Abstract
Study Objective
Patients with syncope are frequently admitted to the hospital, but whether this improves outcome is unknown. We tested whether hospitalization reduced mortality in patients who presented to emergency departments (EDs) with syncope.
Methods
We conducted a propensity analysis of the outcomes of patients ≥18 years old presenting to EDs with a primary diagnosis of syncope in April 2004–March 2013. The model used 1:1 nearest‐neighbor matching to predicted admission using age, sex, urban residence, household income, and 14 significant comorbidities from 4 administrative databases of the province of Alberta. The primary outcome was death.
Results
There were 57,417 ED patients with a primary diagnosis of syncope; 8864 were admitted, and 48,553 were discharged in <24 hours. Admitted patients were older (median 76 vs 49 years), male (53% vs 45%), rural (23% vs 18%), and had lower income (median $58,599 vs $61,422); all P < 0.001. All comorbidities were higher in admitted patients (mean Charlson scores, 1.9 vs 0.7; P < 0.001). The propensity‐matched hospitalized patients had higher 30‐day mortality (3.5% vs 1.0%) and 1‐year mortality (14.1% vs 8.6%); both P < 0.001. Mortality in all propensity quintiles was higher in the hospitalized group (all P < 0.001). The most common causes of death in 2719 patients included chronic ischemic heart disease, 14%; lung cancer, 7.1%; acute myocardial infarction, 6.9%; stroke, 3.7%; chronic obstructive pulmonary disease, 3.6%; dementia, 2.6%; and heart failure, 2.5%.
Conclusions
Hospital admission did not reduce early or late mortality in patients who presented to the ED with syncope. Mortality is associated with comorbidities.
Keywords: emergency department, hospitalization, mortality, propensity analysis, syncope
1. INTRODUCTION
1.1. Background
The assessment and care of patients who present to emergency departments (EDs) with syncope continues to be problematic. The volume is high, with syncope accounting for >1% of ED visits. Estimates of the proportion of visits range from 0.6% to 1.0% in North America and 0.9% to 1.7% in Europe. 1 , 2 Accurate, timely, and efficient delivery of health care for syncope continues to be a significant challenge. 3 The concerns for physicians in the ED include the large number of potential etiologies, the generally benign nature of most syncope syndromes, the occasional high‐risk cause, and the increased complexity and fewer symptom clues in older patients. 4 , 5
1.2. Importance
These factors can lead to admission rates that range from 10% to 15% in some Canadian provinces, 6 to 25% to 40% in many centers in one recent report, 7 and to as high as 54% in a large, urban American teaching hospital. 8 Efforts are underway in several centers to reduce these rates, 9 but the minimal proportion of patients that should be admitted is unknown. The 1‐year mortality of hospitalized syncope patients 9 is 10% to 20%, and whether hospitalization improves this is unknown. There have also been no randomized or controlled studies, and without preliminary evidence such studies would be difficult to propose or conduct. Administrative data provide one such source of evidence.
1.3. Goals
To address whether admission improves fatal outcomes, we conducted a propensity analysis 10 of administrative data of patients with syncope in all Alberta EDs from 2004 to 2012. The primary outcomes were mortality at 30 days and 1 year.
2. METHODS
2.1. Study design and setting
We conducted a retrospective cohort study using 4 linked Alberta health administrative databases. They were the Ambulatory Care database, which includes ED visits; the Discharge Abstract Database, which documents all acute care hospitalizations; and the Population Registry and Vital Statistics, which provide patient demographics and death records. The databases were linked with anonymous patient unique identifiers. We separately used ED and hospital discharge diagnosis codes to classify the discharge diagnosis. The University of Alberta Health Research Ethics Board approved the study (institutional review board number Pro00010852).
The Bottom Line
Patients presenting to the emergency department with syncope are often admitted for further monitoring and testing. In this propensity‐matched study, admitted patients were older, had more comorbidities, and had higher rates of mortality (30 days and 1 year). Hospital admission did not reduce mortality in this cohort.
2.2. Selection of participants
We included all patients aged ≥18 years who were admitted to EDs in Alberta between April 2004 and March 2013 with a primary diagnosis of syncope (International Classification of Diseases Tenth Revision code R55). 11 Patients were categorized into those who were hospitalized within 24 hours of ED visit and those who were discharged home. If a patient had more than 1 ED visit during the study period, only the first visit was selected. Patients were followed for 1 year.
2.3. Measurements
The second digit of the Canadian postal code classified the patient residence as either urban or rural. Patient socioeconomic status was based on the 2006 Canadian census. 12 The hospitals in which patients were admitted were categorized into teaching, community large, community medium, and community small hospitals according to the Canadian Institute for Health Information criteria. The EDs were grouped based on type of hospital (teaching/community large/community medium/community small) or a stand‐alone facility. We used previously validated International Classification of Diseases codes to identify patient comorbidities. 13 A comorbidity was considered present if it was recorded in either an ambulatory care visit or a hospitalization record during the 3 years before the ED index visit. All analyses were performed using Stata version 14 (Stata Corp., College Station, TX); P values < 0.05 were considered significant.
2.4. Objective measures
The primary outcomes were all‐cause mortality at 30 days and 1 year. Causes of death were determined from International Classification of Diseases Tenth Revision codes in the Vital Statistics registry.
2.5. Primary data analysis
Patient characteristics of the 2 cohorts were compared using the χ2 test for categorical variables and the Kruskal‐Wallis test for continuous variables. We used a logistic regression model with hospital admission as the outcome variable, and fiscal year of ED visit, patient sex and age, location of the ED, and comorbidities as the independent variables were used to calculate the propensity score. In the model in which we examined the association between admission versus discharge status on mortality, all patient‐level variables as well as the propensity score were included as independent variables. Backward stepwise selection was used. We used likelihood ratio tests to examine the inclusion of other independent variables that could have an effect on the likelihood of hospital admission. They were comorbidities, urban/rural residence, ambulance or self‐presentation, median household income quartiles (at forward sortation area, ie, neighbourhood level), and sex and age interactions. These variables remained in the final model if the likelihood ratio test was significant at a 5% level. Patients of the 2 cohorts were matched 1:1 with a caliper (0.25 of SD) into 5 quintiles based on the nearest propensity for hospital admission. To account for early death during hospitalization, we performed a sensitivity analysis wherein only survivors of the index hospitalization (8803 patients) were used for propensity matching with their discharged counterparts. A balancing test was used to confirm bias reduction after propensity matching. All analyses were performed using Stata version 14 (Stata Corp., College Station, TX); P values < 0.05 were considered significant.
3. RESULTS
3.1. Characteristics of study subjects
There were 57,429 ED patients with a primary diagnosis of syncope from April 2004 to March 2013, of whom 12 died at the index ED visit and were excluded. Of the remaining 57,417 patients, 8864 (15%) were admitted to the hospital and 48,553 (85%) were discharged from the ED (Table 1). Admitted patients were older (median 76 vs 49 years; P < 0.001), more likely male (53% vs 45%; P < 0.001), and more likely rural (23% vs 18%; P < 0.001). Admitted patients had lower median household incomes ($58,599 vs $61,422; P < 0.001). All comorbidities were significantly higher in admitted patients, and Charlson scores were higher in admitted patients (mean scores 1.9 vs 0.7; P < 0.001). Patients who arrived by ambulance were more likely to be admitted (63% vs 42%). Patients who presented to teaching hospitals were less likely to be admitted (42% vs 48% of admitted or discharged patients), whereas patients who presented to community hospitals were more likely to be admitted (57% vs 49% of admitted or discharged patients).
TABLE 1.
Baseline variables in all patients who were either discharged from or admitted through emergency departments and the corresponding propensity‐matched populations in Alberta, Canada, 2004 to 2012
| Variable | Hospitalized cohort 1, n = 8,864 | Discharged cohort 2, n = 48,553 | P value | Propensity admitted cohort, n = 8,804 | Propensity discharged cohort, n = 8,804 | P value |
|---|---|---|---|---|---|---|
| Demographic characteristics | ||||||
| Age, y, mean (SD) | 71.1 (16.7) | 50.5 (22.2) | <0.001 | 71.1 (16.8) | 71.3 (16.8) | 0.414 |
| Age, y, median (IQR) | 76 (62–83) | 49 (30–70) | <0.001 | 75 (62–83) | 76 (62–83) | 0.292 |
| Age group, n (%) | ||||||
| <40 years | 537 (6.1) | 18,296 (37.7) | <0.001 | 537 (6.1) | 496 (5.6) | 0.313 |
| 40–49 years | 502 (5.7) | 6086 (12.5) | <0.001 | 502 (5.7) | 470 (5.3) | 0.291 |
| 50–59 years | 931 (10.5) | 6566 (13.5) | <0.001 | 931 (10.5) | 884 (10) | 0.224 |
| 60–69 years | 1270 (14.3) | 5334 (11.0) | <0.001 | 1268 (14.4) | 1235 (14) | 0.476 |
| 70–79 years | 2241 (25.3) | 5799 (11.9) | <0.001 | 2229 (25.3) | 2243 (25.5) | 0.808 |
| ≥80 years | 3383 (38.2) | 6472 (13.3) | <0.001 | 3337 (37.9) | 3476 (39.5) | 0.031 |
| Male, n (%) | 4685 (52.9) | 21,689 (44.7) | <0.001 | 4641 (52.7) | 4613 (52.4) | 0.673 |
| Urban residence, n (%) | 6811 (76.8) | 39,654 (81.7) | <0.001 | 6775 (77) | 6783 (77) | 0.886 |
| Charlson score | ||||||
| 0 | 3521 (39.7) | 34,419 (70.9) | <0.001 | 3521 (40) | 3913 (44.5) | <0.001 |
| 1–2 | 2860 (32.3) | 9830 (20.3) | <0.001 | 2860 (32.5) | 2703 (30.7) | 0.011 |
| 3–4 | 1257 (14.2) | 2365 (4.9) | <0.001 | 1249 (14.2) | 1049 (11.9) | <0.001 |
| ≥5 | 1226 (13.8) | 1939 (4.0) | <0.001 | 1174 (13.3) | 1139 (12.9) | 0.435 |
| Comorbidities | ||||||
| Hypertension | 3776 (42.6) | 8328 (17.2) | <0.001 | 3720 (42.3) | 3675 (41.7) | 0.492 |
| Myocardial infarction | 1207 (13.6) | 2077 (4.3) | <0.001 | 1165 (13.2) | 1099 (12.5) | 0.137 |
| Congestive heart failure | 1463 (16.5) | 1896 (3.9) | <0.001 | 1403 (15.9) | 1265 (14.4) | 0.004 |
| Peripheral vascular disease | 622 (7.0) | 1044 (2.2) | <0.001 | 592 (6.7) | 572 (6.5) | 0.544 |
| Cerebrovascular disease | 1286 (14.5) | 2752 (5.7) | <0.001 | 1256 (14.3) | 1230 (14) | 0.574 |
| Dementia | 643 (7.3) | 1406 (2.9) | <0.001 | 640 (7.3) | 636 (7.2) | 0.907 |
| Chronic pulmonary disease | 1613 (18.2) | 4665 (9.6) | <0.001 | 1576 (17.9) | 1469 (16.7) | 0.033 |
| Rheumatoid disease | 222 (2.5) | 479 (1.0) | <0.001 | 216 (2.5) | 206 (2.3) | 0.622 |
| Peptic ulcer disease | 260 (2.9) | 699 (1.4) | <0.001 | 257 (2.9) | 209 (2.4) | 0.024 |
| Mild liver disease | 178 (2.0) | 442 (0.9) | <0.001 | 165 (1.9) | 156 (1.8) | 0.612 |
| Diabetes | 1916 (21.6) | 4487 (9.2) | <0.001 | 1877 (21.3) | 1743 (19.8) | 0.012 |
| Hemiplegia or paraplegia | 147 (1.7) | 355 (0.7) | <0.001 | 144 (1.6) | 155 (1.8) | 0.521 |
| Renal disease | 798 (9.0) | 1282 (2.6) | <0.001 | 767 (8.7) | 710 (8.1) | 0.121 |
| Cancer | 792 (8.9) | 1750 (3.6) | <0.001 | 777 (8.8) | 748 (8.5) | 0.437 |
| Moderate/severe liver disease | 58 (0.7) | 98 (0.2) | <0.001 | 48 (0.6) | 52 (0.6) | 0.688 |
| Metastatic solid tumor | 263 (3.0) | 502 (1.0) | <0.001 | 256 (2.9) | 252 (2.9) | 0.857 |
| AIDS | 3 (0) | 30 (0.1) | 0.313 | 3 (0.03) | 0 (0) | 0.083 |
IQR, interquartile range; SD, standard deviation.
3.2. Main results
Of the 8864 hospitalized patients, 3843 (43.4%) were eventually discharged with a diagnosis of syncope and 5021 (56.6%) were eventually discharged with another diagnosis being listed as the primary cause for hospitalization (Table 2). These included hypotension (I95, 6.2%), other cardiac arrhythmias (I49, 4.8%), and ICD10 category for abnormalities of heartbeat (R00, 3.9%)
TABLE 2.
Top 10 discharge diagnoses of the 8864 hospitalized patients
| Coded diagnosis | N (%) |
|---|---|
| Syncope | 3843 (43.4) |
| Hypotension | 313 (3.5) |
| Other cardiac arrhythmias | 241 (2.7) |
| Abnormalities of heart beat | 197 (2.2) |
| Dizziness and giddiness | 195 (2.2) |
| Atrial fibrillation and flutter | 183 (2.1) |
| Atrioventricular and left bundle‐branch block | 167 (1.9) |
| Transient cerebral ischaemic attacks and related syndromes | 105 (1.2) |
| Acute myocardial infarction | 105 (1.2) |
Overall, 3.6% and 0.3% of admitted and discharged patients, respectively, died within 30 days of the index ED visit (P < 0.001). By 1 year, the mortality rates had increased to 14.3% and 3.0% for admitted and discharged patients, respectively (P < 0.001). The most common causes of death (Table 3) were chronic ischemic heart disease, 14%; lung cancer, 7.1%; acute myocardial infarction, 6.9%; stroke, 3.7%; chronic obstructive pulmonary disease, 3.6%; dementia, 2.6%; and heart failure, 2.5%.
TABLE 3.
Top 10 coded causes of death (n = 2719)
| Coded cause of death | N (%) |
|---|---|
| Chronic ischemic heart disease | 379 (14) |
| Malignant neoplasm of bronchus and lung | 194 (7.1) |
| Acute myocardial infarction | 188 (6.9) |
| Stroke, not specified as hemorrhage or infarction | 99 (3.7) |
| Other chronic obstructive pulmonary disease | 97 (3.6) |
| Unspecified dementia | 70 (2.6) |
| Heart failure | 69 (2.5) |
| Malignant neoplasm of colon | 64 (2.4) |
We created cohorts of 8804 admitted and discharged patients matched on their propensity for hospitalization. Variables of the logistic regression model that predicted propensity to hospitalization are presented (Table 1). The model predicted admission with a C statistic = 0.79. The propensity quintiles of ascending likelihood to be hospitalized were significantly associated with increased 1‐year mortality (second quintile odds ratio [OR] = 3.5, third quintile OR = 5.0, fourth quintile OR = 7.3, fifth quintile OR = 14.9; all P < 0.001; compared with first quintile). Both the 30‐day mortality (3.5% vs 1.0%; P < 0.001) and 1‐year mortality (14.1% vs 8.6%; P < 0.001) continued to be higher in admitted patients compared with discharged patients. The 30‐day and 1‐year mortality in all 5 matched quintiles of propensity for hospitalization were higher in the hospitalized group (Figure 1). At both times and in all quintiles, the patients who were admitted to the hospital were at statistically significant increased risk of death. Comparing unmatched to matched analyses, identifiable baseline risk factors were only associated with a minority of the increased risk borne by admitted patients compared with discharged patients, 1% of the 3.5% 30‐day mortality (29% of risk), and 8.6% of the 14.1% 1‐year mortality (61% of risk).
FIGURE 1.

Left: 30‐day mortality in matched cohorts who were discharged from the emergency department or admitted to the hospital. Right: 1‐year mortality in matched cohorts who were discharged from the emergency department or admitted to the hospital
In a sensitivity analysis, 8564 of 8603 hospital survivors of the index hospital admission were matched with their counterparts who were discharged alive from ED. Survivors of patients who were hospitalized had a higher 1‐year mortality in all quintiles than matched patients who were discharged from the ED (Figure 2). Therefore, the propensity to being admitted predicts 1‐year mortality even after accounting for early in‐hospital mortality.
FIGURE 2.

One‐year mortality of the matched cohort by quintiles of propensity for hospital admission for index hospital survivors and matched discharged patients
4. LIMITATIONS
There are notable limitations. First, these are administrative data, and the interpretation of the results depends on the accuracy and thoroughness of coding, which may lead to misclassification. For example, the International Classification of Diseases Ninth Revision code for syncope has a 99% specificity but only 65% sensitivity. 11 Second, it is from a single Canadian province of about 4 million people, and its geographic, demographic, and income social determinants may not extrapolate completely to other settings. However, it is a broad‐based study that is directly relevant to the disposition of patients in the ED, as the diagnostic code encompasses both syncope and collapse, which are the diagnostic challenges facing emergency physicians. We only included patients with a primary diagnosis of syncope. Therefore, patients with other diagnoses (such as dehydration, arrhythmia, or transient ischemic attack) in the primary diagnosis field with syncope listed as a secondary diagnosis in the ED record were not included.
Conclusions from propensity analyses depend importantly on the baseline factors, and there is a risk that important factors such as frailty were not captured. We did include a broad range of baseline demographic, clinical, and geographic factors. There is no evidence about the history and clinical details of the index syncopal spell, and this information may have both informed the decision to admit and predicted subsequent outcome. This article therefore complements the findings of Probst et al, 10 who included patient‐specific variables in their analyses. Ours is population based using administrative data, whereas their study focused on a smaller and highly relevant population of older syncope patients. Our outcomes did not include morbidities, procedures, quality of life, or costs, but did focus on the worst and most definitive of outcomes: death. Finally, we did not analyze specific causes of death, and it might be that some causes of death are reduced by admission.
5. DISCUSSION
5.1. Summary
This propensity analysis of large administrative data could not find benefit of hospitalization for syncope patients who present to the ED. The results agree with those of Probst et al, 10 who also could not identify a benefit to 30‐day survival in older patients with syncope. This lack of evidence of a benefit of hospitalization stands in contrast to high admission rates for syncope in the United States, 8 Europe, and the United Kingdom. 3 This analysis featured large cohorts with few exclusions, sampling over 10 years, and the use of the hardest of outcomes: 30‐day and 1‐year mortalities. Its internal validity was strengthened by the association of increased propensity to admission and increased likelihood of death. Its external validity was highlighted by the report of Probst et al, 10 which focused on elderly patients and had several morbidity and mortality outcome measures and also could find no benefit from hospitalization.
5.2. Context
The mortality of Canadian syncope patients is high and aligns well with those from previous smaller studies. Costantino et al 14 reported 1‐year mortalities of 2% and 16% in Milan ED syncope patients who were discharged from ED or admitted to the hospital. Similarly, Del Rosso et al 15 reported an 8% 1‐year mortality in syncope patients admitted through the ED to the hospital. The Osservatorio Epidemiologico sulla Sincope nel Lazio (OESIL) investigators 16 reported a 25% 1‐year mortality in syncope patients admitted to the hospital. Solbiati et al 17 reported a systematic review and meta‐analysis of the outcomes of syncope patients seen in the ED. There was an overall estimated 1‐year mortality of 8.3%. The 1‐year mortality in this population approximates those of patients with heart failure 18 or survivors of sudden death, 19 and although much ongoing work is currently focused on preventing unnecessary admissions, these findings suggest the importance of also focusing on identifying and treating this high‐risk group. 7 , 9
The causes of death also resemble those in the Evaluation of Guidelines in SYncope Study (EGSYS) study, 20 which reported that 18 of 40 deaths were attributed to cardiovascular or cerebrovascular causes. Advancing age carries increasingly high ORs, but clinical components are also important. Many of the clinical components in the propensity score are not known risk factors for syncope, suggesting that emergency physicians may be admitting patients on the basis of their perceived overall health status rather than for specific assessment of the cause of syncope. Supporting this is the observation that across all risk‐adjusted quintiles patients who were admitted to the hospital had a higher mortality than patients who were discharged from the ED. Given the unlikelihood that hospitalization could increase the mortality of patients between 30 days and 1 year, this suggests the presence of other, uncoded risk factors for 1‐year mortality that also increase the likelihood of admission to the hospital. One possibility is frailty, whose components regardless of how it is measured may not be diagnostic codes. 21 It is widely if imperfectly recognized at the bedside, 22 is a phenotype that resembles heart failure, and raises appropriate concerns that it portends a poor outcome. 22
Whether syncope itself is a risk factor for death cannot be determined directly from these data. However, there were only 48 deaths by 1 year in the lower 2 quintiles, with a mortality of 0.2%, and mortality generally rose monotonically with the propensity score. This low mortality in patients with few comorbidities suggests that at least in this group syncope is not a risk factor for death. Whether this is also true in patients with several comorbidities and advancing age cannot be estimated without a direct, prospectively assessed comparison group. Determining whether syncope itself is a generalized risk marker such as frailty 22 of a poor outcome remains to be determined.
These findings have several clinical and health service implications. First, current international efforts are focused on preventing unnecessary admissions, and these data suggest the need for efforts to also address identifying, assessing, and treating high‐risk patients. 7 , 9 Many of the deaths may be attributed to preventable cardiovascular or cerebrovascular causes. Second, the range of components in the propensity model for admission suggests that emergency physicians are making broad risk assessments of the patients rather than focusing on simply determining the risk level and etiology of the index syncopal spell. Efforts should address what other factors go into their assessments other than those that appear as diagnostic codes. In particular, whether a bedside assessment of frailty is involved is unknown.
CONFLICTS OF INTEREST
The authors declare no conflicts of interest.
AUTHOR CONTRIBUTIONS
Padma Kaul, Dat T. Tran, Roopinder K. Sandhu, Monica Solbiati, Giorgio Costantino, and Robert S. Sheldon conceived and designed the study. Padma Kaul, Roopinder K. Sandhu, and Robert S. Sheldon obtained research funding. Padma Kaul, Dat T. Tran, and Roopinder K. Sandhu supervised data collection, and Dat T. Tran and Padma Kaul analyzed the data. Robert S. Sheldon provided the first draft, and all authors contributed heavily to its revisions. Robert S. Sheldon takes responsibility for the article as a whole.
Supporting information
Supplementary information
ACKNOWLEDGMENTS
Funding was provided by the Cardiac Arrhythmia Network of Canada, a Network of Centre of Excellence funded by the Canadian Federal Ministry Industry Canada.
Biography
Dr. Padma Kaul, PhD is a Professor, Department of Medicine, University of Alberta; CIHR Sex and Gender Chair in Diabetes and Heart Disease, and the Co‐Director of the Canadian VIGOUR Centre.

Kaul P, Tran DT, Sandhu RK, Solbiati M, Costantino G, Sheldon RS. Lack of benefit from hospitalization in patients with syncope: A propensity analysis. JACEP Open 2020;1:716–722. 10.1002/emp2.12229
Supervising Editor: Kelly N. Sawyer, MD.
Funding and support: By JACEP Open policy, all authors are required to disclose any and all commercial, financial, and other relationships in any way related to the subject of this article as per ICMJE conflict of interest guidelines (see www.icmje.org). The authors have stated that no such relationships exist.
REFERENCES
- 1. Probst MA, Kanzaria HK, Gbedemah M, Richardson LD, Sun BC. National trends in resource utilization associated with ED visits for syncope. Am J Emerg Med 2015;33:998–1001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Thiruganasambandamoorthy V, Williams K, Stiell I, Wells G. Frequency and outcomes of syncope in the emergency department. Can J Emerg Med Care 2008;10:255–295. [Google Scholar]
- 3. Shen WK, Sheldon RS, Benditt DG et al. ACC/AHA/HRS Guideline for the Evaluation and Management of Patients With Syncope: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society. J Am Coll Cardiol 2017;70:620–663. [DOI] [PubMed] [Google Scholar]
- 4. Del Rosso A, Alboni P, Brignole M, Menozzi C, Raviele A. Relation of clinical presentation of syncope to the age of patients. Am J Cardiol 2005;96:1431–1435. [DOI] [PubMed] [Google Scholar]
- 5. Galizia G, Abete P, Mussi C et al. Role of early symptoms in assessment of syncope in elderly people: results from the Italian group for the study of syncope in the elderly. J Am Geriatr Soc 2009;57:18–23. [DOI] [PubMed] [Google Scholar]
- 6. Sandhu RK, Tran DT, Sheldon RS, Kaul P. A population‐based cohort study evaluating outcomes and costs for syncope presentations to the emergency department. JACC Clin Electrophysiol 2018;4:265–273. [DOI] [PubMed] [Google Scholar]
- 7. Sun BC, Costantino G, Barbic F et al. Priorities for emergency department syncope research. Ann Emerg Med 2014;64:649–55.e2. [DOI] [PubMed] [Google Scholar]
- 8. Mechanic OJ, Pascheles CY, Lopez GJ et al. Using the Boston Syncope Observation Management Pathway to Reduce Hospital Admission and Adverse Outcomes. West J Emerg Med 2019;20:250–255. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Costantino G, Casazza G, Reed M et al. Syncope risk stratification tools vs clinical judgment: an individual patient data meta‐analysis. Am J Med 2014;127:1126.e13‐25. [DOI] [PubMed] [Google Scholar]
- 10. Probst MA, Su E, Weiss RE et al. Clinical benefit of hospitalization for older adults with unexplained syncope: a propensity‐matched analysis. Ann Emerg Med 2019;74:260–269. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Ruwald MH, Hansen ML, Lamberts M et al. Accuracy of the ICD‐10 discharge diagnosis for syncope. Europace 2013;15:595–600. [DOI] [PubMed] [Google Scholar]
- 12. Statistics Canada. Available at http://www.statcan.gc.ca/eng/start. Published 2006. Accessed November 14, 2019.
- 13. Quan H, Sundararajan V, Halfon P et al. Coding algorithms for defining comorbidities in ICD‐9‐CM and ICD‐10 administrative data. Medical Care 2005;43:1130–1139. [DOI] [PubMed] [Google Scholar]
- 14. Costantino G, Perego F, Dipaola F et al. Short‐ and long‐term prognosis of syncope, risk factors, and role of hospital admission: results from the STePS (Short‐Term Prognosis of Syncope) study. J Am Coll Cardiol 2008;51:276–283. [DOI] [PubMed] [Google Scholar]
- 15. Del Rosso A, Ungar A, Maggi R et al. Clinical predictors of cardiac syncope at initial evaluation in patients referred urgently to a general hospital: the EGSYS score. Heart 2008;94:1620–1626. [DOI] [PubMed] [Google Scholar]
- 16. Colivicchi F, Ammirati F, Melina D, Guido V, Imperoli G, Santini M. Development and prospective validation of a risk stratification system for patients with syncope in the emergency department: the OESIL risk score. Eur Heart J 2003;24:811–819. [DOI] [PubMed] [Google Scholar]
- 17. Solbiati M, Casazza G, Dipaola F et al. Syncope recurrence and mortality: a systematic review. Europace 2015;17:300–308. [DOI] [PubMed] [Google Scholar]
- 18. Halliday BP, Cleland JGF, Goldberger JJ, Prasad SK. Personalizing risk stratification for sudden death in dilated cardiomyopathy: the past, present, and future. Circulation 2017;136:215–231. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Oscar O, Enrique R, Andres B. Subanalyses of secondary prevention implantable cardioverter‐defibrillator trials: antiarrhythmics versus implantable defibrillators (AVID), Canadian Implantable Defibrillator Study (CIDS), and Cardiac Arrest Study Hamburg (CASH). Curr Opin Cardiol 2004;19:26–30. [DOI] [PubMed] [Google Scholar]
- 20. Ungar A, Del Rosso A, Giada F et al. Early and late outcome of treated patients referred for syncope to emergency department: the EGSYS 2 follow‐up study. Eur Heart J 2010;31:2021–2026. [DOI] [PubMed] [Google Scholar]
- 21. Luc JGY, Graham MM, Norris CM, Al Shouli S, Nijjar YS, Meyer SR. Predicting operative mortality in octogenarians for isolated coronary artery bypass grafting surgery: a retrospective study. BMC Cardiovasc Disord 2017;17:275. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Kim DH, Kim CA, Placide S, Lipsitz LA, Marcantonio ER. Preoperative frailty assessment and outcomes at 6 months or later in older adults undergoing cardiac surgical procedures: a systematic review. Ann Intern Med 2016;165:650–660. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary information
