Plethysmographic Measurements as Novel Predictors of Mortality in Pulmonary Embolism

Plethysmographic Measurements as Novel Predictors of Mortality in Pulmonary Embolism | 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 Plethysmographic Measurements as Novel Predictors of Mortality in Pulmonary Embolism Teslime ERYAVUZ ŞENGÜL, Hamit Hakan ARMAĞAN, Furkan Çağrı OĞUZLAR This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7432049/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Jan, 2026 Read the published version in BMC Pulmonary Medicine → Version 1 posted 11 You are reading this latest preprint version Abstract Background Pulmonary embolism(PE) is a ventilation/perfusion disorder due to obstruction of the pulmonary artery, typically by a thrombus. The Pleth Variability Index(PVI) is a noninvasive measure reflecting dynamic changes in the perfusion index(PI), observed through photoplethysmography over at least one respiratory cycle. This study aims to investigate the prognostic value of plethysmographic parameters in patients with PE, hypothesizing that these parameters may serve as a reliable guide in a condition where impaired perfusion plays a critical role. Methods Data were collected from patients over 18 years old, diagnosed with PE in the emergency department, including demographic information, risk scores, and measurements. PVI and PI values were analyzed across mortality groups. A survival analysis was performed to assess the impact of PVI and PI on 1-year mortality. The study followed the STROBE checklist for evaluation. Results 49-patients were included in this prospective, single-center study. The PVI was significantly higher, and PI was significantly lower in the deceased group compared to the survivors(p = 0.027, p = 0.011). AUC values for PVI, PESI score, and PI were 0.714, 0.820, and 0.745, respectively. The negative predictive value of PESI was 100%, while PVI showed the highest positive predictive value(53.5%). The mean survival time was significantly shorter for patients with PVI > 40 and PI < 1.9(p = 0.001, p = 0.002). An increase in PVI was associated with a 4.04-fold higher risk of death(HR:5.04, 95% CI:1.50-16.92, p = 0.009). Conclusions PVI is a noninvasive, rapid, and objective tool for predicting mortality in PE patients in the emergency department. This study is the first to evaluate PVI in PE. Trial registration: ClinicalTrials.gov Identifier: NCT06508112. Registered on 12 July 2024. Pulmonary embolism perfusion index pleth variability index Figures Figure 1 Figure 2 Figure 3 1. BACKGROUND Ventilation/perfusion(V/Q) dysfunction in pulmonary embolism(PE) occurs due to the mechanical and functional obstruction of the pulmonary vascular bed. Pulmonary artery pressure increase caused by embolism leads to right ventricular(RV) dysfunction and consequently systemic hypotension. The resulting hemodynamic collapse leads to systemic vasoconstriction and decreased peripheral perfusion ( 1 ). The pleth variability index(PVI) is a continuous, noninvasive measurement of the relative change in photoplethysmography during one or more respiratory cycles. It demonstrates changes in the perfusion index(PI), which is the ratio of pulsatile blood flow to non-pulsatile blood flow in photoplethysmographic measurements on a pulse oximeter. Its primary use is to reflect fluid responsiveness in mechanically ventilated adult patients. PVI was recently found to be effective in predicting attack severity and determining treatment response in the triage of pediatric patients with obstructive airway disease ( 2 ). Although its primary indication is to serve as a guide for fluid deficit and replacement, its use has increased due to its ability to detect perfusion abnormalities in respiratory pathologies ( 2 ). The V/Q defect constitutes the fundamental pathophysiology in pulmonary embolism. It is also known to simultaneously cause circulatory collapse. Therefore, we thought that PVI might be effective in determining prognosis and mortality in patients diagnosed with PE. Our literature review revealed that the clinical significance of PVI in PE has not been previously investigated. The aim of this study was to investigate the prognostic value of plethysmographic measurements in patients with PE and to evaluate their impact on survival rates. 2. METHODS 2.1 Study Design This investigation was designed as a prospective, observational study and carried out in the emergency department of a tertiary university hospital in Isparta, Türkiye. The institution has a capacity of over 500 inpatient beds and provides care for approximately 62,000 adult emergency visits annually (Figure 1). Ethical approval was obtained from the Clinical Research Ethics Committee of Süleyman Demirel University (Reference Number: 72867572-050.01.04-752964). All procedures were conducted in accordance with the Declaration of Helsinki (1975, revised 1983) and reported in line with the STROBE recommendations for observational research (http://www.strobe-statement.org). Written informed consent was obtained from all participants. Written informed consent was obtained from each participant prior to enrolment. The trial was prospectively registered at ClinicalTrials.gov. Registry: NCT06508112, Registration date: 12 July 2024. 2.2 Study Population Between April 1, 2023, and March 31, 2024, consecutive adult patients (≥18 years) presenting to the emergency department with a confirmed diagnosis of acute pulmonary embolism (PE) based on computed tomographic pulmonary angiography (CTPA) were screened for eligibility. Exclusion criteria included pregnancy, age under 18 years, acute decompensated heart failure, peripheral arterial disease, acute exacerbation of chronic obstructive pulmonary disease or asthma, and refusal to participate. Patients meeting the inclusion criteria were followed for outcomes. 2.3 Data Collection Demographic information, comorbidities, vital signs (blood pressure, heart rate etc.), and laboratory results including arterial blood gases and high-sensitivity cardiac troponin I (hs-cTnI) were recorded at the time of diagnosis. Classification of patients with acute PE and PESI score measurements were based on the 2019 ESC guideline. Perfusion Index (PI) and Pleth Variability Index (PVI) values were measured immediately after PE confirmation. Measurements were performed while the patient was in a supine position using a Radical-7® Pulse CO-Oximeter (Masimo Corp., Irvine, CA, USA). Data were automatically captured after two minutes of probe stabilization on the index finger. To ensure consistency, all measurements were obtained by the same investigator (1, 2). In addition, cardiac chamber dimensions were assessed on CTPA by a senior emergency medicine resident. Right and left ventricular diameters were measured on axial images at the maximal distance between the endocardial surface and the interventricular septum. Patients were stratified according to survival status during hospitalization, and 1-year mortality was ascertained through the institutional information system and verified using the National Death Notification System. 2.4 Sample Size A priori sample size estimation was performed using G*Power version 3.1. Based on an expected large effect size according to Cohen’s d (d = 0.8), with an α error probability of 0.05 and a statistical power (1–β) of 0.80, the minimum required sample size was calculated as 46 patients. The anticipated effect size was determined by reviewing previously published studies in comparable populations. To safeguard against possible attrition, missing data, or measurement inconsistencies, the final cohort was expanded to 49 patients, ensuring adequate statistical power for the planned analyses (Figure 1). 2.5 Outcome Measures The primary endpoint was all-cause mortality within one year of diagnosis. The prognostic accuracy of PI and PVI for mortality prediction was assessed, alongside conventional clinical predictors such as the Pulmonary Embolism Severity Index (PESI). 2.6 Statistical Analysis All analyses were performed using IBM SPSS Statistics version 29.0 (IBM Corp., Armonk, NY) and MedCalc version 22 (MedCalc Software Ltd., Ostend, Belgium). Normality of distribution was assessed using the Kolmogorov–Smirnov test. Continuous variables were expressed as mean ± standard deviation (SD) or median (minimum–maximum), as appropriate. Group comparisons were conducted using Student’s t-test or Mann–Whitney U test. Categorical variables were presented as frequencies and percentages, and compared using the chi-square or Fisher’s exact test. Receiver operating characteristic (ROC) curves were generated to evaluate the predictive performance of PI, PVI, and other significant variables. The optimal cut-off points were determined according to Youden’s index (sensitivity + specificity − 1), and sensitivity, specificity, positive predictive value, and negative predictive value were subsequently calculated. Cox proportional hazards regression models (univariate and multivariate) were applied to identify independent predictors of one-year mortality, with results expressed as hazard ratios (HR) and 95% confidence intervals (CI). During model construction, variables showing high collinearity or redundancy were carefully examined and excluded to avoid multicollinearity bias. Kaplan–Meier survival curves with log-rank tests were constructed to compare survival according to pre-specified cut-offs. A two-sided p value <0.05 was considered statistically significant. 3. RESULTS A total of 49 patients (26 males [53%], 23 females [47%]; mean age: 71.78±14.1 years; range, 39-93 years) were included in the study. The one-year mortality rate was found to be 24.5% (n=12). When comparing deceased and surviving patients, respiratory rate was significantly higher in the deceased group, while SpO2, systolic blood pressure, and diastolic blood pressure were significantly lower (p<0.05). Among the laboratory parameters examined, lactate levels were significantly elevated in the deceased group compared to the survivors (p<0.001). In plethysmographic measurements, both the Pleth Variability Index (PVI) and Perfusion Index (PI) showed significant differences (p<0.05). Additionally, the Pulmonary Embolism Severity Index (PESI) score was significantly higher in the deceased group compared to the survivors (p<0.001). No significant differences were observed between the groups in terms of gender, ECG findings, or ventricular measurements. The mean PVI was calculated as 29.74±15.77 and the mean PI was 2.97±2.02. PVI and PI values were found to be statistically significant between the groups of survivors and non-survivors (p<0.05). The table below presents the demographic data, measurements, and risk scores of the patients, showing their relationship with mortality (Table 1). Table 1. The summary of demographic, laboratory and clinical data of patients Overall (n=49) Outcome p value Alive (n=37) Exitus (n=12) Age (years) 71.78±14.1 69.24±13.99 79.58±12,22 0.027 Sex Female 23 (47%) 19 (38.8%) 4 (8.2%) 0.33 Male 26 (53%) 18 (36.7%) 8 (16.3%) Vital parameters Fever (°C) 36.63 36.64±0.37 36.60±0.49 0.75 Pulse (beats/min) 104.39 101.70±21.42 112.67±26.04 0.15 RR (breath/min) 26.78 25.24±6.26 31.50±5.79 0.004 SpO 2 (%) 84.76 86.92±7.10 78.08±7.91 <0.001 Systolic BP (mmHg) 118.57 123.49±24.34 103.42±28.00 0.021 Diastolic BP (mmHg) 70.10 72.70±10.05 62.08±18.69 0.015 Laboratory parameters Troponin I (ng/L) 59.93±12.02 54.9±77.2 78.04±98.08 0.43 Lactate (mmol/L) 2.09±1.52 1.69±1.17 3.32±1.87 <0.001 PaO 2 (mmHg) 59.78±20.01 62.51±20.46 51.39±16.61 0.08 ECG findings QT (ms) 362.55±44.53 368±45.64 344±36.79 0.10 QTc (ms) 459.12±35.21 459±33.31 457±42.09 0.81 QT/QTc 0.76±0.07 0.80±0.07 0.75±0.07 0.08 CTPA findings RV diameter (mm) 34.76±8.70 33.78±8.60 37.64±8.68 0.17 LV diameter (mm) 34.56±8.49 35.02±8.23 33.14±9.47 0.51 RV/LV 1.06±0.40 1.01±0.36 1.22±0.48 0.12 Masimo radical-7 oximeter findings PI (%) 2.7±2.02 3.37±2.06 1.72±1.30 0.011 PVI (%) 29.74±15.77 26.93±14.59 38.41±16.70 0.027 Hospitalization Discharged 6 (12.2%) 6 (12.2%) 0 (0%) 0.011 Admitted to the ward 30 (61.2%) 25 (51%) 5 (10.2%) Admitted to the intensive care unit 13 (26.6%) 6 (12.2%) 7 (14.2%) PESI classification Score 76.20±49.76 62.54±43.57 118.33±45.05 <0.001 GCS: Glasgow coma scale, RR: Respiratory rate, SpO 2: Oxygen saturation, BP: Blood pressure, ECG: Electrocardiography, PaO 2 : Arterial oxygen pressure, CTPA: Computed tomography pulmonary angiography, RV: Right ventricle, LV: Left ventricle, PI: Perfusion index, PVI: Pleth variability index, PESI: Pulmonary embolism severity index We performed ROC analysis to determine the mortality predictive power of significantly different parameteres mentioned before. ROC analysis determined the following cutoff values: PESI >60, PI ≤1.9%, PVI >40%, age >74 years, SpO2 ≤84%, respiratory rate >26 breaths/min, and lactate >1.79 mmol/L. The highest AUC value was belong to lactate, while lowers was belong to age. PVI was found to have the highest positive predictive value at 53.8%. The PESI score demonstrated the highest negative predictive value and sensitivity, both at 100%. Table 2 and Fig. 2 presents the results of ROC analysis in detail. Table 2. Roc curve analysis of one year mortality of patients AUC Sensitivity Specifity PPV NPV p value PESI >60 0.820 100% 59.5% 44.4% 100% 0.001 PI (%) ≤1.9 0.745 66.67% 75.68% 47.1% 87.5% 0.011 PVI (%) >40 0.714 58.33% 83.78% 53.8% 86.1% 0.027 Age >74 0.705 83.33% 62.16% 41.7% 92% 0.034 SpO 2 (%) ≤84 0.819 83.33% 70.27% 50% 90.3% 0.001 RR (breath/min) >26 0.766 75.0% 62.16% 39.1% 88.5% 0.006 Lactate (mmol/L) >1.79 0.827 83.33% 72.97% 50% 93.1% 0.001 AUC: Area under the curve, PPV: Positive predictive value, NPV: Negative predictive value, PESI: Pulmonary embolism severity index, PI: Perfusion index, PVI: Pleth variability index, SpO 2: Oxygen saturation, RR: Respiratory rate Univariate Cox regression analysis, showed that PVI, PI, PESI and lactate were significant predictors for mortality. Consequently, independent prognostic factors associated with in-hospital mortality were figured out by multivariate Cox regression analysis. Age, respiratory rate, and SpO2 were excluded from the multivariate model to avoid multicollinearity with the PESI score. An increase in cutoff values for PVI was found to increase mortality by 4.04 times, while a decrease in cutoff values for PI increased mortality by 4.69 times. PVI and PI were strong independent predictors of 1-year mortality (HR:5.04, 95%CI=1.74-17.75, p=0.009; HR:5.69, 95%CI=1.64-23.74 p=0.023, respectively) (Table 3). Table 3. Regression analysis of 1-year mortality of patients Univariate Cox Regression Analysis Multivariate Cox Regression Analysis HR (95%Cl) Lower-Upper p value HR (95%Cl) Lower-Upper p value PVI 5.56 1.74 17.75 0.001 5.04 1.50 16.92 0.009 PI 6.25 1.64 23.74 0.007 5.69 1.27 25.44 0.023 PESI 1.02 1.01 1.04 0.001 1.02 1.00 1.05 0.019 Lactate 1.57 1.19 2.08 0.001 1.30 0.72 2.35 0.379 HR: Hazard Ratio, PI: Perfusion index, PVI: Pleth variability index, PESI: Pulmonary embolism severity index Since lactate was found to be non-significant in the multivariate analysis, Kaplan-Meier analysis was performed using the PESI score, PI, and PVI. The impact of PVI, PI, and PESI on 1-year survival was examined using the Kaplan-Meier survival analysis test. The separation of PVI, PI, and PESI was made according to predetermined cut-off values. In the groups with higher PVI and PESI, and lower PI, mortality was higher (Log Rank p=0.001, 0.001, 0.002) (Figure 3). 4. DISCUSSION PVI has been found useful in predicting fluid responsiveness in targeted fluid therapy, in mechanically ventilated children, and in intraoperative fluid resuscitation ( 3 – 6 ). It has also been found effective in predicting the prognosis of patients in the intensive care unit ( 7 ). Recently, it has been evaluated in pediatric patients with obstructive airway disease, highlighting its importance in respiratory conditions. In a study by Demir G. and colleagues involving 133 patients, PVI was found to be effective in predicting the severity of attacks, response to treatment, and follow-up outcomes in the triage of patients with obstructive airway disease ( 2 ). The primary aim of this study was to evaluate the effectiveness of PVI, which varies with perfusion, in pulmonary embolism(PE), a condition characterized by ventilation/perfusion mismatch. In this study, the mean PVI measured automatically was 29.74 ± 15.77. In the study by Demir G. and colleagues, the mean PVI before treatment was measured as 30 ( 2 ). There is no universally accepted normal range for PVI values. However, in a review encompassing 25 studies, the PVI cut-off value was determined to range between 7% and 20% ( 8 ). In a study conducted by Lu W. et al. to evaluate extracellular fluid volume in mechanically ventilated patients undergoing anesthesia induction, the PVI cut-off value was found to be 14% ( 9 ). Similarly, in a study by Vos J. et al. assessing fluid responsiveness and dynamic arterial tone in patients undergoing major hepatic resection, the PVI cut-off value was determined to be 12% ( 10 ). The fact that the mean systolic and diastolic blood pressures of the patients in this study were within normal limits suggests that PVI may be useful in detecting ventilation/perfusion disorders rather than hypovolemia in our patient group, and could be beneficial in monitoring respiratory disease. When evaluating the outcomes of the patients, the mean PVI was 26.93 ± 14.59 in survivors and 38.41 ± 16.70 in non-survivors, indicating that PVI was significantly higher in non-survivors (p = 0.027). When the predictive power for mortality was evaluated, PVI successfully identified PE patients who were likely to die, outperforming other parameters with the highest specificity of 83.78% and the highest positive predictive value (PPV) of 53.8% (cut-off:>40, AUC:0.714, p = 0.027). Additionally, it was found that patients with PVI values above the cut-off had shorter survival times compared to those with values below the cut-off (Log rank:0.001; 141.29 ± 39.37 days vs. 330.12 ± 24.07 days). It was found that mortality increased by 404% when the PVI value exceeded the cut-off (HR:5.04, p = 0.009). Considering all these factors, this study has demonstrated that PVI is a strong and independent predictor of mortality in PE. PI, defined as the ratio of pulsatile to non-pulsatile blood flow, reflects blood flow adequacy and hemodynamic stability, and is useful for prognosis in critically ill patients ( 11 ). In PE, right ventricular dysfunction causes hemodynamic collapse and vasoconstriction, while thrombus-induced ventilation/perfusion mismatch reduces peripheral perfusion, lowering PI values. PI is valuable in cardiovascular and pulmonary monitoring in neonatal intensive care, predicting postoperative ICU stay, assessing analgesic efficacy in renal colic, detecting hypovolemic shock in the emergency department, and forecasting prognosis in gastrointestinal bleeding ( 12 – 16 ). A recent study by Esen C. et al. on 94 PE patients found that a PI below 1 effectively predicted the need for mechanical ventilation, inotropes, thrombolytics, and mortality ( 1 ). The researchers suggest evaluating both PI and PVI, which reflects dynamic changes in PI, in PE patients. In this study, the mean automatically measured PI was 2.97 ± 2.02, with survivors showing a PI of 3.37 ± 2.06 and deceased patients 1.72 ± 1.30 (p = 0.011). PI was a strong mortality predictor with 66.7% sensitivity, 75.68% specificity, 47% PPV, and 87.5% NPV (cut-off:≤1.9, AUC: 0.745, p = 0.011). Mortality increased by 469% when the PI fell below the cut-off (HR:5.69, p = 0.023). In the study by Esen C. et al., the mean PI was 1.7 ± 1.5 overall, 1.9 ± 1.5 in survivors, and 1.2 ± 1.2 in non-survivors (p = 0.034). In this study, PI sensitivity was found to be 59.3%, specificity 59.7%, PPV 37.2%, and NPV 78.4% (cut-off:1, AUC:0.667, p = 0.011). The regression analysis revealed that a PI below 1 was associated with mortality (OR:0.861, p = 0.042). However, the PESI score was not found to be statistically significant (OR:0.970, p = 0.191) ( 1 ). This study confirmed that PI was lower in PE patients who died compared to those who survived. Additionally, this study found that PI had higher sensitivity, specificity, PPV, and NPV compared to the study by Esen C. et al. Furthermore, it was more clearly and with a higher rate demonstrated that PI is an independent and strong predictor of mortality. However, PI was more clearly and more strongly shown to be an independent and strong predictor of mortality. In the survival analysis, it was found that approximately 80% of patients with PI values above the cut-off were alive at the end of one year. Additionally, it was found that patients with PI values below the cut-off had shorter survival times compared to those with values above the cut-off (Log rank:0.002; 123.91 ± 24.8 days vs. 336.86 ± 24.1 days). There is no study in the literature that includes survival analysis of PI, and this study is the first to evaluate survival in PE patients. In this study, consistent with the literature, the average PESI score was higher in deceased patients than in survivors (p < 0.001) ( 17 ). In the 1-year survival analysis, all patients below the cut-off survived. Regression analysis showed that a one-unit increase in the PESI score, a strong independent mortality predictor, increased mortality by 2%. This study confirmed that lactate levels increased and PaO2 levels decreased in PE patients, consistent with the literature. Additionally, lactate was identified as the best predictor of mortality in PE, with an AUC of 0.827 ( 19 ). This study is the first in the literature to evaluate PVI in PE. It is also the first study to use both PI and PVI in assessing 1-year mortality in PE patients. The researchers believe that the non-invasive and rapid nature of PVI and PI measurements makes them particularly suitable for use in emergency departments, where timely decision-making is critical. 4.1 Conclusion PE is one of the most critical conditions in the emergency department, requiring rapid diagnosis and appropriate treatment. PI and PVI are suitable for emergency department use due to their easy accessibility, rapid results, noninvasive nature, and the simplicity, ease, and objectivity of their measurement. The PESI score includes other causes of mortality that increase the risk of death, such as advanced age, cancer, chronic lung disease, heart failure history, and altered mental status. Therefore, a high PESI score may also include deaths due to causes other than PE. Furthermore, the necessity of patient history information in the evaluation of the PESI score limits its applicability to patients who are unconscious or unable to provide an anamnesis. PI and PVI measurements can be easily utilized in these patients. This parameter, primarily used to regulate the treatment of fluid loss, was found to be highly sensitive to PE in patients with normal blood pressures. Therefore, the researchers believe that in patients with PE, both PI and PVI, which indicates dynamic changes in PI, should be evaluated. The researchers believe that these non-invasive and radiation-free assessment parameters can guide clinicians during follow-up and treatment. 4.2 Limitations This study has several limitations. First, although the power analysis showed that the number of patients was sufficient, the sample size was limited due to the single-center design of this study. Secondly, the possible effects of the medications and treatments administered to the patients during their transportation to the hospital were not evaluated. Third, post-treatment repeat measurements were not performed. This limitation is inherent to the nature of emergency departments, where patients are not typically followed up during these periods. This study is the first to evaluate PVI in pulmonary embolism. Further multicenter studies with larger patient cohorts are needed to validate these findings. Declarations 1. Competing Interests [X] No conflict of interest exists. We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome. 2. Funding [X] No funding was received for this work. 3. Intellectual Property [X] We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property. 4. Research Ethics [X] We further confirm that any aspect of the work covered in this manuscript that has involved human patients has been conducted with the ethical approval of all relevant bodies and that such approvals are acknowledged within the manuscript. [X] IRB approval was obtained (required for studies and series of 3 or more cases) [X] Written consent to publish potentially identifying information, such as details or the case and photographs, was obtained from the patient(s) or their legal guardian(s). 5. Ethics Approval and Consent to Participate [X] Ethical approval and consent were obtained from the Clinical Research and Ethics Committee of Suleyman Demirel University Faculty of Medicine. The committee reference number is 72867572-050.01.04-752964. 6. Consent for Publication [X] Not applicable. No identifiable personal data, images, or case details are included in this manuscript that would require explicit consent for publication. 7. Availability of Data and Materials All data generated or analysed during this study are included in this published article [and its supplementary information files]. 8. Acknowledgements [X] The data that support the findings of this study are available from Electronic Document Management System (EBYS) of the Ministry of Health of the Republic of Turkiye but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Electronic Document Management System (EBYS) of the Ministry of Health of the Republic of Turkiye. 9. Authorship The International Committee of Medical Journal Editors (ICMJE) recommends that authorship be based on the following four criteria: Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; AND Drafting the work or revising it critically for important intellectual content; AND Final approval of the version to be published; AND Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. [X] All listed authors meet the ICMJE criteria. We attest that all authors contributed significantly to the creation of this manuscript, each having fulfilled criteria as established by the ICMJE. [X] We confirm that the manuscript has been read and approved by all named authors. 10. Author's Contributions TEŞ: Conceptualization, Data Curation, Formal analysis, Methodology, Resources, Software, Investigation, Writing-Original draft preparation. HHA: Conceptualization, Investigation, Project administration, Resources, Supervision, Validation. Visualization. FÇO: Formal analysis, Supervision, Writing-Original draft preparation, Writing-Reviewing and Editing. 11. Contact with the Editorial Office The Corresponding Author declared on the title page of the manuscript is: Dr. Teslime ERYAVUZ ŞENGÜL [X] This author submitted this manuscript using her account in EVISE. [X] We understand that this Corresponding Author is the sole contact for the Editorial process (including EVISE and direct communications with the office). She is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs. [X] We confirm that the email address shown below is accessible by the Corresponding Author, is the address to which Corresponding Author’s EVISE account is linked, and has been configured to accept email from the editorial office of American Journal of Ophthalmology Case Reports: [email protected] [X] We understand that this author is the sole contact for the Editorial process (including EVISE and direct communications with the office). He/she is responsible for communicating with the other authors, including the Corresponding Author, about progress, submissions of revisions and final approval of proofs. References Esen CI, Satar S, Gulen M, Acehan S, Sevdımbas S, Ince C. Perfusion index: could it be a new tool for early identification of pulmonary embolism severity? Intern Emerg Med. 2024. Demir G, Berksoy E, Bardak Ş, Elibol P, Çiçek A, Özön A, et al. Use of the pleth variability index in children with obstructive respiratory disease. Am J Emerg Med. 2022;56:28–32. 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Ozakin E, Yazlamaz NO, Kaya FB, Karakilic EM, Bilgin M. Perfusion Index Measurement in Predicting Hypovolemic Shock in Trauma Patients. J Emerg Med. 2020;59(2):238–45. Firat BT, Gulen M, Satar S, Firat A, Acehan S, Isikber C, et al. Perfusion index: Could this be a new triage tool for upper gastrointestinal system bleeding in the emergency department? A prospective cohort study. Sao Paulo Med J. 2021;139(6):583–90. Zhou XY, Ben SQ, Chen HL, Ni SS. The prognostic value of pulmonary embolism severity index in acute pulmonary embolism: a meta-analysis. Respir Res. 2012;13(1):111. Wang Y, Yang H, Qiao L, Tan Z, Jin J, Yang J, et al. The predictive value of PaO2/FIO2 and additional parameters for in-hospital mortality in patients with acute pulmonary embolism: an 8-year prospective observational single-center cohort study. BMC Pulm Med. 2019;19(1):242. Wang Y, Feng Y, Yang X, Mao H. Prognostic role of elevated lactate in acute pulmonary embolism: A systematic review and meta-analysis. Phlebology. 2022;37(5):338–47. Additional Declarations No competing interests reported. Supplementary Files HIGHLIGHTS.docx graphicalabstract.jpg Dataset.sav Cite Share Download PDF Status: Published Journal Publication published 23 Jan, 2026 Read the published version in BMC Pulmonary Medicine → Version 1 posted Editorial decision: Revision requested 25 Nov, 2025 Reviews received at journal 24 Nov, 2025 Reviews received at journal 18 Nov, 2025 Reviewers agreed at journal 08 Nov, 2025 Reviewers agreed at journal 06 Nov, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers invited by journal 11 Sep, 2025 Editor assigned by journal 08 Sep, 2025 Editor invited by journal 03 Sep, 2025 Submission checks completed at journal 01 Sep, 2025 First submitted to journal 01 Sep, 2025 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-7432049","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":516170269,"identity":"a061195d-ee7f-4690-8fd0-5951beb90426","order_by":0,"name":"Teslime ERYAVUZ 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1","display":"","copyAsset":false,"role":"figure","size":122470,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart\u003c/p\u003e","description":"","filename":"Figure1.Flowchart.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7432049/v1/0929e60fd8556a2d68b311cd.jpg"},{"id":91841348,"identity":"f564dc37-92c9-44ad-af46-e713ad2b4cbe","added_by":"auto","created_at":"2025-09-22 09:55:32","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":295424,"visible":true,"origin":"","legend":"\u003cp\u003eROC Analysis Showing the Relationship Between Survivors vs Non-survivors\u003cbr\u003e\nReceiver operating characteristic curves for the age, oxygen saturation, respiratory rate, systolic and diastolic blood pressure and lactate (A); PESI, PI and PVI (B); Age, SpO\u003csub\u003e2\u003c/sub\u003e, Systolic BP, Diastolic BP, RR and lactate\u003cbr\u003e\nPESI: Pulmonary embolism severity index, PVI: Pleth variability index, PI: Perfusion index, SpO\u003csub\u003e2:\u003c/sub\u003e Oxygen saturation, BP: Blood pressure\u003c/p\u003e","description":"","filename":"Figure2.ROCAnalysis.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7432049/v1/1bd0fc84aab8eadb2b877564.jpg"},{"id":91841351,"identity":"b91543f7-bd98-4c0b-aa62-9f291665171a","added_by":"auto","created_at":"2025-09-22 09:55:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":122087,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curve according to PI (A), PESI (B), and PVI (C) levels above and below optimal cut-off value (40, 1,9, 60)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7432049/v1/29f50ac4856837b2591a1e28.png"},{"id":101151722,"identity":"384556ad-dec7-4145-b899-f0040ec500b7","added_by":"auto","created_at":"2026-01-26 16:03:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1497712,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7432049/v1/642d09fd-e07f-4eb7-9478-634f6b4e5182.pdf"},{"id":91841354,"identity":"27b9576b-a1be-45bc-b18f-cf74476a65a8","added_by":"auto","created_at":"2025-09-22 09:55:33","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":18187,"visible":true,"origin":"","legend":"","description":"","filename":"HIGHLIGHTS.docx","url":"https://assets-eu.researchsquare.com/files/rs-7432049/v1/86d5442f308f33d88870321d.docx"},{"id":91841325,"identity":"aaf09980-e96a-4678-ab65-4341a7575b3b","added_by":"auto","created_at":"2025-09-22 09:55:30","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":183233,"visible":true,"origin":"","legend":"","description":"","filename":"graphicalabstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7432049/v1/61c65ae68ad33cfeb194ee93.jpg"},{"id":91841374,"identity":"9938fe9d-130d-4f02-9919-1ec55f324d1d","added_by":"auto","created_at":"2025-09-22 09:55:34","extension":"sav","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":13913,"visible":true,"origin":"","legend":"","description":"","filename":"Dataset.sav","url":"https://assets-eu.researchsquare.com/files/rs-7432049/v1/7e7110fc97681aa8c809fcb8.sav"}],"financialInterests":"No competing interests reported.","formattedTitle":"Plethysmographic Measurements as Novel Predictors of Mortality in Pulmonary Embolism","fulltext":[{"header":"1. BACKGROUND","content":"\u003cp\u003eVentilation/perfusion(V/Q) dysfunction in pulmonary embolism(PE) occurs due to the mechanical and functional obstruction of the pulmonary vascular bed. Pulmonary artery pressure increase caused by embolism leads to right ventricular(RV) dysfunction and consequently systemic hypotension. The resulting hemodynamic collapse leads to systemic vasoconstriction and decreased peripheral perfusion (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe pleth variability index(PVI) is a continuous, noninvasive measurement of the relative change in photoplethysmography during one or more respiratory cycles. It demonstrates changes in the perfusion index(PI), which is the ratio of pulsatile blood flow to non-pulsatile blood flow in photoplethysmographic measurements on a pulse oximeter. Its primary use is to reflect fluid responsiveness in mechanically ventilated adult patients. PVI was recently found to be effective in predicting attack severity and determining treatment response in the triage of pediatric patients with obstructive airway disease (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough its primary indication is to serve as a guide for fluid deficit and replacement, its use has increased due to its ability to detect perfusion abnormalities in respiratory pathologies (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The V/Q defect constitutes the fundamental pathophysiology in pulmonary embolism. It is also known to simultaneously cause circulatory collapse. Therefore, we thought that PVI might be effective in determining prognosis and mortality in patients diagnosed with PE. Our literature review revealed that the clinical significance of PVI in PE has not been previously investigated. The aim of this study was to investigate the prognostic value of plethysmographic measurements in patients with PE and to evaluate their impact on survival rates.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003ch2\u003e2.1 Study Design\u003c/h2\u003e\n\u003cp\u003eThis investigation was designed as a prospective, observational study and carried out in the emergency department of a tertiary university hospital in Isparta, T\u0026uuml;rkiye. The institution has a capacity of over 500 inpatient beds and provides care for approximately 62,000 adult emergency visits annually (Figure 1). Ethical approval was obtained from the Clinical Research Ethics Committee of S\u0026uuml;leyman Demirel University (Reference Number: 72867572-050.01.04-752964). All procedures were conducted in accordance with the Declaration of Helsinki (1975, revised 1983) and reported in line with the STROBE recommendations for observational research (http://www.strobe-statement.org). Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from each participant prior to enrolment. The trial was prospectively registered at ClinicalTrials.gov. Registry: NCT06508112, Registration date: 12 July 2024.\u003c/p\u003e\n\u003ch2\u003e2.2 Study Population\u003c/h2\u003e\n\u003cp\u003eBetween April 1, 2023, and March 31, 2024, consecutive adult patients (\u0026ge;18 years) presenting to the emergency department with a confirmed diagnosis of acute pulmonary embolism (PE) based on computed tomographic pulmonary angiography (CTPA) were screened for eligibility. Exclusion criteria included pregnancy, age under 18 years, acute decompensated heart failure, peripheral arterial disease, acute exacerbation of chronic obstructive pulmonary disease or asthma, and refusal to participate. Patients meeting the inclusion criteria were followed for outcomes.\u003c/p\u003e\n\u003ch2\u003e2.3 Data Collection\u003c/h2\u003e\n\u003cp\u003eDemographic information, comorbidities, vital signs (blood pressure, heart rate etc.), and laboratory results including arterial blood gases and high-sensitivity cardiac troponin I (hs-cTnI) were recorded at the time of diagnosis. Classification of patients with acute PE and PESI score measurements were based on the 2019 ESC guideline. Perfusion Index (PI) and Pleth Variability Index (PVI) values were measured immediately after PE confirmation. Measurements were performed while the patient was in a supine position using a Radical-7\u0026reg; Pulse CO-Oximeter (Masimo Corp., Irvine, CA, USA). Data were automatically captured after two minutes of probe stabilization on the index finger. To ensure consistency, all measurements were obtained by the same investigator (1, 2).\u003c/p\u003e\n\u003cp\u003eIn addition, cardiac chamber dimensions were assessed on CTPA by a senior emergency medicine resident. Right and left ventricular diameters were measured on axial images at the maximal distance between the endocardial surface and the interventricular septum. Patients were stratified according to survival status during hospitalization, and 1-year mortality was ascertained through the institutional information system and verified using the National Death Notification System.\u003c/p\u003e\n\u003ch2\u003e2.4 Sample Size\u003c/h2\u003e\n\u003cp\u003eA priori sample size estimation was performed using G*Power version 3.1. Based on an expected large effect size according to Cohen\u0026rsquo;s d (d = 0.8), with an \u0026alpha; error probability of 0.05 and a statistical power (1\u0026ndash;\u0026beta;) of 0.80, the minimum required sample size was calculated as 46 patients. The anticipated effect size was determined by reviewing previously published studies in comparable populations. To safeguard against possible attrition, missing data, or measurement inconsistencies, the final cohort was expanded to 49 patients, ensuring adequate statistical power for the planned analyses (Figure 1).\u003c/p\u003e\n\u003ch2\u003e2.5 Outcome Measures\u003c/h2\u003e\n\u003cp\u003eThe primary endpoint was all-cause mortality within one year of diagnosis. The prognostic accuracy of PI and PVI for mortality prediction was assessed, alongside conventional clinical predictors such as the Pulmonary Embolism Severity Index (PESI).\u003c/p\u003e\n\u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e\n\u003cp\u003eAll analyses were performed using IBM SPSS Statistics version 29.0 (IBM Corp., Armonk, NY) and MedCalc version 22 (MedCalc Software Ltd., Ostend, Belgium). Normality of distribution was assessed using the Kolmogorov\u0026ndash;Smirnov test. Continuous variables were expressed as mean \u0026plusmn; standard deviation (SD) or median (minimum\u0026ndash;maximum), as appropriate. Group comparisons were conducted using Student\u0026rsquo;s t-test or Mann\u0026ndash;Whitney U test. Categorical variables were presented as frequencies and percentages, and compared using the chi-square or Fisher\u0026rsquo;s exact test.\u003c/p\u003e\n\u003cp\u003eReceiver operating characteristic (ROC) curves were generated to evaluate the predictive performance of PI, PVI, and other significant variables. The optimal cut-off points were determined according to Youden\u0026rsquo;s index (sensitivity + specificity \u0026minus; 1), and sensitivity, specificity, positive predictive value, and negative predictive value were subsequently calculated. Cox proportional hazards regression models (univariate and multivariate) were applied to identify independent predictors of one-year mortality, with results expressed as hazard ratios (HR) and 95% confidence intervals (CI). During model construction, variables showing high collinearity or redundancy were carefully examined and excluded to avoid multicollinearity bias. Kaplan\u0026ndash;Meier survival curves with log-rank tests were constructed to compare survival according to pre-specified cut-offs. A two-sided p value \u0026lt;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003eA total of 49 patients (26 males [53%], 23 females [47%]; mean age: 71.78\u0026plusmn;14.1 years; range, 39-93 years) were included in the study. The one-year mortality rate was found to be 24.5% (n=12). When comparing deceased and surviving patients, respiratory rate was significantly higher in the deceased group, while SpO2, systolic blood pressure, and diastolic blood pressure were significantly lower (p\u0026lt;0.05). Among the laboratory parameters examined, lactate levels were significantly elevated in the deceased group compared to the survivors (p\u0026lt;0.001). In plethysmographic measurements, both the Pleth Variability Index (PVI) and Perfusion Index (PI) showed significant differences (p\u0026lt;0.05). Additionally, the Pulmonary Embolism Severity Index (PESI) score was significantly higher in the deceased group compared to the survivors (p\u0026lt;0.001). No significant differences were observed between the groups in terms of gender, ECG findings, or ventricular measurements.\u003c/p\u003e\n\u003cp\u003eThe mean PVI was calculated as 29.74\u0026plusmn;15.77 and the mean PI was 2.97\u0026plusmn;2.02. PVI and PI values were found to be statistically significant between the groups of survivors and non-survivors (p\u0026lt;0.05). The table below presents the demographic data, measurements, and risk scores of the patients, showing their relationship with mortality (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003e The summary of demographic, laboratory and clinical data of patients\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall (n=49)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 242px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlive (n=37)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExitus (n=12)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e71.78\u0026plusmn;14.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e69.24\u0026plusmn;13.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e79.58\u0026plusmn;12,22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;Female\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e23 (47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e19 (38.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e4 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;Male\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e26 (53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e18 (36.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e8 (16.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 604px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVital parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFever (\u0026deg;C)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e36.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e36.64\u0026plusmn;0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e36.60\u0026plusmn;0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePulse (beats/min)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e104.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e101.70\u0026plusmn;21.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e112.67\u0026plusmn;26.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRR (breath/min)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e26.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e25.24\u0026plusmn;6.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e31.50\u0026plusmn;5.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpO\u003csub\u003e2\u003c/sub\u003e (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e84.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e86.92\u0026plusmn;7.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e78.08\u0026plusmn;7.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSystolic BP (mmHg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e118.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e123.49\u0026plusmn;24.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e103.42\u0026plusmn;28.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiastolic BP (mmHg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e70.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e72.70\u0026plusmn;10.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e62.08\u0026plusmn;18.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 604px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTroponin I (ng/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e59.93\u0026plusmn;12.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e54.9\u0026plusmn;77.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e78.04\u0026plusmn;98.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLactate (mmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e2.09\u0026plusmn;1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1.69\u0026plusmn;1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e3.32\u0026plusmn;1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePaO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003e(mmHg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e59.78\u0026plusmn;20.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e62.51\u0026plusmn;20.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e51.39\u0026plusmn;16.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 604px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eECG findings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQT (ms)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e362.55\u0026plusmn;44.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e368\u0026plusmn;45.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e344\u0026plusmn;36.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQTc (ms)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e459.12\u0026plusmn;35.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e459\u0026plusmn;33.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e457\u0026plusmn;42.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQT/QTc\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.76\u0026plusmn;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.80\u0026plusmn;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.75\u0026plusmn;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 604px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCTPA findings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRV diameter (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e34.76\u0026plusmn;8.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e33.78\u0026plusmn;8.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e37.64\u0026plusmn;8.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLV diameter (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e34.56\u0026plusmn;8.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e35.02\u0026plusmn;8.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e33.14\u0026plusmn;9.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRV/LV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1.06\u0026plusmn;0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1.01\u0026plusmn;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1.22\u0026plusmn;0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 604px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMasimo radical-7 oximeter findings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePI (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e2.7\u0026plusmn;2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e3.37\u0026plusmn;2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1.72\u0026plusmn;1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePVI (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e29.74\u0026plusmn;15.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e26.93\u0026plusmn;14.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e38.41\u0026plusmn;16.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 604px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHospitalization\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDischarged\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e6 (12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e6 (12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0 (0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdmitted to the ward\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e30 (61.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e25 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e5 (10.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdmitted to the intensive care unit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e13 (26.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e6 (12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e7 (14.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 604px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePESI classification\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScore\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e76.20\u0026plusmn;49.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e62.54\u0026plusmn;43.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e118.33\u0026plusmn;45.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eGCS: Glasgow coma scale, RR: Respiratory rate, SpO\u003csub\u003e2:\u003c/sub\u003e Oxygen saturation, BP: Blood pressure, ECG: Electrocardiography, PaO\u003csub\u003e2\u003c/sub\u003e: Arterial oxygen pressure, CTPA: Computed tomography pulmonary angiography, RV: Right ventricle, LV: Left ventricle, PI: Perfusion index, PVI: Pleth variability index, PESI: Pulmonary embolism severity index\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe performed ROC analysis to determine the mortality predictive power of significantly different parameteres mentioned before. ROC analysis determined the following cutoff values: PESI \u0026gt;60, PI \u0026le;1.9%, PVI \u0026gt;40%, age \u0026gt;74 years, SpO2 \u0026le;84%, respiratory rate \u0026gt;26 breaths/min, and lactate \u0026gt;1.79 mmol/L. The highest AUC value was belong to lactate, while lowers was belong to age. PVI was found to have the highest positive predictive value at 53.8%. The PESI score demonstrated the highest negative predictive value and sensitivity, both at 100%. Table 2 and Fig. 2 presents the results of ROC analysis in detail.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e\u0026nbsp;\u0026nbsp; Roc curve analysis of one year mortality of patients\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"547\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecifity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePESI \u0026gt;60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e59.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e44.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePI (%) \u0026le;1.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e66.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e75.68%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e47.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e87.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePVI (%) \u0026gt;40\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e58.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e83.78%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e53.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e86.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge \u0026gt;74\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e83.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e62.16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e41.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e92%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.034\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpO\u003csub\u003e2\u003c/sub\u003e (%) \u0026le;84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e83.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e70.27%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e90.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRR (breath/min) \u0026gt;26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e75.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e62.16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e39.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e88.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLactate (mmol/L) \u0026gt;1.79\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.827\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e83.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e72.97%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e93.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAUC: Area under the curve, PPV: Positive predictive value, NPV: Negative predictive value, PESI: Pulmonary embolism severity index,\u003c/em\u003e\u003cem\u003e\u0026nbsp;PI: Perfusion index, PVI: Pleth variability index, SpO\u003csub\u003e2:\u003c/sub\u003e Oxygen saturation, RR: Respiratory rate\u003c/em\u003e\u003cstrong\u003e\u003cem\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate Cox regression analysis, showed that PVI, PI, PESI and lactate were significant predictors for mortality. \u0026nbsp;Consequently, independent prognostic factors associated with in-hospital mortality were figured out by multivariate Cox regression analysis. Age, respiratory rate, and SpO2 were excluded from the multivariate model to avoid multicollinearity with the PESI score. An increase in cutoff values for PVI was found to increase mortality by 4.04 times, while a decrease in cutoff values for PI increased mortality by 4.69 times. PVI and PI were strong independent predictors of 1-year mortality (HR:5.04, 95%CI=1.74-17.75, p=0.009; HR:5.69, 95%CI=1.64-23.74 p=0.023, respectively) (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u0026nbsp;\u003c/strong\u003eRegression analysis of 1-year mortality of patients\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate Cox Regression Analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate Cox Regression Analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95%Cl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower-Upper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95%Cl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower-Upper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePVI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e5.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e17.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e5.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e16.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e23.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e5.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e25.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePESI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLactate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eHR: Hazard Ratio, PI: Perfusion index, PVI: Pleth variability index, PESI: Pulmonary embolism severity index\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSince lactate was found to be non-significant in the multivariate analysis, Kaplan-Meier analysis was performed using the PESI score, PI, and PVI. The impact of PVI, PI, and PESI on 1-year survival was examined using the Kaplan-Meier survival analysis test. The separation of PVI, PI, and PESI was made according to predetermined cut-off values. In the groups with higher PVI and PESI, and lower PI, mortality was higher (Log Rank p=0.001, 0.001, 0.002) (Figure 3).\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003ePVI has been found useful in predicting fluid responsiveness in targeted fluid therapy, in mechanically ventilated children, and in intraoperative fluid resuscitation (\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). It has also been found effective in predicting the prognosis of patients in the intensive care unit (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Recently, it has been evaluated in pediatric patients with obstructive airway disease, highlighting its importance in respiratory conditions. In a study by Demir G. and colleagues involving 133 patients, PVI was found to be effective in predicting the severity of attacks, response to treatment, and follow-up outcomes in the triage of patients with obstructive airway disease (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The primary aim of this study was to evaluate the effectiveness of PVI, which varies with perfusion, in pulmonary embolism(PE), a condition characterized by ventilation/perfusion mismatch. In this study, the mean PVI measured automatically was 29.74\u0026thinsp;\u0026plusmn;\u0026thinsp;15.77. In the study by Demir G. and colleagues, the mean PVI before treatment was measured as 30 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). There is no universally accepted normal range for PVI values. However, in a review encompassing 25 studies, the PVI cut-off value was determined to range between 7% and 20% (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In a study conducted by Lu W. et al. to evaluate extracellular fluid volume in mechanically ventilated patients undergoing anesthesia induction, the PVI cut-off value was found to be 14% (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Similarly, in a study by Vos J. et al. assessing fluid responsiveness and dynamic arterial tone in patients undergoing major hepatic resection, the PVI cut-off value was determined to be 12% (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The fact that the mean systolic and diastolic blood pressures of the patients in this study were within normal limits suggests that PVI may be useful in detecting ventilation/perfusion disorders rather than hypovolemia in our patient group, and could be beneficial in monitoring respiratory disease. When evaluating the outcomes of the patients, the mean PVI was 26.93\u0026thinsp;\u0026plusmn;\u0026thinsp;14.59 in survivors and 38.41\u0026thinsp;\u0026plusmn;\u0026thinsp;16.70 in non-survivors, indicating that PVI was significantly higher in non-survivors (p\u0026thinsp;=\u0026thinsp;0.027). When the predictive power for mortality was evaluated, PVI successfully identified PE patients who were likely to die, outperforming other parameters with the highest specificity of 83.78% and the highest positive predictive value (PPV) of 53.8% (cut-off:\u0026gt;40, AUC:0.714, p\u0026thinsp;=\u0026thinsp;0.027). Additionally, it was found that patients with PVI values above the cut-off had shorter survival times compared to those with values below the cut-off (Log rank:0.001; 141.29\u0026thinsp;\u0026plusmn;\u0026thinsp;39.37 days vs. 330.12\u0026thinsp;\u0026plusmn;\u0026thinsp;24.07 days). It was found that mortality increased by 404% when the PVI value exceeded the cut-off (HR:5.04, p\u0026thinsp;=\u0026thinsp;0.009). Considering all these factors, this study has demonstrated that PVI is a strong and independent predictor of mortality in PE.\u003c/p\u003e\u003cp\u003ePI, defined as the ratio of pulsatile to non-pulsatile blood flow, reflects blood flow adequacy and hemodynamic stability, and is useful for prognosis in critically ill patients (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). In PE, right ventricular dysfunction causes hemodynamic collapse and vasoconstriction, while thrombus-induced ventilation/perfusion mismatch reduces peripheral perfusion, lowering PI values. PI is valuable in cardiovascular and pulmonary monitoring in neonatal intensive care, predicting postoperative ICU stay, assessing analgesic efficacy in renal colic, detecting hypovolemic shock in the emergency department, and forecasting prognosis in gastrointestinal bleeding (\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). A recent study by Esen C. et al. on 94 PE patients found that a PI below 1 effectively predicted the need for mechanical ventilation, inotropes, thrombolytics, and mortality (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The researchers suggest evaluating both PI and PVI, which reflects dynamic changes in PI, in PE patients. In this study, the mean automatically measured PI was 2.97\u0026thinsp;\u0026plusmn;\u0026thinsp;2.02, with survivors showing a PI of 3.37\u0026thinsp;\u0026plusmn;\u0026thinsp;2.06 and deceased patients 1.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30 (p\u0026thinsp;=\u0026thinsp;0.011). PI was a strong mortality predictor with 66.7% sensitivity, 75.68% specificity, 47% PPV, and 87.5% NPV (cut-off:\u0026le;1.9, AUC: 0.745, p\u0026thinsp;=\u0026thinsp;0.011). Mortality increased by 469% when the PI fell below the cut-off (HR:5.69, p\u0026thinsp;=\u0026thinsp;0.023). In the study by Esen C. et al., the mean PI was 1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 overall, 1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 in survivors, and 1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 in non-survivors (p\u0026thinsp;=\u0026thinsp;0.034). In this study, PI sensitivity was found to be 59.3%, specificity 59.7%, PPV 37.2%, and NPV 78.4% (cut-off:1, AUC:0.667, p\u0026thinsp;=\u0026thinsp;0.011). The regression analysis revealed that a PI below 1 was associated with mortality (OR:0.861, p\u0026thinsp;=\u0026thinsp;0.042). However, the PESI score was not found to be statistically significant (OR:0.970, p\u0026thinsp;=\u0026thinsp;0.191) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This study confirmed that PI was lower in PE patients who died compared to those who survived. Additionally, this study found that PI had higher sensitivity, specificity, PPV, and NPV compared to the study by Esen C. et al. Furthermore, it was more clearly and with a higher rate demonstrated that PI is an independent and strong predictor of mortality. However, PI was more clearly and more strongly shown to be an independent and strong predictor of mortality. In the survival analysis, it was found that approximately 80% of patients with PI values above the cut-off were alive at the end of one year. Additionally, it was found that patients with PI values below the cut-off had shorter survival times compared to those with values above the cut-off (Log rank:0.002; 123.91\u0026thinsp;\u0026plusmn;\u0026thinsp;24.8 days vs. 336.86\u0026thinsp;\u0026plusmn;\u0026thinsp;24.1 days). There is no study in the literature that includes survival analysis of PI, and this study is the first to evaluate survival in PE patients.\u003c/p\u003e\u003cp\u003eIn this study, consistent with the literature, the average PESI score was higher in deceased patients than in survivors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). In the 1-year survival analysis, all patients below the cut-off survived. Regression analysis showed that a one-unit increase in the PESI score, a strong independent mortality predictor, increased mortality by 2%.\u003c/p\u003e\u003cp\u003eThis study confirmed that lactate levels increased and PaO2 levels decreased in PE patients, consistent with the literature. Additionally, lactate was identified as the best predictor of mortality in PE, with an AUC of 0.827 (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study is the first in the literature to evaluate PVI in PE. It is also the first study to use both PI and PVI in assessing 1-year mortality in PE patients. The researchers believe that the non-invasive and rapid nature of PVI and PI measurements makes them particularly suitable for use in emergency departments, where timely decision-making is critical.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Conclusion\u003c/h2\u003e\u003cp\u003ePE is one of the most critical conditions in the emergency department, requiring rapid diagnosis and appropriate treatment. PI and PVI are suitable for emergency department use due to their easy accessibility, rapid results, noninvasive nature, and the simplicity, ease, and objectivity of their measurement. The PESI score includes other causes of mortality that increase the risk of death, such as advanced age, cancer, chronic lung disease, heart failure history, and altered mental status. Therefore, a high PESI score may also include deaths due to causes other than PE. Furthermore, the necessity of patient history information in the evaluation of the PESI score limits its applicability to patients who are unconscious or unable to provide an anamnesis. PI and PVI measurements can be easily utilized in these patients. This parameter, primarily used to regulate the treatment of fluid loss, was found to be highly sensitive to PE in patients with normal blood pressures. Therefore, the researchers believe that in patients with PE, both PI and PVI, which indicates dynamic changes in PI, should be evaluated. The researchers believe that these non-invasive and radiation-free assessment parameters can guide clinicians during follow-up and treatment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Limitations\u003c/h2\u003e\u003cp\u003eThis study has several limitations. First, although the power analysis showed that the number of patients was sufficient, the sample size was limited due to the single-center design of this study. Secondly, the possible effects of the medications and treatments administered to the patients during their transportation to the hospital were not evaluated. Third, post-treatment repeat measurements were not performed. This limitation is inherent to the nature of emergency departments, where patients are not typically followed up during these periods. This study is the first to evaluate PVI in pulmonary embolism. Further multicenter studies with larger patient cohorts are needed to validate these findings.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003e1. Competing Interests\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e[X]\u0026nbsp;No conflict of interest exists.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e2. Funding\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e[X]\u0026nbsp;No funding was received for this work.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e3. Intellectual Property\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e[X]\u0026nbsp;We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e4. Research Ethics\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e[X]\u0026nbsp;We further confirm that any aspect of the work covered in this manuscript that has involved human patients has been conducted with the ethical approval of all relevant bodies and that such approvals are acknowledged within the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[X]\u0026nbsp;IRB approval was obtained (required for studies and series of 3 or more cases)\u003c/p\u003e\n\u003cp\u003e[X]\u0026nbsp;Written consent to publish potentially identifying information, such as details or the case and photographs, was obtained from the patient(s) or their legal guardian(s).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e5. Ethics Approval and Consent to Participate\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e[X]\u0026nbsp;Ethical approval and consent were obtained from the Clinical Research and Ethics Committee of Suleyman Demirel University Faculty of Medicine.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe committee reference number is 72867572-050.01.04-752964.\u003c/p\u003e\n\u003cp\u003e6. \u003cu\u003eConsent for Publication\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e[X]\u0026nbsp;Not applicable. No identifiable personal data, images, or case details are included in this manuscript that would require explicit consent for publication.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e7. Availability of Data and Materials\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article [and its supplementary information files].\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e8. Acknowledgements\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e[X]\u0026nbsp;The data that support the findings of this study are available from Electronic Document Management System (EBYS) of the Ministry of Health of the Republic of Turkiye but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Electronic Document Management System (EBYS) of the Ministry of Health of the Republic of Turkiye.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e9. Authorship\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe International Committee of Medical Journal Editors (ICMJE) recommends that authorship be based on the following four criteria:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eSubstantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; AND\u003c/li\u003e\n \u003cli\u003eDrafting the work or revising it critically for important intellectual content; AND\u003c/li\u003e\n \u003cli\u003eFinal approval of the version to be published; AND\u003c/li\u003e\n \u003cli\u003eAgreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e[X]\u0026nbsp;All listed authors meet the ICMJE criteria. We attest that all authors contributed significantly to the creation of this manuscript, each having fulfilled criteria as established by the ICMJE.\u003c/p\u003e\n\u003cp\u003e[X]\u0026nbsp;We confirm that the manuscript has been read and approved by all named authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e10. Author\u0026apos;s Contributions\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eTEŞ: Conceptualization, Data Curation, Formal analysis, Methodology, Resources, Software, Investigation, Writing-Original draft preparation.\u003c/p\u003e\n\u003cp\u003eHHA: Conceptualization, Investigation, Project administration, Resources, Supervision, Validation. Visualization.\u003c/p\u003e\n\u003cp\u003eF\u0026Ccedil;O: Formal analysis, Supervision, Writing-Original draft preparation, Writing-Reviewing and Editing.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e11. Contact with the Editorial Office\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe Corresponding Author declared on the title page of the manuscript is:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDr. Teslime ERYAVUZ ŞENG\u0026Uuml;L\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e[X]\u0026nbsp;This author submitted this manuscript using her account in EVISE.\u003c/p\u003e\n\u003cp\u003e[X]\u0026nbsp;We understand that this Corresponding Author is the sole contact for the Editorial process (including EVISE and direct communications with the office). She is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[X]\u0026nbsp;We confirm that the email address shown below is accessible by the Corresponding Author, is the address to which Corresponding Author\u0026rsquo;s EVISE account is linked, and has been configured to accept email from the editorial office of American Journal of Ophthalmology Case Reports:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\[email protected]\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e[X] We understand that this author is the sole contact for the Editorial process (including EVISE and direct communications with the office). He/she is responsible for communicating with the other authors, including the Corresponding Author, about progress, submissions of revisions and final approval of proofs.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEsen CI, Satar S, Gulen M, Acehan S, Sevdımbas S, Ince C. Perfusion index: could it be a new tool for early identification of pulmonary embolism severity? Intern Emerg Med. 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDemir G, Berksoy E, Bardak Ş, Elibol P, \u0026Ccedil;i\u0026ccedil;ek A, \u0026Ouml;z\u0026ouml;n A, et al. Use of the pleth variability index in children with obstructive respiratory disease. Am J Emerg Med. 2022;56:28\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCannesson M, Desebbe O, Rosamel P, Delannoy B, Robin J, Bastien O, et al. Pleth variability index to monitor the respiratory variations in the pulse oximeter plethysmographic waveform amplitude and predict fluid responsiveness in the operating theatre. Br J Anaesth. 2008;101(2):200\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDesgranges FP, Bouvet L, Pereira de Souza Neto E, Evain JN, Terrisse H, Joosten A, et al. Non-invasive measurement of digital plethysmographic variability index to predict fluid responsiveness in mechanically ventilated children: A systematic review and meta-analysis of diagnostic test accuracy studies. Anaesth Crit Care Pain Med. 2023;42(3):101194.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eForget P, Lois F, de Kock M. Goal-directed fluid management based on the pulse oximeter-derived pleth variability index reduces lactate levels and improves fluid management. Anesth Analg. 2010;111(4):910\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu Y, Dong J, Xu Z, Shen H, Zheng J. Pleth variability index-directed fluid management in abdominal surgery under combined general and epidural anesthesia. J Clin Monit Comput. 2015;29(1):47\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlbayrak T, Yuksel B. Prognostic value of Pleth Variability Index in patients followed up in the Intensive Care Unit. Eur Rev Med Pharmacol Sci. 2024;28(4):1392\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu T, Xu C, Wang M, Niu Z, Qi D. Reliability of pleth variability index in predicting preload responsiveness of mechanically ventilated patients under various conditions: a systematic review and meta-analysis. BMC Anesthesiol. 2019;19(1):67.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu W, Dong J, Xu Z, Shen H, Zheng J. The pleth variability index as an indicator of the central extracellular fluid volume in mechanically ventilated patients after anesthesia induction: comparison with initial distribution volume of glucose. Med Sci Monit. 2014;20:386\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVos JJ, Kalmar AF, Struys MMRF, Wietasch JKG, Hendriks HGD, Scheeren TWL. Comparison of arterial pressure and plethysmographic waveform-based dynamic preload variables in assessing fluid responsiveness and dynamic arterial tone in patients undergoing major hepatic resection. Br J Anaesth. 2013;110(6):940\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePinto Lima A, Beelen P, Bakker J. Use of a peripheral perfusion index derived from the pulse oximetry signal as a noninvasive indicator of perfusion. Crit Care Med. 2002;30(6):1210\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePiasek CZ, Van Bel F, Sola A. Perfusion index in newborn infants: a noninvasive tool for neonatal monitoring. Acta Paediatr. 2014;103(5):468\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShi X, Xu M, Yu X, Lu Y. Peripheral perfusion index predicting prolonged ICU stay earlier and better than lactate in surgical patients: an observational study. BMC Anesthesiol. 2020;20(1):153.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGulen M, Satar S, Acehan S, Yildiz D, Aslanturkiyeli EF, Aka Satar D, et al. Perfusion index versus visual analogue scale: as an objective tool of renal colic pain in emergency department. Heliyon. 2022;8(9):e10606.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOzakin E, Yazlamaz NO, Kaya FB, Karakilic EM, Bilgin M. Perfusion Index Measurement in Predicting Hypovolemic Shock in Trauma Patients. J Emerg Med. 2020;59(2):238\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFirat BT, Gulen M, Satar S, Firat A, Acehan S, Isikber C, et al. Perfusion index: Could this be a new triage tool for upper gastrointestinal system bleeding in the emergency department? A prospective cohort study. Sao Paulo Med J. 2021;139(6):583\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou XY, Ben SQ, Chen HL, Ni SS. The prognostic value of pulmonary embolism severity index in acute pulmonary embolism: a meta-analysis. Respir Res. 2012;13(1):111.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, Yang H, Qiao L, Tan Z, Jin J, Yang J, et al. The predictive value of PaO2/FIO2 and additional parameters for in-hospital mortality in patients with acute pulmonary embolism: an 8-year prospective observational single-center cohort study. BMC Pulm Med. 2019;19(1):242.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, Feng Y, Yang X, Mao H. Prognostic role of elevated lactate in acute pulmonary embolism: A systematic review and meta-analysis. Phlebology. 2022;37(5):338\u0026ndash;47.\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-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pulmonary embolism, perfusion index, pleth variability index","lastPublishedDoi":"10.21203/rs.3.rs-7432049/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7432049/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePulmonary embolism(PE) is a ventilation/perfusion disorder due to obstruction of the pulmonary artery, typically by a thrombus. The Pleth Variability Index(PVI) is a noninvasive measure reflecting dynamic changes in the perfusion index(PI), observed through photoplethysmography over at least one respiratory cycle. This study aims to investigate the prognostic value of plethysmographic parameters in patients with PE, hypothesizing that these parameters may serve as a reliable guide in a condition where impaired perfusion plays a critical role.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eData were collected from patients over 18 years old, diagnosed with PE in the emergency department, including demographic information, risk scores, and measurements. PVI and PI values were analyzed across mortality groups. A survival analysis was performed to assess the impact of PVI and PI on 1-year mortality. The study followed the STROBE checklist for evaluation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003e49-patients were included in this prospective, single-center study. The PVI was significantly higher, and PI was significantly lower in the deceased group compared to the survivors(p\u0026thinsp;=\u0026thinsp;0.027, p\u0026thinsp;=\u0026thinsp;0.011). AUC values for PVI, PESI score, and PI were 0.714, 0.820, and 0.745, respectively. The negative predictive value of PESI was 100%, while PVI showed the highest positive predictive value(53.5%). The mean survival time was significantly shorter for patients with PVI\u0026thinsp;\u0026gt;\u0026thinsp;40 and PI\u0026thinsp;\u0026lt;\u0026thinsp;1.9(p\u0026thinsp;=\u0026thinsp;0.001, p\u0026thinsp;=\u0026thinsp;0.002). An increase in PVI was associated with a 4.04-fold higher risk of death(HR:5.04, 95% CI:1.50-16.92, p\u0026thinsp;=\u0026thinsp;0.009).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003ePVI is a noninvasive, rapid, and objective tool for predicting mortality in PE patients in the emergency department. This study is the first to evaluate PVI in PE.\u003c/p\u003e\u003ch2\u003eTrial registration:\u003c/h2\u003e\u003cp\u003eClinicalTrials.gov Identifier: NCT06508112. 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