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Meta-Analysis
. 2016 Sep;23(9):963-1003.
doi: 10.1111/acem.12984. Epub 2016 Sep 6.

Spontaneous Subarachnoid Hemorrhage: A Systematic Review and Meta-analysis Describing the Diagnostic Accuracy of History, Physical Examination, Imaging, and Lumbar Puncture With an Exploration of Test Thresholds

Christopher R Carpenter  1 , Adnan M Hussain  2 , Michael J Ward  3 , Gregory J Zipfel  4 , Susan Fowler  5 , Jesse M Pines  6 , Marco L A Sivilotti  7
Affiliations
Meta-Analysis

Spontaneous Subarachnoid Hemorrhage: A Systematic Review and Meta-analysis Describing the Diagnostic Accuracy of History, Physical Examination, Imaging, and Lumbar Puncture With an Exploration of Test Thresholds

Christopher R Carpenter et al. Acad Emerg Med. 2016 Sep.

Abstract

Background: Spontaneous subarachnoid hemorrhage (SAH) is a rare, but serious etiology of headache. The diagnosis of SAH is especially challenging in alert, neurologically intact patients, as missed or delayed diagnosis can be catastrophic.

Objectives: The objective was to perform a diagnostic accuracy systematic review and meta-analysis of history, physical examination, cerebrospinal fluid (CSF) tests, computed tomography (CT), and clinical decision rules for spontaneous SAH. A secondary objective was to delineate probability of disease thresholds for imaging and lumbar puncture (LP).

Methods: PubMed, Embase, Scopus, and research meeting abstracts were searched up to June 2015 for studies of emergency department patients with acute headache clinically concerning for spontaneous SAH. QUADAS-2 was used to assess study quality and, when appropriate, meta-analysis was conducted using random effects models. Outcomes were sensitivity, specificity, and positive (LR+) and negative (LR-) likelihood ratios. To identify test and treatment thresholds, we employed the Pauker-Kassirer method with Bernstein test indication curves using the summary estimates of diagnostic accuracy.

Results: A total of 5,022 publications were identified, of which 122 underwent full-text review; 22 studies were included (average SAH prevalence = 7.5%). Diagnostic studies differed in assessment of history and physical examination findings, CT technology, analytical techniques used to identify xanthochromia, and criterion standards for SAH. Study quality by QUADAS-2 was variable; however, most had a relatively low risk of biases. A history of neck pain (LR+ = 4.1; 95% confidence interval [CI] = 2.2 to 7.6) and neck stiffness on physical examination (LR+ = 6.6; 95% CI = 4.0 to 11.0) were the individual findings most strongly associated with SAH. Combinations of findings may rule out SAH, yet promising clinical decision rules await external validation. Noncontrast cranial CT within 6 hours of headache onset accurately ruled in (LR+ = 230; 95% CI = 6 to 8,700) and ruled out SAH (LR- = 0.01; 95% CI = 0 to 0.04); CT beyond 6 hours had a LR- of 0.07 (95% CI = 0.01 to 0.61). CSF analyses had lower diagnostic accuracy, whether using red blood cell (RBC) count or xanthochromia. At a threshold RBC count of 1,000 × 10(6) /L, the LR+ was 5.7 (95% CI = 1.4 to 23) and LR- was 0.21 (95% CI = 0.03 to 1.7). Using the pooled estimates of diagnostic accuracy and testing risks and benefits, we estimate that LP only benefits CT-negative patients when the pre-LP probability of SAH is on the order of 5%, which corresponds to a pre-CT probability greater than 20%.

Conclusions: Less than one in 10 headache patients concerning for SAH are ultimately diagnosed with SAH in recent studies. While certain symptoms and signs increase or decrease the likelihood of SAH, no single characteristic is sufficient to rule in or rule out SAH. Within 6 hours of symptom onset, noncontrast cranial CT is highly accurate, while a negative CT beyond 6 hours substantially reduces the likelihood of SAH. LP appears to benefit relatively few patients within a narrow pretest probability range. With improvements in CT technology and an expanding body of evidence, test thresholds for LP may become more precise, obviating the need for a post-CT LP in more acute headache patients. Existing SAH clinical decision rules await external validation, but offer the potential to identify subsets most likely to benefit from post-CT LP, angiography, or no further testing.

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Figures

Figure 1
Figure 1
Study Selection Process
Figure 2
Figure 2
Forest Plots for Diagnostic Elements of History for SAH
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Figure 2
Forest Plots for Diagnostic Elements of History for SAH
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Figure 2
Forest Plots for Diagnostic Elements of History for SAH
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Figure 2
Forest Plots for Diagnostic Elements of History for SAH
Figure 2
Figure 2
Forest Plots for Diagnostic Elements of History for SAH
Figure 2
Figure 2
Forest Plots for Diagnostic Elements of History for SAH
Figure 2
Figure 2
Forest Plots for Diagnostic Elements of History for SAH
Figure 2
Figure 2
Forest Plots for Diagnostic Elements of History for SAH
Figure 2
Figure 2
Forest Plots for Diagnostic Elements of History for SAH
Figure 2
Figure 2
Forest Plots for Diagnostic Elements of History for SAH
Figure 2
Figure 2
Forest Plots for Diagnostic Elements of History for SAH
Figure 2
Figure 2
Forest Plots for Diagnostic Elements of History for SAH
Figure 2
Figure 2
Forest Plots for Diagnostic Elements of History for SAH
Figure 2
Figure 2
Forest Plots for Diagnostic Elements of History for SAH
Figure 2
Figure 2
Forest Plots for Diagnostic Elements of History for SAH
Figure 3
Figure 3
Forest Plots for Diagnostic Elements of Physical Exam for SAH
Figure 3
Figure 3
Forest Plots for Diagnostic Elements of Physical Exam for SAH
Figure 3
Figure 3
Forest Plots for Diagnostic Elements of Physical Exam for SAH
Figure 4
Figure 4
Forest Plots for Diagnostic Accuracy of CT for SAH
Figure 4
Figure 4
Forest Plots for Diagnostic Accuracy of CT for SAH
Figure 4
Figure 4
Forest Plots for Diagnostic Accuracy of CT for SAH
Figure 5
Figure 5
Forest Plots for Diagnostic Accuracy of CSF Analysis for SAH
Figure 5
Figure 5
Forest Plots for Diagnostic Accuracy of CSF Analysis for SAH
Figure 5
Figure 5
Forest Plots for Diagnostic Accuracy of CSF Analysis for SAH
Figure 6
Figure 6. Bernstein Test-Indication Curves for CT and LP
Test Indication Curves with 95% CI natural scale in panels A through D. Diagnostic accuracy of computed tomography or lumbar puncture for subarachnoid hemorrhage. Raw test-indication curves as proposed by Bernstein are shown, providing a graphical representation of the Bayesian post-test probability (y-axis) based on either a positive (upper curved black line, with 95% confidence intervals in grey) or negative (lower curved lines) test result as a function of the pre-test probability (x-axis). The graphs use the same principle as the Fagan nomogram, but provide more intuitive representations of the diagnostic accuracy of a test. The four tests considered are cranial CT obtained within 6 hours (panel A) or later (panel B) from headache onset, or LP with more than 1,000×106/L erythrocytes (panel C) or visible xanthochromia (panel D). The distance of the curves from the main diagonal of zero diagnostic information provide a visual representation of the information gained from the test result.
Figure 6
Figure 6. Bernstein Test-Indication Curves for CT and LP
Test Indication Curves with 95% CI natural scale in panels A through D. Diagnostic accuracy of computed tomography or lumbar puncture for subarachnoid hemorrhage. Raw test-indication curves as proposed by Bernstein are shown, providing a graphical representation of the Bayesian post-test probability (y-axis) based on either a positive (upper curved black line, with 95% confidence intervals in grey) or negative (lower curved lines) test result as a function of the pre-test probability (x-axis). The graphs use the same principle as the Fagan nomogram, but provide more intuitive representations of the diagnostic accuracy of a test. The four tests considered are cranial CT obtained within 6 hours (panel A) or later (panel B) from headache onset, or LP with more than 1,000×106/L erythrocytes (panel C) or visible xanthochromia (panel D). The distance of the curves from the main diagonal of zero diagnostic information provide a visual representation of the information gained from the test result.
Figure 6
Figure 6. Bernstein Test-Indication Curves for CT and LP
Test Indication Curves with 95% CI natural scale in panels A through D. Diagnostic accuracy of computed tomography or lumbar puncture for subarachnoid hemorrhage. Raw test-indication curves as proposed by Bernstein are shown, providing a graphical representation of the Bayesian post-test probability (y-axis) based on either a positive (upper curved black line, with 95% confidence intervals in grey) or negative (lower curved lines) test result as a function of the pre-test probability (x-axis). The graphs use the same principle as the Fagan nomogram, but provide more intuitive representations of the diagnostic accuracy of a test. The four tests considered are cranial CT obtained within 6 hours (panel A) or later (panel B) from headache onset, or LP with more than 1,000×106/L erythrocytes (panel C) or visible xanthochromia (panel D). The distance of the curves from the main diagonal of zero diagnostic information provide a visual representation of the information gained from the test result.
Figure 6
Figure 6. Bernstein Test-Indication Curves for CT and LP
Test Indication Curves with 95% CI natural scale in panels A through D. Diagnostic accuracy of computed tomography or lumbar puncture for subarachnoid hemorrhage. Raw test-indication curves as proposed by Bernstein are shown, providing a graphical representation of the Bayesian post-test probability (y-axis) based on either a positive (upper curved black line, with 95% confidence intervals in grey) or negative (lower curved lines) test result as a function of the pre-test probability (x-axis). The graphs use the same principle as the Fagan nomogram, but provide more intuitive representations of the diagnostic accuracy of a test. The four tests considered are cranial CT obtained within 6 hours (panel A) or later (panel B) from headache onset, or LP with more than 1,000×106/L erythrocytes (panel C) or visible xanthochromia (panel D). The distance of the curves from the main diagonal of zero diagnostic information provide a visual representation of the information gained from the test result.
Figure 7
Figure 7. Test-indication curves illustrating when benefits of testing outweigh the risks for subarachnoid hemorrhage
The complete test-indication curves as proposed by Bernstein are shown which incorporate the Pauker-Kassirer threshold approach to deciding whether to perform the test in question. This technique compares the risks versus benefits for treatment (in this case proceeding to CT or formal angiography), and calculates the corresponding treatment threshold (shown as horizontal dashed lines, with the point estimate in black and the upper and lower bands of the sensitivity analysis in grey). The vertical line dashed line (or rightmost line when two are visible) therefore represents the pre-test probability range below which the diagnostic test might be appropriate, assuming the test in question had neither risks nor costs; when the pre-test probability is higher, empirical treatment is recommended since the post-test probability exceeds this threshold even if the test is negative. Because tests have a non-zero risk, the actual pre-test range for which performing the test is rational is narrower, and is shown by the thick line between the arrowheads. The thinner solid line extending beyond the arrows illustrates the effect of reducing the test risk by half. At pre-test probabilities to the left of this range, no further testing is indicated. For example, this approach suggests proceeding directly to angiography when the pre-CT probability exceeds 10% (panel B), unless the unenhanced CT can be obtained within 6 hours (panel A) in which case a pre-test probability of nearly 70% seems appropriate before proceeding directly to angiography. On the other hand, the use of CT is warranted even in very low risk patients, down to perhaps 0.7% pre-test probability as determined primarily by the risks of the CT itself. The LP curves, however, illustrate that the pre-LP probabilities that justify performing LP are very narrow, ranging from 2% to 4% in panel C and 2% to 7% in panel D. Moreover, these pre-LP probabilities in turn can only arise after an implausibly high pre-CT probability (>>20%). Testing Thresholds
Figure 7
Figure 7. Test-indication curves illustrating when benefits of testing outweigh the risks for subarachnoid hemorrhage
The complete test-indication curves as proposed by Bernstein are shown which incorporate the Pauker-Kassirer threshold approach to deciding whether to perform the test in question. This technique compares the risks versus benefits for treatment (in this case proceeding to CT or formal angiography), and calculates the corresponding treatment threshold (shown as horizontal dashed lines, with the point estimate in black and the upper and lower bands of the sensitivity analysis in grey). The vertical line dashed line (or rightmost line when two are visible) therefore represents the pre-test probability range below which the diagnostic test might be appropriate, assuming the test in question had neither risks nor costs; when the pre-test probability is higher, empirical treatment is recommended since the post-test probability exceeds this threshold even if the test is negative. Because tests have a non-zero risk, the actual pre-test range for which performing the test is rational is narrower, and is shown by the thick line between the arrowheads. The thinner solid line extending beyond the arrows illustrates the effect of reducing the test risk by half. At pre-test probabilities to the left of this range, no further testing is indicated. For example, this approach suggests proceeding directly to angiography when the pre-CT probability exceeds 10% (panel B), unless the unenhanced CT can be obtained within 6 hours (panel A) in which case a pre-test probability of nearly 70% seems appropriate before proceeding directly to angiography. On the other hand, the use of CT is warranted even in very low risk patients, down to perhaps 0.7% pre-test probability as determined primarily by the risks of the CT itself. The LP curves, however, illustrate that the pre-LP probabilities that justify performing LP are very narrow, ranging from 2% to 4% in panel C and 2% to 7% in panel D. Moreover, these pre-LP probabilities in turn can only arise after an implausibly high pre-CT probability (>>20%). Testing Thresholds
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