How to Answer Quality Studies Included Questions (Complete Guide)

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T he quality of the studies included in the meta-analysis was evaluated using the QUADAS-2 tool, which is spe cifically designed for diagnostic accuracy studies. QUA DAS-2 focuses on four main areas: patient selection, the index test, the reference standard, and the flow and tim ing of the study. We examined all domains for potential risk of bias (ROB) and evaluated the first three for appli cability concerns. The risk of bias was categorized as ‘low’, ‘high’, or ‘unclear’ [28]. Two independent research ers conducted the QUADAS-2 assessment, and any dis crepancies were resolved through consensus among the researchers. Statistical analysis Data analysis was performed using Stata (versions 17.0 and 14.0) and Meta-Disc 1.4 software. Heterogene ity among studies was assessed using the Cochran-Q test and I2 statistic; significant heterogeneity was indi cated by P < 0.05 and I2 > 50%, in which case a random effects model was employed. If heterogeneity was not significant, a fixed-effect model was used. Calculations included pooled sensitivity, specificity, positive and nega tive likelihood ratios, and the 95% confidence intervals for the diagnostic odds ratio. Forest plots, hierarchical summary receiver operating characteristic (HSROC) curves, and summary receiver operating characteristic (SROC) curves were also constructed, and the area under the curve (AUC) was estimated. If heterogeneity was detected, Meta-Disc 1.4 was used to explore the presence of a threshold effect (determined by Spearman correla tion coefficient), and meta-regression was conducted to identify potential sources of heterogeneity. Additionally, subgroup analyses were performed to compare sensitiv ity and specificity across different subgroups. Potential publication bias was assessed using Deek’s funnel plot, Egger’s test, and Begg’s test, with a P-value of less than 0.10 indicating the presence of publication bias. Results Literature search results A total of 10,023 articles were initially retrieved from var ious databases. After screening titles and abstracts, 894 duplicate articles were excluded, along with 8,853 articles irrelevant to the research objectives. Furthermore, 223 experience summaries, conference abstracts, 13 ani mal studies, 13 articles involving patients already diag nosed with pulmonary embolism, 5 articles where it was not possible to calculate all TP, TN, FP, FN values, and 4 articles lacking a reference or gold standard were also excluded. After a detailed screening process, 18 articles [8-25] involving 1,264 participants were finally included. T he detailed screening process is shown in Fig. 1, and the basic information of the articles is presented in Table 1. Quality assessment results of included studies T he results of the quality assessment of the included studies are presented in Table 2. Heterogeneity and threshold effect analysis T he combined sensitivity and specificity of MRI for diag nosing pulmonary embolism had I² values of 73.11% and 79.45%, respectively. The P values for Cochran’s Q test for combined sensitivity and specificity were both 0.00, indicating significant heterogeneity among the included studies. Therefore, a threshold effect analysis was neces sary. Using Meta Disc 1.4, a Spearman correlation coef f icient of -0.235 with a P value of 0.440 was calculated, indicating the presence of non-threshold effect heteroge neity. Consequently, a random effects model was used to estimate the combined effect sizes. Meta-analysis results Forest plots and SROC curves were generated to calcu late the combined sensitivity, specificity, positive like lihood ratio, negative likelihood ratio, and diagnostic odds ratio, along with the AUC of the SROC curve. The combined sensitivity and specificity were 0.89 (95% CI: 0.79-0.94) and 0.94 (95% CI: 0.89-0.97), respectively. T he combined positive likelihood ratio and negative like lihood ratio were 14.6 (95% CI: 8.0-26.7) and 0.12 (95% CI: 0.06-0.23), respectively. The diagnostic odds ratio was 121 (95% CI: 49-299), with an AUC of 0.97 for the SROC curve. The forest plots and SROC curves for the MRI diagnosis of pulmonary embolism are shown in Figs. 2 and 3. Additionally, the HSROC curve provides further insight by accounting for the variability between studies, with its 95% CI and prediction region displayed, as shown in Fig. 4. Publication bias Publication bias for MRI diagnosis of pulmonary embo lism was assessed using Deek’s funnel plot, which showed asymmetry, as shown in Fig. 5. Although the P values for Egger’s test and Begg’s test were 0.929 and 0.436, respec tively, the P value for the linear regression test was 0.01, suggesting the potential presence of publication bias. Meta-regression and subgroup analysis Heterogeneity due to threshold effects has been excluded. T herefore, univariate meta-regression analysis was con ducted using publication year, country, sample size, gold standard for the diagnosis of pulmonary embolism, and MRI field strength as variables. The results indicated that the country was the primary cause of heterogeneity in sensitivity, while the gold standard for the diagnosis of pulmonary embolism was the primary cause of heteroge neity in specificity. The results of the subgroup analysis are detailed in Table 3. Yang et al. BMC Medical Imaging (2025) 25:92 Page 4 of 9 Fig. 1 Study screening process Discussion Pulmonary embolism is a serious cardiovascular disease that, without timely diagnosis and treatment, can lead to high mortality [29]. Traditional diagnostic methods such as CTPA and DSA, although highly sensitive, pose potential risks to certain patients (e.g., those with renal insufficiency, pregnant women) due to radiation expo sure and the use of contrast agents [30, 31]. Therefore, exploring a safe and effective alternative diagnostic tool is particularly important. MRI, as a radiation-free imaging technique, has shown great potential in the diagnosis of pulmonary embolism in recent years [32]. MRI utilizes a strong magnetic field and radio waves to detect changes in water molecules within the body, thereby produc ing high-resolution images [33]. In the diagnosis of pul monary embolism, MRI not only can reveal structural abnormalities in the blood vessels but also assists in the diagnosis through hemodynamic changes and perfusion status of lung tissue [34, 35]. For example, Magnetic Res onance Pulmonary Angiography (MRPA) with MRI can visually display the condition of vascular blockage, while magnetic resonance perfusion imaging helps assess areas of blood flow deficit [36, 37] T he results of this systematic review and meta-analysis demonstrate that MRI exhibits high sensitivity and speci f icity in diagnosing pulmonary embolism. With a pooled sensitivity of 0.89 and a specificity of 0.94, MRI effectively distinguishes between patients with and without pulmo nary embolism. Additionally, the high values of the posi tive and negative likelihood ratios further reinforce the diagnostic value of MRI, indicating its significant clinical relevance in confirming or excluding pulmonary embo lism. A positive likelihood ratio of 14.6 suggests that the odds of having pulmonary embolism are about 15 times higher if the MRI result is positive. Conversely, a nega tive likelihood ratio of 0.12 implies that the odds of not having pulmonary embolism increase about 8 times if the MRI result is negative. Such remarkable diagnostic per formance is invaluable in clinical settings, especially in emergency environments where rapid and accurate diag nosis is crucial. Page 5 of 9 Yang et al. BMC Medical Imaging (2025) 25:92 The AUC of SROC curve reached 0.97, indicating that MRI’s diagnostic capability for pulmonary embolism is nearly perfect. This high value underscores the poten tial of MRI as a diagnostic tool for pulmonary embolism. High AUC values typically indicate high accuracy of a diagnostic test, and the results of this analysis support the use of MRI as a reliable tool for diagnosing pulmo nary embolism. The clinical implications of these results are particularly significant, as misdiagnosis or missed diagnosis of pulmonary embolism can lead to severe or even fatal consequences. Furthermore, the forest plots and SROC curves visually display the heterogeneity Table 1 Basic characteristics of included studies Study Country Sample size (M/F) Age (years) Gold standard for the diagnosis of PE Magnetic field strength of MRI) MRI brand TP FP FN TN Blum 2005 [8] France 89 (45/44) 64 ± 16* CTPA 1.5T GE 47 5 16 21 Ersoy 2007 [9] USA 24 (Na/Na) 62 (35-92)# CTPA 1.5-T GE 2 2 7 13 Grist 1993 [10] USA 14 (7/7) 35-82** CTPA 1.5 T GE 6 3 0 5 Gupta 1999 [11] Australia 36 (17/19) 28-84** DSA 1.5-T Siemens 11 1 2 22 Kluge 2006 [12] Germany 62 (Na/Na) 60.9 ± 15.7* CTPA 1.5-T Siemens 19 3 0 40 Loubeyre 1994 [13] France 23 (12/11) 20-66** DSA 1.5-T Na 10 0 2 11 Meaney 1997 [14] USA 30 (15/15) 52(22-83)# DSA 1.5-T GE 8 1 0 21 Meng 2005 [15] China 56 (35/21) 32-63** Ventilation/Perfusion 1.5-T Na 36 2 3 15 Ohno 2004 [16] Japan 48 (26/22) 22-73** DSA 1.5-T Philips 11 2 1 34 Osman 2016 [17] Egypt 50 (15/35) 45-70** CTPA 1.5-T Na 31 2 4 13 Oudkerk 2002 [18] Netherlands 118 (Na/Na) 53(16-87)# DSA 1.5T Siemens 27 2 8 81 Pleszewski 2006 [19] Switzerland 48 (20/28) 55(22-84) # CTPA /DSA 1.5-T GE 9 0 2 37 Revel 2012 [20] France 274 (137/147) 59.8 ± 19.0* CTPA 1.5-T GE 87 2 16 169 Sostman 1996 [21] USA 25 (Na/Na) 26-80** DSA 1.5-T GE 3 2 4 16 Stein 2010 [22] USA 279 (Na/Na) 49 ± 15* CTPA 1.5-T Na 59 2 17 201 Yu 2005 [23] China 38 (19/19) 37-76** DSA 1.5-T Na 30 2 0 6 Zhang 2013 [24] China 27 (18/9) 38.9 ± 14.4* CTPA 3-T Siemens 24 0 0 3 Zhao 2016 [25] China 23 (19/4) 37.8 ± 14.6* CTPA 3.0-T Na 13 3 1 6 *: Mean age and standard deviation; **: Range of ages; #: Median age and range of age data CTPA: Computed Tomography Pulmonary Angiography; DSA: Digital Subtraction Angiography; F: Female; FN: False Negative; FP: False Positive; GE: General Electric; M: Male; Na: Not Available; TN: True Negative; TP: True Positive Table 2 Quality assessment results of included studies Study Risk of bias Applicability concerns â‘ â‘¡ â‘¢ â‘£ â‘ â‘¡ â‘¢ Blum 2005 [8] Low Risk High Risk Low Risk Low Risk Low Risk Low Risk Low Risk Ersoy 2007 [9] Low Risk Unclear Risk Unclear Risk Unclear Risk Low Risk Low Risk Low Risk Grist 1993 [10] Unclear Risk Unclear Risk Unclear Risk Unclear Risk Unclear Risk Unclear Risk Low Risk Gupta 1999 [11] Low Risk Low Risk Low Risk Low Risk Low Risk Low Risk Low Risk Kluge 2006 [12] Unclear Risk Unclear Risk Low Risk Low Risk Low Risk Low Risk Low Risk Loubeyre 1994 [13] Low Risk Low Risk Low Risk Low Risk Low Risk Low Risk Low Risk Meaney 1997 [14] Low Risk Low Risk Low Risk Low Risk Low Risk Low Risk Low Risk Meng 2005 [15] Unclear Risk Unclear Risk Unclear Risk Unclear Risk Low Risk Low Risk Unclear Risk Ohno 2004 [16] Low Risk Low Risk Low Risk Low Risk Low Risk Low Risk Low Risk Osman 2016 [17] Unclear Risk Unclear Risk Low Risk Unclear Risk Low Risk Low Risk Low Risk Oudkerk 2002 [18] Low Risk Low Risk Low Risk Low Risk Low Risk Low Risk Low Risk Pleszewski 2006 [19] Low Risk Low Risk Low Risk Low Risk Low Risk Low Risk Low Risk Revel 2012 [20] Unclear Risk Low Risk Unclear Risk Low Risk Unclear Risk Low Risk Low Risk Sostman 1996 [21] Unclear Risk Unclear Risk Low Risk Low Risk Unclear Risk Low Risk Low Risk Stein 2010 [22] Low Risk Low Risk Low Risk Low Risk Low Risk Low Risk Low Risk Yu 2005 [23] Unclear Risk Unclear Risk Unclear Risk Unclear Risk Low Risk Low Risk Unclear Risk Zhang 2013 [24] Unclear Risk Low Risk Low Risk Low Risk Unclear Risk Low Risk Low Risk Zhao 2016 [25] Unclear Risk Low Risk Low Risk Low Risk Low Risk Low Risk Low Risk â‘ : Patient Selection; â‘¡: Index Test; â‘¢: Reference Standard; â‘£: Flow and Timing Yang et al. BMC Medical Imaging (2025) 25:92between different studies and the overall efficacy trend. Although the results of individual studies show some variability, the overall trend confirms the strong diag nostic potential of MRI in this field. This meta-analysis reveals high diagnostic performance of MRI, but Deek’s funnel plot suggests potential publication bias. This bias may arise from a tendency to publish only those studies that show favorable MRI performance. This calls for cau tion in interpreting these results and indicates the need for more representative multicenter studies to validate these findings and promote measures such as registered study protocols. The presence of publication bias may affect our comprehensive understanding of MRI’s diag nostic efficiency for pulmonary embolism; thus, broader and more in-depth research is essential before MRI can be considered a standard diagnostic tool. MRI provides a safe and effective diagnostic option for the diagnosis of pulmonary embolism. However, practi cal application must consider the complexity of opera tion and the skill requirements for operators, as well as limitations related to cost, equipment availability, and longer scanning times in clinical practice [38, 39]. Future Yang et al. BMC Medical Imaging (2025) 25:92 Page 7 of 9 Fig. 4 HSROC curve for MRI diagnosis of pulmonary embolism. The curve shows the summary point (blue square) along with the 95% confidence region (green dashed line) and 95% prediction region (orange dashed line). The HSROC curve (red solid line) accounts for variability between studies in the meta-analysis research should explore the relative efficacy and cost effectiveness of MRI compared to CTPA and other diag nostic methods, such as ultrasound and D-dimer tests, and aim to reduce costs through technological innova tion. Globally, especially in resource-limited settings, cost and accessibility are key determinants in the adoption of medical technology. Although MRI provides excellent diagnostic data, its high costs and operational complexity limit its prevalence in low-income countries [40]. There fore, researchers and policymakers need to evaluate not only the diagnostic benefits of MRI but also its economic burden and feasibility of implementation. T his study also has certain limitations: (1) The rela tively small sample size may limit the generalizability and extrapolation of our analysis results. Moreover, the qual ity and design heterogeneity of the included studies may affect the interpretation of the results, despite the assess ment of study quality according to QUADAS standards. (2) Funnel plot analysis suggests the presence of potential publication bias, which may indicate a tendency to pub lish studies that demonstrate high sensitivity and speci f icity of MRI. This bias could lead to an overly optimistic assessment of MRI’s diagnostic efficacy. (3) All studies were conducted up to May 12, 2024, and the continual emergence of new technologies and methods may limit the timeliness of our conclusions. Future research might reveal new evidence that could support or contradict our f indings. Fig. 5 Deek’s funnel plot Page 8 of 9 Yang et al. BMC Medical Imaging (2025) 25:92 Conclusion This meta-analysis confirms the high sensitivity and specificity of MRI in the diagnosis of pulmonary embo lism, its clinical application remains limited by equip ment costs and operational requirements. Additionally, the publication bias identified in the study underscores the need for more high-quality, multicenter research to further validate the broad applicability of these results. With improvements in MRI technology and increased accessibility, MRI has the potential to become an impor tant tool for diagnosing pulmonary embolism and other complex diseases in the future What are the main study variable(s)? How would you describe the variables (independent vs dependent; what level of measurement are they; are they continuous or discrete; etc)?

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