Shock index, modified shock index, and age shock index for prediction of mortality in Emergency Severity Index level 3

Age shock index as an early predictor of cardiovascular death in acute coronary syndrome patients

BACKGROUND

The shock index (SI), reflecting heart rate (HR) to SBP ratio, is established for predicting adverse outcomes in acute coronary syndrome (ACS) patients. Exploring the age shock index (ASI), obtained by multiplying SI with age, could offer further insights into ACS prognosis.

OBJECTIVES

Assess ASI's effectiveness in predicting in-hospital death in individuals with ACS.

METHODS

This study encompassed patients with acute myocardial infarction, drawn from a national registry spanning October 2010 to January 2022. The optimal ASI threshold was established using receiver operating characteristic (ROC) curve analysis. The primary outcome was in-hospital mortality.

RESULTS

A total of 27 312 patients were enrolled, exhibiting a mean age of 66 ± 13 years, with 72.3% being male and 47.5% having ST-elevation myocardial infarction. ROC analysis yielded an area under the curve (AUC) of 0.80, identifying the optimal ASI cutoff as 44. Multivariate regression analysis, adjusting for potential confounders, established ASI ≥ 44 as an independent predictor of in-hospital death [hazard ratio: 3.09, 95% confidence interval: 2.56-3.71, P  < 0.001]. Furthermore, ASI emerged as a notably superior predictor of in-hospital death compared to the SI (AUC ASI  = 0.80 vs. AUC SI  = 0.72, P  < 0.0001), though it did not outperform the Global Registry of Acute Coronary Events (GRACE) score (AUC ASI  = 0.80 vs. AUC GRACE  = 0.85, P  < 0.001) or thrombolysis in myocardial infarction (TIMI) risk index (AUC ASI  = 0.80 vs. AUC TIMI  = 0.84, P  < 0.001).

CONCLUSION

The ASI offers an expedient mean to promptly identify ACS patients at elevated risk of in-hospital death. Its simplicity and effectiveness could render it a valuable tool for early risk stratification in this population.

Hyperkalaemia in bleeding trauma patients: A potential marker of disease severity - A retrospective cohort study

Background

Hyperkalaemia is a common electrolyte abnormality seen in critically ill patients. In haemorrhagic shock, it may contribute to cardiac arrest and has been identified as a potential marker for tissue hypoxia. However, the significance of its role in haemorrhagic shock and its contribution to mortality remains unclear. This study aimed to examine the potential underlying pathophysiology and evaluate the incidence and characteristics of patients with hyperkalaemia on hospital arrival in bleeding trauma patients before transfusions and its mortality.

Methods

A retrospective cohort study was conducted on adult patients with traumatic bleeding admitted to a European Major Trauma Centre between January 2016 and December 2021. Patients were classified according to their serum potassium levels on arrival, and relevant clinical parameters between non-hyperkalaemic and hyperkalaemic patients were compared.

Results

Among the 83 patients in this study, 8 (9.6 %) presented with hyperkalaemia on arrival. The median shock index showed a higher tendency in the hyperkalaemic group. Hyperkalaemia was found to be more common among younger patients who sustained penetrating trauma. Mortality rates were higher in the hyperkalaemic group, but the difference was not statistically significant.

Conclusion

Our results suggest that hyperkalaemia occurs frequently in bleeding trauma patients on hospital arrival pre-transfusions, indicating a more severe illness. Our findings provide insights into the pathophysiology and characteristics of hyperkalaemia in bleeding trauma patients. Further studies are required to investigate the mechanisms by which hyperkalaemia contributes to mortality in haemorrhagic shock patients.

The Shock Index among Children Presenting to the Emergency Department: Analysis of Nationally Representative Sample

BACKGROUND

The Shock Index (SI) is emerging as a potentially useful measure among children with injury or suspected sepsis.

OBJECTIVE

The aim of this study was to evaluate the distribution of the SI and evaluate its association with clinical outcomes among all children presenting to the emergency department (ED).

METHODS

A complex survey of nonfederal U.S. ED encounters from 2016 through 2021 was analyzed. Among children, the Pediatric Age-Adjusted Shock Index (SIPA), Pediatric Shock Index (PSI), and the Temperature- and Age-Adjusted Shock Index (TAMSI) were analyzed. The association of these criteria with disposition, acuity, medication administration, diagnoses and procedures was analyzed.

RESULTS

A survey-weighted 81.5 million ED visits were included for children aged 4-16 years and 117.2 million visits were included for children aged 1-12 years. SI could be calculated for 78.6% of patients aged 4-16 years and 57.9% of patients aged 1-12 years. An abnormal SI was present in 15.9%, 11.1%, and 31.7% when using the SIPA, PSI, and TAMSI, respectively. With all criteria, an elevated SI was associated with greater hospitalization. The SIPA and PSI were associated with triage acuity. All criteria were associated with medical interventions, including provision of IV fluids and acquisition of blood cultures.

CONCLUSIONS

An elevated SI is indicative of greater resource utilization needs among children in the ED. When using any criteria, an elevated SI was associated with clinically important outcomes. Further research is required to evaluate the distribution of the SI in children and to investigate its potential role within existing triage algorithms for children in the ED.

RELATIONSHIP BETWEEN SHOCK INDEX, MODIFIED SHOCK INDEX, AND AGE SHOCK INDEX AND 28-DAY MORTALITY AMONG PATIENTS WITH PREHOSPITAL SEPTIC SHOCK

BACKGROUND

A relative hypovolemia occurs during septic shock (SS); the early phase is clinically reflected by tachycardia and low blood pressure. In the prehospital setting, simple objective tools to assess hypovolemia severity are needed to optimize triaging.

OBJECTIVE

The aim of this study was to evaluate the relationship between shock index (SI), diastolic SI (DSI), modified SI (MSI), and age SI (ASI) and 28-day mortality of patients with SS initially cared for in a prehospital setting of a mobile intensive care unit (MICU).

METHODS

From April 6, 2016 through December 31, 2021, 530 patients with SS cared for at a prehospital MICU were analyzed retrospectively. Initial SI, MSI, DSI, and ASI values, that is, first measurement after MICU arrival to the scene were calculated. A propensity score analysis with inverse probability of treatment weighting (IPTW) method was used to assess the relationship between SI, DSI, MSI, and ASI and 28-day mortality.

RESULTS

SS resulted mainly from pulmonary, digestive, and urinary infections in 44%, 25%, and 17% of patients. The 28-day overall mortality was 31%. IPTW propensity score analysis indicated a significant relationship between 28-day mortality and SI (adjusted odds ratio [aOR] 1.13; 95% CI 1.01-1.26; p = 0.04), DSI (aOR 1.16; 95% CI 1.06-1.34; p = 0.03), MSI (aOR 1.03; 95% CI 1.01-1.17; p = 0.03), and ASI (aOR 3.62; 95% CI 2.63-5.38; p < 10-6).

CONCLUSIONS

SI, DSI, MSI, and ASI were significantly associated with 28-day mortality among patients with SS cared for at a prehospital MICU. Further studies are needed to confirm the usefulness of SI and SI derivates for prehospital SS optimal triaging.

Vital indicators in predicting burn mortality: A comparison of shock indices and burn shock indices

Background/aim

In many studies, shock indices have proven to be good tools for predicting mortality. In the present study, burn shock index (BSI), percentage of total body surface area burned (TBSA%) multiplied by shock index; burn modified shock index (BMSI), TBSA% multiplied by modified shock index; burn age shock index (BASI), TBSA% multiplied by age shock index; burn rivers shock index (BrSI), TBSA% multiplied by rivers shock index; burn rivers shock index multiplied by Glasgow Coma Scale score (BrSIG) were examined in burn patients. We defined these burn shock indices for the first time. This study aimed to evaluate the effectiveness of shock indices and burn shock indices in predicting mortality in burn patients.

Materials and methods

This study examines retrospectively of burn patients admitted to the emergency department of Dicle University Hospital between January 2010 and December 2022. The patients' vital signs were obtained at the time of presentation to the emergency department, and shock indices were calculated. The effectiveness of shock indices in predicting mortality was compared.

Results

A total of 2445 patients were included in the study. Of the patients, 1793 were pediatric, and 652 were adults. BSI (AUC: 0.872, 95% confidence interval (CI): 0.812-0.931, p < 0.001) had the highest area under the curve (AUC) value in predicting mortality in children. The optimal cut-off value for BSI in children was 21.79 and its was sensitivity 83.05%, specificity 79.64%, positive predictive value (PPV) 12.19%, negative predictive value (NPV) 99.28%. In adults, BASI had the highest value of AUC (AUC: 0.936, 95% CI: 0.887-0.984, p < 0.001). The optimal cut-off value for BASI in adults was 62.5 and its sensitivity was 86.49%, specificity was 91.71%, PPV was 38.55%, and NPV was 99.12%.

Conclusion

Shock indices are easy to calculate and effective in predicting mortality in burn patients admitted to the emergency department. Among the shock indices in the study, BSI was the best in predicting mortality in children, and BASI was the best in adults.

The new combination of oxygen saturation with age shock index predicts the outcome of COVID-19 pneumonia

Introduction

Emergency departments around the world have been struggling to deal with patients with COVID-19 and presumed COVID-19. Triaging patients who need further medical support is the key matter to emergency physicians as the delay of proper treatment may worsen the results. The aim of this study was to validate the ability of age shock index and hypoxia-age-shock index at the time of presentation to the emergency department to predict case fatality in patients with COVID-19 pneumonia.

Methods

We only included patients who had COVID-19-associated pneumonia who needed in-hospital treatment. The vital signs and oxygen saturation used in the study were collected, especially from the triage sector, before patients were given supplemental oxygen.

Results

A total of 241 patients enrolled in the study. The case fatality rate was 27%. The median age of the study samples was 78 (66-86) years with 133 male and 108 female patients. Hypoxia-age-shock index showed the best performance in analysis (odds ratio 15.1, 95% confidence interval: 5.1-44.4; adjusted odds ratio 8.6, 95% confidence interval: 2.8-26.8).

Conclusion

The hypoxia-age-shock index was a strong predictor for in-hospital mortality of COVID-19 pneumonia. Furthermore, when it was compared with age shock index, hypoxia-age-shock index showed better performance in predicting fatality of the disease.

Predicting acute clinical deterioration with interpretable machine learning to support emergency care decision making

The emergency department (ED) is a fast-paced environment responsible for large volumes of patients with varied disease acuity. Operational pressures on EDs are increasing, which creates the imperative to efficiently identify patients at imminent risk of acute deterioration. The aim of this study is to systematically compare the performance of machine learning algorithms based on logistic regression, gradient boosted decision trees, and support vector machines for predicting imminent clinical deterioration for patients based on cross-sectional patient data extracted from electronic patient records (EPR) at the point of entry to the hospital. We apply state-of-the-art machine learning methods to predict early patient deterioration, based on their first recorded vital signs, observations, laboratory results, and other predictors documented in the EPR. Clinical deterioration in this study is measured by in-hospital mortality and/or admission to critical care. We build on prior work by incorporating interpretable machine learning and fairness-aware modelling, and use a dataset comprising 118, 886 unplanned admissions to Salford Royal Hospital, UK, to systematically compare model variations for predicting mortality and critical care utilisation within 24 hours of admission. We compare model performance to the National Early Warning Score 2 (NEWS2) and yield up to a 0.366 increase in average precision, up to a [Formula: see text] reduction in daily alert rate, and a median 0.599 reduction in differential bias amplification across the protected demographics of age and sex. We use Shapely Additive exPlanations to justify the models' outputs, verify that the captured data associations align with domain knowledge, and pair predictions with the causal context of each patient's most influential characteristics. Introducing our modelling to clinical practice has the potential to reduce alert fatigue and identify high-risk patients with a lower NEWS2 that might be missed currently, but further work is needed to trial the models in clinical practice. We encourage future research to follow a systematised approach to data-driven risk modelling to obtain clinically applicable support tools.

Discriminant ability of the shock index, modified shock index, and reverse shock index multiplied by the Glasgow coma scale on mortality in adult trauma patients: a PATOS retrospective cohort study

BACKGROUND

The shock index (SI) predicts short-term mortality in trauma patients. Other shock indices have been developed to improve discriminant accuracy. The authors examined the discriminant ability of the SI, modified SI (MSI), and reverse SI multiplied by the Glasgow Coma Scale (rSIG) on short-term mortality and functional outcomes.

METHODS

The authors evaluated a cohort of adult trauma patients transported to emergency departments. The first vital signs were used to calculate the SI, MSI, and rSIG. The areas under the receiver operating characteristic curves and test results were used to compare the discriminant performance of the indices on short-term mortality and poor functional outcomes. A subgroup analysis of geriatric patients with traumatic brain injury, penetrating injury, and nonpenetrating injury was performed.

RESULTS

A total of 105 641 patients (49±20 years, 62% male) met the inclusion criteria. The rSIG had the highest areas under the receiver operating characteristic curve for short-term mortality (0.800, CI: 0.791-0.809) and poor functional outcome (0.596, CI: 0.590-0.602). The cutoff for rSIG was 18 for short-term mortality and poor functional outcomes with sensitivities of 0.668 and 0.371 and specificities of 0.805 and 0.813, respectively. The positive predictive values were 9.57% and 22.31%, and the negative predictive values were 98.74% and 89.97%. rSIG also had better discriminant ability in geriatrics, traumatic brain injury, and nonpenetrating injury.

CONCLUSION

The rSIG with a cutoff of 18 was accurate for short-term mortality in Asian adult trauma patients. Moreover, rSIG discriminates poor functional outcomes better than the commonly used SI and MSI.

A Comparative Study on Predictive Validity of Modified Shock Index, Shock Index, and Age Shock Index in Predicting the Need for Mechanical Ventilation among Sepsis Patients in a Tertiary Care Hospital

Introduction

The shock index (SI), modified shock index (MSI), and age multiplied by SI (ASI) are used to assess the severity of shock. They are also used to predict the mortality of trauma patients, but their validity for sepsis patients is controversial. The aim of this study is to assess the predictive value of the SI, MSI, and ASI in predicting the need for mechanical ventilation after 24 h of admission among sepsis patients.

Methods

A prospective observational study was conducted in a tertiary care teaching hospital. Patients with sepsis (235) diagnosed based on systemic inflammatory response syndrome criteria and quick sequential organ failure assessment were included in the study. The need for mechanical ventilation after 24 h is the outcome variables MSI, SI, and ASI were considered as predictor variables. The utility of MSI, SI, and ASI in predicting mechanical ventilation was assessed by receiver operative curve analysis. Data were analyzed using coGuide.

Results

Among the study population, the mean age was 56.12 ± 17.28 years. MSI value at the time of disposition from the emergency room had good predictive validity in predicting mechanical ventilation after 24 h, as indicated by the area under the curve (AUC) of 0.81 (P < 0.001), SI and ASI had fair predictive validity for mechanical ventilation as indicated by AUC (0.78, P < 0.001) and (0.802, P < 0.001), respectively.

Conclusion

SI had better sensitivity (78.57%) and specificity (77.07%) compared to ASI and MSI in predicting the need for mechanical ventilation after 24 h in sepsis patients admitted to intensive care units.

The relationship between age shock index, and severity of stroke and in-hospital mortality in patients with acute ischemic stroke

BACKGROUND

Shock index (SI) has been reported to help us predict adverse prognosis in patients with acute ischemic stroke (AIS). However, the prognostic value of age SI and age modified shock index (MSI) in acute ischemic stroke is unknown. In our study, we aimed to examine the association between the severity of the stroke and in-hospital mortality, age SI and age MSI in patients with AIS.

METHODS

A total of 256 patients were enrolled in this study. The National Institutes of Health Stroke Scale (NIHSS) was used to determine the severity of stroke. Patients were divided into two groups according to the NIHSS score calculated during hospitalization (NIHSS>14: severe disability group, NIHSS<15: moderate and mild disability group). Shock indexes were calculated using the blood pressure and heart rate values measured as a result of the cardiovascular examinations of the patients. We looked for correlations between increased NIHSS and in-hospital mortality with age shock index and age modified shock index.

RESULTS

Age SI and age MSI values were higher in the severe disability group than those without severe disability, and the results were statistically significant (p<0.001, p<0.001, respectively). Also, a positive correlation was determined between the height of NIHSS and the age SI and the age MSI (p=0.002, r=0.197, p=0.001, r=0.215, respectively). Thirty-two (12.5%) of 256 patients included in the study died during hospitalization. Patients who died were older (77.1±11.0 vs. 67.5±13.5, respectively; p<0.001). According to Point-Biserial correlation analysis, there was a positive correlation between mortality and age SI, and age MSI (p<0.001, r=0.258 ve p<0.001, r=0.274, respectively).

CONCLUSIONS

As a result of our study, the relationship between stroke severity and increasing age SI and age MSI was significant and there was a positive correlation. In addition, there was a significant and positive relationship between in-hospital mortality and age SI and age MSI.

Patient disposition using the Emergency Severity Index: a retrospective observational study at an interdisciplinary emergency department

OBJECTIVES

Early patient disposition is crucial to prevent crowding in emergency departments (EDs). Our study aimed to characterise the need of in-house resources for patients treated in the ED according to the Emergency Severity Index (ESI) and the presenting complaint at the timepoint of triage.

DESIGN

A retrospective single-centre study was conducted.

SETTING

Data of all patients who presented to the interdisciplinary ED of a tertiary care hospital in Munich, Germany, from 2014 to 2017 were analysed.

PARTICIPANTS

n=113 694 patients were included.

MEASURES

ESI Score, medical speciality according to the chief complaint, mode of arrival, admission rates and discharge destination from the ED were evaluated.

RESULTS

Patient disposition varied according to ESI scores in combination with the chief complaint. Patients with low ESI scores were more likely to be admitted after treatment in the ED than patients with high ESI scores. Highly prioritised patients (ESI 1) mainly required admission to an intensive care unit (ICU, 27%), intermediate care unit (IMC, 37%) or immediate intervention (11%). In this critical patient group, 30% of patients with neurological or medical symptoms required immediate intensive care, whereas only 17% of patients with surgical problems were admitted to an ICU. A significant number of patients (particularly with neurological or medical problems) required hospital (and in some cases even ICU or IMC) admission despite high ESI scores.

CONCLUSIONS

Overall, ESI seems to be a useful tool to anticipate the need for specialised in-hospital resources on arrival. Patients with symptoms pointing at neurological or medical problems need particular attention as ESI may fail to sufficiently predict the care facility level for this patient group.

Prognostic Performance of Shock Index, Diastolic Shock Index, Age Shock Index, and Modified Shock Index in COVID-19 Pneumonia

OBJECTIVE

We aimed to compare the prognostic accuracy of shock indexes in terms of mortality in patients hospitalized with coronavirus disease 2019 (COVID-19) pneumonia.

METHODS

Hospitalized patients whose COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR) test results were positive, had thoracic computed tomography (CT) scan performed, and had typical thoracic CT findings for COVID-19 were included in the study.

RESULTS

Eight hundred one patients were included in the study. Chronic obstructive pulmonary disease, congestive heart failure, chronic neurological diseases, chronic renal failure, and a history of malignancy were found to be chronic diseases that were significantly associated with mortality in patients with COVID-19 pneumonia. White blood cell, neutrophil, lymphocyte, C reactive protein, creatinine, sodium, aspartate aminotransferase, alanine aminotransferase, total bilirubin, high sensitive troponin, d-dimer, hemoglobin, and platelet had a statistically significant relationship with in-hospital mortality in patients with COVID-19 pneumonia. The area under the curve (AUC) values of shock index (SI), age shock index (aSI), diastolic shock index (dSI), and modified shock index (mSI) calculated to predict mortality were 0.772, 0.745, 0.737, 0.755, and Youden Index J (YJI) values were 0.523, 0.396, 0.436, 0.452, respectively.

CONCLUSIONS

The results of this study show that SI, dSI, mSI, and aSI are effective in predicting in-hospital mortality.

Modified Shock Index as a Predictor for Mortality and Hospitalization Among Patients With Dementia

BACKGROUND

One in four older adults in the Emergency Department (ED) suffers from severe cognitive impairment, creating great difficulty for the emergency physicians who determine the urgency of their patients' condition, which informs decisions regarding discharge or hospitalization.

OBJECTIVE

Our objective was to determine whether modified shock index (MSI) can be a clinical mortality and hospitalization predictor when applied to older patients with dementia in the ED.

METHODS

Included in the research were all patients with dementia, > 65 years old, who arrived at the Soroka University Medical Center ED during 2014-2017. The population was divided into three groups according to their MSI score, calculated as heart rate/mean arterial pressure: MSI < 0.7; 0.7 > MSI < 1.3; and MSI > 1.3. We performed multivariable logistic regression as a predictor of death within 30 days, Cox analysis for number of days to death, and a negative binominal regression for predicting the number of admission days.

RESULTS

Included were 1437 patients diagnosed with dementia. Patients with an MSI > 1.3 vs. those with MSI < 0.7 had an odds ratio of 8.23 (95% confidence interval [CI] 4.64-4.54) for mortality within 30 days, increased mortality risk within 180 days (hazard ratio 4.42; 95% CI 2.64-7.41), and longer hospitalization duration (incidence rate ratio 1.8; 95% CI 1.32-2.45).

CONCLUSIONS

High MSI scores were associated with high mortality rates and longer hospitalization duration for patients diagnosed with dementia who were > 65 years old. We suggest performing prospective studies utilizing the MSI score as an indicator in ED triage settings to classify patients with dementia by their severity of risk, to determine if this benefits health, minimizes expenses, and prevents unnecessary hospitalizations.

Outcomes of Trauma Patients Present to the Emergency Department with a Shock Index of ≥1.0

Introduction

The study aimed primarily to evaluate the association between the initial shock index (SI) ≥1.0 with blood transfusion requirement in the emergency department (ED) after acute trauma. The study's secondary aim was to look at the outcomes regarding patients' disposition from ED, intensive care unit (ICU) and hospital length of stay, and deaths.

Methods

It was a retrospective, cross-sectional study and utilized secondary data from the Saudi Trauma Registry (STAR) between September 2017 and August 2020. We extracted the data related to patient demographics, mechanism of injuries, the intent of injuries, mode of arrival at the hospital, characteristics on presentation to ED, length of stay, and deaths from the database and compared between two groups of SI <1.0 and SI ≥1.0. A P < 0.05 was statistically considered significant.

Results

Of 6667 patients in STAR, 908 (13.6%) had SI ≥1.0. With SI ≥1.0, there was a significantly higher incidence of blood transfusion in ED compared to SI <1.0 (8.9% vs. 2.4%, P < 0.001). Furthermore, SI ≥ 1.0 was associated with significant ICU admission (26.4% vs. 12.3%, P < 0.001), emergency surgical intervention (8.5% vs. 2.8%, P < 0.001), longer ICU stay (5.0 ± 0.36 vs. 2.2 ± 0.11days, P < 0.001), longer hospital stays (14.8 ± 0.61 vs. 13.3 ± 0.24 days, P < 0.001), and higher deaths (8.4% vs. 2.8%, P < 0.001) compared to the patient with SI <1.0.

Conclusions

In our cohort, a SI ≥ 1.0 on the presentation at the ED carried significantly worse outcomes. This simple calculation based on initial vital signs may be used as a screening tool and therefore incorporated into initial assessment protocols to manage trauma patients.

Modified Shock Index as an Indicator for Prognosis Among Sepsis Patients With and Without Comorbidities Presenting to the Emergency Department

OBJECTIVES

Modified shock index (MSI) is a simple bedside tool used in the emergency department. There are a few studies suggesting MSI as a good prognostic indicator than shock index in sepsis patients. However, there is not enough research emphasizing the role of MSI in patients with comorbidities. Hence, this study aims to assess the predictive validity of MSI in predicting the prognosis of sepsis patients with and without co-morbidities.

METHODS

From January to December 2020, a prospective observational study was conducted in a tertiary care teaching hospital. Patients with sepsis diagnosed based on systemic inflammatory response syndrome criteria and quick sequential organ failure assessment (qSOFA) were included. The need for mechanical ventilation and step down from the intensive care unit were outcome variables, MSI was considered as a predictor variable, and co-morbidities as an explanatory variable.

RESULTS

Among people with co-morbidities, the MSI value on arrival to the emergency department had fair predictive validity in predicting the need for mechanical ventilation after 24 hours, as indicated by the area under the curve of 0.749 (95% CI: 0.600-0.897; p-value = 0.002) and a sensitivity of 68.75% in predicting mechanical ventilation after 24 hours (MSI ≥ 1.59). Among people without co-morbidities, the MSI value on arrival to the emergency department had fair predictive validity in predicting the need for mechanical ventilation after 24 hours, as indicated by the area under the curve of 0.879 (95% CI: 0.770-0.988; p-value <0.001) and a sensitivity of 83.33% in predicting the need for mechanical ventilation after 24 hours (MSI ≥ 1.67).

CONCLUSION

MSI can be used as an indicator in predicting the prognosis of sepsis patients in the emergency department. A simple bedside calculation of the MSI can indicate the need for mechanical ventilation and step down from the intensive care unit after 24 hours in patients with co-morbidities and without co-morbidities.

Vasopressin Loading for Refractory Septic Shock: A Preliminary Analysis of a Case Series

Background: Vasopressin is one of the strong vasopressor agents associated with ischemic events. Responses to the administration of vasopressin differ among patients with septic shock. Although the administration of a high dose of vasopressin needs to be avoided, the effects of bolus loading have not yet been examined. Since the half-life of vasopressin is longer than that of catecholamines, we hypothesized that vasopressin loading may be effective for predicting responses to its continuous administration. Methods: We retrospectively analyzed consecutive cases of septic shock for which vasopressin was introduced with loading under noradrenaline at >0.2 μg/kg/min during the study period. Vasopressin was administered in a 1 U bolus followed by its continuous administration at 1 U/h. The proportion of patients with a negative catecholamine index (CAI) change 6 h after the introduction of vasopressin was set as the primary outcome. We defined non-responders for exploration as those with a mean arterial pressure change <18 mmHg 1 min after vasopressin loading, among whom none had a change in CAI <0. Results: Twenty-one consecutive cases were examined in the present study, and included 14 responders and 7 non-responders. The primary outcome accounted for 71.4% of responders and 0% of non-responders, with a significant difference (p = 0.0039). Median CAI changes 2, 4, and 6 h after the administration of vasopressin were 0, -5, and -10 in responders and +20, +10, and +10 in non-responders, respectively. CAI was not reduced in any non-responder. Outcomes including mortality were not significantly different between responders and non-responders. Digital ischemia (1/21) and mesenteric ischemia (1/21) were observed. Conclusions: Vasopressin loading may predict responses to its continuous administration in septic shock patients. Further investigations involving a safety analysis are needed.

Use of age shock index in determining severity of illness in patients presenting to the emergency department with gastrointestinal bleeding

OBJECTIVES

This study aimed to make a comparison between classical shock index (SI), modified shock index (MSI), and age shock index (age SI) for predicting critical patients presenting to the emergency department (ED) with gastrointestinal bleeding (GIS).

METHODS

The study, which was planned retrospectively, consisted of patients diagnosed with GIS bleeding at the ED admission. Triage time vital signs were used to calculate SI, MSI, and age SI. These results were compared with intensive care admission, endoscopic/colonoscopic (E/C) intervention, blood transfusion, and mortality criteria, which we define as adverse outcomes.

RESULTS

The study included 151 patients. Seventy-nine (52.32%) of the patients had at least one adverse outcome. Of the 151 patients, 19 (12.58%) had ICU admission, 27 (17.88%) underwent endoscopic/colonoscopic (E/C) intervention, 68 (45.03%) received a blood transfusion, and 6 (3.97%) died. There was a significant difference between patients who had no adverse outcome and those who had at least one adverse outcome in terms of SI, age SI, and MSI. We performed ROC curve analyses to evaluate the diagnostic performances of all indices for predicting adverse outcomes. AUC (area under the curve) values for age SI was the highest (age SI AUC = 0.711, p < 0.001; SI AUC = 0.616; MSI AUC = 0.617). The performance of the age SI was significantly higher than the SI (p = 0.013) and the MSI (p = 0.024) for predicting adverse outcomes. The cut-off value for the age shock index was 45.12.

CONCLUSIONS

In patients with GIS bleeding, age SI, which can be easily calculated in triage, is more significant than SI and MSI for predicting the critical patient.

Predicting In-Hospital Mortality at Admission to the Medical Ward: A Big-Data Machine Learning Model

BACKGROUND

General medical wards admit high-risk patients. Artificial intelligence algorithms can use big data for developing models to assess patients' risk stratification. The aim of this study was to develop a mortality prediction machine learning model using data available at the time of admission to the medical ward.

METHODS

We included consecutive patients (ages 18-100) admitted to medical wards at a single medical center (January 1, 2013-December 31, 2018). We constructed a machine learning model using patient characteristics, comorbidities, laboratory tests, and patients' emergency department (ED) management. The model was trained on data from the years 2013 to 2017 and validated on data from the year 2018. The area under the curve (AUC) for mortality prediction was used as an outcome metric. Youden index was used to find an optimal sensitivity-specificity cutoff point.

RESULTS

Of the 118,262 patients admitted to the medical ward, 6311 died (5.3%). The single variables with the highest AUCs were medications administered in the ED (AUC = 0.74), ED diagnosis (AUC = 0.74), and albumin (AUC = 0.73). The machine learning model yielded an AUC of 0.924 (95% confidence interval [CI]: 0.917-0.930). For Youden index, a sensitivity of 0.88 (95% CI: 0.86-0.89) and specificity of 0.83 (95% CI: 0.83-0.83) were observed. This corresponds to a false-positive rate of 1:5.9 and negative predictive value of 0.99.

CONCLUSION

A machine learning model outperforms single variables predictions of in-hospital mortality at the time of admission to the medical ward. Such a decision support tool has the potential to augment clinical decision-making regarding level of care needed for admitted patients.

Shock index is better than conventional vital signs for assessing higher level of care and mortality in severe sepsis or shock

BACKGROUND

Conventional vital signs alone have limitations in determining the physiological status. Age-adjusted shock-index (SIPA), a comprehensive physiological variable, defined as the ratio of heart rate (HR) and systolic blood pressure (SBP) may be better at predicting hemodynamic stability and outcome than vital signs.

OBJECTIVES

To compare discriminatory power of SIPA against vital signs in assessing higher level of care (vasopressor use and mechanical ventilation) and early mortality in severe sepsis/septic shock.

METHODS

Prospective cohort study of 116children <14 years with severe sepsis/septic shock admitted at emergency department of a tertiary care hospital. Association between abnormal signs (raised heart-rate; HR, lower systolic blood-pressure; SBP, high SIPA) and higher level of care and early mortality at 0 and completed 6 hours (t0, t6) were assessed using univariate/multivariate analysis. Area-under-receiver-operating-characteristic curves (AUROC) of SIPA and conventional vital signs for outcome variables and their correlation with arterial lactate using Pearson's-coefficient were noted.

RESULTS

High SIPA was independently associated with higher level of care i.e. vasopressor use, mechanical ventilation (AUROC t0: 0.698, 0.730; AUROC t6; 0.733, 0.735) as well as early mortality (AUROC t0: 0.638; AUROC t6:0.721) at t0 and t6. At t0, only high SIPA (r² = 0.313) fairly correlated with arterial lactate (4.5 mmol/L). At t6, HR and SBP showed weak and SIPA (r² = 0.434) demonstrated moderate correlation with arterial lactate.

CONCLUSIONS

SIPA performs better than conventional vital-signs in recognising higher-level-of-care and early mortality.

Utility of triage shock index in predicting patient outcome in calcium channel blocker poisoning

Introduction:

Shock index is defined as heart rate divided by systolic blood pressure. It is reported as a predictor of morbidity and mortality in trauma and acute myocardial infarction in previous studies. It may be valuable in evaluation of calcium channel blocker poisoning. The objective of this study is to examine the probability of serious outcome based on first measured shock index in patients who presented to emergency department with calcium channel blocker poisoning.

Methods:

A retrospective chart review was conducted on calcium channel blocker poisoning cases in Hong Kong Poison Information Centre from 1 July 2008 to 30 June 2017. Shock index was calculated with blood pressure and pulse measurement at emergency department triage. Odds ratios of various variables for major outcome, mortality, intensive care unit admission, length of stay in acute hospital were calculated by multivariate analysis or negative binomial regression where appropriate. The performance of shock index in predicting major outcome was evaluated with receiver operating characteristic curve.

Results:

A total of 390 cases were identified, of whom 25.1% developed major outcome and 5.6% died. Shock index showed significant association with major outcome (odds ratio: 17.017, 95% confidence interval: 5.521–52.455). The area under the receiver operating characteristic curve for shock index in predicting major outcome was 0.7008 (95% confidence interval: 0.64–0.76).

Conclusion:

Higher shock index is associated with worse patient outcome in calcium channel blocker poisoning. However, shock index alone is not reliable in predicting patient outcome. Further research is needed before shock index can be incorporated for use in patient management in poisoning with calcium channel blocker or other anti-hypertensives.

Prognostic performance of peripheral perfusion index and shock index combined with ESI to predict hospital outcome

INTRODUCTION

Peripheral perfusion index (PPI) and shock index (SI) are considered valuable predictors of hospital outcome and mortality in various operative and intensive care settings. In the present study, we evaluated the prognostic capabilities of these parameters for performing emergency department (ED) triage, as represented by the emergency severity index (ESI).

METHODS

This prospective cross-sectional study included 367 patients aged older than 18 years who visited the ED of a tertiary referral hospital. The ESI triage levels with PPI, SI, and other basic vital sign parameters were recorded for each patient. The hospital outcome of the patients at the end of the ED period, such as discharge, admission to the hospital and death were recorded.

RESULTS

A total of 367 patients (M/F: 178/189) admitted to the ED were categorized according to ESI and included in the study. A decrease in diastolic BP, SpO2 and PPI increased the likelihood of hospitalization and 30-day mortality. Based on univariate analysis, a significant improvement in performance was found by using age, diastolic BP, mean arterial pressure, SpO2, SI and PPI in terms of predicting high acuity level patients (ESI < 3). In the multivariable analysis only SpO2 and PPI were found to predict ESI < 3 patients.

CONCLUSION

Peripheral perfusion index and SI as novel triage instruments might provide useful information for predicting hospital admission and mortality in ED patients. The addition of these parameters to existing triage instruments such as ESI could enhance the triage specificity in unselected patients admitted to ED.

Age shock index and age-modified shock index are strong predictors of outcomes in ST-segment elevation myocardial infarction patients undergoing emergency percutaneous coronary intervention

BACKGROUND

Early identification of high-risk patients provides clinicians with greater decision-making time and better informs strategies to cope with disease. The predictive values of age shock index (age SI) and age-modified shock index (age MSI) in ST-segment elevation myocardial infarction (STEMI) patients undergoing emergency percutaneous coronary intervention (PCI) have rarely been reported, especially compared with those for SI, MSI, and the Global Registry of Acute Coronary Events (GRACE) risk score.

PATIENTS AND METHODS

Nine hundred and eighty-three STEMI patients undergoing emergency PCI between January 2014 and September 2017 were analyzed in a retrospective cohort study. The primary outcomes were rates of in-hospital cardiovascular events, and 6-month and long-term all-cause mortality.

RESULTS

In multivariate analyses, the predictive values of age SI and age MSI were comparable to that of the GRACE score, but superior to those of SI and MSI for in-hospital cardiac mortality [age SI: odds ratio (OR) = 1.05, P < 0.001, area under the receiver operating characteristic (ROC-AUC) = 0.805, P < 0.001; age MSI: OR = 1.04, P < 0.001, ROC-AUC = 0.813, P < 0.001; GRACE score: OR = 1.03, P < 0.001, ROC-AUC = 0.827, P < 0.001], 6-month all-cause mortality (age SI: OR = 1.04, P < 0.001, ROC-AUC = 0.791, P < 0.001; age MSI: OR = 1.03, P < 0.001, ROC-AUC = 0.801, P < 0.001; GRACE score: ROC-AUC = 0.828, P < 0.001), long-term all-cause mortality [age SI: hazard ratio (HR) = 1.06, P < 0.001, ROC-AUC = 0.798, P < 0.001; age MSI: HR = 1.04, P < 0.001, ROC-AUC = 0.84, P < 0.001; GRACE score: ROC-AUC = 0.822, P < 0.001] and post-discharge all-cause mortality (age SI: HR = 1.05, P < 0.001, ROC-AUC = 0.78, P = 0.001; age MSI: HR = 1.05, P < 0.001, ROC-AUC = 0.789, P < 0.001; GRACE score: ROC-AUC = 0.812, P < 0.001).

CONCLUSION

Age SI and age MSI are stronger predictors than SI and MSI for in-hospital cardiovascular events, and 6-month and long-term all-cause mortality in STEMI patients undergoing emergency PCI. Age SI and age MSI appear to be convenient and simpler indicators than the GRACE score.

A Gradient Boosting Machine Learning Model for Predicting Early Mortality in the Emergency Department Triage: Devising a Nine-Point Triage Score

BACKGROUND

Emergency departments (ED) are becoming increasingly overwhelmed, increasing poor outcomes. Triage scores aim to optimize the waiting time and prioritize the resource usage. Artificial intelligence (AI) algorithms offer advantages for creating predictive clinical applications.

OBJECTIVE

Evaluate a state-of-the-art machine learning model for predicting mortality at the triage level and, by validating this automatic tool, improve the categorization of patients in the ED.

DESIGN

An institutional review board (IRB) approval was granted for this retrospective study. Information of consecutive adult patients (ages 18-100) admitted at the emergency department (ED) of one hospital were retrieved (January 1, 2012-December 31, 2018). Features included the following: demographics, admission date, arrival mode, referral code, chief complaint, previous ED visits, previous hospitalizations, comorbidities, home medications, vital signs, and Emergency Severity Index (ESI). The following outcomes were evaluated: early mortality (up to 2 days post ED registration) and short-term mortality (2-30 days post ED registration). A gradient boosting model was trained on data from years 2012-2017 and examined on data from the final year (2018). The area under the curve (AUC) for mortality prediction was used as an outcome metric. Single-variable analysis was conducted to develop a nine-point triage score for early mortality.

KEY RESULTS

Overall, 799,522 ED visits were available for analysis. The early and short-term mortality rates were 0.6% and 2.5%, respectively. Models trained on the full set of features yielded an AUC of 0.962 for early mortality and 0.923 for short-term mortality. A model that utilized the nine features with the highest single-variable AUC scores (age, arrival mode, chief complaint, five primary vital signs, and ESI) yielded an AUC of 0.962 for early mortality.

CONCLUSION

The gradient boosting model shows high predictive ability for screening patients at risk of early mortality utilizing data available at the time of triage in the ED.

Shock index in the emergency department: utility and limitations

Shock index (SI) is defined as the heart rate (HR) divided by systolic blood pressure (SBP). It has been studied in patients either at risk of or experiencing shock from a variety of causes: trauma, hemorrhage, myocardial infarction, pulmonary embolism, sepsis, and ruptured ectopic pregnancy. While HR and SBP have traditionally been used to characterize shock in these patients, they often appear normal in the compensatory phase of shock and can be confounded by factors such as medications (eg, antihypertensives, beta-agonists). SI >1.0 has been widely found to predict increased risk of mortality and other markers of morbidity, such as need for massive transfusion protocol activation and admission to intensive care units. Recent research has aimed to study the use of SI in patients immediately on arrival to the emergency department (ED). In this review, we summarize the literature pertaining to use of SI across a variety of settings in the management of ED patients, in order to provide context for use of this measure in the triage and management of critically ill patients.

The value of shock index in prediction of cardiogenic shock developed during primary percutaneous coronary intervention

BACKGROUND

Shock index(SI) is a conventional predictive marker for haemodynamic state. Its breakpoint varies by different conditions according to previous studies. The current study was performed to evaluate the capability of SI in prediction of cardiogenic shock(CS) developed during primary percutaneous coronary intervention (pPCI).

METHODS

Total 870 patients of ST segment elevation myocardial infarction(STEMI) who were haemodynamic stable before pPCI were involved in the study. In this cohort, 625 consecutive patients composed analysis series and 245 consecutive patients composed validation series. Multivariate regression analysis was used to evaluate whether SI was a significant predictor of developed CS and Hosmer-Lemeshow test was used to assess the goodness of model fitness. Receiver-operating characteristics (ROC) analysis was used to compare the predictive capability of SI with other predictors. The sensitivity, specificity, accuracy, positive and negative predictive values of SI at different cutoff values was compared to identify a best breakpoint.

RESULTS

In the analysis series, SI and Killips classification were identified as independent predictors. ROC analysis demonstrated the diagnostic capability of SI was superior to pre-procedural systolic blood pressure(SBP) or heart rate(HR) alone (0.8113 vs 0.7582, P = 0.04 and 0.8113 vs 0.7111, P < 0.001). The diagnostic capability of SI was equivalent to that of combination of SBP, HR and Killips claasification(0.8133 vs 0.8137, P = 0.97). SI had a high specificity and low sensitivity. When the cutoff value was set at 0.93, the positive predictive value, negative predictive value and diagnostic accuracy was 42.6%, 95.1% and 87.4% respectively. In validation series, the area under ROC curve was 0.8245, which was similar to that in the analysis series. The positive predictive value, negative predictive value and diagnostic accuracy at the cutoff value of 0.93 was 53.8%, 93.2% and 88.9% respectively.

CONCLUSIONS

SI has a high predictive accuracy for developing CS during pPCI in STEMI patients. It is an excellent exclusion diagnosis index rather than confirmative diagnosis index.

Ultrasound-guided interscalene nerve block vs procedural sedation by propofol and fentanyl for anterior shoulder dislocations

BACKGROUND

Few studies were performed to compare ultrasound guided brachial plexus block with procedural sedation for reduction of shoulder dislocations in the Emergency Department (ED). This study was done to provide further evidence regarding this comparison.

METHODS

This was a randomized clinical trial performed on patients presenting with anterior shoulder dislocations to the emergency department of an academic level 2 trauma center. Exclusion criteria were any contraindications to the drugs used, any patient which may not be potentially assigned into both groups because of an underlying medical condition, presence of neurovascular compromise related to the dislocation, presence of concomitant fractures, and patient refusal to participate in the study. Patients were randomly assigned into the Procedural Sedation and Analgesia (PSA) group with propofol and fentanyl or ultrasound guided Inter-Scalene Brachial Plexus Block (ISBPB) with lidocaine and epinephrine.

RESULTS

A total of 60 patients (30 in each group) were included in the study. The emergency room length of stay was significantly lower in the ISBPB group, with mean (SD) values of 108.6 (42.1) vs. 80.2 (25.2) minutes (p=0.005). However, pain scores in the PSA group during reduction showed advantage over ISBPB [0.38 vs. 3.43 (p<0.001)]. Moreover, patient satisfaction was higher with PSA (p<0.001).

CONCLUSION

Using ISBPB for reduction of anterior shoulder dislocations takes less time to discharge and may make it more feasible in conditions mandating faster discharge of the patient. However, since pain scores may be lower using PSA, this method may be preferred by many physicians in some other situations.