The ratio of shock index to pulse oxygen saturation predicting mortality of emergency trauma patients

Qi, Junfang; Ding, Li; Bao, Lei; Du, Chen

doi:10.1371/journal.pone.0236094

Cited by 9 publications

(9 citation statements)

References 25 publications

(48 reference statements)

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“…In this study, SpO2 values and the presence of oxygen administration were added to the characteristics with reference to the NEWS. The literature on the relationship between SpO2 and the prognosis of trauma patients is limited, and the results have been inconsistent 17 . Some studies have reported that SpO2 values are not a predictor of mortality in trauma patients upon arrival at the hospital 18 , while others have reported that they are 1920 .…”

Section: Discussionmentioning

confidence: 99%

Development of a Machine Learning Model to Predict Cardiac Arrest during Transport of Trauma Patients

et al. 2023

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Background: Trauma is a serious medical and economic problem worldwide, and patients with trauma injuries have a poor survival rate following cardiac arrest. This study aimed to create a prediction model specific to prehospital trauma care and to achieve greater accuracy with techniques of machine learning.Methods: This retrospective observational study investigated data of patients who had blunt trauma injuries due to traffic accident and fall trauma from January 1, 2018, to December 31, 2019, using the National Emergency Medical Services Information System, which stores emergency medical service activity records nationwide in the United States. Random forest was used to develop a machine learning model. Results:Per the prediction model, the area under the curve of the predictive model was 0.95 and negative predictive value was 0.99. The feature importance of the predictive model was the highest for the AVPU scale (an acronym from "Alert, Verbal, Pain, Unresponsive"), followed by oxygen saturation (SpO2). Among patients who were progressing to cardiac arrest, the cutoff value was 89% for SpO2 in unalert patients.Conclusions: Patients whose conditions did not progress to cardiac arrest could be identified with high accuracy by machine learning model techniques.

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Section: Discussionmentioning

confidence: 99%

Development of a Machine Learning Model to Predict Cardiac Arrest during Transport of Trauma Patients

et al. 2023

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“…To assess the performance of oxygen perfusion without laboratory facilities, the only parameter that can be used clinically is oxygen saturation (SpO 2 ). Oxygen saturation in critically ill patients may reflect microcirculatory conditions, which will also be associated with laboratory markers such as lactate and acid-base deficiency [ 32 ]. Imanieh et al are also in line with this study, integrating oxygen saturation as one of the scoring categories [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

“…Du et al examined COVID-19 patients based on clinical and laboratory criteria and revealed some severe complaints and comorbidities. The five most common complaints in COVID-19 patients were fever (98.9%), dry cough (81.6%), dyspnea (49.7%), fatigue (39.1%), and productive cough (30.7%) [ 32 ]. On the other hand, headaches, fatigue, and productive coughs were more common in the survivor group.…”

Section: Discussionmentioning

confidence: 99%

Hemodynamic, Oxygenation and Lymphocyte Parameters Predict COVID-19 Mortality

Windradi,

Asmarawati,

Rosyid

et al. 2023

Pathophysiology

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The mortality of COVID-19 patients has left the world devastated. Many scoring systems have been developed to predict the mortality of COVID-19 patients, but several scoring components cannot be carried out in limited health facilities. Herein, the authors attempted to create a new and easy scoring system involving mean arterial pressure (MAP), PF Ratio, or SF ratio-respiration rate (SF Ratio-R), and lymphocyte absolute, which were abbreviated as MPL or MSLR functioning, as a predictive scoring system for mortality within 30 days for COVID-19 patients. Of 132 patients with COVID-19 hospitalized between March and November 2021, we followed up on 96 patients. We present bivariate and multivariate analyses as well as the area under the curve (AUC) and Kaplan–Meier charts. From 96 patients, we obtained an MPL score of 3 points: MAP < 75 mmHg, PF Ratio < 200, and lymphocyte absolute < 1500/µL, whereas the MSLR score was 6 points: MAP < 75 mmHg, SF Ratio < 200, lymphocyte absolute < 1500/µL, and respiration rate 24/min. The MPL cut-off point is 2, while the MSLR is 4. MPL and MSLR have the same sensitivity (79.1%) and specificity (75.5%). The AUC value of MPL vs. MSLR was 0.802 vs. 0.807. The MPL ≥ 2 and MSLR ≥ 4 revealed similar predictions for survival within 30 days (p < 0.05). Conclusion: MPL and MSLR scores are potential predictors of mortality in COVID-19 patients within 30 days in a resource-limited country.

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“…12 Similarly, respiratory adjusted shock index (RASI) and shock index to SpO 2 ratio (SS) have been validated in acute respiratory distress syndrome (ARDS) and community-acquired pneumonia patients to predict the need for mechanical ventilation and hospitalization. 13,14 Jiang and colleagues have successfully applied RASI in sepsis for identifying occult shock and subsequent triage of the patients for the level of care required. 13 Adjusting the cutoff appropriate for age, Ray and colleagues 15 highlighted a better predictive efficacy of on admission SI pediatric age-adjusted (SIPA) over the conventional hemodynamic parameters (SBP, HR) and positive correlation with arterial lactate level.…”

Section: Encouraging Literatures Diverse Patient Populationsmentioning

confidence: 99%

Shock Index in COVID Era

Dey

Magoon

Kohli

et al. 2021

Journal of Cardiac Critical Care TSS

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The health care burden and risks to health care workers imposed by novel coronavirus disease 2019 (COVID-19) mandated the need for a simple, noninvasive, objective, and parsimonious risk stratification system predicting the level of care, need for definitive airway, and titration of the ongoing patient care. Shock index (SI = heart rate/systolic blood pressure) has been evaluated in emergency triage, sepsis, and trauma settings including different age group of patients. The ever accumulating girth of evidences demonstrated a superior predictive value of SI over other hemodynamic parameters. Inclusion of respiratory and/or neurological parameters and adjustment of the cutoffs appropriate to patient age increase the predictability in the trauma and sepsis scenario. Being reproducible, dynamic, and simple, SI can be a valuable patient risk stratification tool in this ongoing era of COVID-19 pandemic.

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The ratio of shock index to pulse oxygen saturation predicting mortality of emergency trauma patients

Cited by 9 publications

References 25 publications

Development of a Machine Learning Model to Predict Cardiac Arrest during Transport of Trauma Patients

Development of a Machine Learning Model to Predict Cardiac Arrest during Transport of Trauma Patients

Hemodynamic, Oxygenation and Lymphocyte Parameters Predict COVID-19 Mortality

Shock Index in COVID Era

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