OBJECTIVEDigital transformation enables new possibilities to assess objective functional impairment (OFI) in patients with lumbar degenerative disc disease (DDD). This study examines the psychometric properties of an app-based 6-minute walking test (6WT) and determines OFI in patients with lumbar DDD.METHODSThe maximum 6-minute walking distance (6WD) was determined in patients with lumbar DDD. The results were expressed as raw 6WDs (in meters), as well as in standardized z-scores referenced to age- and sex-specific values of spine-healthy volunteers. The 6WT results were assessed for reliability and content validity using established disease-specific patient-reported outcome measures.RESULTSSeventy consecutive patients and 330 volunteers were enrolled. The mean 6WD was 370 m (SD 137 m) in patients with lumbar DDD. Significant correlations between 6WD and the Core Outcome Measures Index for the back (r = −0.31), Zurich Claudication Questionnaire (ZCQ) symptom severity (r = −0.32), ZCQ physical function (r = −0.33), visual analog scale (VAS) for back pain (r = −0.42), and VAS for leg pain (r = −0.32) were observed (all p < 0.05). The 6WT revealed good test-retest reliability (intraclass correlation coefficient 0.82), and the standard error of measurement was 58.3 m. A 4-tier severity stratification classified patients with z-scores > −1 (no OFI), −1 to −1.9 (mild OFI), −2 to −2.9 (moderate OFI), and ≤ −3 (severe OFI).CONCLUSIONSThe smartphone app-based self-measurement of the 6WT is a convenient, reliable, and valid way to determine OFI in patients with lumbar DDD. The 6WT app facilitates the digital evaluation and monitoring of patients with lumbar DDD.
F-FET-PET is a promising biomarker for early response assessment in Gd-negative gliomas undergoing chemotherapy. It might be helpful for a timely adjustment of potentially ineffective treatment concepts and overcomes limitations of conventional structural imaging.
BACKGROUND Current prognostic tools in aneurysmal subarachnoid hemorrhage (aSAH) are constrained by being primarily based on patient and disease characteristics on admission. OBJECTIVE To develop and validate a complication- and treatment-aware outcome prediction tool in aSAH. METHODS This cohort study included data from an ongoing prospective nationwide multicenter registry on all aSAH patients in Switzerland (Swiss SOS [Swiss Study on aSAH]; 2009-2015). We trained supervised machine learning algorithms to predict a binary outcome at discharge (modified Rankin scale [mRS] ≤ 3: favorable; mRS 4-6: unfavorable). Clinical and radiological variables on admission (“Early” Model) as well as additional variables regarding secondary complications and disease management (“Late” Model) were used. Performance of both models was assessed by classification performance metrics on an out-of-sample test dataset. RESULTS Favorable functional outcome at discharge was observed in 1156 (62.0%) of 1866 patients. Both models scored a high accuracy of 75% to 76% on the test set. The “Late” outcome model outperformed the “Early” model with an area under the receiver operator characteristics curve (AUC) of 0.85 vs 0.79, corresponding to a specificity of 0.81 vs 0.70 and a sensitivity of 0.71 vs 0.79, respectively. CONCLUSION Both machine learning models show good discrimination and calibration confirmed on application to an internal test dataset of patients with a wide range of disease severity treated in different institutions within a nationwide registry. Our study indicates that the inclusion of variables reflecting the clinical course of the patient may lead to outcome predictions with superior predictive power compared to a model based on admission data only.
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