Predicting who will benefit from treatment with immune checkpoint inhibition (ICI) in patients with advanced melanoma is challenging. We developed a multivariable prediction model for response to... Show morePredicting who will benefit from treatment with immune checkpoint inhibition (ICI) in patients with advanced melanoma is challenging. We developed a multivariable prediction model for response to ICI, using routinely available clinical data including primary melanoma characteristics. We used a population-based cohort of 3525 patients with advanced cutaneous melanoma treated with anti-PD-1-based therapy. Our prediction model for predicting response within 6 months after ICI initiation was internally validated with bootstrap resampling. Performance evaluation included calibration, discrimination and internal-external cross-validation. Included patients received anti-PD-1 monotherapy (n = 2366) or ipilimumab plus nivolumab (n = 1159) in any treatment line. The model included serum lactate dehydrogenase, World Health Organization performance score, type and line of ICI, disease stage and time to first distant recurrence-all at start of ICI-, and location and type of primary melanoma, the presence of satellites and/or in-transit metastases at primary diagnosis and sex. The over-optimism adjusted area under the receiver operating characteristic was 0.66 (95% CI: 0.64-0.66). The range of predicted response probabilities was 7%-81%. Based on these probabilities, patients were categorized into quartiles. Compared to the lowest response quartile, patients in the highest quartile had a significantly longer median progression-free survival (20.0 vs 2.8 months; P < .001) and median overall survival (62.0 vs 8.0 months; P < .001). Our prediction model, based on routinely available clinical variables and primary melanoma characteristics, predicts response to ICI in patients with advanced melanoma and discriminates well between treated patients with a very good and very poor prognosis. Show less
Diermen, L. van; Versyck, P.; Ameele, S. van den; Madani, Y.; Vermeulen, T.; Fransen, E.; ... ; Schrijvers, D. 2019
Objectives The presence of psychotic symptoms is an important predictor of responsiveness to electroconvulsive therapy (ECT). This study investigates whether a continuous severity measure, the... Show moreObjectives The presence of psychotic symptoms is an important predictor of responsiveness to electroconvulsive therapy (ECT). This study investigates whether a continuous severity measure, the Psychotic Depression Assessment Scale (PDAS), is a more accurate predictor. Methods Depression severity was assessed before and after the ECT course using the Montgomery-Asberg Depression Rating Scale (MADRS) in 31 patients with psychotic depression and 34 depressed patients without psychotic symptoms. Logistic regression models for MADRS response and remission were fitted, with either the PDAS total score or the dichotomous predictors "absence/presence of psychotic symptoms" as the independent variables. Age, episode duration, and treatment resistance were added as covariates. Results Both the asserted presence of psychotic symptoms and a higher PDAS total score reflected MADRS response (areas under the curve, 0.83 and 0.85, respectively), with MADRS remission also being predicted by the presence of psychotic symptoms and higher PDAS scores (areas under the curves, 0.86 and 0.84, respectively). Age was a contributor to these prediction models, with response and remission rates being highest in the older patients. Psychotic Depression Assessment Scale scores decreased significantly during ECT: at end point, 81.5% of the patients showed significant response and 63.9% had achieved remission. Conclusions The PDAS indeed accurately predicts response to and remission after ECT in (psychotic) depression and most pronouncedly so in older patients but seems to have no clear advantage over simply verifying the presence of psychotic symptoms. This could be the consequence of a ceiling effect, as ECT was extremely effective in patients with psychotic depression. ClinicalTrials.gov: Identifier: NCT02562846. Show less
