This meta-analysis aimed to evaluate the accuracy of hyperechogenicity of the substantia nigra (SN) for the differential diagnosis of Parkinson’s disease (PD) and other movement disorders. We systematically searched the PubMed, EMBASE, Cochrane Library, and China National Knowledge Infrastructure databases for relevant studies published between January 2015 and May 2020. Eligible articles comparing the echogenicity of the SN between patients with PD and those with other movement disorders were screened, and two independent reviewers extracted data according to the inclusion and exclusion criteria. Statistical analyses were conducted using STATA (version 15.0) (Stata Corporation, College Station, TX, USA), Review Manager 5.3 (Cochrane Collaboration), and Meta-DiSc1.4 to assess the pooled diagnostic value of transcranial sonography (TCS) for PD. Nine studies with a total of 1046 participants, including 669 patients with PD, were included in the final meta-analysis. Our meta-analysis demonstrated that hyperechogenicity of the SN had a pooled sensitivity and specificity of 0.85 (0.82, 0.87) and 0.71 (0.66, 0.75), respectively, for distinguishing idiopathic Parkinson’s disease from other movement disorders. Furthermore, the area under the curve of the summary receiver operating characteristic was 0.94. Transcranial sonography of the SN is a valuable tool for the differential diagnosis of PD and other movement disorders.
Parkinson’s disease (PD) is a common neurodegenerative disease. Its primary motor symptoms include tremor, rigidity, bradykinesia, and postural instability. However, depression, dementia, rapid eye movement sleep disorder, olfactory dysfunction, and other nonmotor symptoms often co-occur in patients with PD [
Substantia nigra (SN) hyperechogenicity in patients with PD was first proposed by Becker et al. in 1995 [
We conducted this meta-analysis according to the Preferred Reporting Items of Systematic Reviews and Meta-Analyses (PRISMA) statement [
Two independent reviewers (Zheng-Rong Wu and Ying Yang) identified relevant studies published between January 2015 and May 2020 by searching the EMBASE, PubMed, Cochrane Library, and China National Knowledge Infrastructure databases. We searched the existing literature on the diagnostic value of TCS for the differential diagnosis of PD and other movement disorders. Medical Subject Heading terms or keywords including “Parkinson’s disease” and “Ultrasonography, Doppler, Transcranial” and entry terms such as “Idiopathic Parkinson’s Disease,” “Parkinson’s Disease,” “Idiopathic, Parkinson Disease,” “Idiopathic, Primary Parkinsonism,” “Parkinsonism, Primary,” “Transcranial Doppler Sonography,” “Doppler Sonography, Transcranial,” and “Doppler Transcranial Sonography” were used. Moreover, we attempted to acquire unpublished data but were unable to find studies that were appropriate for inclusion.
Two reviewers evaluated all the articles independently. Studies were included if they met the following criteria: (1) focused on the diagnostic evaluation of hyperechogenicity of the SN for the diagnosis of PD (only those studies that were published between January 2015 and May 2020 were included); (2) must include participants with PD and other movement disorders; and (3) true positive, false positive, true negative, and false negative cases could be extracted from the studies. Review articles, letters, conference reports, editorial comments, prefaces, and articles not published in English were excluded. The other exclusion criteria were as follows: (1) repeatedly published studies, (2) studies whose full texts were not available, (3) studies whose control groups only contained healthy volunteers, and (4) articles on Parkinsonism but not idiopathic PD.
All the relevant data of the 9 included studies were extracted in a unified manner by two independent reviewers (Zheng-Rong Wu and Ying Yang). Any disagreements were settled by discussion with the third reviewer (Jing Yang). The principal parameters of data extraction included the name of the first author, publication year, number of patients with PD, control group, TCS device, diagnostic criteria for PD, and overall number of true-positives, false-negatives, true-negatives, and false-positives each. The quality of each article was assessed with the revised version of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool [
In the present study, data analyses were conducted using the statistical software STATA, version 15.0 (Stata Corporation, College Station, TX, USA), Review Manager 5.3 (Cochrane Collaboration), and Meta-Disc, version 1.4 for Windows (XI Cochrane Colloquium, Barcelona, Spain). We calculated Spearman correlation coefficients between sensitivity and 1−specificity to explore the potential threshold heterogeneity. The Cochrane
A total of 168 related English studies were obtained after a search of the electronic databases. Nine studies were finally selected for inclusion in the meta-analysis based on the inclusion and exclusion criteria. The main study selection process is shown in Figure
Flow chart of the selection process of the included studies.
Study quality assessment was performed using the QUADAS-2 criteria, and the study quality scores ranged from 3 to 5, as shown in Figures
Included studies according to Quality Assessment of Diagnostic Accuracy Studies-2 tool guidelines.
The risk of bias was measured via the Quality Assessment of Diagnostic Accuracy Studies tool.
Nine related studies [
Characteristics of the included studies.
Author | Year | PD cases | PD age (years) | Control groups | Cutoff value | TCS device | Diagnostic criteria | TP | FP | FN | TN |
---|---|---|---|---|---|---|---|---|---|---|---|
Alonso-Canovas A | 2018 | 138 | 71.0 (25–90) | AP, ET, VP | 21 mm or 25 mm | 2.5 MHz | UK Brain Bank criteria | 111 | 11 | 27 | 23 |
Grippe TC | 2018 | 39 | 67.0 (17–88) | AP, ET, EPD | 20 mm | 2.0–3.5 MHz | UK Brain Bank criteria | 37 | 3 | 2 | 23 |
Ghourchian S | 2019 | 18 | 65.4 (SD 5.8) | PSP | 25 mm | 2.0–2.5 MHz | UK Brain Bank criteria | 16 | 5 | 2 | 12 |
Smajlović D | 2017 | 44 | 64.9 (SD 7.8) | PSP, CBD, MSA, VP | 20 mm | 2.5 MHz | UK Brain Bank criteria | 39 | 8 | 5 | 14 |
Jesus-Ribeiro J | 2016 | 32 | 62.0 (IQR 13) | ET | 24 mm | 3.0 MHz | UK Brain Bank criteria | 28 | 1 | 4 | 25 |
Štenc Bradvica I | 2015 | 59 | 67.2 (SD 7.6) | ET,HCs | 20 mm | 2.0–4.0 MHz | Not mentioned | 56 | 6 | 3 | 45 |
Alonso-Canovas A | 2019 | 254 | 69.0 (SD 11) | PSP, CBD, MSA | 21 mm or 25 mm | 2.5 MHz | UK Brain Bank criteria | 203 | 61 | 51 | 94 |
Svetel M | 2017 | 55 | 58.9 (SD 10.9) | DRB, FD, HCs | 20 mm | 2–4 MHz | UK Brain Bank criteria | 48 | 13 | 7 | 28 |
Sanzaro E | 2016 | 30 | 45.0–85.0 | MSA, PSP | 25 mm | 2.5 MHz | Not mentioned | 27 | 2 | 3 | 3 |
AP: atypical Parkinsonism; ET: essential tremor; VP: vascular Parkinsonism; EPD: excluded PD; PSP: progressive supranuclear palsy; CBD: corticobasal degeneration; MSA: multiple system atrophy; HCs: healthy controls; DRB: dopa-responsive dystonia; FD: isolated adult-onset focal dystonia; TCS: transcranial sonography; TP: true positive; FP: false positive; FN: false negative; PD: Parkinson’s disease; TN: true negative; IQR: interquartile range; SD: standard deviation.
The Spearman correlation coefficient was −0.350 (
The random effects model was used for the statistical analysis because of the heterogeneity resulting from the nonthreshold effect. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic ratio, and their 95% confidence interval (CI) of TCS for the differential diagnosis of PD and other movement disorders among the 9 studies were 0.85 (0.82, 0.87), 0.71 (0.66, 0.75), 3.27 (2.25, 4.74), 0.18 (0.12, 0.27), and 22.49 (9.99, 50.61), respectively. The forest plots of TCS in the differential diagnosis of PD and other movement disorders are displayed in Figure
Forest plots of the diagnostic accuracy of transcranial sonography of the substantia nigra in the differential diagnosis of Parkinson’s disease.
Summary receiver operating characteristic (SROC) curve for transcranial sonography for the differentiation of Parkinson’s disease from other movement disorders. AUC = area under curve; SE = standard error;
We conducted a subgroup analysis revealing that sample size and TCS device may be the major source of heterogeneity, as shown in Table
Subgroup analysis results.
Subgroup | Pooled sensitivity | Pooled specificity | Pooled diagnostic odds ratio | ||
---|---|---|---|---|---|
Age | ≥65.0 | 84.7 | 0.83 (0.80, 0.86) | 0.70 (0.64, 0.75) | 24.14 (7.17, 81.63) |
<65.0 | 53.4 | 0.88 (0.81, 0.93) | 0.75 (0.65, 0.84) | 23.28 (7.01, 77.34) | |
Sample | ≥100 | 88.1 | 0.82 (0.78, 0.85) | 0.68 (0.61, 0.73) | 16.39 (4.07, 66.03) |
<100 | 0.41 | 0.89 (0.85, 0.93) | 0.77 (0.69, 0.83) | 26.96 (11.37, 63.96) | |
Cutoff value | 20 mm | 71.1 | 0.91 (0.87, 0.95) | 0.79 (0.71, 0.85) | 39.63 (11.17, 140.62) |
Not 20 mm | 59.2 | 0.82 (0.78, 0.85) | 0.66 (0.60, 0.72) | 12.16 (5.28, 27.98) | |
TCS device | 2.5 MHz | 0 | 0.82 (0.78, 0.85) | 0.62 (0.55, 0.69) | 7.18 (4.96, 10.39) |
Not 2.5 MHz | 62.1 | 0.91 (0.86, 0.95) | 0.83 (0.76, 0.88) | 43.35 (22.92, 81.96) |
The potential presence of publication bias was analyzed using Deeks’ funnel plots, which were drawn using STATA 15.0 software (Figure
Funnel plot for the assessment of the potential publication bias of the 9 included studies.
The results of our meta-analysis, which included 669 patients with PD from 9 studies, demonstrated a high clinical value of TCS in the diagnosis of PD and other movement disorders. The sensitivity and specificity of TCS ranged from 0.80 to 0.95 and 0.60 to 0.96, respectively, for individual studies. The pooled sensitivity and specificity of TCS for the differential diagnosis of PD were 0.85 and 0.71, respectively. Moreover, the pooled positive likelihood and negative likelihood ratios of this meta-analysis were 3.27 and 0.18, respectively, suggesting that the probability of a positive diagnosis with TCS was 3.27 times higher in patients with PD than that in patients without PD, and the possibility of the correct exclusion of a PD diagnosis was 5.56 times higher than that of a missed diagnosis. Our study demonstrated the utility of TCS in the differential diagnosis of PD. Moreover, the AUC of this meta-analysis was 0.94, which was indicative of a high diagnostic accuracy.
A previously published meta-analysis [
The pathological change in the SN of patients with PD is the basic principle underlying the diagnosis of PD with TCS [
The principal pathological changes occur in the SN in patients with PD, but TCS can also detect enlargement of the third ventricle in patients of Parkinson’s disease dementia (PDD) [
The 9 original studies included in this meta-analysis had some heterogeneity, which was mainly caused by nonthreshold effects and may have affected the reliability of the results to some extent. The subgroup analysis results revealed that the sample size and TCS device may be the major source of heterogeneity. We found that the pooled sensitivity and specificity of different age subgroups were similar, indicating the applicability of TCS in the early diagnosis of PD. To our surprise, the results of subgroup analysis showed that the pooled sensitivity and specificity of the subgroup with a smaller sample size (<100) were higher than those of the subgroup with a larger sample size (≥100). We speculated that this may be related to the fact that the control group of the original study with a larger sample size had a greater proportion of other types of Parkinson’s syndrome that were not easily differentiated from PD. Sensitivity analysis showed that the pooled sensitivity and specificity did not change dramatically when other studies were excluded one by one.
In conclusion, our meta-analysis suggested that TCS had a high diagnostic accuracy in the differential diagnosis of PD from other movement disorders.
The data used to support the findings of this meta-analysis are included within the article.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
Yan-Liang Mei and Jing Yang contributed equally to the work.
This work was supported by grants from the National Natural Science Foundation of China to Yuming Xu (grant number: 81530037) and Jing Yang (grant number: 81600946).