Objective

The purpose of this presentation is to familiarize the reader with the often subtle findings of peroneal tendon derangement on MRI examination.

Materials and methods

We present four patients who were studied with MR (1.5 T) prior to surgical exploration of the peroneal tunnel. All patients had lateral ankle symptoms for > 6 months. Normal MR anatomy and an example of the peroneus quartus tendon are also shown.

Results

Surgery revealed two peroneus brevis and three peroneus longus lesions. A "multipartite" appearance of the peroneus tendon on axial MR images, especially when paired with a "flame-shaped" appearance on more distal axial images, indicates a longitudinal tendon split. Both patients with peroneus brevis tendon splits had insufficient superior peroneal retinacula.

Conclusion

Partial ruptures of the peroneal tendons characteristically take the form of longitudinal tendon splits. Axial MR images through the ankle and hindfoot can help distinguish peroneal tendonitis from longitudinal tendon splits.

Degenerative plantar plate failure is an under-recognized cause of lesser metatarsalgia. We performed magnetic resonance imaging (MRI) with a small receiver coil in 13 patients in whom plantar plate ruptures of the second or third metatarsophalangeal joint were clinically suspected. In eight patients, MRI showed focal hyperintensity in the plantar plate that was interpreted as a rupture of the plate. Ruptures were confirmed in all five patients who underwent an operative procedure to treat the unstable, painful metatarsophalangeal joint. MRI is a noninvasive technique that can visualize plantar plate abnormalities and aid the clinical evaluation of problematic lesser metatarsalgia.

This article demonstrates normal anatomy of the foot and ankle as visualized with magnetic resonance imaging (MRI) in the sagittal, axial, and coronal planes. Additionally, selected cases chosen from our experience with more than 100 clinical scans are shown to highlight the primary areas in which we have found MRI to be clinically useful: bone marrow abnormalities, especially osteomyelitis and osteonecrosis, soft tissue injuries and masses, and cases in which metallic fixators make CT evaluation problematic.

Background

Two acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS.

Methods

In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables, and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity, specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational, observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory pressure (PEEP) strategy, with 90-day mortality as the dependent variable.

Findings

The primary clinical classifier model had an area under receiver operating characteristic curve (AUC) of 0·92 (95% CI 0·90-0·95) in EARLI and 0·88 (0·84-0·91) in VALID. Performance of the primary model was similar when using exclusively EHR-derived predictors compared with manually curated predictors (AUC=0·88 [95% CI 0·81-0·94] vs 0·92 [0·88-0·97]). In LUNG SAFE, 90-day mortality was higher in patients assigned the hyperinflammatory subphenotype than in those with the hypoinflammatory phenotype (414 [57%] of 725 vs 694 [33%] of 2088; p<0·0001). There was a significant treatment interaction with PEEP strategy and ARDS subphenotype (p=0·041), with lower 90-day mortality in the high PEEP group of patients with the hyperinflammatory subphenotype (hyperinflammatory subphenotype: 169 [54%] of 313 patients in the high PEEP group vs 127 [62%] of 205 patients in the low PEEP group; hypoinflammatory subphenotype: 231 [34%] of 675 patients in the high PEEP group vs 233 [32%] of 734 patients in the low PEEP group).

Interpretation

Classifier models using clinical variables alone can accurately assign ARDS subphenotypes in observational cohorts. Application of these models can provide valuable prognostic information and could inform management strategies for personalised treatment, including application of PEEP, once prospectively validated.

Funding

US National Institutes of Health and European Society of Intensive Care Medicine.