Risk Factors for Diagnostic Delay in Acute Aortic Dissection

Article Outline

• Abstract
• Methods
• Results
• Discussion
• References
• Copyright

In acute aortic dissection (AAD), timely diagnosis is challenging. However, dedicated studies of the entity and determinants of delay are currently lacking. We surveyed pre-/in-hospital time to diagnosis and explored risk factors for diagnostic delay. We analyzed the dedicated database of a metropolitan AAD network (161 patients diagnosed since 1996; 115 Stanford type A) in terms of hospital arrival times (from pain to presentation at any hospital) and in-hospital diagnostic times (presentation to final diagnosis). Median (interquartile range) in-hospital diagnostic times were approximately twofold greater than hospital arrival times (177 minutes, 644, vs 75 minutes, 124, p = 0.0001, Wilcoxon test). Median annual in-hospital diagnostic times were most often ∼3 hours (spread was wide, but decreased after 2001; ρ = −0.94, p = 0.005). Risk factors (univariate analysis) for in-hospital diagnostic time >75th percentile (12 hours) included pleural effusion (odds ratio 3.96, 95% confidence interval 1.80 to 8.69), dyspneic presentation (odds ratio 3.33, 95% confidence interval 1.93 to 8.59), and age <70 years (odds ratio 2.34, 95% confidence interval 1.03 to 5.36). Systolic arterial pressure ≤105 mm Hg decreased the likelihood of lengthy diagnosis (odds ratio 0.08, 95% confidence interval 0.01 to 0.59). In patients (n = 82) with routine values (since 2000), troponin positivity (odds ratio 3.63, 95% confidence interval 1.12 to 11.84) and an acute coronary syndrome–like electrocardiogram (odds ratio 2.88, 95% confidence interval 1.01 to 8.17) were also risk factors. In conclusion, in a metropolitan setting, most of the diagnostic delay may occur in hospital. At presentation, pleural effusion, troponin positivity, acute coronary syndrome–like electrocardiogram, and dyspnea are possible “clinical confounders” associated with particularly long in-hospital diagnostic times.

Rapid diagnosis of acute aortic dissection (AAD) is crucial for the outcome of medical and surgical therapies.¹, ², ³, ⁴, ⁵, ⁶ When undiagnosed (and therefore untreated), AAD has a linearized mortality rate of up to 1% per hour.¹, ² Correct diagnosis is often challenging due to the ability of AAD (including intramural hematomas) to mimic the onset of many other diseases, not only of the cardiovascular system.⁷, ⁸, ⁹, ¹⁰, ¹¹ Although these diagnostic challenges have been widely discussed, to our knowledge no study is currently available that is dedicated to the entity of diagnostic delay and its possible determinants (such as clinical confounders). We surveyed secular trends in pre-/in-hospital time to diagnosis in our AAD treatment network and explored risk factors for lengthy in-hospital diagnostic delay (i.e., diagnostic confounders).

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Methods 

Since 1996 our institution, which provides the centralized public health service AAD treatment referral center in a metropolitan hospital network covering Bologna and its hinterland, has kept a dedicated database of all patients referred to our institution who received a final diagnosis of AAD (confirmed at surgery, imaging, or any subsequent autopsy). The database provides information on time to diagnosis (i.e., interval from onset of pain to final diagnosis of AAD at any hospital) in terms of hospital arrival times (interval from pain to presentation at any hospital) and in-hospital diagnostic times (hospital presentation to final diagnosis of AAD in any hospital). We surveyed distributions of these 3 measurements during the overall study period and across calendar years. The study was based on records of all patients who received a final diagnosis of spontaneous AAD in any of the network hospitals from 1996 to 2006. AAD comprised “classic” aortic dissection and intramural hematoma and was defined and classified according to the Stanford classification. We also used detailed reviews of patients' original presentation, cardiac tomographic scans, magnetic resonance imaging examinations, transesophageal and transthoracic echocardiographic recordings, and electrocardiograms. In brief, all available echocardiographic recordings were blindly reviewed by a team of 3 expert echocardiographers who noted the anatomic details of the AAD and identified cases of coronary ostia involvement of the dissection/hematoma by assessment of the dynamic spatial relations between the ostia and the intimal flap (or intramural hematoma). Magnetic resonance imaging and cardiac tomographic scans were reviewed by 2 expert cardiovascular radiologists to define anatomic details. In a subgroup of patients, data were available on routine cardiac troponin testing (since around 2000), performed according to the standard protocol used in chest pain units (blood samples taken at presentation and after 8 hours or until a correct diagnosis of aortic dissection was reached). Presentation electrocardiograms were blindly reviewed by 3 experienced cardiologists who identified (according to standard diagnostic criteria) myocardial infarction patterns, bundle branch blocks, hemiblocks, and repolarization abnormalities.

The study was conducted in accordance with the guiding principles of the Helsinki Declaration and all patients provided written informed consent for anonymous data publication. Application for formal approval from an ethical committee would not have been deemed appropriate for this observational study.

According to current guidelines,¹², ¹³ an electrocardiogram was considered acute coronary syndrome (ACS)-like in the presence of ≥1 of the following characteristics in ≥2 contiguous leads: ST-segment elevation ≥0.1 mV, ST-segment depression ≥0.1 mV, and T-wave inversion ≥0.2 mV. Shock was defined as sustained hypotension (systolic blood pressure <90 mm Hg for ≥30 minutes) accompanied by clinical signs indicating peripheral/cerebral hypoperfusion.¹⁴ Cardiac tamponade was defined as coexistence of ≥2 of the following signs: jugular venous distention, a pulsus paradoxus of ≥10 mm Hg, a heart rate >100 beats/min, and a systolic blood pressure <100 mm Hg.¹⁵ Severe and moderate-to-severe aortic regurgitation at transthoracic/transesophageal echocardiography was considered hemodynamically significant. Pleural effusion was diagnosed by chest x-ray or cardiac tomographic scan. Pericardial effusion was diagnosed by transthoracic/transesophageal echocardiogram, cardiac tomogram, or magnetic resonance imaging. Periaortic hematoma was diagnosed by transthoracic/transesophageal echocardiogram, cardiac tomogram, or magnetic resonance imaging.¹⁶

Continuous variables were summarized as mean ± SD or median (interquartile range [IQR]), as appropriate and categorical variables as number (percentage). Chi-square test or Student's t test were used for 2-way comparisons, as appropriate. To assess secular trends, we constructed a histogram and box plots of delays (i.e., hospital arrival time, in-hospital diagnostic time, and overall time to diagnosis) by calendar year and performed Spearman rank correlation coefficient, if appropriate. We used 75th percentiles as cutoffs to define unusually long delays within the overall population and each Stanford subtype and explored possible associations with clinical, instrumental, and laboratory factors (based on variables listed in Table 1). For analysis of (univariate) predictors of long delays, we focused on in-hospital diagnostic times (rather than hospital arrival times or overall time to diagnosis) based on the diagnostic relevance of this period and the distribution of the delays. Of note, given the objective of the study to identify confounders, we used only univariate analysis.

Table 1. Patients' characteristics at presentation in overall study population and according to Stanford type

Variable	Overall (n = 161)	Type A (n = 115)	Type B (n = 46)
Age (yrs), mean ± SD	63±14	63±13	62±6
Men	110(68%)	73(63%)	37(80%)
Intramural hematoma	22(14%)	14(12%)	8(17%)
Hypertension(history)	108(67%)	75(65%)	33(72%)
Antihypertensive therapy	99/159(62%)	69/113(61%)	30(65%)
Marfan syndrome	7(4%)	4(3%)	3(7%)
Bicuspid aortic valve	4(2%)	2(2%)	2(4%)
Coronary artery disease (history)	10/155(6%)	5/111(5%)	5/44(11%)
Previous stroke	8/155(5%)	6/111(5%)	2/44(5%)
Known thoracic aortic aneurysm	11/155(7%)	8/111(7%)	3/44(7%)
Previous ascending aorta and/or valve surgery	5/155(3%)	1/111(1%)	4/44(9%)
Systolic blood pressure (mm Hg)	146±25	133±25	178±35
Systolic blood pressure ≤90 mm Hg	30(19%)	26(23%)	4(9%)
Systolic blood pressure >160 mm Hg	58(36%)	27(23%)	31(67%)
Chest pain	112(70%)	89(77%)	23(50%)
Abdominal pain	53(33%)	34(30%)	19(41%)
Pain plus syncope	17(11%)	16(14%)	1(2%)
Pain plus shock	25(16%)	24(21%)	1(2%)
≥1 “characteristic” finding	103/137(75%)	64/92(70%)	39/45(87%)
Back pain	70(43%)	37(32%)	33(72%)
Migratory pain	24(15%)	13(11%)	11(24%)
Pain plus pulse deficit	36/110(33%)	27/81(33%)	9/29(31%)
Pain plus cerebrovascular accident	8(5%)	5(4%)	3(7%)
Other types of pain	20/160(12%)	14/114(12%)	6(13%)
No pain	8(5%)	8(7%)	0(0%)
Dyspnea	21(13%)	15(13%)	6(13%)
Syncope	21(13%)	20(17%)	1(2%)
Paraplegia	6(4%)	3(3%)	3(7%)
Shock within 12 h of admission	32/152(21%)	30/109(28%)	2/43(5%)
Cardiac tamponade	26(16%)	26(23%)	0(0%)
Pericardial effusion	64(40%)	56(49%)	8(17%)
Pleural effusion	39/157(25%)	14/111(13%)	25(54%)
Periaortic effusion	34/150(23%)	13/104(13%)	21(46%)
Moderate/severe aortic regurgitation	53/152(35%)	52/109(48%)	1/43(2%)
ACS-like electrocardiogram	47(29%)	33(29%)	14(30%)
Troponin positivity	15/82(18%)	10/59(17%)	5/23(22%)
No. of diagnostic tests performed	1.6±0.7	1.5±0.7	1.7±0.8

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Results 

A total of 161 patients received a final diagnosis of spontaneous AAD during the study period (in-hospital mortality, Stanford type A, 27%, 31 of 115; type B, 15%, 7 of 46) based on chest computed tomogram in 109 (68%), angiogram in 27 (17%), transesophageal echocardiogram in 20 (12%), and magnetic resonance imaging in 5 (3%). Of note, 12 patients (7 type A) received thrombolytic therapy and 3 patients (2 type A) were sent to the catheter laboratory for coronary angiography. Routinely performed troponin test results were available for 82 patients (51%, 59 Stanford type A, 23 type B). Table 1 presents patients' clinical characteristics according to Stanford type. Only a minority of patients had ≥1 clinical finding considered characteristic of the disease (i.e., migratory pain, back pain, pain plus pulse deficit, or pain plus cerebrovascular accident).¹, ¹⁷ Regarding known risk factors for AAD,¹, ¹⁷ most patients had a history of hypertension, whereas only a small minority of patients had Marfan syndrome or a bicuspid aortic valve.

Globally, median overall time to diagnosis (i.e., hospital arrival time plus in-hospital diagnostic time) was 330 minutes (IQR 893). Over the entire period, in-hospital diagnostic times were about twofold greater than hospital arrival times (median 177 minutes, IQR 644, vs 75 minutes, IQR 124, p = 0.0001, Wilcoxon test), an imbalance that was observed in each of the 11 years under consideration (Figure 1). Distributions of annual values of hospital arrival time, in-hospital diagnostic time, and overall time to diagnosis are presented in Figure 2. No secular trend was observable in the median values of any of the 3 measurements. Spread of in-hospital diagnostic times decreased during the years from 2001 to 2006 (ρ = −0.94, p = 0.005). No trend was apparent for spread of hospital arrival time.

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• Figure 1. 

  Annual median values of overall time to diagnosis in terms of hospital arrival time (prehosp) (light gray) and in-hospital diagnostic time (inhosp) (dark gray).

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• Figure 2. 

  Distributions of annual values of overall time to diagnosis (A), hospital arrival time (B), and in-hospital diagnostic time (C), with medians (boxes) and IQRs (lines).

Regarding hospital arrival time, no significant association was apparent between lengthy delay (defined by cutoffs based on the 75th percentile of the entire population) and any of the variables analyzed for the overall study population or the 2 Stanford types, including gender, age, and presence of pain (data not shown). Table 2, Table 3 list values of patients' characteristics according to length of in-hospital diagnostic time in the entire population (cutoff 12 hours) and for Stanford type A (cutoff 10 hours). In the overall population and the Stanford type A subset, differences or trends were recorded for pleural effusion, dyspnea at presentation, troponin positivity (all more frequent in patients with lengthy diagnosis), systolic blood pressure (higher in lengthy diagnosis), and numbers of diagnostic tests performed. Of note, in the Stanford type B subgroup (data not shown), the only appreciable difference regarded between patients with early and late diagnosis (cutoff 20 hours) was prevalence of pleural effusion (41%, 14 of 34, vs 92%, 11 of 12, p = 0.007).

Table 2. Presentation characteristics in the overall population according to in-hospital diagnostic time (cutoff, 75th percentile)

Variable	Short In-Hospital Diagnostic Time (≤12 h)	Long In-Hospital Diagnostic Time (>12 h)	p Value
	(n = 121)	(n = 40)
Age (yrs), mean ± SD	64±14	60±13	0.11
Men	79(65%)	31(78%)	0.2
AAD, Stanford type A	76(88%)	25(86%)	1.0
Intramural hematoma	19(16%)	3(8%)	0.3
Hypertension (history)	79(65%)	29(73%)	0.6
Antihypertensive therapy	73/119(61%)	26/40(65%)	0.8
Marfan syndrome	6(5%)	1(3%)	0.8
Systolic blood pressure (mm Hg)	142±43	158±33	0.03
Systolic blood pressure ≤90 mm Hg	28(23%)	2(5%)	0.02
Systolic blood pressure >160 mm Hg	40(33%)	18(45%)	0.2
Chest pain	86(71%)	26(65%)	0.6
Abdominal pain	41(34%)	12(30%)	0.8
Pain plus syncope	13(11%)	4(10%)	1.0
Pain plus shock	21(17%)	4(10%)	0.4
≥1 “characteristic” finding	79/106(75%)	24/31(77%)	0.9
Back pain	54(45%)	16(40%)	0.7
Migratory pain	17(14%)	7(18%)	0.8
Pain plus pulse deficit	40/106(38%)	11/37(30%)	0.5
Pain plus cerebrovascular accident	6(5%)	2(5%)	0.7
No pain	6(5%)	2(5%)	0.7
Dyspnea	11(9%)	10(25%)	0.02
Syncope	17(14%)	4(10%)	0.7
Shock within 12 h of admission	28/115(24%)	4/37(11%)	0.13
Cardiac tamponade	23(19%)	3(8%)	0.14
Pericardial effusion	49(40%)	15(38%)	0.9
Pleural effusion	21/118(18%)	18/39(46%)	0.0001
Moderate/severe aortic regurgitation	40/115(35%)	13/37(35%)	0.9
ACS-like electrocardiogram	33(27%)	14(35%)	0.5
Troponin positivity	8/62(13%)	7/20(35%)	0.06
In-hospital death	31(26%)	7(18%)	0.4
No. of diagnostic tests performed	1.5±0.7	1.8±0.8	0.008

Table 3. Presentation characteristics by in-hospital diagnostic time (cutoff 75th percentile) in Stanford type A subset

Variable	Short In-Hospital Diagnostic Time (≤10.75 h)	Long In-Hospital Diagnostic Time (>10.75 h)	p Value
	(n = 86)	(n = 29)
Age (yrs), mean ± SD	64±13	60±13	0.2
Men	52(61%)	21(72%)	0.4
AAD, Stanford type A	76(88%)	25(86%)	1.0
Intramural hematoma	10(12%)	4(14%)	1.0
Hypertension (history)	54(63%)	21(72%)	0.5
Antihypertensive therapy	50/84(60%)	19/29(66%)	0.7
Marfan syndrome	3(4%)	1(3%)	0.6
Systolic blood pressure (mm Hg)	129±39	144±34	0.07
Systolic blood pressure ≤90 mm Hg	23(27%)	3(10%)	0.12
Systolic blood pressure >160 mm Hg	18(21%)	9(31%)	0.4
Chest pain	68(79%)	21(72%)	0.6
Abdominal pain	27(31%)	7(24%)	0.6
Pain plus syncope	13(15%)	3(10%)	0.7
Pain plus shock	20(23%)	4(14%)	0.4
≥1 “characteristic” finding	51/71(72%)	14/22(64%)	0.6
Back pain	30(35%)	7(24%)	0.4
Migratory pain	10(12%)	3(10%)	1.0
Pain plus pulse deficit	27/72(38%)	8/26(31%)	0.7
Pain plus cerebrovascular accident	6(7%)	2(7%)	0.7
No pain	5(6%)	3(10%)	0.7
Dyspnea	7(8%)	8(28%)	0.02
Syncope	16(19%)	4(14%)	0.8
Shock within 12 h of admission	25/82(30%)	5/27(19%)	0.4
Cardiac tamponade	23(27%)	3(10%)	0.12
Pericardial effusion	42(49%)	14(48%)	0.9
Pleural effusion	8/83(10%)	6/28(21%)	0.2
Moderate/severe aortic regurgitation	39/82(48%)	13/27(48%)	0.9
ACS-like electrocardiogram	24(28%)	9(31%)	0.9
Troponin positivity	4/44(9%)	6/15(40%)	0.02
In-hospital death	24(28%)	7(24%)	0.9
No. of diagnostic tests performed	1.4±0.7	1.7±0.7	0.09

Results of univariate analysis regarding risk factors for late diagnosis in the overall study population are presented in Table 4 and Figure 3. Excess risk of lengthy in-hospital diagnostic time was apparent for pleural effusion (approximately fourfold increase), dyspnea (approximately threefold), and age <70 years (approximately twofold). Conversely, low systolic blood pressure (<105 mm Hg) was associated with a greatly decreased risk of long in-hospital diagnostic time (∼90% decrease). Results of a similar analysis restricted to the subset of patients with routine troponin test data are presented in Table 4 and Figure 3. Remarkably, in addition to pleural effusion and dyspnea, troponin positivity and ACS-like electrocardiogram turned out to be associated with long in-hospital diagnostic time (three- to fourfold excess risk).

Table 4. Univariate analysis of risk factors for late in-hospital diagnosis (>75th percentile) in overall population and within subset of patients (n = 82) with routine troponin test values

CI = confidence interval; NA = not available.

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• Figure 3. 

  Risk factors at univariate analysis for late in-hospital diagnosis (>75th percentile) in the overall population (A) and within the subset of patients (n = 82) with routine troponin test values (B). ECG = electrocardiogram; SBP = systolic blood pressure.

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Discussion 

This is the first study focusing on diagnostic times (and their possible determinants) before successful recognition of AAD. The results provide a pilot exploration of this relevant topic. In our rather densely populated (mainly urban/suburban) setting, in-hospital diagnostic times were about 2 times as long as hospital arrival times. Certain clinical/instrumental characteristics at presentation appeared to be relevant risk factors for longer in-hospital diagnostic times. An encouraging finding was that spread of in-hospital diagnostic times tapered in the previous 5 years (whereas median values remained relatively constant across the 11-year study period).

Hospital arrival times appeared reasonably short (averaging about 1 hour to 3 hours across the study period) and were rather similar to those reported by patients with ACS in similar geographic areas during these years.¹⁸, ¹⁹ The constantly low absolute values also likely reflect the effects of an articulated, long-term public health campaign in Italy regarding the relevance of chest pain and avoidable delay in the treatment of acute myocardial infarction (according to the Gruppo Italiano per lo Studio della Streptochinasi nell'Infarto Miocardico [GISSI] study group's work since the mid-1980s).²⁰, ²¹ Our data did not allow us to assess separately the 2 separate phases of prehospital delay (i.e., time from onset of symptoms to decision to call an ambulance or go to a hospital and time taken to reach a hospital). However, in our “metropolitan” setting it is reasonable to suppose that decision times often account for much of the prehospital delay (similarly to what occurs with ACS¹⁸, ¹⁹). In any case, the in-hospital (i.e., diagnostic) phase generally took about 2 times as long as the prehospital phase, thus accounting for the majority of overall delay.

We recorded considerable spread (and variations in spread between years) for in-hospital diagnostic times (Figure 2). This observation can reasonably be explained by the cognitive challenge (need for deep clinical reasoning) involved in appropriately formulating a diagnostic hypothesis regarding a relatively infrequent condition that can mimic a wide range of other diseases. We noted a marked linear decrease in the spread of in-hospital diagnostic times from 2001 to 2006. This secular trend can likely be attributed to increasingly easy access to cardiac tomography in our emergency departments, which may have lowered the threshold of diagnostic suspicion that physicians felt they needed to reach to call for this key imaging examination.

Final diagnosis of AAD still depends on imaging techniques—most often cardiac tomography and transesophageal echocardiography—which, although widely available, fall outside the battery of tests routinely performed on admission. Despite ongoing biological research,²², ²³, ²⁴ no routinely available plasma test can serve as a prompt diagnostic marker (in the way troponin does for acute myocardial infarction). Consequently, arrival at congruous diagnostic suspicion of spontaneous AAD is an essential prerequisite to order the appropriate imaging examinations, which can then rapidly test the diagnostic hypothesis. The problem in this “2-step” diagnostic process is timely suspicion of spontaneous AAD. The literature (including medical textbooks) generally underscores the importance of looking for highly specific findings (in terms of back pain, migratory/radiating pain, pain plus pulse deficit, or pain plus cerebral ischemic accident). Unfortunately, in the International Registry of Acute Aortic Dissection (IRAD)¹, ², ³, ⁴, ⁵, ⁶ and the present study, the frequency of each of these “characteristic” findings (when considered singly) was relatively low. Nevertheless, we did record ≥1 of these 4 characteristic findings in about 3/4 of patients (irrespective of early or late in-hospital diagnosis). We therefore explored possible diagnostic confounders (findings that may act as clinical “red herrings”) in terms of risk factors for an unusually long in-hospital diagnostic time in terms of the top quartile. In the overall study population, 2 strong clinical confounders appeared to be pleural effusion and dyspnea (associated with a three- to fourfold excess risk of a lengthy in-hospital diagnosis), which affect a consistent minority of patients with AAD (in the case of pleural effusion, particularly those with Stanford type B disease). These 2 clinical manifestations may induce clinicians to formulate a primary diagnostic hypothesis of pulmonary or cardiac disease, potentially leading to a time-consuming red herring. Patients <70 years of age also appeared to have about double the risk of late in-hospital diagnosis. This association may be tentatively attributed to a lower index of diagnostic suspicion of spontaneous AAD in patients with less strong age-related risk factors and co-morbidities. Interestingly, in the subset of patients who routinely underwent troponin testing, troponin positivity and an ACS-like electrocardiographic pattern were also associated with long in-hospital diagnosis. This observation is broadly in line with the findings of a study focusing on misdiagnosis of AAD,⁸ where ACS was the most common erroneous judgment and frequently led to inappropriate antithrombotic treatment. In the current absence of a specific biomarker for AAD, troponin positivity may constitute a tricky red herring, especially given the high frequency of ACS among emergency admissions and the shared causal risk factors for AAD and ACS. It also has to be borne in mind that in many cases of AAD, electrocardiographic repolarization abnormalities and/or increased troponin levels do actually reflect coexistence of a true myocardial ischemia (with a multifactorial pathogenesis).²⁵ Conversely, low systolic blood pressure (<105 mm Hg) was associated with a greatly decreased risk of a long in-hospital diagnostic time (∼90% decrease). Taken together, these findings underline the need for clinicians to suspect AAD whenever plausible, even in cases where the initial diagnostic hypothesis is ACS, pneumonia, or unexplained pleural effusion.

The study was conducted in a favorable organizational setting within a rather densely populated urban center and its surrounding hinterland. The findings regarding hospital arrival times obviously cannot be generalized to more challenging geographic settings. The numbers available from this single network study (even over a 11-year period) limited the statistical power to explore possible determinants of diagnostic delay and precluded meaningful analysis of the impact on mortality. We were able to analyze only a limited number of potentially relevant variables based on our prospectively collected database. Inevitably, this registry study was based only on patients who reached a final diagnosis of AAD and could not take into account those patients for whom a diagnosis of AAD was never made or was made only at autopsy (and were not included in this clinical registry).

This work may be considered a pilot study providing an initial analysis of risk factors for diagnostic delay in a predominantly urban/suburban context. More extensive studies may help orient clinicians to reach appropriate diagnostic suspicion more rapidly and perhaps guide educational campaigns regarding this important cause of avoidable death.

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