Archives of Academic Emergency Medicine. 2020; 8(1): e79 REV I EW ART I C L E Accuracy of Canadian CT Head Rule and New Orleans Cri- teria for Minor Head Trauma; a Systematic Review and Meta-Analysis Abeer Kadum Abass Alzuhairy1∗ 1. Lecturer in Diagnostic Imaging - Surgery Department, College of Medicine, University of Sulimani Kurdistan Region, Iraq. Received: August 2020; Accepted: September 2020; Published online: 8 September 2020 Abstract: Introduction: The present meta-analysis has two objectives; primarily, the predictive values of Canadian com- puted tomography (CT) Head Rule (CCHR) and New Orleans Criteria (NOC) will be compared. Secondly, the possibility of interchangeable use of the two models in cases of contraindication will be evaluated. Method: An extensive search was performed in Medline, Embase, Scopus and Web of Science electronic databases from the inception of databases until the end of July 2020. All prospective and retrospective observational and diagnostic accuracy studies comparing NOC and CCHR on a single group of patients were included. Data were entered to STATA 14.0 statistical program, and analyses were performed using "metandi" command. Results: Data from 14 articles were included (21140 samples). Summary sensitivity, specificity, and diagnostic odds ratio of CCHR in prediction of positive CT findings were 89.8% (95% CI: 79.6 to 95.2), 38.3% (95% CI: 34.0 to 42.8), and 5.5 (95% CI: 2.3 to 13.1), respectively. In addition, summary sensitivity, specificity, and diagnostic odds ratio of NOC in prediction of positive CT findings were 97.2% (95% CI: 89.7 to 99.2), 12.3% (95% CI: 7.4 to 19.8), and 4.8 (95% CI: 1.2 to 18.3), respectively. Summary sensitivity, specificity, and diagnostic odds ratio of CCHR in prediction of clinically important TBI (ciTBI) in mild TBI patients were 92.5% (95% CI: 79.5 to 97.5), 40.1% (95% CI: 34.8 to 45.6), and 8.3 (95% CI: 2.4 to 29.2), respectively. In addition, summary sensitivity, specificity, and diagnostic odds ratio of NOC in prediction of ciTBI were 98.3% (95% CI: 93.8 to 99.6), 8.5% (95% CI: 4.8 to 14.5), and 5.4 (95% CI: 1.5 to 20.0), respectively. Conclusion: The present meta-analysis demonstrated that both CCHR and NOC scores have a good predictive value in predicting the presence of abnormal findings in CT scan and ciTBI. The similar performance of CCHR and NOC models results in their interchangeable use in cases of contraindication. Keywords: Sensitivity and Specificity; Predictive Value of Tests; Craniocerebral Trauma; Systematic Review; Meta-Analysis Cite this article as: Alzuhairy A K A. Accuracy of Canadian CT Head Rule and New Orleans Criteria for Minor Head Trauma; a Systematic Review and Meta-Analysis. Arch Acad Emerg Med. 2020; 8(1): e79. 1. Introduction Traumatic brain injury (TBI) is one of the most common causes of emergency department referrals worldwide. The global burden of TBI has been increasing in recent years, with an increasing prevalence rate of 8.4% between 1990 and 2016, accompanied by a increasing incidence rate of 3.6% in years of life lived with disability (1). The gold standard in diagnosis of intracranial complications after TBI is computed tomography (CT) scan. However, mild TBI is the most prevalent type of TBI, in which brain CT scans ∗Corresponding Author: Abeer kadum Abass Alzuhairy;Surgery Department, College of Medicine, University of Sulimani Kurdistan Region, Iraq. Tel: +9847702213543, Email: drabealzuhairy@yahoo.com are usually normal (2). Therefore, unnecessary CT scans are quite prevalent. In an attempt to reduce the number of un- necessary CT scans, several scoring systems have been intro- duced (3-10), including the Canadian CT Head Rule (CCHR) and the New Orleans Criteria (NOC) (11, 12). The perfor- mance of these two models has been validated in various studies (13-16), but limitations have been attributed to each of these scoring systems. For instance, NOC is only applica- ble in patients with a Glasgow Coma Scale (GCS) of 15, and cannot be used for patients with a GCS of 14 or 13. On the other hand, CCHR cannot be applied to patients under 18 years of age, patients on blood thinners and patients having seizures after a head trauma. These limitations have caused an uncertainty regarding which of the two scoring systems has better performance in identifying high-risk patients, and whether these tools can be used interchangeably. To evaluate This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem A. Kadum Abass Alzuhairy 2 the performance of a rule out criteria in identifying high risk patients in mild TBI, two major outcomes will be assessed, including positive CT scan findings and clinically important TBI (ciTBI). Existing studies have attempted to evaluate the diagnostic value of these two decision rules in predicting positive CT findings and ciTBI to reduce unnecessary CT scans (13, 17- 20). However, a consensus over the subject is yet to be achieved. Hence, the present meta-analysis has two objec- tives; primarily, the predictive values of CCHR and NOC will be compared; and secondly, the possibility of interchange- able use of the two models in cases of contraindication will be evaluated. 2. Method 2.1. Search strategy To achieve the objectives of the present study, an extensive search was performed in Medline, (via PubMed), Embase, Scopus and Web of Science electronic databases from the inception of databases until the end of July 2020. In addi- tion, PubMed Central records were also added, so that no articles would be missed. Initially, keywords related to trau- matic brain injury in combination with keywords related to Canadian CT Head Rule or New Orleans Criteria were used in the search. However, the number of the achieved records was low. Hence, to perform a more extensive search, only keywords related to Canadian CT Head Rule or New Orleans Criteria were used in the search. The search term in Medline database is presented below. "Canadian computed tomog- raphy Head Rule" [Title / Abstract] OR "Canadian CT Head Rule" [Title / Abstract] OR "New Orleans criteria" [Title / Ab- stract] In addition to the systematic search, a manual search was also performed via Google and Google scholar search engines and in related articles’ bibliography to include articles that were not indexed or not found. 2.2. Selection criteria All prospective and retrospective observational and diagnos- tic accuracy studies, performing a comparison between the two models of NOC and CCHR on a single group of patients, were entered to the present study. Exclusion criteria were lack of reporting sensitivity and specificity or true positive, true negative, false positive and false negative. Other ex- clusion criteria were lack of assessment of CT scan findings or ciTBI, studies performed on children, failure to compare CCHR and NOC criteria and review studies. 2.3. Data extraction and quality assessment Two independent researchers screened the titles and ab- stracts of the retrieved records, based on the inclusion and exclusion criteria. Next, potentially relevant studies were screened more carefully, and finally, related articles were included. Then, the two researchers independently sum- marized the included articles into a checklist, composed of data including first author’s name, publication year, coun- try, study design, sampling method, sample size, age, gen- der distribution, outcome, sensitivity, specificity, true posi- tive, true negative, false positive and false negative. Any dis- agreements were resolved by discussion. Furthermore, eval- uated outcomes included positive findings on CT and ciTBI. Quality control of the studies was performed based on the Quality Assessment of Diagnostic Accuracy Studies version 2.0 (QUADAS-2) (21). 2.4. Statistical analysis Data were entered to STATA 14.0 statistical program, and analyses were performed using "metandi" command. Analy- ses were performed in two separate sections according to the evaluated outcomes. Initially, the predictive values of CCHR and NOC in CT scan positive findings were assessed. Next, the predictive values of CCHR and NOC in ciTBI were evalu- ated. To assess publication bias, Deek’s funnel plot asymme- try test was used. Results are presented as sensitivity, speci- ficity, positive and negative likelihood ratios, and diagnostic odds ratio with 95% confidence interval (95% CI). 3. Results 3.1. Characteristics The search resulted in 406 records. After eliminating dupli- cates and screening, 64 titles of potentially relevant studies were screened in more detail, and finally data from 14 articles were included in the present meta-analysis (11, 13, 14, 17-19, 22-29) (Figure 1). These articles entailed 21140 samples. Of these, 1940 patients had a positive CT scan finding and 19180 patients had a negative CT scan. Of the 1940 patients with positive CT scan, 594 ciTBIs were observed. The evaluated outcome was only positive CT scan findings in eight articles, only ciTBI in two studies, and both outcomes in 5 studies. Ta- ble 1 summarizes the characteristics of the included studies. 3.2. Accuracy of CCHR and NOC in prediction of positive CT findings Summary sensitivity, specificity, and diagnostic odds ratio of CCHR in prediction of positive CT findings were 89.8% (95% CI: 79.6 to 95.2), 38.3% (95% CI: 34.0 to 42.8), and 5.5 (95% 2.3 to 13.1), respectively. In addition, summary sensitivity, specificity, and diagnostic odds ratio of NOC in prediction of positive CT findings were 97.2% (95% CI: 89.7 to 99.2), 12.3% (95% CI: 7.4 to 19.8), and 4.8 (95% CI: 1.2 to 18.3), respectively (Table 2 and Figure 2). This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 3 Archives of Academic Emergency Medicine. 2020; 8(1): e79 3.3. Accuracy of CCHR and NOC in prediction of ciTBI Summary sensitivity, specificity, and diagnostic odds ratio of CCHR in prediction of ciTBI in patients with mild traumatic brain injuries were 92.5% (95% CI: 79.5 to 97.5), 40.1% (95% CI: 34.8 to 45.6), and 8.3 (95% CI: 2.4 to 29.2), respectively. In addition, summary sensitivity, specificity, and diagnostic odds ratio of NOC in prediction of ciTBI were 98.3% (95% CI: 93.8 to 99.6), and 8.5% (95% CI: 4.8 to 14.5), 5.4 (95% CI: 1.5 to 20.0), respectively (Table 2 and Figure 2). 3.4. Quality assessment and risk of bias Risk of bias assessment based on QUADAS-2 scale showed that risk of bias regarding patient selection was high in four studies, and unclear in 2 studies. In addition, flow and timing was high risk in 2 studies and unclear 2 other studies. Other items were categorized as low risk in risk of bias assessment and applicability domains (Table 3 and figure 3). Deeks’ fun- nel asymmetry plots showed that there was no publication bias in assessment of accuracy of CCHR and NOC in predic- tion of positive CT findings (p for CCHR = 0.65; p for NOC = 0.13) and ciTBI (p for CCHR = 0.84; p for NOC = 0.50) (Figure 3). 4. Discussion The present meta-analysis showed that both CCHR and NOC scores have good predictive value in predicting abnormal findings in CT scan and ciTBI. Although NOC’s sensitivity was slightly higher than that of CCHR, but CCHR’s specificity was much higher than that of NOC. However, in interpreting the results of the two models, bear in mind that both models are designed for screening patients and identifying high risk ones to decrease the number of unnecessary CT scans. In screen- ing tests, sensitivity is more important than specificity, be- cause the task of these tests is to select eligible patients for CT scan. Hence, these two models are not designed for a definite diagnosis determination, and a low specificity is not consid- ered a weakness of the tests. Accordingly, it can be concluded that the values of both CCHR and NOC models in screening minor head trauma patients are similar. The similarity of the two models in predicting positive CT findings and ciTBI is a profound finding in the present study, because both CCHR and NOC models have limitations that affect their performance. For instance, there is no indica- tion for using NOC in patients with a GCS less than 15, while CCHR is recommended for patients having a GCS between 13 and 15. On the other hand, patients under the age of 18 years, patients on blood thinners and patients with seizures after head traumas are the exclusion criteria for CCHR. As a result, in cases of contraindication, the other score can be used in- stead. In a similar systematic review in 2017, Webster et al. showed that both CCHR and NOC models have the same sensitivity in predicting ciTBI, while the specificity of CCHR is much higher than that of NOC (10). The findings of the current study are also in line with that of Webster et al. However, in the present article, more studies were included and at the end, a meta-analysis was performed, unlike in the review by Webster et al. The quality control was performed according to QUADAS-2 instructions, showing an acceptable status regarding most of the included studies in the fields of risk of bias and applica- bility, so the results of the present meta-analysis are authen- tic. On the other hand, no publication bias was observed in the present study, confirming the validity of the findings of our study. The current study demonstrated that the values of CCHR and NOC models in predicting positive CT find- ings and ciTBI are desirable. However, in addition to the diagnostic value, cost-effectiveness is also important. For this purpose, a meta-analysis evaluating 24 economic studies showed that CCHR is an economically attractive tool in man- agement of mild TBI (30). Nevertheless, there existed limita- tions in the present study. One of these limitations was the lack of real-time score assessments in most of the included studies. The studies were designed in a way that in most of them, risk factors for intracranial complications used in the CCHR and NOC were collected and then their performance was evaluated. While, it was more desirable if the physician or the technician would have determined and recorded the decision whether to perform a CT scan based on the two cri- teria of NOC and CCHR, using a decision tree, and eventually compared the CT findings with their decision. Hence, it is recommended that in future studies, real-time value of both models in predicting positive CT findings and ciTBI be com- pared. 5. Conclusion The present meta-analysis demonstrated that both CCHR and NOC scores have a good predictive value in predicting the presence of abnormal findings in CT scan and ciTBI. The similar performance of CCHR and NOC models results in their interchangeable use in cases of contraindication. 6. Declarations 6.1. Acknowledgements I would like to thank Dr. Mohammed IM Gubari for his valu- able helps in screening process of the paper. 6.2. Authors Contributions Abeer Kadum Abass Alzuhairy contributed to all parts of the study. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem A. Kadum Abass Alzuhairy 4 Authors ORCIDs Abeer Kadum Abass Alzuhairy: 0000-0002-2131-4717 6.3. Funding Support None. 6.4. 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Kadum Abass Alzuhairy 6 Table 1: Characteristics of the included studies Author; year; country Design) Sampling method Total sample Total age Male number Outcome Chobdari; 2018; Iran Prospective observational Convenience 264 >14 211 Positive CT Foks; 2018; Netherlands Prospective observational Consecutive 4557 16 to 101 2656 Positive CT; ciTBI Jones; 2020; USA Prospective observational Consecutive 679 ≥16 420 Positive CT Kavalci; 2014; Turkey Prospective observational NR 175 ≥18 106 Positive CT Korley; 2013; USA Prospective observational Convenience 130 ≥14 63 Positive CT Lo; 2016; Hong Kong Retrospective observational Consecutive 383 All ages NR Positive CT Mata-Mbemba; 2016; Japan Prospective observational Consecutive 142 17 to 88 96 ciTBI Papa; 2012; USA Prospective observational Consecutive 314 18 to 89 201 Positive CT; ciTBI Ro; 2011; Korea Prospective observational Consecutive 696 46.1+18.9 447 Positive CT; ciTBI Smith; 2005; Netherlands Prospective observational Consecutive 3181 16 to 102 2244 Positive CT; ciTBI Stein; 2009; USA Prospective observational Consecutive 7955 >10 4415 Positive CT Stiell; 2005; Canada Prospective observational Consecutive 1822 16 to 99 1246 ciTBI Valle Alonso; 2016; Spain Prospective observational NR 217 16 to 102 135 Positive CT Yang; 2017; China Retrospective observational Consecutive 625 >18 339 Positive CT ciTBI: Clinically important traumatic brain injuries; CT: Computed tomography; NR: Not reported. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 7 Archives of Academic Emergency Medicine. 2020; 8(1): e79 Table 2: Prognostic performance of Canadian computed tomography Head Rule and New Orleans criteria according to outcome Positive CT ciTBI Value 95% CI Value 95% CI Canadian computed tomography Head Rule True positive 1554 — 524 — True negative 7576 — 4300 — False Positive 9782 — 5818 — False negative 244 — 70 — Sensitivity 89.8 79.6 - 95.2 92.5 79.5 - 97.5 Specificity 38.3 34.1 - 42.8 40.1 34.8 - 45.6 Positive likelihood ratio 1.5 1.3 - 1.6 1.5 1.3 - 1.8 Negative likelihood ratio 0.3 0.1 - 0.6 0.2 0.1 - 0.6 Diagnostic odds ratio 5.5 2.3 - 13.1 8.3 2.4 - 29.2 New Orleans criteria True positive 1643 — 562 — True negative 3278 — 664 — False Positive 14109 — 9436 — False negative 134 — 14 — Sensitivity 97.2 89.7 - 99.3 98.3 93.8 - 99.6 Specificity 12.3 7.4 - 19.8 8.5 4.8 - 14.5 Positive likelihood ratio 1.1 1.0 - 1.2 1.1 1.0 - 1.1 Negative likelihood ratio 0.2 0.1 - 0.8 0.2 0.1 - 0.7 Diagnostic odds ratio 4.8 1.3 - 18.3 5.4 1.5 - 20.0 CI: Confidence interval; ciTBI: Clinically important traumatic brain injuries; CT: Computed tomography. Table 3: Risk of bias assessment of included studies Author; Year Risk of bias Applicability Patients selection Index test Reference test Flow and timing Patients selection Index test Reference test Chobdari; 2018 High Low Low High Low Low Low Foks; 2018 Low Low Low Low Low Low Low Jones; 2020 Low Low Low Low Low Low Low Kavalci; 2014 Unclear Low Low Low Low Low Low Korley; 2013 High Low Low High Low Low Low Lo; 2016 High Low Low Unclear Low Low Low Mata-Mbemba; 2016 Low Low Low Low Low Low Low Papa; 2012 Low Low Low Low Low Low Low Ro; 2011 Low Low Low Low Low Low Low Smith; 2005 Low Low Low Low Low Low Low Stein; 2009 Low Low Low Low Low Low Low Stiell; 2005 Low Low Low Low Low Low Low Valle alonso; 2016 Unclear Low Low Low Low Low Low Yang; 2017 High Low Low Unclear Low Low Low Low: Low risk; High: High risk; Unclear: Unclear risk of bias. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem A. Kadum Abass Alzuhairy 8 Figure 1: PRISMA flow diagram of the present meta-analysis. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 9 Archives of Academic Emergency Medicine. 2020; 8(1): e79 Figure 2: Hierarchical summary receiver-operating characteristic (HSROC) curves of Canadian computed tomography Head Rule and New Orleans criteria in prediction of computed tomography findings and clinically important traumatic brain injury. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem A. Kadum Abass Alzuhairy 10 Figure 3: Risk of bias assessment and publication bias in Canadian computed tomography Head Rule and New Orleans criteria according to outcome. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem Introduction Method Results Discussion Conclusion Declarations References