Equivalency Between the Shock Index and Subtracting the Systolic Blood Pressure From the Heart Rate: An Observational Cohort Study

Equivalency Between the Shock Index and Subtracting the Systolic Blood Pressure From the Heart Rate: An Observational Cohort 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 Equivalency Between the Shock Index and Subtracting the Systolic Blood Pressure From the Heart Rate: An Observational Cohort Study Yohei Kamikawa, Hiroyuki Hayashi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-76143/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Oct, 2020 Read the published version in BMC Emergency Medicine → Version 1 posted 10 You are reading this latest preprint version Abstract Background: Although the shock index is known to predict mortality and other severe outcomes, deriving it requires complex calculations. Subtracting the systolic blood pressure from the heart rate may produce a simple shock index that would be a clinically useful substitute for the shock index. In this study, we investigated whether the simple shock index was equivalent to the shock index. Methods: This observational cohort study was conducted at 2 tertiary care hospitals. Patients who were transported by ambulance were recruited for this study and were excluded if they were aged <15 years, had experienced prehospital cardiopulmonary arrest, or had undergone inter-hospital transfer. Pearson’s product-moment correlation coefficient and regression equation were calculated, and two one-sided tests were performed to examine their equivalency. Results: Among 5,429 eligible patients, the correlation coefficient between the shock index and simple shock index was extremely high (0.917, 95% confidence interval 0.912 to 0.921, P <.001). The regression equation was estimated as sSI = 258.55 log SI. The two one-sided tests revealed a very strong equivalency between the shock index and the index estimated by the above equation using the simple shock index (mean difference was 0.004, 90% confidence interval 0.003 to 0.005). Conclusion: The simple shock index strongly correlated with the shock index. Critical Care & Emergency Medicine Ambulance blood pressure cohort study critical illness heart rate shock index tertiary care hospital vital signs Figures Figure 1 Figure 2 Figure 3 Background The shock index (SI) is an indicator of the severity of hypovolemic shock and is calculated by dividing the heart rate (HR) by systolic blood pressure (SBP) [ 1 ]. It serves to predict the mortality, need for blood transfusion, or necessity of intensive care unit admission among patients with trauma [ 2 – 7 ], postpartum haemorrhage [ 8 , 9 ], acute myocardial infarction [ 10 , 11 ], stroke [ 12 , 13 ], sepsis [ 14 , 15 ], and other critical conditions [ 16 , 17 ]. Numerous previous studies have demonstrated that the SI demonstrates superior prediction for mortality to traditional vital signs, although it has some limitations, including its low sensitivity especially for the elderly or obstetric patients [ 2 – 17 ]. However, in clinical practice, calculating the SI for all patients is difficult. An SI value > 0.9 is generally accepted as a cut-off point for an increased risk of mortality [ 16 ], but it is sometimes difficult to quickly calculate whether the patient meets this cut-off when the value of the quotient (particularly when considering the second decimal place) is extremely close to 0.9 (e.g. When a patient has an HR of 103 beats per minute and SBP of 114 mmHg, the quotient is approximately 0.904 and it technically meets the cut-off but it is exceedingly difficult to calculate promptly without a calculator). Recent studies have attempted to validate revised SI measurements meant to improve its ability to predict mortality [ 18 – 21 ]; however, such calculations are more complicated and tend to be avoided by clinicians. If the calculation of the SI can be made simpler, it would lead to rapid progress in terms of the clinical research using SI. Considering that SI is used to represent the different dynamics of HR and SBP [ 22 , 23 ], it is possible that simply subtracting the SBP from the HR may provide a useful substitute for SI, improving the availability of a calculated value as it is easier to mentally subtract integers than to divide them. In this study, we identify the proposed new index the simple shock index (sSI) and investigated whether the sSI predicted SI equivalently among patients transported to hospitals via ambulance. Methods Study design and setting This observational cohort study was conducted at two urban tertiary hospitals that annually receive via ambulance transport patients (> 2,500 and > 4,000 respectively). Written informed consent was waived because of the retrospective observational nature of the study, which was conducted using the opt-out method on the hospital websites. All data were fully anonymized. The institutional ethical review board of the University of Fukui Hospital (20160131) and the Fukui Prefectural Hospital (16–60) approved the study’s protocol. All methods were carried out in accordance with relevant guidelines and regulations. Patients were considered eligible if they were transported to either hospital via ambulance between July 1, 2015 and June 30, 2016. Patients who were aged < 15 years, experienced prehospital cardiopulmonary arrest, or underwent inter-hospital transfer were excluded. Study protocol The collected data included HR in the emergency department (ED), SBP in the ED, age, sex, trauma, pregnancy status, acute myocardial infarction, sepsis, chronic respiratory disease (previous history of chronic obstructive pulmonary disease, chronic bronchitis, asthma, bronchiectasis, interstitial pneumonia, pulmonary tuberculosis, or lung cancer), and intracranial disease (having suffered from stroke, transient ischemic attack, encephalitis, encephalopathy, seizure, brain tumour, hydrocephalus, concussion, cerebral contusion, or traumatic subarachnoid haemorrhage at arrival to ED). These specific patient characteristics were included since many previous studies have examined the ability of the SI to predict mortality or other critical conditions in those with trauma, pregnancy, acute myocardial infarction, sepsis, and intracranial disease [ 2 – 15 ], and because HR and SBP of aged or chronic respiratory disease patients are known to exhibit specific dynamics [ 24 – 26 ]. HR and SBP were documented immediately following a patient’s arrival to ED. These data were extracted from the electronic medical records. When available, prehospital vital signs documented in emergency service records were used to substitute for missing ED vital sign data. The bedside monitor models BSM-3562 (NIHON KOHDEN, Tokyo, Japan) and PVM-2703 (NIHON KOHDEN, Tokyo, Japan) were used to measure prehospital and in-hospital vital signs, respectively. Any remaining missing data were complemented using the multiple imputation method [ 27 , 28 ]. To minimize selection or operator bias, all data were collected retrospectively and were fully anonymized before analysis. The SI and sSI were calculated from the HR and SBP as mentioned above (SI = HR/SBP; sSI = HR − SBP). Statistical analysis Categorical variables were reported as numbers and percentages, while continuous variables were reported as the median and interquartile range (IQR). Patients aged 65 years and older were classified as aged individuals according to the definition widely adopted in developed countries [ 29 ]. First, a correlation plot for SI and sSI derived from all subjects was constructed, and the Pearson’s product-moment correlation coefficient was calculated. Regression analysis was performed using the least-squares method where possible. According to the regression equation, the sSI value, which corresponds to the SI value of 0.9, was determined. Next, an equivalence test with two one-sided test (TOST) was used to examine the mean difference between the SI and estimated value of SI derived by sSI from the regression equation mentioned above. It was necessary to convert sSI to the same scale as SI using the procedure noted previously since TOST compares mean difference between two groups. In a TOST, equivalency is determined when the 90% confidence interval (CI) of mean difference is settled within a predetermined equivalence margin [ 30 ]. Since the equivalence margin between 0.25 and 0.5 of effect size adjusted for standard deviation is usually chosen in practice [ 31 ], we chose 0.25 of the standardized effect size of the SI as the equivalent margin. Power values of 0.8 were considered statistically significant. Equivalence tests were also performed for 10 patient subgroups including aged, non-aged, female, male, trauma, pregnant, acute myocardial infarction, sepsis, chronic respiratory disease, and intracranial disease. The potential risk of type I errors due to multiple subgroup analyses would be expected to occur in up to 0.5 nominally statistically significant interaction tests (P < 0.05) by chance alone, which was an extremely low possibility [ 32 ]. Finally, we performed a sensitivity analysis to validate robustness with respect to missing data with an equivalence test using all cases that were not missing any data [ 33 ]. R software, version 3.4.1 (The R Foundation, Vienna, Austria) was used for all statistical analyses. Results Characteristics of the study subjects There were 6,687 patients who were transported to the two hospitals via ambulance during the study period; 1,258 of these patients were excluded including 527 aged < 15 years, 136 who experienced prehospital cardiopulmonary arrest, and 595 who underwent inter-hospital transfer. Thus, 5,429 patients were ultimately evaluated (Fig. 1 ). The patients’ characteristics are shown in Table 1 . The median age was 68 (IQR 47–81), and the median ages of those aged 15–64 and aged ≥ 65 were 43 (IQR 29–56) and 80 (IQR 73–85), respectively. The median HR and SBP values were 84 (IQR 72–97) and 140 (IQR 121–163) mmHg, respectively. Prehospital HR of 765 cases (14.1%) and prehospital SBP of 721 cases (13.3%) were substituted for missing values of the ED. Remaining missing HR and SBP values were complemented by using the multiple imputation method for 958 (17.6%) and 912 (16.8%) patients, respectively. No other data were missing. Table 1 Patients’ Characteristics N (%) Age, years 15–64 2420 (44.6) ≥ 65 3009 (55.4) Sex Female 2677 (49.3) Male 2752 (50.7) Trauma 1653 (30.4) Pregnancy 91 (1.7) Acute myocardial infarction 97 (1.8) Sepsis 65 (1.2) Chronic respiratory disease 114 (2.1) Intracranial disease 643 (11.8) Main results The Pearson’s product-moment correlation coefficient between the SI and sSI was 0.917 (95% CI 0.912–0.921, P < .001), indicating an extremely high correlation. The log SI/sSI correlation plot represented a proportional relationship (Fig. 2 ) and the regression equation was estimated as sSI = 258.55 log SI using the least-squares method with logarithmic transformation. According to this equation, an sSI value of − 12 was found to correspond to the SI value of 0.9. Next, an equivalence test with TOST was performed for comparisons between SI and the estimated value of SI derived by sSI. The estimated value of SI was calculated as 10 sSI/258.55 according to the regression equation mentioned above. Equivalence margin was determined as ± 0.052, since it was 0.25 of the standardized effect size of SI. The TOST revealed an equivalency between the SI and sSI (mean difference, 0.004; 90% CI, 0.003 to 0.005; statistical power, 100.00%). Similar consequences were also derived from subgroup analyses (Table 2 , Fig. 3 ). The statistical power for all analyses of each subgroup and all subjects were over 99.99%. Table 2 SI and SI derived by sSI (HR-SBP) equivalence tests N Mean difference (90% CI) Age, years 15–64 2420 0.009 (0.008 to 0.011) ≥ 65 3009 0.000 (− 0.001 to 0.002) Sex Female 2677 0.003 (0.001 to 0.004) Male 2752 0.005 (0.004 to 0.007) Trauma 1653 0.000 (− 0.002 to 0.002) Pregnancy 91 0.008 (0.004 to 0.012) Acute myocardial infarction 97 0.016 (0.006 to 0.026) Sepsis 65 −0.022 (− 0.038 to − 0.006) Chronic respiratory disease 114 −0.003 (− 0.011 to 0.005) Intracranial disease 643 −0.005 (− 0.008 to − 0.002) Total 5429 0.004 (0.003 to 0.005) SI: shock index, sSI: simple shock index, HR: heart rate, SBP: systolic blood pressure, CI: confidence interval. Equivalence margin is 0.052. SI derived by sSI was calculated according to the following estimation equation: sSI = 258.55 log SI. In the sensitivity analysis, the robustness of equivalency was validated using 3,629 cases (66.8%) that did not lack any data of ED (mean difference was 0.006, 90% CI 0.004 to 0.007, statistical power 100.00%). Discussion In this study, we revealed that the sSI, which was derived by subtracting the SBP from the HR, strongly correlated with the SI among the patients transported via ambulance. Meanwhile, an sSI value of > − 12 was observed to correspond to the known SI cut-off value of > 0.9, which is the most common optimized cut-off point, as has been previously described [ 16 ]. This finding confirms the utility of using the sSI for more rapid assessment of the condition of patients admitted for emergency care than the more complicated SI. Assuming that a calculator is not available, judging whether the SI is more than 1.0 is quite easy because the hypothesis is true when the value of HR is greater than that of SBP. However, when the SI cut-off value is 0.9, the judgement becomes difficult. As calculating HR/SBP mentally can cause confusion, we often calculate SBP times 0.9 mentally and then compare it with HR. For example, when an HR of 103 and SBP of 114 are known, we calculate 114 × 0.9 = 102.6 and then compare it with 103. Thus, we can judge SI to be > 0.9 because 103 > 102.6. Obviously, this procedure is complicated and tends to lead to miscalculation. On the other hand, an alternative criterion of sSI > − 12 makes the procedure much simpler. For example, using the same values of HR of 103 and SBP of 114, calculating HR plus 12 is the first step to solve the hypothesis. When the sum (e.g. 103 + 12 = 115) is compared with SBP, we are able to judge that sSI is > − 12 because 115 > 114. Since this addition is quite easy, mental calculation can be performed at a glance. While attempts have been made to improve the predictive ability of SI for mortality or other outcomes, these endeavours have made the process more complicated. Examples of such previously proposed predictors include an index called ‘age shock index’ derived by multiplying the SI with the patient’s age, or another referred to as the ‘modified shock index’ obtained by dividing the HR by the mean blood pressure [ 18 , 19 ]. Other complicated predictors were also proposed such as ‘respiratory adjusted shock index’ calculated by multiplying the SI with the respiratory rate/10 and ‘reverse shock index multiplied by Glasgow Coma Scale score’ derived by dividing the Glasgow Coma Scale by the SI [ 20 , 21 ]. To the best of our knowledge, this is the first study aimed at simplifying the calculation using subtraction, as no previous groups have proposed the idea of subtracting the SBP from the HR for purposes of estimating the SI. Regarding the moderate number of missing values, when comparing analysis using values generated from the imputation method and from using only cases without missing values our results indicated good concordance between the measures examined and indicated that sSI is a useful and precise tool. This study has several limitations. We were unable to investigate patients of different ethnicities because this study was conducted in a single geographic area. Moreover, there is a dearth of a theoretical framework to support the sSI, given that this study was intended as merely a proposal of a pragmatic alternative to the SI. Additionally, although the SI is used to predict mortality, necessity of blood transfusion, or necessity for intensive care unit admission, our study did not address these outcomes; we tested only the correlation between the SI and sSI here. Furthermore, there is certainly a possibility of multiplicity in the subgroup analyses, although this possibility was estimated to be extremely low. These issues should be addressed in future studies intended to clarify the scientific underpinnings of sSI, to further validate sSI as an accurate substitute calculation for SI, or to justify the clinical utility of sSI. Conclusions The sSI was demonstrated to highly correlate with the SI among patients transported to hospitals in our study via ambulance, though further studies are needed to validate its clinical utility. Given that the sSI is easier to calculate and use for performing evaluations, it can be a useful and highly precise alternative to the SI. Abbreviations SI shock index, HR:heart rate, SBP:systolic blood pressure, sSI:simple shock index, ED:emergency department, IQR:interquartile range, TOST:two one-sided test, CI:confidence interval. Declarations Ethics approval and consent to participate: This study’s protocol was approved by the research ethics committee of the University of Fukui Hospital (20160131) and the Fukui Prefectural Hospital (16-60). Informed consent was not required because the information was sufficiently anonymized. Consent for publication: Not applicable. Availability of data and materials: The datasets generated and/or analysed during the current study are available in the Open Science Framework repository, https://osf.io/n94zs/ or DOI 10.17605/OSF.IO/N94ZS. 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: YK conceived the study, designed the trial, collected data, handled the recruitment of patients, managed the data including quality control, analysed the data, and drafted the manuscript. 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Cite Share Download PDF Status: Published Journal Publication published 31 Oct, 2020 Read the published version in BMC Emergency Medicine → Version 1 posted Review # 2 received at journal 04 Oct, 2020 Editorial decision: Minor revision 04 Oct, 2020 Reviewer # 2 agreed at journal 23 Sep, 2020 Editor assigned by journal 17 Sep, 2020 Reviewers invited by journal 17 Sep, 2020 Reviewer # 1 agreed at journal 17 Sep, 2020 Review # 1 received at journal 17 Sep, 2020 Submission checks completed at journal 16 Sep, 2020 Editor invited by journal 14 Sep, 2020 First submitted to journal 10 Sep, 2020 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-76143","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":2420336,"identity":"3fba8d18-266d-4f54-aebc-bbb67d8fe508","order_by":0,"name":"Yohei Kamikawa","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-9680-6791","institution":"Department of Emergency Medicine, University of Fukui Hospital, Fukui, Japan ","correspondingAuthor":true,"prefix":"","firstName":"Yohei","middleName":"","lastName":"Kamikawa","suffix":""},{"id":2420337,"identity":"3e6a2aab-8c13-4770-b1c4-7258b9604148","order_by":1,"name":"Hiroyuki Hayashi","email":"","orcid":"","institution":"University of Fukui Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hiroyuki","middleName":"","lastName":"Hayashi","suffix":""}],"badges":[],"createdAt":"2020-09-11 10:47:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-76143/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-76143/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12873-020-00383-2","type":"published","date":"2020-10-31T15:02:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":2481130,"identity":"00693e59-5942-4613-aef8-790eba8bc4a4","added_by":"auto","created_at":"2020-09-18 16:27:04","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":290839,"visible":true,"origin":"","legend":"Flowchart of the study.","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-76143/v1/Figure1.jpg"},{"id":2481131,"identity":"9834c9b6-571a-43b0-84f5-06293b97c71d","added_by":"auto","created_at":"2020-09-18 16:27:04","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":741709,"visible":true,"origin":"","legend":"Correlation plot of the SI and sSI (HR−SBP). \nSI: shock index, sSI: simple shock index, HR: heart rate, SBP: systolic blood pressure.\nSI plotted on logarithmic scale.\n","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-76143/v1/Figure2.jpg"},{"id":2481132,"identity":"f5fc10c8-8d2b-4875-bb22-00195afd337e","added_by":"auto","created_at":"2020-09-18 16:27:04","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":644751,"visible":true,"origin":"","legend":"Equivalency tests between SI and SI derived by sSI (HR−SBP).\nSI: shock index, sSI: simple shock index, HR: heart rate, SBP: systolic blood pressure. Equivalence margin is 0.052. SI derived by sSI was calculated according to the following estimation equation: sSI = 258.55 log SI.\n","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-76143/v1/Figure3.jpg"},{"id":13594294,"identity":"02800531-6821-4a5b-aa3a-730e8dc513de","added_by":"auto","created_at":"2021-09-17 05:19:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":452674,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-76143/v1/5f88bfa4-d548-4e16-a559-b7e4aec01729.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eEquivalency Between the Shock Index and Subtracting the Systolic Blood Pressure From the Heart Rate: An Observational Cohort Study\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003eThe shock index (SI) is an indicator of the severity of hypovolemic shock and is calculated by dividing the heart rate (HR) by systolic blood pressure (SBP) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It serves to predict the mortality, need for blood transfusion, or necessity of intensive care unit admission among patients with trauma [\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], postpartum haemorrhage [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], acute myocardial infarction [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], stroke [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], sepsis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and other critical conditions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Numerous previous studies have demonstrated that the SI demonstrates superior prediction for mortality to traditional vital signs, although it has some limitations, including its low sensitivity especially for the elderly or obstetric patients [\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, in clinical practice, calculating the SI for all patients is difficult. An SI value\u0026thinsp;\u0026gt;\u0026thinsp;0.9 is generally accepted as a cut-off point for an increased risk of mortality [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], but it is sometimes difficult to quickly calculate whether the patient meets this cut-off when the value of the quotient (particularly when considering the second decimal place) is extremely close to 0.9 (e.g. When a patient has an HR of 103 beats per minute and SBP of 114\u0026nbsp;mmHg, the quotient is approximately 0.904 and it technically meets the cut-off but it is exceedingly difficult to calculate promptly without a calculator). Recent studies have attempted to validate revised SI measurements meant to improve its ability to predict mortality [\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]; however, such calculations are more complicated and tend to be avoided by clinicians. If the calculation of the SI can be made simpler, it would lead to rapid progress in terms of the clinical research using SI.\u003c/p\u003e \u003cp\u003eConsidering that SI is used to represent the different dynamics of HR and SBP [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], it is possible that simply subtracting the SBP from the HR may provide a useful substitute for SI, improving the availability of a calculated value as it is easier to mentally subtract integers than to divide them.\u003c/p\u003e \u003cp\u003eIn this study, we identify the proposed new index the simple shock index (sSI) and investigated whether the sSI predicted SI equivalently among patients transported to hospitals via ambulance.\u003c/p\u003e "},{"header":"Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eThis observational cohort study was conducted at two urban tertiary hospitals that annually receive via ambulance transport patients (\u0026gt;\u0026thinsp;2,500 and \u0026gt;\u0026thinsp;4,000 respectively). Written informed consent was waived because of the retrospective observational nature of the study, which was conducted using the opt-out method on the hospital websites. All data were fully anonymized. The institutional ethical review board of the University of Fukui Hospital (20160131) and the Fukui Prefectural Hospital (16\u0026ndash;60) approved the study\u0026rsquo;s protocol. All methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e \u003cp\u003ePatients were considered eligible if they were transported to either hospital via ambulance between July 1, 2015 and June 30, 2016. Patients who were aged\u0026thinsp;\u0026lt;\u0026thinsp;15 years, experienced prehospital cardiopulmonary arrest, or underwent inter-hospital transfer were excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy protocol\u003c/h2\u003e \u003cp\u003eThe collected data included HR in the emergency department (ED), SBP in the ED, age, sex, trauma, pregnancy status, acute myocardial infarction, sepsis, chronic respiratory disease (previous history of chronic obstructive pulmonary disease, chronic bronchitis, asthma, bronchiectasis, interstitial pneumonia, pulmonary tuberculosis, or lung cancer), and intracranial disease (having suffered from stroke, transient ischemic attack, encephalitis, encephalopathy, seizure, brain tumour, hydrocephalus, concussion, cerebral contusion, or traumatic subarachnoid haemorrhage at arrival to ED). These specific patient characteristics were included since many previous studies have examined the ability of the SI to predict mortality or other critical conditions in those with trauma, pregnancy, acute myocardial infarction, sepsis, and intracranial disease [\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and because HR and SBP of aged or chronic respiratory disease patients are known to exhibit specific dynamics [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHR and SBP were documented immediately following a patient\u0026rsquo;s arrival to ED. These data were extracted from the electronic medical records. When available, prehospital vital signs documented in emergency service records were used to substitute for missing ED vital sign data. The bedside monitor models BSM-3562 (NIHON KOHDEN, Tokyo, Japan) and PVM-2703 (NIHON KOHDEN, Tokyo, Japan) were used to measure prehospital and in-hospital vital signs, respectively. Any remaining missing data were complemented using the multiple imputation method [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. To minimize selection or operator bias, all data were collected retrospectively and were fully anonymized before analysis.\u003c/p\u003e \u003cp\u003eThe SI and sSI were calculated from the HR and SBP as mentioned above (SI\u0026thinsp;=\u0026thinsp;HR/SBP; sSI\u0026thinsp;=\u0026thinsp;HR\u0026thinsp;\u0026minus;\u0026thinsp;SBP).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eCategorical variables were reported as numbers and percentages, while continuous variables were reported as the median and interquartile range (IQR). Patients aged 65\u0026nbsp;years and older were classified as aged individuals according to the definition widely adopted in developed countries [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFirst, a correlation plot for SI and sSI derived from all subjects was constructed, and the Pearson\u0026rsquo;s product-moment correlation coefficient was calculated. Regression analysis was performed using the least-squares method where possible. According to the regression equation, the sSI value, which corresponds to the SI value of 0.9, was determined.\u003c/p\u003e \u003cp\u003eNext, an equivalence test with two one-sided test (TOST) was used to examine the mean difference between the SI and estimated value of SI derived by sSI from the regression equation mentioned above. It was necessary to convert sSI to the same scale as SI using the procedure noted previously since TOST compares mean difference between two groups. In a TOST, equivalency is determined when the 90% confidence interval (CI) of mean difference is settled within a predetermined equivalence margin [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Since the equivalence margin between 0.25 and 0.5 of effect size adjusted for standard deviation is usually chosen in practice [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], we chose 0.25 of the standardized effect size of the SI as the equivalent margin. Power values of 0.8 were considered statistically significant. Equivalence tests were also performed for 10 patient subgroups including aged, non-aged, female, male, trauma, pregnant, acute myocardial infarction, sepsis, chronic respiratory disease, and intracranial disease. The potential risk of type I errors due to multiple subgroup analyses would be expected to occur in up to 0.5 nominally statistically significant interaction tests (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) by chance alone, which was an extremely low possibility [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, we performed a sensitivity analysis to validate robustness with respect to missing data with an equivalence test using all cases that were not missing any data [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. R software, version 3.4.1 (The R Foundation, Vienna, Austria) was used for all statistical analyses.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":" \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the study subjects\u003c/h2\u003e \u003cp\u003eThere were 6,687 patients who were transported to the two hospitals via ambulance during the study period; 1,258 of these patients were excluded including 527 aged\u0026thinsp;\u0026lt;\u0026thinsp;15 years, 136 who experienced prehospital cardiopulmonary arrest, and 595 who underwent inter-hospital transfer. Thus, 5,429 patients were ultimately evaluated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The patients\u0026rsquo; characteristics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age was 68 (IQR 47\u0026ndash;81), and the median ages of those aged 15\u0026ndash;64 and aged\u0026thinsp;\u0026ge;\u0026thinsp;65 were 43 (IQR 29\u0026ndash;56) and 80 (IQR 73\u0026ndash;85), respectively. The median HR and SBP values were 84 (IQR 72\u0026ndash;97) and 140 (IQR 121\u0026ndash;163) mmHg, respectively. Prehospital HR of 765 cases (14.1%) and prehospital SBP of 721 cases (13.3%) were substituted for missing values of the ED. Remaining missing HR and SBP values were complemented by using the multiple imputation method for 958 (17.6%) and 912 (16.8%) patients, respectively. No other data were missing.\u003c/p\u003e \u003cp\u003e \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\u003ePatients\u0026rsquo; Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2420 (44.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3009 (55.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2677 (49.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2752 (50.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrauma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1653 (30.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91 (1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute myocardial infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97 (1.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSepsis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65 (1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic respiratory disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114 (2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntracranial disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e643 (11.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMain results\u003c/h2\u003e \u003cp\u003eThe Pearson\u0026rsquo;s product-moment correlation coefficient between the SI and sSI was 0.917 (95% CI 0.912\u0026ndash;0.921, P\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating an extremely high correlation. The log SI/sSI correlation plot represented a proportional relationship (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and the regression equation was estimated as sSI\u0026thinsp;=\u0026thinsp;258.55 log SI using the least-squares method with logarithmic transformation. According to this equation, an sSI value of \u0026minus;\u0026thinsp;12 was found to correspond to the SI value of 0.9.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, an equivalence test with TOST was performed for comparisons between SI and the estimated value of SI derived by sSI. The estimated value of SI was calculated as 10\u003csup\u003esSI/258.55\u003c/sup\u003e according to the regression equation mentioned above. Equivalence margin was determined as \u0026plusmn;\u0026thinsp;0.052, since it was 0.25 of the standardized effect size of SI. The TOST revealed an equivalency between the SI and sSI (mean difference, 0.004; 90% CI, 0.003 to 0.005; statistical power, 100.00%). Similar consequences were also derived from subgroup analyses (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The statistical power for all analyses of each subgroup and all subjects were over 99.99%.\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\u003eSI and SI derived by sSI (HR-SBP) equivalence tests\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean difference (90% CI)\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\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009 (0.008 to 0.011)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000 (\u0026minus;\u0026thinsp;0.001 to 0.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003 (0.001 to 0.004)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005 (0.004 to 0.007)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrauma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000 (\u0026minus;\u0026thinsp;0.002 to 0.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008 (0.004 to 0.012)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute myocardial infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016 (0.006 to 0.026)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSepsis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.022 (\u0026minus;\u0026thinsp;0.038 to \u0026minus;\u0026thinsp;0.006)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic respiratory disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.003 (\u0026minus;\u0026thinsp;0.011 to 0.005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntracranial disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.005 (\u0026minus;\u0026thinsp;0.008 to \u0026minus;\u0026thinsp;0.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004 (0.003 to 0.005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSI: shock index, sSI: simple shock index, HR: heart rate, SBP: systolic blood pressure, CI: confidence interval. Equivalence margin is 0.052. SI derived by sSI was calculated according to the following estimation equation: sSI\u0026thinsp;=\u0026thinsp;258.55 log SI.\u003c/p\u003e \u003cp\u003eIn the sensitivity analysis, the robustness of equivalency was validated using 3,629 cases (66.8%) that did not lack any data of ED (mean difference was 0.006, 90% CI 0.004 to 0.007, statistical power 100.00%).\u003c/p\u003e \u003c/div\u003e "},{"header":"Discussion","content":" \u003cp\u003eIn this study, we revealed that the sSI, which was derived by subtracting the SBP from the HR, strongly correlated with the SI among the patients transported via ambulance. Meanwhile, an sSI value of \u0026gt;\u0026thinsp;\u0026minus;\u0026thinsp;12 was observed to correspond to the known SI cut-off value of \u0026gt;\u0026thinsp;0.9, which is the most common optimized cut-off point, as has been previously described [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis finding confirms the utility of using the sSI for more rapid assessment of the condition of patients admitted for emergency care than the more complicated SI. Assuming that a calculator is not available, judging whether the SI is more than 1.0 is quite easy because the hypothesis is true when the value of HR is greater than that of SBP. However, when the SI cut-off value is 0.9, the judgement becomes difficult. As calculating HR/SBP mentally can cause confusion, we often calculate SBP times 0.9 mentally and then compare it with HR. For example, when an HR of 103 and SBP of 114 are known, we calculate 114\u0026thinsp;\u0026times;\u0026thinsp;0.9\u0026thinsp;=\u0026thinsp;102.6 and then compare it with 103. Thus, we can judge SI to be \u0026gt;\u0026thinsp;0.9 because 103\u0026thinsp;\u0026gt;\u0026thinsp;102.6. Obviously, this procedure is complicated and tends to lead to miscalculation. On the other hand, an alternative criterion of sSI\u0026thinsp;\u0026gt;\u0026thinsp;\u0026minus;\u0026thinsp;12 makes the procedure much simpler. For example, using the same values of HR of 103 and SBP of 114, calculating HR plus 12 is the first step to solve the hypothesis. When the sum (e.g. 103\u0026thinsp;+\u0026thinsp;12\u0026thinsp;=\u0026thinsp;115) is compared with SBP, we are able to judge that sSI is \u0026gt;\u0026thinsp;\u0026minus;\u0026thinsp;12 because 115\u0026thinsp;\u0026gt;\u0026thinsp;114. Since this addition is quite easy, mental calculation can be performed at a glance.\u003c/p\u003e \u003cp\u003eWhile attempts have been made to improve the predictive ability of SI for mortality or other outcomes, these endeavours have made the process more complicated. Examples of such previously proposed predictors include an index called \u0026lsquo;age shock index\u0026rsquo; derived by multiplying the SI with the patient\u0026rsquo;s age, or another referred to as the \u0026lsquo;modified shock index\u0026rsquo; obtained by dividing the HR by the mean blood pressure [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Other complicated predictors were also proposed such as \u0026lsquo;respiratory adjusted shock index\u0026rsquo; calculated by multiplying the SI with the respiratory rate/10 and \u0026lsquo;reverse shock index multiplied by Glasgow Coma Scale score\u0026rsquo; derived by dividing the Glasgow Coma Scale by the SI [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. To the best of our knowledge, this is the first study aimed at simplifying the calculation using subtraction, as no previous groups have proposed the idea of subtracting the SBP from the HR for purposes of estimating the SI.\u003c/p\u003e \u003cp\u003eRegarding the moderate number of missing values, when comparing analysis using values generated from the imputation method and from using only cases without missing values our results indicated good concordance between the measures examined and indicated that sSI is a useful and precise tool.\u003c/p\u003e \u003cp\u003eThis study has several limitations. We were unable to investigate patients of different ethnicities because this study was conducted in a single geographic area. Moreover, there is a dearth of a theoretical framework to support the sSI, given that this study was intended as merely a proposal of a pragmatic alternative to the SI. Additionally, although the SI is used to predict mortality, necessity of blood transfusion, or necessity for intensive care unit admission, our study did not address these outcomes; we tested only the correlation between the SI and sSI here. Furthermore, there is certainly a possibility of multiplicity in the subgroup analyses, although this possibility was estimated to be extremely low. These issues should be addressed in future studies intended to clarify the scientific underpinnings of sSI, to further validate sSI as an accurate substitute calculation for SI, or to justify the clinical utility of sSI.\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eThe sSI was demonstrated to highly correlate with the SI among patients transported to hospitals in our study via ambulance, though further studies are needed to validate its clinical utility. Given that the sSI is easier to calculate and use for performing evaluations, it can be a useful and highly precise alternative to the SI.\u003c/p\u003e "},{"header":"Abbreviations","content":" \u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eshock index, HR:heart rate, SBP:systolic blood pressure, sSI:simple shock index, ED:emergency department, IQR:interquartile range, TOST:two one-sided test, CI:confidence interval.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate: \u003c/strong\u003eThis study\u0026rsquo;s protocol was approved by the research ethics committee of the University of Fukui Hospital (20160131) and the Fukui Prefectural Hospital (16-60). Informed consent was not required because the information was sufficiently anonymized.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e The datasets generated and/or analysed during the current study are available in the Open Science Framework repository, https://osf.io/n94zs/ or DOI 10.17605/OSF.IO/N94ZS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests: \u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This 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' contributions:\u003c/strong\u003e YK conceived the study, designed the trial, collected data, handled the recruitment of patients, managed the data including quality control, analysed the data, and drafted the manuscript. HH supervised the conduct of the trial, provided statistical advice on study design, and chaired the data oversight committee. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements: \u003c/strong\u003eWe would like to thank Editage (www.editage.jp) for English language editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e \u003cspan\u003eAllgower M, Burri C. Shock Index. Dtsch Med Wochenschr. 1967;92:1947\u0026ndash;50.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eSloan EP, Koenigsberg M, Clark JM, Weir WB, Philbin N. Shock index and prediction of traumatic hemorrhagic shock 28-day mortality: data from the DCLHb resuscitation clinical trials. West J Emerg Med. 2014;15:795\u0026ndash;802.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eCannon CM, Braxton CC, Kling-Smith M, Mahnken JD, Carlton E, Moncure M. Utility of the shock index in predicting mortality in traumatically injured patients. J Trauma. 2009;67:1426\u0026ndash;30.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eOdom SR, Howell MD, Gupta A, Silva G, Cook CH, Talmor D. Extremes of shock index predicts death in trauma patients. J Emerg Trauma Shock. 2016;9:103\u0026ndash;6.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eSchroll R, Swift D, Tatum D, Couch S, Heaney JB, Llado-Farrulla M, et al. Accuracy of shock index versus ABC score to predict need for massive transfusion in trauma patients. Injury. 2018;49:15\u0026ndash;9.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eMcNab A, Burns B, Bhullar I, Chesire D, Kerwin A. A prehospital shock index for trauma correlates with measures of hospital resource use and mortality. Surgery. 2012;152:473\u0026ndash;6.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eBruijns SR, Guly HR, Bouamra O, Lecky F, Wallis LA. The value of the difference between ED and prehospital vital signs in predicting outcome in trauma. Emerg Med J. 2014;31:579\u0026ndash;82.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eNathan HL, Ayadi AEl, Hezelgrave NL, Seed P, Butrick E, Miller S, et al. Shock index: an effective predictor of outcome in postpartum haemorrhage? BJOG. 2015;122:268\u0026ndash;75.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eNathan HN, Seed PT, Hezelgrave NL, Greeff AD, Lawley E, Anthony J, et al. Shock index thresholds to predict adverse outcomes in maternal hemorrhage and sepsis: A prospective cohort study. Acta Obstet Gynecol Scand. 2019;98:1178\u0026ndash;86.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eAbe N, Miura T, Miyashita Y, Hashizume N, Ebisawa S, Motoki H, et al. Long-term prognostic implications of the admission shock index in patients with acute myocardial infarction who received percutaneous coronary intervention. Angiology. 2017;68:339\u0026ndash;45.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eWang Q, Shen H, Mao H, Yu F, Wang H, Zheng J. Shock index on admission is associated with coronary slow/no reflow in patients with acute myocardial infarction undergoing emergent percutaneous coronary intervention. J Interv Cardiol. 2019;2019:7873468.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eMcCall SJ, Musgrave SD, Potter JF, Hale R, Clark AB, Mamas MA, et al. The shock index predicts acute mortality outcomes in stroke. Int J Cardiol. 2015;182:523\u0026ndash;7.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eMyint PK, Sheng S, Xian Y, Matsouaka RA, Reeves MJ, Saver JL, et al. Shock index predicts patient-related clinical outcomes in stroke. J Am Heart Assoc. 2018;7:e007581.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eBerger T, Green J, Horeczko T, Hagar Y, Garg N, Suarez A, et al. Shock index and early recognition of sepsis in the emergency department: pilot study. West J Emerg Med. 2013;14:168\u0026ndash;74.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eMiddleton DJ, Smith TO, Bedford R, Neilly M, Myint PK. Shock index predicts outcome in patients with suspected sepsis or community-acquired pneumonia: A systematic review. J Clin Med. 2019;8:1144.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eRady MY, Smithline HA, Blake H, Nowak R, Rivers E. A comparison of the shock index and conventional vital signs to identify acute, critical illness in the emergency department. Ann Emerg Med. 1994;24:685\u0026ndash;90.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eKamikawa Y, Hayashi H. Predicting in-hospital mortality among non-trauma patients based on vital sign changes between prehospital and in-hospital: An observational cohort study. PLoS One. 2019;14:e0211580.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eZarzaur BL, Croce MA, Magnotti LJ, Fabian TC. Identifying life-threatening shock in the older injured patient: an analysis of the National Trauma Data Bank. J Trauma. 2010;68:1134\u0026ndash;8.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eLiu Y, Liu J, Fang ZA, Shan G, Xu J, Qi Z, et al. Modified shock index and mortality rate of emergency patients. World J Emerg Med. 2012;3:114\u0026ndash;7.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eCaputo N, Reilly J, Kanter M, West J. A retrospective analysis of the respiratory adjusted shock index to determine the presence of occult shock in trauma patients. J Trauma Acute Care Surg. 2018;84:674\u0026ndash;8.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eKimura A, Tanaka N. Reverse shock index multiplied by Glasgow Coma Scale score (rSIG) is a simple measure with high discriminant ability for mortality risk in trauma patients: an analysis of the Japan Trauma Data Bank. Crit Care. 2018;22:87.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eGraham LN, Smith PA, Stoker JB, Mackintosh AF, Mary DA. Sympathetic neural hyperactivity and its normalization following unstable angina and acute myocardial infarction. Clin Sci. 2004;106:605\u0026ndash;11.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eBarcroft H, Edholm OG, McMichael J, Sharpey-Schafer EP. Posthaemorrhagic fainting: Study by cardiac output and forearm flow. Lancet. 1944;243:489\u0026ndash;91.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eLamantia MA, Stewart PW, Platts-Mills TF, Biese KJ, Forbach C, Zamora E, et al. Predictive value of initial triage vital signs for critically ill older adults. West J Emerg Med. 2013;14:453\u0026ndash;60.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eChester JG, Rudolph JL. Vital signs in older patients: age-related changes. J Am Med Dir Assoc. 2011;12:337\u0026ndash;43.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eEccles SR, Subbe C, Hancock D, Thomson N. CREWS: improving specificity whilst maintaining sensitivity of the National Early Warning Score in patients with chronic hypoxaemia. Resuscitation. 2014;85:109\u0026ndash;11.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eHaukoos JS, Newgard CD. Advanced statistics: missing data in clinical research-part 1: an introduction and conceptual framework. Acad Emerg Med. 2007;14:662\u0026ndash;8.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eNewgard CD, Haukoos JS. Advanced statistics: missing data in clinical research-part 2: multiple imputation. Acad Emerg Med. 2007;14:669\u0026ndash;78.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eWorld Health Organization. Proposed working definition of an older person in Africa for the MDS Project. 2002. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.who.int/healthinfo/survey/ageingdefnolder/en/\u003c/span\u003e\u003c/span\u003e Accessed 25 July 2020.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eWalker E, Nowacki AS. Understanding equivalence and noninferiority testing. J Gen Intern Med. 2010;26:192\u0026ndash;6.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eChow S, Shao J, Wang H, Lokhnygina Y. Sample Size Calculations in Clinical Research. Third Edition. Boca Raton, FL: CRC Press 2017.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eWang R, Lagakos SW, Ware JH, Hunter DJ, Drazen JM. Statistics in medicine \u0026mdash; Reporting of subgroup analyses in clinical trials. N Engl J Med. 2007;357:2189\u0026ndash;94.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eThabane L, Mbuagbaw L, Zhang S, Samaan Z, Marcucci M, Ye C, et al. A tutorial on sensitivity analyses in clinical trials: the what, why, when and how. BMC Med Res Methodol. 2013;13:92.\u0026atilde;࿽࿽.\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":"[email protected]","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":"Ambulance, blood pressure, cohort study, critical illness, heart rate, shock index, tertiary care hospital, vital signs","lastPublishedDoi":"10.21203/rs.3.rs-76143/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-76143/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eAlthough the shock index is known to predict mortality and other severe outcomes, deriving it requires complex calculations. Subtracting the systolic blood pressure from the heart rate may produce a simple shock index that would be a clinically useful substitute for the shock index. In this study, we investigated whether the simple shock index was equivalent to the shock index.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This observational cohort study was conducted at 2 tertiary care hospitals. Patients who were transported by ambulance were recruited for this study and were excluded if they were aged \u0026lt;15 years, had experienced prehospital cardiopulmonary arrest, or had undergone inter-hospital transfer. Pearson’s product-moment correlation coefficient and regression equation were calculated, and two one-sided tests were performed to examine their equivalency. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Among 5,429 eligible patients, the correlation coefficient between the shock index and simple shock index was extremely high (0.917, 95% confidence interval 0.912 to 0.921, P \u0026lt;.001). The regression equation was estimated as sSI = 258.55 log SI. The two one-sided tests revealed a very strong equivalency between the shock index and the index estimated by the above equation using the simple shock index (mean difference was 0.004, 90% confidence interval 0.003 to 0.005). \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The simple shock index strongly correlated with the shock index.\u003c/p\u003e","manuscriptTitle":"Equivalency Between the Shock Index and Subtracting the Systolic Blood Pressure From the Heart Rate: An Observational Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-09-18 16:27:02","doi":"10.21203/rs.3.rs-76143/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2020-10-04T12:00:00+00:00","index":2,"fulltext":"Recommendation: Accept without revision\nForm responses:\n---\n\nComments to Author:\n---\nthe paper „Equivalency between the shock index and subtracting the systolic blood pressure from the heart rate: An observational cohort study\" by Yohei Kamikawa and Hiroyuki Hayashi is quite interesting - the idea is interesting. It asks if an already easy equation can be improved by making it even more easier.\nBut wouldn`t it be even easier if the positiv results were not negative. Taking to your example with the HR of 103 and a systolic blood pressure of 114 - 114 - 103 = 11. All results from 12 upwards are positiv - predicting a better outcome.\nNevertheless the topic ist interesting - the result is clear - even if it will not change our behavior - I would advice to publish the paper in your journal.\n* Publons Reviewer Recognition. Springer Nature can send verification of this review directly to Publons (a subsidiary of Clarivate Analytics). If you would like to take advantage of this service, please click on the “Yes” option below. Your name, email address, title of the reviewed manuscript, name of the journal, and date of your review submission (the “Review Data”) will then be transmitted to Publons upon publication of the manuscript. If you have already registered at Publons, they will notify you of the receipt of this review and update your profile as per your settings and their policy. If you are not registered with Publons, you will receive an email from them asking you to register in order for them to be able to recognize your review on your new profile page. Publons may use the Review Data to generate derivative metadata for the benefit of Publons and you as a reviewer, carefully considering the sensitivity of such information. For example, Publons may verify your record as a reviewer by updating your profile published on its webservice if you have registered for such service or help editors to identify candidate reviewers. Please find the details of processing in Publons’ privacy policy https://publons.com/about/terms: **Yes**\n* Declaration of competing interests: **I declare that I have no competing interests**\n* Reviewer Publication Consent. I agree for my report to be made available under an Open Access Creative Commons CC-BY License (http://creativecommons.org/licenses/by/4.0) if this manuscript is accepted for publication. Any comments that I do not wish to be included in the published report have been included as confidential comments to the editor, which will not be published.: **I agree to the terms of the CC-BY 4.0 license; please publish my name with my report.**\n* Is the study design appropriate to answer the research question (including the use of appropriate controls), and are the conclusions supported by the evidence presented?: **Yes**\n* Are the methods sufficiently described to allow the study to be repeated?: **Yes**\n* Is the use of statistics and treatment of uncertainties appropriate?: **Yes**\n* Is the presentation of the work clear?: **Yes**\n* Are the images in this manuscript (including electrophoretic gels and blots) free from apparent manipulation?: **Yes**\n"},{"type":"decision","content":"Minor revision","date":"2020-10-04T12:00:00+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2020-09-23T12:00:00+00:00","index":2,"fulltext":""},{"type":"editorAssigned","content":"","date":"2020-09-17T12:00:00+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2020-09-17T12:00:00+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2020-09-17T12:00:00+00:00","index":1,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-09-17T12:00:00+00:00","index":1,"fulltext":"Recommendation: Accept after minor essential revisions\nForm responses:\n---\n\nComments to Author:\n---\nDear Authors\nI received your paper as a reviewer. In my opinion, it is a simple interesting paper; although I'm not in favor of your statement that \"... it is sometimes difficult to quickly calculate whether the patient meets this cut-off when the value of the quotient is extremely close to 0.9.\" Because, shock is not just a measurable mathematical value, and considering lots of variables, the in-charge physician can estimate the patients' situation.\nAll in all, the paper is still interesting but I have one suggestion for you to consider, that I think it can improve your paper usefulness. I want you to divide your patients in various blood pressure categories (sever hypotensive, hypertensive, normotensive, and even hypertensive) and re-checked your statistics in sub-categories. Thereafter, please report that whether your final claim as conclusion is true in each category or not?\nAnd also, as a limitation, it is better to mention that you could not proof that whether the assessed patients were clinically in shock state or not (for example you have not any data regarding their cardiac output, lactate level, urine output, blood gas analysis, mental status, etc.)\nKind regards* Publons Reviewer Recognition. Springer Nature can send verification of this review directly to Publons (a subsidiary of Clarivate Analytics). If you would like to take advantage of this service, please click on the “Yes” option below. Your name, email address, title of the reviewed manuscript, name of the journal, and date of your review submission (the “Review Data”) will then be transmitted to Publons upon publication of the manuscript. 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I agree for my report to be made available under an Open Access Creative Commons CC-BY License (http://creativecommons.org/licenses/by/4.0) if this manuscript is accepted for publication. Any comments that I do not wish to be included in the published report have been included as confidential comments to the editor, which will not be published.: **I agree to the terms of the CC-BY 4.0 license; please do not publish my name with my report. (default)**\n* Is the study design appropriate to answer the research question (including the use of appropriate controls), and are the conclusions supported by the evidence presented?: **Yes**\n* Are the methods sufficiently described to allow the study to be repeated?: **Yes**\n* Is the use of statistics and treatment of uncertainties appropriate?: **Yes**\n* Is the presentation of the work clear?: **Yes**\n* Are the images in this manuscript (including electrophoretic gels and blots) free from apparent manipulation?: **Yes**\n"},{"type":"checksComplete","content":"","date":"2020-09-16T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-09-14T12:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"","date":"2020-09-10T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"8d3064ac-49ad-45ae-8d68-cf9d5a036447","owner":[],"postedDate":"September 18th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":551563,"name":"Critical Care \u0026 Emergency Medicine"}],"tags":[],"updatedAt":"2020-11-01T15:03:35+00:00","versionOfRecord":{"articleIdentity":"rs-76143","link":"https://doi.org/10.1186/s12873-020-00383-2","journal":{"identity":"bmc-emergency-medicine","isVorOnly":false,"title":"BMC Emergency Medicine"},"publishedOn":"2020-10-31 15:02:27","publishedOnDateReadable":"October 31st, 2020"},"versionCreatedAt":"2020-09-18 16:27:02","video":"","vorDoi":"10.1186/s12873-020-00383-2","vorDoiUrl":"https://doi.org/10.1186/s12873-020-00383-2","workflowStages":[]},"version":"v1","identity":"rs-76143","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-76143","identity":"rs-76143","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}