Dataset Information

Unnecessary ordering of magnetic resonance imaging of the knee: A retrospective chart review of referrals to orthopedic surgeons.

ABSTRACT: There is a noticeable increase in the unnecessary ordering of Magnetic Resonance Imaging (MRI) of the knee in older patients. This quality improvement study assessed the frequency of unnecessary pre-consultation knee MRIs and investigated the effect on the outcome of the patients' consultation with the orthopedic surgeon. 650 medical charts of patients aged 55 years or older referred to an orthopedic clinic with knee complaints were reviewed. Patients arriving with a pre-consultation MRI were identified, and the usefulness of the MRI was evaluated using the appropriateness criteria developed to support this study. Of the 650 patient charts reviewed, 225 patients presented with a pre-consultation MRI, 76% of which were not useful for the orthopedic surgeon. The ordered knee MRI scans were considered not useful because they were requested for confirmed meniscal tear for patients ?55 years, suspected degenerative disorder and ligament/tendon injury, or for patients with severe osteoarthritis without locking or extension. These MRI scans were done despite the absence of signs of effusion, tenderness, soft tissue swelling, decreased range of motion, or difficulty of weight-bearing, a lack of persistent knee joint pain at the time of assessment, or with no x-ray before ordering MRI. Half of the patients with a pre-consult MRI did not present with plain radiographs of their knee, however, 35% of those still required an x-ray to be ordered at the time of the surgical consult. A logistic regression analysis on post-consult disposition found that patients with pre-consult MRI were less likely to be considered for total knee arthroplasty (TKA) (OR 0.424, CI 0.258-0.698, p = 0.001). Patients assessed by an advanced practice physiotherapist prior to referral for surgical consult were 4.47 more likely to have TKA (CI 2.844-7.039, p< 0.000). Most of the pre-consult knee MRIs were deemed as unnecessary for the orthopedic surgeon's clinical decision-making. This study highlights the potential benefits of following a comprehensive model of care within the referral process to reduce the unnecessary high orders of pre-consult MRI scans.

SUBMITTER: Mohammed HT

PROVIDER: S-EPMC7605700 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Unnecessary ordering of magnetic resonance imaging of the knee: A retrospective chart review of referrals to orthopedic surgeons.

Mohammed Heba Tallah HT Yoon Samuel S Hupel Thomas T Payson Lori-Anne LA

PloS one 20201102 11

There is a noticeable increase in the unnecessary ordering of Magnetic Resonance Imaging (MRI) of the knee in older patients. This quality improvement study assessed the frequency of unnecessary pre-consultation knee MRIs and investigated the effect on the outcome of the patients' consultation with the orthopedic surgeon. 650 medical charts of patients aged 55 years or older referred to an orthopedic clinic with knee complaints were reviewed. Patients arriving with a pre-consultation MRI were id ...[more]

PMID: 33137163

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Project description:BackgroundThe overdiagnosis of prostate cancer (PCa) caused by unnecessary prostate biopsy has become a worldwide problem that urgently requires a solution. We aimed to reduce the unnecessary prostate biopsies and increase the detection rate of clinically significant PCa (csPCa) by creating a novel multiparametric magnetic resonance imaging (mpMRI)-based strategy.MethodsA total of 1,194 eligible patients who underwent transperineal prostate biopsies from January 2018 to December 2022 were included in this retrospective study. Of these patients, 1,080 who received prostate biopsies from January 2018 to July 2022 were regarded as cohort 1 for primary analysis, and 114 patients who received prostate biopsies from August 2022 to December 2022 were collected in cohort 2 for validation. All the mpMRI images were quantitatively evaluated by the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v. 2.1). The diagnostic performances were assessed through the receiver operating characteristic (ROC) curve and area under the curve (AUC) and were compared with the DeLong test. Cancer diagnosis-free survival analysis was performed using the Kaplan-Meier method and log-rank test. The primary endpoint of this study was clinically significant PCa with an International Society of Urological Pathology (ISUP) grade ≥2.ResultsIn cohort 1, the results of ROC curves demonstrated that the PI-RADS score had a higher diagnostic accuracy (AUC =0.898 for any-grade PCa; AUC =0.917 for csPCa) than did the other clinical variables (P<0.001). Under the novel mpMRI-based biopsy strategy, all patients with PI-RADS 1 can safely avoid prostate biopsy. For patients with PI-RADS 2, prostate biopsy should be considered for patients with prostate-specific antigen density (PSAD) ≥0.3 ng/mL2 and prostate volume <65 mL. As for patients with PI-RADS 3, structured surveillance programs can be a viable option if PSAD <0.3 ng/mL2 and prostate volume ≥65 mL. Finally, patients with a PI-RADS score of 4 and 5 should undergo prostate biopsy due to the high probability of clinically significant PCa. In the validation analysis of cohort 2, 48 patients were placed into a biopsy-spared group with no csPCa cases, while 66 patients were placed in a biopsy-needed group, with an csPCa detection rate of 50.0%. Overall, the novel strategy demonstrated a sensitivity, specificity, positive predictive value, and negative predictive value of 98.9%, 57.5%, 50.5%, and 99.2%, respectively, for diagnosing csPCa.ConclusionsAn mpMRI-based biopsy strategy can effectively avoid about 40% of prostate biopsies and maintain a high detection rate for clinically significant PCa. It can further provide valuable guidance for patients and physicians in considering the necessity of prostate biopsy.

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Project description:BACKGROUND:Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries. However, interpretation of knee MRI is time-intensive and subject to diagnostic error and variability. An automated system for interpreting knee MRI could prioritize high-risk patients and assist clinicians in making diagnoses. Deep learning methods, in being able to automatically learn layers of features, are well suited for modeling the complex relationships between medical images and their interpretations. In this study we developed a deep learning model for detecting general abnormalities and specific diagnoses (anterior cruciate ligament [ACL] tears and meniscal tears) on knee MRI exams. We then measured the effect of providing the model's predictions to clinical experts during interpretation. METHODS AND FINDINGS:Our dataset consisted of 1,370 knee MRI exams performed at Stanford University Medical Center between January 1, 2001, and December 31, 2012 (mean age 38.0 years; 569 [41.5%] female patients). The majority vote of 3 musculoskeletal radiologists established reference standard labels on an internal validation set of 120 exams. We developed MRNet, a convolutional neural network for classifying MRI series and combined predictions from 3 series per exam using logistic regression. In detecting abnormalities, ACL tears, and meniscal tears, this model achieved area under the receiver operating characteristic curve (AUC) values of 0.937 (95% CI 0.895, 0.980), 0.965 (95% CI 0.938, 0.993), and 0.847 (95% CI 0.780, 0.914), respectively, on the internal validation set. We also obtained a public dataset of 917 exams with sagittal T1-weighted series and labels for ACL injury from Clinical Hospital Centre Rijeka, Croatia. On the external validation set of 183 exams, the MRNet trained on Stanford sagittal T2-weighted series achieved an AUC of 0.824 (95% CI 0.757, 0.892) in the detection of ACL injuries with no additional training, while an MRNet trained on the rest of the external data achieved an AUC of 0.911 (95% CI 0.864, 0.958). We additionally measured the specificity, sensitivity, and accuracy of 9 clinical experts (7 board-certified general radiologists and 2 orthopedic surgeons) on the internal validation set both with and without model assistance. Using a 2-sided Pearson's chi-squared test with adjustment for multiple comparisons, we found no significant differences between the performance of the model and that of unassisted general radiologists in detecting abnormalities. General radiologists achieved significantly higher sensitivity in detecting ACL tears (p-value = 0.002; q-value = 0.019) and significantly higher specificity in detecting meniscal tears (p-value = 0.003; q-value = 0.019). Using a 1-tailed t test on the change in performance metrics, we found that providing model predictions significantly increased clinical experts' specificity in identifying ACL tears (p-value < 0.001; q-value = 0.006). The primary limitations of our study include lack of surgical ground truth and the small size of the panel of clinical experts. CONCLUSIONS:Our deep learning model can rapidly generate accurate clinical pathology classifications of knee MRI exams from both internal and external datasets. Moreover, our results support the assertion that deep learning models can improve the performance of clinical experts during medical imaging interpretation. Further research is needed to validate the model prospectively and to determine its utility in the clinical setting.

Dataset Information

Unnecessary ordering of magnetic resonance imaging of the knee: A retrospective chart review of referrals to orthopedic surgeons.

Publications

Unnecessary ordering of magnetic resonance imaging of the knee: A retrospective chart review of referrals to orthopedic surgeons.

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