Dataset Information

Accuracy of using serum D-dimer for diagnosis of acute intestinal ischemia: A meta-analysis.

ABSTRACT: The purpose of this meta-analysis is to comprehensively assess the accuracy of serum D-dimer for the diagnosis of acute intestinal ischemia.Diagnostic studies of D-dimer for accurate diagnosis of acute intestinal ischemia were extracted from 6 databases, and prospective and retrospective studies that provided adequate data on sensitivity and specificity were included here. Sensitivity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were calculated. The overall diagnostic performance of D-dimer was assessed by plotting a summary receiver operating characteristic curve (SROC) and calculating the area under the curve (AUC).A total of 1300 patients with suspected acute intestinal ischemia from 12 studies met the inclusion criteria. The combined sensitivity, specificity, PLR, NLR, and DOR were 0.94 (95% CI: 0.87-0.97), 0.50 (95% CI: 0.40-0.61), 1.9 (95% CI: 1.5-2.3), 0.12 (95% CI: 0.05-0.26), and 16 (95% CI: 7-39), respectively. The AUC was 0.81 (95% CI: 0.78-0.84).The results of this meta-analysis suggested that plasma D-dimer detection might be a useful means of identifying patients with acute intestinal ischemia of the abdomen.

SUBMITTER: Sun DL

PROVIDER: S-EPMC5380248 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Accuracy of using serum D-dimer for diagnosis of acute intestinal ischemia: A meta-analysis.

Sun Da-Li DL Li Shu-Min SM Cen Yun-Yun YY Xu Qing-Wen QW Li Yi-Jun YJ Sun Yan-Bo YB Qi Yu-Xing YX Lin Yue-Ying YY Yang Ting T An Li-Ya LY Su Kun K Li Wei-Ming WM Xu Peng-Yuan PY

Medicine 20170301 13

<h4>Objective</h4>The purpose of this meta-analysis is to comprehensively assess the accuracy of serum D-dimer for the diagnosis of acute intestinal ischemia.<h4>Methods</h4>Diagnostic studies of D-dimer for accurate diagnosis of acute intestinal ischemia were extracted from 6 databases, and prospective and retrospective studies that provided adequate data on sensitivity and specificity were included here. Sensitivity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnos ...[more]

PMID: 28353564

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Project description:Importance:The recent advances in the field of machine learning have raised expectations that computer-aided diagnosis will become the standard for the diagnosis of melanoma. Objective:To critically review the current literature and compare the diagnostic accuracy of computer-aided diagnosis with that of human experts. Data Sources:The MEDLINE, arXiv, and PubMed Central databases were searched to identify eligible studies published between January 1, 2002, and December 31, 2018. Study Selection:Studies that reported on the accuracy of automated systems for melanoma were selected. Search terms included melanoma, diagnosis, detection, computer aided, and artificial intelligence. Data Extraction and Synthesis:Evaluation of the risk of bias was performed using the QUADAS-2 tool, and quality assessment was based on predefined criteria. Data were analyzed from February 1 to March 10, 2019. Main Outcomes and Measures:Summary estimates of sensitivity and specificity and summary receiver operating characteristic curves were the primary outcomes. Results:The literature search yielded 1694 potentially eligible studies, of which 132 were included and 70 offered sufficient information for a quantitative analysis. Most studies came from the field of computer science. Prospective clinical studies were rare. Combining the results for automated systems gave a melanoma sensitivity of 0.74 (95% CI, 0.66-0.80) and a specificity of 0.84 (95% CI, 0.79-0.88). Sensitivity was lower in studies that used independent test sets than in those that did not (0.51; 95% CI, 0.34-0.69 vs 0.82; 95% CI, 0.77-0.86; P?<?.001); however, the specificity was similar (0.83; 95% CI, 0.71-0.91 vs 0.85; 95% CI, 0.80-0.88; P?=?.67). In comparison with dermatologists' diagnosis, computer-aided diagnosis showed similar sensitivities and a 10 percentage points lower specificity, but the difference was not statistically significant. Studies were heterogeneous and substantial risk of bias was found in all but 4 of the 70 studies included in the quantitative analysis. Conclusions and Relevance:Although the accuracy of computer-aided diagnosis for melanoma detection is comparable to that of experts, the real-world applicability of these systems is unknown and potentially limited owing to overfitting and the risk of bias of the studies at hand.

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Dataset Information

Accuracy of using serum D-dimer for diagnosis of acute intestinal ischemia: A meta-analysis.

Publications

Accuracy of using serum D-dimer for diagnosis of acute intestinal ischemia: A meta-analysis.

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