid diseases of the patients as chronic heart failure, chronic obstructive pulmonary disease, cancer, dementia, end-stage renal disease, and other end-stage diseases. Similar to our study, there was no mortality risk in the presence of chronic heart failure or dementia. Still, an increased mortality risk was observed in end-stage renal and other end-stage diseases ( 6 ). It has been shown that the vital signs and comorbidities affect mortality, transfer to the ICU, and the frequency of re-admissions to the ED ( 6 , 9 , 12 , 14 , 15 ). Charlson Comorbidity index skoru arttıkça mortalite ihtimalinin de arttığı görülmüştür ( 18 , 19 ). It is also recommended that comorbidities be used to determine trauma patients' initial triage and prognosis. ( 20 ). It is seen that the importance of comorbidities in terms of triage is underestimated, especially in elderly trauma patients. ( 21 ). Likewise, it is reported that comorbidities should be considered to predict the prognosis after surgery ( 22 , 23 ). Even an artificial intelligence-based triage system was created in which comorbidities were encountered. ( 24 ). Although there are similar studies, the difference in the outcome part and the calculation of the emergency risk based on comorbid diseases are the most valuable aspects of the study. Limitations There are some limitations in the study. It was a single-center study with the patient population of a single region. Other limitations in the study are that other early warning systems, triage grading, and re-measurements of vital signs were not evaluated. CONCLUSION Hypoxia, low DBP, tachypnea, tachycardia, and some comorbidities are good predictors of a MEC. Adding DBP and comorbidities to the early warning scoring systems should be considered. Healthcare providers should consider that MECs may occur in patients without abnormal vital signs. While performing triage, the patient should be evaluated as a whole with vital signs, comorbidities, and general condition. Abbreviations ED Emergency Department MEC Medical Emergency Condition OR the Odd’s Ratio RR Respiratory Rate HR Heart Rate CCI Charlson Comorbidity Index Declarations Ethics approval and consent to participate: Ethics Committee approval was obtained before the study to derivate the patient files from the Ethics Committee of Ankara University. (number: İ04-211-22). Informed consent was obtained from patients or legal guardians. Consent for publication: Not Applicable Availability of data and materials: The datasets generated and/or analyzed during the current study are not publicly available due the patients’ files were derived from archives of Hacettepe University but are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests. Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors' contributions: MY and OP have set up the main idea and hypothesis of the study. MY, ASY, and SG developed the theory and organized the material and method section. MGE and ABO evaluated the data given in the conclusion. MY, ASY, and AK wrote the discussion part of the article, after OP reviewed it, made necessary regulations, and approved it. All authors discussed the entire study and approved the final version of the manuscript. Acknowledgments: Not Applicable Authors’ information: Maral YAZICI MD: Pazarcık State Hospital, Emergency Service, Kahramanmaraş, Türkiye Ahmet Sefa YETER MD: Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Emergency Service, Ankara, Türkiye Sinan GENÇ; MD: Ankara University School of Medicine Department of Emergency Medicine, Ankara, Türkiye Ayça KOCA; MD: Ankara University School of Medicine Department of Emergency Medicine, Ankara, Türkiye Ahmet Burak OĞUZ; MD: Ankara University School of Medicine Department of Emergency Medicine, Ankara, Türkiye Müge GÜNALP ENEYLİ; Prof: Ankara University School of Medicine Department of Emergency Medicine, Ankara, Türkiye Onur POLAT; Prof: Ankara University School of Medicine Department of Emergency Medicine, Ankara, Türkiye References Oredsson S, Jonsson H, Rognes J, Lind L, Göransson KE, Ehrenberg A, et al. A systematic review of triage-related interventions to improve patient flow in emergency departments. Scand J Trauma Resusc Emerg Med. 2011;19(1):1–9. Jobé J, Ghuysen A, D'Orio V. Advanced nurse triage for emergency department. Rev Med Liege. 2018;73(5–6):229–36. Gilboy N, Tanabe P, Travers D, Rosenau AM. Emergency Severity Index (ESI): a triage tool for emergency department care, version 4. Implement Handb. 2012;2012:12–0014. Kuriyama A, Urushidani S, Nakayama T. Five-level emergency triage systems: variation in assessment of validity. Emerg Med J. 2017;34(11):703–10. DeVita MA, Hillman K, Bellomo R, Odell M, Jones DA, Winters BD, et al. Textbook of rapid response systems: concept and implementation. Springer; 2017. Bleyer AJ, Vidya S, Russell GB, Jones CM, Sujata L, Daeihagh P, et al. Longitudinal analysis of one million vital signs in patients in an academic medical center. Resuscitation. 2011;82(11):1387–92. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J chronic Dis. 1987;40(5):373–83. Egol A. Guidelines for intensive care unit admission, discharge, and triage. Crit Care Med. 1999;27:633–8. Gabayan GZ, Gould MK, Weiss RE, Derose SF, Chiu VY, Sarkisian CA. Emergency department vital signs and outcomes after discharge. Acad Emerg Med. 2017;24(7):846–54. Stensrud MJ, Strohmaier S. Diastolic hypotension due to intensive blood pressure therapy: is it harmful? Atherosclerosis. 2017;265:29–34. Flack JM, Adekola B. Blood pressure and the new ACC/AHA hypertension guidelines. Trends Cardiovasc Med. 2020;30(3):160–4. Nguyen OK, Makam AN, Clark C, Zhang S, Xie B, Velasco F, et al. Vital signs are still vital: instability on discharge and the risk of post-discharge adverse outcomes. J Gen Intern Med. 2017;32:42–8. Levin R, Dolgin M, Fox C, Gorlin R, The Criteria Committee of the New York Heart Association. Nomenclature and criteria for diagnosis of diseases of the heart and great vessels. LWW Handbooks. 1994;9:344. Kellett J, Kim A. Validation of an abbreviated Vitalpac™ Early Warning Score (ViEWS) in 75,419 consecutive admissions to a Canadian regional hospital. Resuscitation. 2012;83(3):297–302. Churpek MM, Adhikari R, Edelson DP. The value of vital sign trends for detecting clinical deterioration on the wards. Resuscitation. 2016;102:1–5. Churpek MM, Yuen TC, Huber MT, Park SY, Hall JB, Edelson DP. Predicting cardiac arrest on the wards: a nested case-control study. Chest. 2012;141(5):1170–6. Kusumoto FM, Schoenfeld MH, Barrett C, Edgerton JR, Ellenbogen KA, Gold MR, et al. 2018 ACC/AHA/HRS guideline on the evaluation and management of patients with bradycardia and cardiac conduction delay: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society. J Am Coll Cardiol. 2019;74(7):e51–156. Sundararajan V, Henderson T, Perry C, Muggivan A, Quan H, Ghali WA. New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality. J Clin Epidemiol. 2004;57(12):1288–94. Bannay A, Chaignot C, Blotière P-O, Basson M, Weill A, Ricordeau P, et al. The best use of the Charlson comorbidity index with electronic health care database to predict mortality. Med Care. 2016;54(2):188–94. Alshibani A, Singler B, Conroy S. Towards improving prehospital triage for older trauma patients. Zeitschrift für Gerontologie und Geriatrie. 2021;54(2):125–9. Rhodes HX, Locklear TD, Pepe AP. Under-triage of elderly trauma patients with comorbid conditions. Am Surg. 2022;88(8):1925–7. Payá-Llorente C, Martínez-López E, Sebastián-Tomás JC, Santarrufina-Martínez S, de’Angelis N, Martínez-Pérez A. The impact of age and comorbidity on the postoperative outcomes after emergency surgical management of complicated intra-abdominal infections. Sci Rep. 2020;10(1):1631. Cinar F, Parlak G, Aslan FE. The effect of comorbidity on mortality in elderly patients undergoing emergencyabdominal surgery: a systematic review and metaanalysis. Turk J Med Sci. 2021;51(1):61–7. Raita Y, Goto T, Faridi MK, Brown DF, Camargo CA, Hasegawa K. Emergency department triage prediction of clinical outcomes using machine learning models. Crit Care. 2019;23(1):1–13. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Oct, 2024 Read the published version in BMC Emergency Medicine → Version 1 posted Editorial decision: Revision requested 10 Sep, 2024 Reviews received at journal 09 Sep, 2024 Reviews received at journal 06 Sep, 2024 Reviewers agreed at journal 03 Sep, 2024 Reviewers agreed at journal 02 Sep, 2024 Reviews received at journal 24 Aug, 2024 Reviewers agreed at journal 22 Aug, 2024 Reviewers agreed at journal 21 Aug, 2024 Reviewers invited by journal 21 Aug, 2024 Editor invited by journal 16 Aug, 2024 Editor assigned by journal 16 Aug, 2024 Submission checks completed at journal 16 Aug, 2024 First submitted to journal 14 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4913657\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":351815047,\"identity\":\"f9923596-ab18-4bc8-9274-a5da549e6430\",\"order_by\":0,\"name\":\"Maral YAZICI\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Pazarcık State Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Maral\",\"middleName\":\"\",\"lastName\":\"YAZICI\",\"suffix\":\"\"},{\"id\":351815048,\"identity\":\"48a32f8e-3b3d-4b4a-841a-7c384504af7e\",\"order_by\":1,\"name\":\"Ahmet Sefa YETER\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYNACGws5AzDDwIJYLWkSxgYMzCAtEsRrSdwA1sJAhBb+/jMGDD8SJNK3s/cf3fCjQIKBv707Aa8WiRs5Bow9CRK5O3sOs93sATpM4szZDfitucFjwMD7QyJ3w41kNiBbAuidXPxa5M+fMWD8A3SYAVDLzT/EaDE4kGPAzJMgkQDScpsoWwxvpBUwyyRIGG44c9jstoyBBA9Bv8idP7yB8U2CjbzB8cZnN9/8sZHjb+8l4H0GDvMfyFweAspBgP0BEYpGwSgYBaNgRAMAAupC5j7jOHsAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Ahmet\",\"middleName\":\"Sefa\",\"lastName\":\"YETER\",\"suffix\":\"\"},{\"id\":351815049,\"identity\":\"49db84d7-7690-4e75-a05d-27db02e89fa6\",\"order_by\":2,\"name\":\"Sinan GENÇ\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Ankara University School of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sinan\",\"middleName\":\"\",\"lastName\":\"GENÇ\",\"suffix\":\"\"},{\"id\":351815050,\"identity\":\"39515ae2-6db5-43d6-a7a4-6c16af412f76\",\"order_by\":3,\"name\":\"Ayça KOCA\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Ankara University School of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ayça\",\"middleName\":\"\",\"lastName\":\"KOCA\",\"suffix\":\"\"},{\"id\":351815051,\"identity\":\"1cd759a7-6434-450b-93af-b3fceb9a27b1\",\"order_by\":4,\"name\":\"Ahmet Burak OĞUZ\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Ankara University School of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ahmet\",\"middleName\":\"Burak\",\"lastName\":\"OĞUZ\",\"suffix\":\"\"},{\"id\":351815052,\"identity\":\"5a69302d-8bc7-4622-9be0-14289f8f4c3b\",\"order_by\":5,\"name\":\"Müge GÜNALP ENEYLİ\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Ankara University School of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Müge\",\"middleName\":\"GÜNALP\",\"lastName\":\"ENEYLİ\",\"suffix\":\"\"},{\"id\":351815053,\"identity\":\"6a0ab7a3-8176-4784-b0d7-25d34be05474\",\"order_by\":6,\"name\":\"Onur POLAT\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Ankara University School of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Onur\",\"middleName\":\"\",\"lastName\":\"POLAT\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-08-14 12:33:30\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4913657/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4913657/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1186/s12873-024-01101-y\",\"type\":\"published\",\"date\":\"2024-10-10T15:57:39+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":66563648,\"identity\":\"1456f0bb-09eb-40e8-b2d5-3bf18423bb11\",\"added_by\":\"auto\",\"created_at\":\"2024-10-14 10:27:40\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":12344,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFlow Chart of the Study\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Onlinedrawingimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4913657/v1/59d2dd003fab05e49c435d41.png\"},{\"id\":66597168,\"identity\":\"6e5bdc2d-c49a-40f5-ab88-b23179da88df\",\"added_by\":\"auto\",\"created_at\":\"2024-10-14 16:07:55\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":568092,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4913657/v1/68aeb9dd-ab90-4767-8c86-f434e768b59a.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Predictability of Adult Patient Medical Emergency Condition from Triage Vital Signs and Comorbidities: A Single-Center, Observational Study\",\"fulltext\":[{\"header\":\"BACKGROUND\",\"content\":\"\\u003cp\\u003eOver the years, overcrowding in emergency departments (ED) and high patient density have increased the importance of triage in the ED (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e). Triage is the primary tool to assess patients\\u0026rsquo; severity of illness or injury upon arrival to the ED (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e). The purpose of triage is to determine the patient\\u0026rsquo;s emergency status and prioritize emergent patients (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e). The critical step of triage is a dynamic process related to various components.\\u003c/p\\u003e \\u003cp\\u003eAn initial clinical assessment is essential to distinguish the patients and recognize those needing immediate care. According to established international triage scores used worldwide, the parameters of these scores may vary (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e). Common characteristics components are initial vital signs, chief complaint and discriminators, and prediction of resource requirements. However, the joint entity of these scoring systems is vital signs.\\u003c/p\\u003e \\u003cp\\u003eVital signs, with their dynamic and instantaneous variability, are among the simplest and most important information obtained about admitted patients. Vital signs are measurements of the body's most basic functions; they help detect or monitor medical problems or patients\\u0026rsquo; progress. The four classic vital signs are body temperature, heart rate (HR), and blood pressure (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e), respiratory rate (RR). These vital signs may significantly impact patient triage scores and clinical outcomes. For example, a patient admitted with a headache may have a different trajectory if their blood pressure is elevated. Similarly, the number of critical vital signs is associated with mortality; as the number of abnormal vital signs increases, mortality rates increase (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eBesides vital signs, another entity that may be relevant to illness or injury severity and clinical outcome is the comorbid illness of a patient. Likewise, a patient admitted with a headache should be categorized differently if they have a history of a previous brain aneurysm.\\u003c/p\\u003e \\u003cp\\u003eAlthough initially developed for breast cancer patients, the Charlson comorbidity index (CCI) is a widely used index accepted as valid for predicting disease burden and mortality (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThis study aims to determine the effect of vital signs and comorbidities on predicting medical emergency conditions (MEC) within 24 hours\\u003c/p\\u003e\"},{\"header\":\"METHODS\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy Type and Design\\u003c/h2\\u003e \\u003cp\\u003eThis was a prospective study conducted in an academic ED environment. The study was approved by our institution's Health Research Ethics Board (number: İ04-211-22).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy Setting and Population\\u003c/h2\\u003e \\u003cp\\u003e The prospective observational study was conducted in a large tertiary care hospital with an approximate annual of 50,000 ED admissions. All adult patients admitted to the ED with any complaint between 16.06.2022 and 09.09.2022 were eligible for inclusion. Patients younger than 18 years, re-admitted to the ED within 24 hours, and presented with no vital signs due to cardiac arrest were excluded from the study. Informed consent was obtained from patients or legal guardians.\\u003c/p\\u003e \\u003cp\\u003eThe vital signs recorded from patients were body temperature, heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), respiratory rate (RR), and oxygen saturation (SaO\\u003csub\\u003e2\\u003c/sub\\u003e). Mindray Beneheart D6 was used for RR, HR, SaO\\u003csub\\u003e2,\\u003c/sub\\u003e and blood pressure, and Unaan YNA-800 was used for body temperature. These data were recorded in the study form. According to the modified Charlson comorbid index, comorbidities were recorded in the study form as yes/no (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e). Information regarding medical history was obtained from patients\\u0026rsquo; first-degree relatives or informal caregivers when patients were incompetent to give it themselves. A medical emergency condition was defined as mortality within 24 hours, need for intensive care unit admission, need for emergency surgery, and life-saving procedures. The MEC, as mentioned earlier, was reviewed through the \\u0026ldquo;Hospital Information Management System\\u0026rdquo; and if one of these four criteria was positive, the patient's status was recorded as in a \\\"medical emergency condition\\\" (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e). Assisted ventilation, intubation, surgical airway, emergent non-invasive positive pressure ventilation, defibrillation, emergent cardioversion, external pacing, chest needle decompression, pericardiocentesis, open thoracotomy, bronchoscopy, intraosseous access, significant intravenous fluid resuscitation, blood administration, control of major bleeding, hemodialysis, and use of medications like naloxone-dextrose-dopamine-dobutamine-noradrenaline-adrenalin-atropine-adenosine were considered as life-saving procedures. Patients who will benefit from the treatments to be applied in the intensive care unit, who cannot be followed and treated outside the intensive care unit, who need mechanical ventilation, who need non-invasive mechanical ventilation, who need continuous invasive hemodynamic monitoring, and who require vasoactive drug therapy were considered as patients who need for intensive care unit admission.\\u003c/p\\u003e \\u003cp\\u003eAbnormal vital signs were defined according to the following cutoff values: body temperature\\u0026thinsp;\\u0026gt;\\u0026thinsp;37.8 C\\u0026deg; - body temperature\\u0026thinsp;\\u0026lt;\\u0026thinsp;35 C\\u0026deg;, HR\\u0026thinsp;\\u0026lt;\\u0026thinsp;60 beats/minute\\u0026ndash;HR\\u0026thinsp;\\u0026gt;\\u0026thinsp;100 beats/minute, SBP\\u0026thinsp;\\u0026lt;\\u0026thinsp;90 mmHg \\u0026ndash; SBP\\u0026thinsp;\\u0026gt;\\u0026thinsp;140 mmHg, DBP\\u0026thinsp;\\u0026lt;\\u0026thinsp;60 mmHg \\u0026ndash; DBP\\u0026thinsp;\\u0026gt;\\u0026thinsp;90 mmHg, RR\\u0026thinsp;\\u0026gt;\\u0026thinsp;24 breaths/minute, and SaO\\u003csub\\u003e2\\u003c/sub\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;94%, RR\\u0026thinsp;\\u0026lt;\\u0026thinsp;12/minute \\u0026ndash; RR\\u0026thinsp;\\u0026gt;\\u0026thinsp;24/minute (\\u003cspan additionalcitationids=\\\"CR10 CR11 CR12\\\" citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical Analysis\\u003c/h2\\u003e \\u003cp\\u003eAll statistical analyses were performed using SPSS 26.0 (IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp. 2019) package program.\\u003c/p\\u003e \\u003cp\\u003eThe mean and standard deviation with frequency and percentage were used as descriptive statistics for normally distributed data. The median and minimum-maximum values were used for non-normally distributed data. A comparison of categorical variables between groups was carried out with a Chi-square test or Fisher's exact test. The relationship between initial vital signs, comorbidities, and MECs was determined using binary logistic regression analysis, and the odds ratio was calculated to determine the factors affecting MEC. A p-value less than 0.05 was considered significant.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"RESULTS\",\"content\":\"\\u003cp\\u003eThe study evaluated 10577 patients admitted to the ED between 16.06.2022 and 09.09.2022 (Figure.1). Five hundred fifty-five patients were excluded because of re-admission, age under 18, and cardiac arrest. Of 10022 patients participating in the study, 5056 (50.4%) were female, and 4966 (49.6%) were male. The median age of the patients was 46 (min-max: 18\\u0026ndash;104). Hypoxia was detected in 992 (9.9%) patients. 236 (2.4%) patients had diastolic hypotension, while 1691 (16.9%) had diastolic hypertension. Systolic hypotension was observed in 50 (0.5%) patients. 1986 (19.8%) patients had systolic hypertension. Tachypnea and high body temperature were detected in 170 (1.7%) and 593 (5.9%) patients. Pulse counts were outside the normal range in 2077 (20.7%) patients.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eIn 6360 patients without any comorbidity or MEC, mean DBP and SBP were 80\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.7 mmHg and 127\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;18.9 mmHg. Median RR, HR, and SaO\\u003csub\\u003e2\\u003c/sub\\u003e were 18/min (min-max: 10\\u0026ndash;29/min), 88/min (min-max: 49\\u0026ndash;170/min), and 97% (min-max: 75\\u0026ndash;100%) relatively. The mean body temperature was 36.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.6\\u0026deg;C.\\u003c/p\\u003e \\u003cp\\u003eAt least one MEC was detected in 604 (6%) of all patients (Table.1). At least one of the life-saving procedures was applied to 320 (3.2%) patients. Non-invasive mechanical ventilation, endotracheal intubation, and surgical airway were applied in 44, 58, and 2 patients. Defibrillation, medical/electrical cardioversion, and external pacing were required in 2, 22, and 4 patients, respectively. Fluid resuscitation was performed in 60 patients, blood replacement was performed in 92 patients, vasopressor agents were performed in 66 patients, and hemodialysis was performed in 38 patients. Adrenaline was administered in 64 patients for anaphylaxis. Six patients needed needle/tube thoracostomy, 48 needed bleeding control surgery, and ten needed bronchoscopy.\\u003c/p\\u003e \\u003cp\\u003eOf the 604 patients with one of the MEC, 206 (34.1%) had hypoxia, and 97 (16.1%) had diastolic hypotension. Elevated DBP was recorded in 112 (18.5%) patients. Tachypnea, bradycardia, and tachycardia were detected in 101 (59.4%), 91 (14.3%), and 188 (31.1%) patients, respectively. Female gender and increasing age were associated with MECs. Fever and SBP were not statistically significant in predicting the MEC. Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e shows the frequencies and the odds ratios of vital signs and comorbidities regarding MEC. Hemiplegia, leukemia, moderate-severe liver disease, and any tumor presence had the most significant relationship with MECs. Other comorbidities in the modified Charlson comorbidity index did not significantly predict the MEC at admission.\\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\\u003eFrequency of Medical Emergency Conditions\\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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMedical Emergency Conditions\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003en *\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePercentage\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePercentage in all patients (n\\u0026thinsp;=\\u0026thinsp;10022)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMortality within 24 hours\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eICU requirement\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e410\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e67.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eEmergency Surgery requirement\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e86\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e14.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eLife-Saving procedures\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e320\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e52.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003e*: Some of the patients have a coexistence of medical emergency conditions. ICU: intensive care unit\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\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\\u003eRisk Factors in Patients with the MEC\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"10\\\"\\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 \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" morerows=\\\"1\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eSignificant Risks\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" morerows=\\\"1\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eNon-MEC\\u003c/p\\u003e \\u003cp\\u003en (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eAny MEC\\u003c/p\\u003e \\u003cp\\u003en (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eOR\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c9\\\" namest=\\\"c8\\\"\\u003e \\u003cp\\u003e95% CI\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c10\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eLower\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eupper\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eCAD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e775 (83.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e152 (16.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1,566\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1,231\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e1,993\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003ePVD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e234 (85.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e40 (14.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1,530\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1,023\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e2,288\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.038\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eCTD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e288 (89.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e34 (10.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1,619\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1,066\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e2,459\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.024\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eDiabetes Mellitus\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e1146 (87.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e166 (12.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1,318\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1,052\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e1,651\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.016\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eHemiplegia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e20 (62.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e12 (37.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e5,378\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e2,248\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e12,870\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eModerate-Severe Kidney Disease\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e276 (80.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e66 (19.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1,583\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1,139\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e2,201\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.006\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eAny Tumor Presence\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e361 (84.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e64 (15.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e2,268\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1,666\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e3,088\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eLeukemia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e46 (85.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e8 (14.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e4,239\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1,935\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e9,284\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eModerate-Severe Liver Disease\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e90 (75%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e30 (25%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e2,990\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1,857\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e4,814\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eMetastatic Solid Tumor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e228 (78.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e58 (21.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e2,360\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1,647\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e3,382\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eHypoxia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e786 (79.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e206 (20.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1,739\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1,366\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e2,215\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eDiastolic Hypotension\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e236 (70.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e97 (29.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e3,718\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e2,711\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e5,100\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eDiastolic Hypertension\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e1691 (93.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e112 (6.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.945\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.748\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e1,195\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.638\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eTachypnea\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e69 (40.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e101 (59.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e8,090\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e5,528\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e11,840\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eBradycardia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e78 (85.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e13 (14.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1,291\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.662\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e2,519\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.453\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eTachycardia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e1798 (90.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e188 (9.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1,611\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1,309\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e1,983\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"10\\\"\\u003eMEC: Medical Emergency Condition, CAD: Coronary Artery Disease, PVD: Peripheral Vascular Disease, CTD: Connective Tissue Disease, OR: Odd's ratio, CI: Confidence interval, p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 was considered significant.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eAt least one MEC was detected in 3.6% (n\\u0026thinsp;=\\u0026thinsp;186) of the patients with no abnormal vital signs or comorbidities. As the number of abnormal vital signs increased, the rate of MEC increased. The relationship between the number of abnormal vital signs, including the mean arterial pressure and the presence of MEC, is shown in 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\\u003eAbnormal Vital Findings and Medical Emergency Conditions Frequencies\\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\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eTotal Abnormal Vital Findings\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eMedical Emergency Conditions\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003en\\u0026thinsp;=\\u0026thinsp;10022\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNone\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAny\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4991 (96.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e186 (3.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5177\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e661 (92.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e57 (7.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e718\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1653 (93.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e108 (6.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1761\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e373 (88%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e51 (12%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e424\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e398 (91.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e39 (8.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e437\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e73 (73%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e27 (27%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e100\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e767 (92.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e63 (7.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e830\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e122 (84.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e23 (15.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e145\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e298 (90%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e33 (10%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e331\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e77 (88.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10 (11.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e87\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5 (41.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7 (58.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e\"},{\"header\":\"DISCUSSION\",\"content\":\"\\u003cp\\u003eThe measurement, recording, and reporting of vital signs are integral to patient management. These findings vary in disease and provide immediate information for the experienced healthcare professional about the underlying pathology. In addition, comorbidities give information on the patient\\u0026rsquo;s prognosis and lead to different ways of patient management. The study showed the importance of vital signs such as DBP and comorbidities in determining a patient\\u0026rsquo;s emergency status, such as MECs.\\u003c/p\\u003e \\u003cp\\u003eThe oxygen saturation expresses the percentage of hemoglobin molecules saturated with oxygen. This indicates the state of hypoxemia. As in our study, hypoxia carries a risk for mortality, and mortality rates increase as hypoxia deepens (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e). It is a marker for hospitalized patients' intensive care unit (ICU) admission (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e). The low oxygen saturation value before discharge is even significant for re-admission to the ED (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e). Diastolic blood pressure is the resting pressure on the arteries between each cardiac contraction. Low DBP causes high pulse pressure, and increased pulse pressure may indicate arterial stiffness, often due to aging or cardiovascular disease. High diastolic blood pressure causes low pulse pressure, which may be a marker of poor heart function with decreased cardiac output. Although not mentioned in the literature, which is similar to our study (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e), low DBP can predict mortality (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e); it has even been shown that low DBP is a better predictor of cardiac arrest (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e). Even though high DBP was not found significant in predicting the emergency status in the study, Bleyer et al. showed an increase in mortality at values of 120\\u0026ndash;130 mmHg and above for DBP, unlike our study (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e). Systolic blood pressure is the maximum pressure on the arteries during left ventricular contraction. Low SBP causes low pulse pressure, a marker of poor heart function, and reduced cardiac output. On the other hand, high SBP may be due to renal, endocrine, intracranial, pregnancy-related, and cardiovascular causes, as well as essential. In the study, low and high SBP was not statistically significant in predicting the MEC. Contrary to the survey, low SBP was a risk factor for mortality, and mortality rates increase as the low SBP deepens (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e). The low SBP before discharge resulted in re-admission to the ED and mortality (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e). An increase in mortality was found at values of 200 mmHg and above for SBP (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eRespiratory rates vary with age. The average resting RR for adults is 10\\u0026ndash;20 breaths per minute. An increased number of breaths carries a significant risk for mortality, and mortality rates increase as tachypnea worsens (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e). A high RR before discharge is a good indicator for re-admission to the ED. As the number of breaths increases, the transfer rates of hospitalized patients to the ICU and mortality increase (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e). Heart rate is an important variable that determines cardiovascular risk. Tachycardia is a resting HR above 100 beats/min in adults. Tachycardias may occur due to physiological processes such as effort, anemia, pain, and anxiety. It may also arise for compensation in pathological processes such as hypoxia, fever, acidosis, hyperthyroidism, shock, and coronary ischemia. A heart rate below 60 beats/minute is bradycardia in adults except for athletes (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e). A high HR is significant for mortality; the mortality rates increase with an increase in heart rate (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e). Tachycardia is a good indicator of in-hospital cardiac arrest, and a high heart rate before discharge increases the re-admission to the ED (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e). While low heart rate was not statistically significant in predicting emergencies in the study, an increase in mortality was found in low heart rates (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e). Body temperature is a vital sign affected by many internal and external sources. A healthy person's body temperature ranges from 36.5 to 37.8\\u0026deg; C. Like the study, Nguyen et al. did not find an increase in re-admission to the ED and 30-day mortality with high body temperature before discharge (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e). However, there are also studies showing an increase in mortality as the body temperature increases, and there is an increase in re-admissions to the ED in patients with high body temperature before discharge (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e). Current findings have not clarified the role of body temperature in predicting an emergency status. Bleyer et al. classified the comorbid diseases of the patients as chronic heart failure, chronic obstructive pulmonary disease, cancer, dementia, end-stage renal disease, and other end-stage diseases. Similar to our study, there was no mortality risk in the presence of chronic heart failure or dementia. Still, an increased mortality risk was observed in end-stage renal and other end-stage diseases (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e). It has been shown that the vital signs and comorbidities affect mortality, transfer to the ICU, and the frequency of re-admissions to the ED (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e). Charlson Comorbidity index skoru arttık\\u0026ccedil;a mortalite ihtimalinin de arttığı g\\u0026ouml;r\\u0026uuml;lm\\u0026uuml;şt\\u0026uuml;r (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e). It is also recommended that comorbidities be used to determine trauma patients' initial triage and prognosis. (\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e). It is seen that the importance of comorbidities in terms of triage is underestimated, especially in elderly trauma patients. (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e). Likewise, it is reported that comorbidities should be considered to predict the prognosis after surgery (\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e). Even an artificial intelligence-based triage system was created in which comorbidities were encountered. (\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e). Although there are similar studies, the difference in the outcome part and the calculation of the emergency risk based on comorbid diseases are the most valuable aspects of the study.\\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eLimitations\\u003c/strong\\u003e \\u003cp\\u003eThere are some limitations in the study. It was a single-center study with the patient population of a single region. Other limitations in the study are that other early warning systems, triage grading, and re-measurements of vital signs were not evaluated.\\u003c/p\\u003e \\u003c/p\\u003e\"},{\"header\":\"CONCLUSION\",\"content\":\"\\u003cp\\u003eHypoxia, low DBP, tachypnea, tachycardia, and some comorbidities are good predictors of a MEC. Adding DBP and comorbidities to the early warning scoring systems should be considered. Healthcare providers should consider that MECs may occur in patients without abnormal vital signs. While performing triage, the patient should be evaluated as a whole with vital signs, comorbidities, and general condition.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cdiv class=\\\"DefinitionList\\\"\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eED\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eEmergency Department\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eMEC\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eMedical Emergency Condition\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eOR\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003ethe Odd\\u0026rsquo;s Ratio\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eRR\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eRespiratory Rate\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eHR\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eHeart Rate\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eCCI\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eCharlson Comorbidity Index\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate:\\u0026nbsp;\\u003c/strong\\u003eEthics Committee approval was obtained before the study to derivate the patient files from the Ethics Committee of Ankara University. (number: İ04-211-22). Informed consent was obtained from patients or legal guardians.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication:\\u0026nbsp;\\u003c/strong\\u003eNot Applicable\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials:\\u0026nbsp;\\u003c/strong\\u003eThe datasets generated and/or analyzed during the current study are not publicly available due the patients\\u0026rsquo; files were derived from archives of Hacettepe University but are available from the corresponding author on reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests:\\u0026nbsp;\\u003c/strong\\u003eThe authors declare that they have no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding:\\u0026nbsp;\\u003c/strong\\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026apos; contributions:\\u0026nbsp;\\u003c/strong\\u003eMY and OP have set up the main idea and hypothesis of the study. MY, ASY, and SG developed the theory and organized the material and method section. MGE and ABO evaluated the data given in the conclusion. MY, ASY, and AK wrote the discussion part of the article, after OP reviewed it, made necessary regulations, and approved it. All authors discussed the entire study and approved the final version of the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments:\\u0026nbsp;\\u003c/strong\\u003eNot Applicable\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026rsquo; information:\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMaral YAZICI MD: Pazarcık State Hospital, Emergency Service, Kahramanmaraş, T\\u0026uuml;rkiye\\u003c/p\\u003e\\n\\u003cp\\u003eAhmet Sefa YETER MD: Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Emergency Service, Ankara,\\u0026nbsp;T\\u0026uuml;rkiye\\u003c/p\\u003e\\n\\u003cp\\u003eSinan GEN\\u0026Ccedil;; MD: Ankara University School of Medicine Department of Emergency Medicine, Ankara,\\u0026nbsp;T\\u0026uuml;rkiye\\u003c/p\\u003e\\n\\u003cp\\u003eAy\\u0026ccedil;a KOCA; MD: Ankara University School of Medicine Department of Emergency Medicine, Ankara,\\u0026nbsp;T\\u0026uuml;rkiye\\u003c/p\\u003e\\n\\u003cp\\u003eAhmet Burak OĞUZ; MD: Ankara University School of Medicine Department of Emergency Medicine, Ankara,\\u0026nbsp;T\\u0026uuml;rkiye\\u003c/p\\u003e\\n\\u003cp\\u003eM\\u0026uuml;ge G\\u0026Uuml;NALP ENEYLİ; Prof:\\u0026nbsp;Ankara University School of Medicine Department of Emergency Medicine, Ankara,\\u0026nbsp;T\\u0026uuml;rkiye\\u003c/p\\u003e\\n\\u003cp\\u003eOnur POLAT; Prof: Ankara University School of Medicine Department of Emergency Medicine, Ankara, T\\u0026uuml;rkiye\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eOredsson S, Jonsson H, Rognes J, Lind L, G\\u0026ouml;ransson KE, Ehrenberg A, et al. A systematic review of triage-related interventions to improve patient flow in emergency departments. Scand J Trauma Resusc Emerg Med. 2011;19(1):1\\u0026ndash;9.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJob\\u0026eacute; J, Ghuysen A, D'Orio V. Advanced nurse triage for emergency department. Rev Med Liege. 2018;73(5\\u0026ndash;6):229\\u0026ndash;36.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGilboy N, Tanabe P, Travers D, Rosenau AM. Emergency Severity Index (ESI): a triage tool for emergency department care, version 4. Implement Handb. 2012;2012:12\\u0026ndash;0014.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKuriyama A, Urushidani S, Nakayama T. Five-level emergency triage systems: variation in assessment of validity. Emerg Med J. 2017;34(11):703\\u0026ndash;10.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDeVita MA, Hillman K, Bellomo R, Odell M, Jones DA, Winters BD, et al. Textbook of rapid response systems: concept and implementation. Springer; 2017.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBleyer AJ, Vidya S, Russell GB, Jones CM, Sujata L, Daeihagh P, et al. Longitudinal analysis of one million vital signs in patients in an academic medical center. Resuscitation. 2011;82(11):1387\\u0026ndash;92.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCharlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J chronic Dis. 1987;40(5):373\\u0026ndash;83.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eEgol A. Guidelines for intensive care unit admission, discharge, and triage. Crit Care Med. 1999;27:633\\u0026ndash;8.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGabayan GZ, Gould MK, Weiss RE, Derose SF, Chiu VY, Sarkisian CA. Emergency department vital signs and outcomes after discharge. Acad Emerg Med. 2017;24(7):846\\u0026ndash;54.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eStensrud MJ, Strohmaier S. Diastolic hypotension due to intensive blood pressure therapy: is it harmful? Atherosclerosis. 2017;265:29\\u0026ndash;34.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFlack JM, Adekola B. Blood pressure and the new ACC/AHA hypertension guidelines. Trends Cardiovasc Med. 2020;30(3):160\\u0026ndash;4.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNguyen OK, Makam AN, Clark C, Zhang S, Xie B, Velasco F, et al. Vital signs are still vital: instability on discharge and the risk of post-discharge adverse outcomes. J Gen Intern Med. 2017;32:42\\u0026ndash;8.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLevin R, Dolgin M, Fox C, Gorlin R, The Criteria Committee of the New York Heart Association. Nomenclature and criteria for diagnosis of diseases of the heart and great vessels. LWW Handbooks. 1994;9:344.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKellett J, Kim A. Validation of an abbreviated Vitalpac\\u0026trade; Early Warning Score (ViEWS) in 75,419 consecutive admissions to a Canadian regional hospital. Resuscitation. 2012;83(3):297\\u0026ndash;302.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChurpek MM, Adhikari R, Edelson DP. The value of vital sign trends for detecting clinical deterioration on the wards. Resuscitation. 2016;102:1\\u0026ndash;5.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChurpek MM, Yuen TC, Huber MT, Park SY, Hall JB, Edelson DP. Predicting cardiac arrest on the wards: a nested case-control study. Chest. 2012;141(5):1170\\u0026ndash;6.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKusumoto FM, Schoenfeld MH, Barrett C, Edgerton JR, Ellenbogen KA, Gold MR, et al. 2018 ACC/AHA/HRS guideline on the evaluation and management of patients with bradycardia and cardiac conduction delay: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society. J Am Coll Cardiol. 2019;74(7):e51\\u0026ndash;156.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSundararajan V, Henderson T, Perry C, Muggivan A, Quan H, Ghali WA. New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality. J Clin Epidemiol. 2004;57(12):1288\\u0026ndash;94.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBannay A, Chaignot C, Bloti\\u0026egrave;re P-O, Basson M, Weill A, Ricordeau P, et al. The best use of the Charlson comorbidity index with electronic health care database to predict mortality. Med Care. 2016;54(2):188\\u0026ndash;94.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAlshibani A, Singler B, Conroy S. Towards improving prehospital triage for older trauma patients. Zeitschrift f\\u0026uuml;r Gerontologie und Geriatrie. 2021;54(2):125\\u0026ndash;9.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRhodes HX, Locklear TD, Pepe AP. Under-triage of elderly trauma patients with comorbid conditions. Am Surg. 2022;88(8):1925\\u0026ndash;7.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePay\\u0026aacute;-Llorente C, Mart\\u0026iacute;nez-L\\u0026oacute;pez E, Sebasti\\u0026aacute;n-Tom\\u0026aacute;s JC, Santarrufina-Mart\\u0026iacute;nez S, de\\u0026rsquo;Angelis N, Mart\\u0026iacute;nez-P\\u0026eacute;rez A. The impact of age and comorbidity on the postoperative outcomes after emergency surgical management of complicated intra-abdominal infections. Sci Rep. 2020;10(1):1631.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCinar F, Parlak G, Aslan FE. The effect of comorbidity on mortality in elderly patients undergoing emergencyabdominal surgery: a systematic review and metaanalysis. Turk J Med Sci. 2021;51(1):61\\u0026ndash;7.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRaita Y, Goto T, Faridi MK, Brown DF, Camargo CA, Hasegawa K. Emergency department triage prediction of clinical outcomes using machine learning models. Crit Care. 2019;23(1):1\\u0026ndash;13.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-emergency-medicine\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"emmd\",\"sideBox\":\"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/emmd\",\"title\":\"BMC Emergency Medicine\",\"twitterHandle\":\"@BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Vital Signs, Comorbidity, Mortality, Medical Emergency Condition, Life-Saving Procedure\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4913657/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4913657/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eVital signs and comorbid diseases are the first information evaluated in patients admitted to the emergency department (ED). This study aims to assess the relationship between initial vital signs, comorbid diseases, and medical emergency conditions (MEC) in patients admitted to the ED.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eThis prospective study was designed as a single-center observational study, including patients admitted to a tertiary ED between 16.06.2022 and 09.09.2022. Patients younger than 18, readmitted to the ED within 24 hours, or absence of vital signs due to cardiac arrest were excluded from the study. Vital signs and comorbid diseases of all patients were recorded. The mortality within 24 hours, the need for intensive care unit admission, emergency surgery, and life-saving procedures were considered \\u0026ldquo;medical emergency conditions\\u0026rdquo;. The role of vital signs and comorbid diseases in predicting emergencies was analyzed by binary logistic regression.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eA total of 10022 patients were included in the study; 5056 (50.4%) were female, and 4966 (49.6%) were male. The median age of patients was 46 (min-max: 18\\u0026ndash;104). Six hundred four patients presented with a MEC. 3480 (34.7%) patients had at least one comorbidity, while 5031 (50.2%) patients had at least one abnormal vital sign. Hypoxia (Odd\\u0026rsquo;s Ratio [OR]: 1.73), diastolic hypotension (OR: 3.71), tachypnea (OR: 8.09), and tachycardia (OR: 1.61) were associated with MECs. Hemiplegia (OR: 5.7), leukemia (OR: 4.23), and moderate-severe liver disease (OR: 2.99) were the most associated comorbidities with MECs. In our study, a MEC was detected in 3.6% (186 patients) of the patients with no abnormal vital signs and without any comorbidities.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003eAmong the vital signs, hypoxia, diastolic hypotension, tachypnea, and tachycardia should be considered indicators of a MEC. Hemiplegia, leukemia, and moderate-severe liver disease are the most relevant comorbidities that may accompany the MECs.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Predictability of Adult Patient Medical Emergency Condition from Triage Vital Signs and Comorbidities: A Single-Center, Observational Study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-10-14 10:27:36\",\"doi\":\"10.21203/rs.3.rs-4913657/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2024-09-10T04:02:39+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-09-09T05:41:02+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-09-06T11:16:25+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"335169425937064780494051577678017418526\",\"date\":\"2024-09-03T13:21:44+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"302918167115247178498291384733529118226\",\"date\":\"2024-09-02T09:44:42+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-08-24T18:30:11+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"313103619454501116223005638846054869539\",\"date\":\"2024-08-22T17:28:00+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"109646995500240333117894661343650364317\",\"date\":\"2024-08-21T18:27:09+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-08-21T17:48:58+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2024-08-16T07:56:20+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-08-16T07:54:31+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-08-16T07:54:21+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Emergency Medicine\",\"date\":\"2024-08-14T12:32:04+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-emergency-medicine\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"emmd\",\"sideBox\":\"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/emmd\",\"title\":\"BMC Emergency Medicine\",\"twitterHandle\":\"@BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"ef074885-72c9-4af6-9a05-75519576d911\",\"owner\":[],\"postedDate\":\"October 14th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-10-14T16:01:42+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-4913657\",\"link\":\"https://doi.org/10.1186/s12873-024-01101-y\",\"journal\":{\"identity\":\"bmc-emergency-medicine\",\"isVorOnly\":false,\"title\":\"BMC Emergency Medicine\"},\"publishedOn\":\"2024-10-10 15:57:39\",\"publishedOnDateReadable\":\"October 10th, 2024\"},\"versionCreatedAt\":\"2024-10-14 10:27:36\",\"video\":\"\",\"vorDoi\":\"10.1186/s12873-024-01101-y\",\"vorDoiUrl\":\"https://doi.org/10.1186/s12873-024-01101-y\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4913657\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4913657\",\"identity\":\"rs-4913657\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}