Determinants of frailty in primary care patients with COPD: the Greek UNLOCK study

Abstract Background Frailty is a state of increased vulnerability that has a significant risk of unfavorable outcomes such as increased dependency and/or death, but little is known about frailty in people with chronic obstructive pulmonary disease (COPD). Method We aimed to determine the prevalence of frailty in COPD patients and to identify the associated risk factors. Two hundred fifty-seven COPD patients enrolled from primary care in Greece between 2015 and 2016. Physicians used structured interviews to collect cross-sectional data including demographics, medical history, symptoms and COPD Assessment Tool (CAT) or modified Medical Research Council Dyspnea scale (mMRC) score. Patients were classified into severity groups according to GOLD 2017 guidelines. Participants completed the The Frail Non-Disabled (FiND) questionnaire, exploring the frailty and disability domains. In the present analyses, frail patients with and without mobility disability were pooled and were compared to non-frail patients. Factors associated with frailty were analyzed using univariate and multivariate logistic regression. Results Mean (SD) age was 65 (12.3) with 79% males. The majority of patients suffered with frailty (82%) of which 76.8% had mobility disability. 84.2% were married/with partner and 55.4% retired. 55.6% were current smokers. Uncontrolled disease (≥10 CAT score) was reported in 91.1% and 37.2% of patients had ≥2 exacerbations in the past year. Dyspnea (38%) and cough (53.4%) were the main symptoms. Main comorbidities were hypertension (72.9%), hyperlipidaemia (24.6%) and diabetes (11%). Risk of frailty was significantly increased with age (OR; 95%CI: 1.05; 1.02–1.08), hypertension (2.25; 1.14–4.45), uncontrolled disease (≥10 CAT score 4.65; 1.86–11.63, ≥2 mMRC score 5.75 (2.79–11.85) or ≥ 2 exacerbations 1.73; 1.07–2.78), smoking cessation (ex compared to current smokers: 2.37; 1.10–5.28) and GOLD status (B&D compared to A&C groups: CAT-based 4.65; 1.86–11.63; mMRC-based: 5.75; 2.79–11.85). In multivariate regression smoking cessation and GOLD status remained significant. Gender, body mass index, occupational or marital status, symptoms and other comorbidities were not significant. Conclusions Frailty with mobility disability is common in COPD patients and severity of disease increases the risk. It is possible that frail patients are more likely to quit smoking perhaps because of their disability and uncontolled disease. Routine assessment of frailty in addition to COPD control may allow early interventions for preventing or delaying progression of frailty and improvement in COPD disease

Impact of a preceding radiotherapy on the outcome of immune checkpoint inhibition in metastatic melanoma: a multicenter retrospective cohort study of the DeCOG

Background Immune checkpoint inhibition (ICI) is an essential treatment option in melanoma. Its outcome may be improved by a preceding radiation of metastases. This study aimed to investigate the impact of a preceding radiotherapy on the clinical outcome of ICI treatment.Methods This multicenter retrospective cohort study included patients who received anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) or anti-programmed cell death protein 1 (PD-1) ICI with or without preceding radiotherapy for unresectable metastatic melanoma. ICI therapy outcome was measured as best overall response (BOR), progression-free (PFS) and overall survival (OS). Response and survival analyses were adjusted for confounders identified by directed acyclic graphs. Adjusted survival curves were calculated using inverse probability treatment weighting.Results 835 patients who received ICI (anti-CTLA-4, n=596; anti-PD-1, n=239) at 16 centers were analyzed, whereof 235 received a preceding radiotherapy of metastatic lesions in stage IV disease. The most frequent organ sites irradiated prior to ICI therapy were brain (51.1%), lymph nodes (17.9%) and bone (17.9%). After multivariable adjustment for confounders, no relevant differences in ICI therapy outcome were observed between cohorts with and without preceding radiotherapy. BOR was 8.7% vs 13.0% for anti-CTLA-4 (adjusted relative risk (RR)=1.47; 95% CI=0.81 to 2.65; p=0.20), and 16.5% vs 25.3% for anti-PD-1 (RR=0.93; 95% CI=0.49 to 1.77; p=0.82). Survival probabilities were similar for cohorts with and without preceding radiotherapy, for anti-CTLA-4 (PFS, adjusted HR=1.02, 95% CI=0.86 to 1.25, p=0.74; OS, HR=1.08, 95% CI=0.81 to 1.44, p=0.61) and for anti-PD-1 (PFS, HR=0.84, 95% CI=0.57 to 1.26, p=0.41; OS, HR=0.73, 95% CI=0.43 to 1.25, p=0.26). Patients who received radiation last before ICI (n=137) revealed no better survival than those who had one or more treatment lines between radiation and start of ICI (n=86). In 223 patients with brain metastases, we found no relevant survival differences on ICI with and without preceding radiotherapy.Conclusions This study detected no evidence for a relevant favorable impact of a preceding radiotherapy on anti-CTLA-4 or anti-PD-1 ICI treatment outcome in metastatic melanoma

Genetic and Clinical Characteristics of ARID1A Mutated Melanoma Reveal High Tumor Mutational Load without Implications on Patient Survival

(1) Background: Melanoma has the highest mortality of all cutaneous tumors, despite recent treatment advances. Many relevant genetic events have been identified in the last decade, including recurrent ARID1A mutations, which in various tumors have been associated with improved outcomes to immunotherapy. (2) Methods: Retrospective analysis of 116 melanoma samples harboring ARID1A mutations. Assessment of clinical and genetic characteristics was performed as well as correlations with treatment outcome applying Kaplan–Meier (log-rank test), Fisher’s exact and Chi-squared tests. (3) Results: The majority of ARID1A mutations were in cutaneous and occult melanoma. ARID1A mutated samples had a higher number of mutations than ARID1A wild-type samples and harbored UV-mutations. A male predominance was observed. Many samples also harbored NF1 mutations. No apparent differences were noted between samples harboring genetically inactivating (frame-shift or nonsense) mutations and samples with other mutations. No differences in survival or response to immunotherapy of patients with ARID1A mutant melanoma were observed. (4) Conclusions: ARID1A mutations primarily occur in cutaneous melanomas with a higher mutation burden. In contrast to findings in other tumors, our data does not support ARID1A mutations being a biomarker of favorable response to immunotherapies in melanoma. Larger prospective studies would still be warranted

Impact of a preceding radiotherapy on the outcome of immune checkpoint inhibition in metastatic melanoma: a multicenter retrospective cohort study of the DeCOG

Background Immune checkpoint inhibition (ICI) is an essential treatment option in melanoma. Its outcome may be improved by a preceding radiation of metastases. This study aimed to investigate the impact of a preceding radiotherapy on the clinical outcome of ICI treatment. Methods This multicenter retrospective cohort study included patients who received anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) or anti-programmed cell death protein 1 (PD-1) ICI with or without preceding radiotherapy for unresectable metastatic melanoma. ICI therapy outcome was measured as best overall response (BOR), progression-free (PFS) and overall survival (OS). Response and survival analyses were adjusted for confounders identified by directed acyclic graphs. Adjusted survival curves were calculated using inverse probability treatment weighting. Results 835 patients who received ICI (anti-CTLA-4, n=596; anti-PD-1, n=239) at 16 centers were analyzed, whereof 235 received a preceding radiotherapy of metastatic lesions in stage IV disease. The most frequent organ sites irradiated prior to ICI therapy were brain (51.1%), lymph nodes (17.9%) and bone (17.9%). After multivariable adjustment for confounders, no relevant differences in ICI therapy outcome were observed between cohorts with and without preceding radiotherapy. BOR was 8.7% vs 13.0% for anti-CTLA-4 (adjusted relative risk (RR)=1.47; 95% CI=0.81 to 2.65; p=0.20), and 16.5% vs 25.3% for anti-PD-1 (RR=0.93; 95% CI=0.49 to 1.77; p=0.82). Survival probabilities were similar for cohorts with and without preceding radiotherapy, for anti-CTLA-4 (PFS, adjusted HR=1.02, 95% CI=0.86 to 1.25, p=0.74; OS, HR=1.08, 95% CI=0.81 to 1.44, p=0.61) and for anti-PD-1 (PFS, HR=0.84, 95% CI=0.57 to 1.26, p=0.41; OS, HR=0.73, 95% CI=0.43 to 1.25, p=0.26). Patients who received radiation last before ICI (n=137) revealed no better survival than those who had one or more treatment lines between radiation and start of ICI (n=86). In 223 patients with brain metastases, we found no relevant survival differences on ICI with and without preceding radiotherapy. Conclusions This study detected no evidence for a relevant favorable impact of a preceding radiotherapy on anti-CTLA-4 or anti-PD-1 ICI treatment outcome in metastatic melanoma

A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task

Background: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively trained with dermoscopic images in a clinical image classification task in direct competition with a large number of dermatologists has not been measured to date. This study compares the performance of a convolutional neuronal network trained with dermoscopic images exclusively for identifying melanoma in clinical photographs with the manual grading of the same images by dermatologists. Methods: We compared automatic digital melanoma classification with the performance of 145 dermatologists of 12 German university hospitals. We used methods from enhanced deep learning to train a CNN with 12,378 open-source dermoscopic images. We used 100 clinical images to compare the performance of the CNN to that of the dermatologists. Dermatologists were compared with the deep neural network in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with clinical images was 89.4% (range: 55.0%-100%) and 64.4% (range: 22.5%-92.5%). At the same sensitivity, the CNN exhibited a mean specificity of 68.2% (range 47.5%-86.25%). Among the dermatologists, the attendings showed the highest mean sensitivity of 92.8% at a mean specificity of 57.7%. With the same high sensitivity of 92.8%, the CNN had a mean specificity of 61.1%. Interpretation: For the first time, dermatologist-level image classification was achieved on a clinical image classification task without training on clinical images. The CNN had a smaller variance of results indicating a higher robustness of computer vision compared with human assessment for dermatologic image classification tasks. (C) 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

Background: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy. Methods: We used methods from enhanced deep learning to train a convolutional neural network (CNN) with 12,378 open-source dermoscopic images. We used 100 images to compare the performance of the CNN to that of the 157 dermatologists from 12 university hospitals in Germany. Outperformance of dermatologists by the deep neural network was measured in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with dermoscopic images was 74.1% (range 40.0%-100%) and 60% (range 21.3%-91.3%), respectively. At a mean sensitivity of 74.1%, the CNN exhibited a mean specificity of 86.5% (range 70.8%-91.3%). At a mean specificity of 60%, a mean sensitivity of 87.5% (range 80%-95%) was achieved by our algorithm. Among the dermatologists, the chief physicians showed the highest mean specificity of 69.2% at a mean sensitivity of 73.3%. With the same high specificity of 69.2%, the CNN had a mean sensitivity of 84.5%. Interpretation: A CNN trained by open-source images exclusively outperformed 136 of the 157 dermatologists and all the different levels of experience (from junior to chief physicians) in terms of average specificity and sensitivity. (C) 2019 The Author(s). Published by Elsevier Ltd