Treatment of non-small-cell lung cancer (NSCLC) might take into account comorbidities as an important variable. The aim of this study was to generate a new simplified comorbidity score (SCS) and to determine whether or not it improves the possibility of predicting prognosis of NSCLC patients. A two-step methodology was used.Step 1: An SCS was developed and its prognostic value was compared with classical prognostic determinants in the outcome of 735 previously untreated NSCLC patients.Step 2: the SCS reliability as a prognostic determinant was tested in a different population of 136 prospectively accrued NSCLC patients with a formal comparison between SCS and the classical Charlson comorbidity index (CCI). Prognosis was analysed using both univariate and multivariate (Cox model) statistics. The SCS summarised the following variables: tobacco consumption, diabetes mellitus and renal insufficiency (respective weightings 7, 5 and 4), respiratory, neoplastic and cardiovascular comorbidities and alcoholism (weighting ¼ 1 for each item). In step 1, aside from classical variables such as age, stage of the disease and performance status, SCS was a statistically significant prognostic variable in univariate analyses. In the Cox model weight loss, stage grouping, performance status and SCS were independent determinants of a poor outcome. There was a trend towards statistical significance for age (P ¼ 0.08) and leucocytes count (P ¼ 0.06). In Step 2, both SCS and well-known prognostic variables were found as significant determinants in univariate analyses. There was a trend towards a negative prognostic effect for CCI. In multivariate analysis, stage grouping, performance status, histology, leucocytes, lymphocytes, lactate dehydrogenase, CYFRA 21-1 and SCS were independent determinants of a poor prognosis. CCI was removed from the Cox model. In conclusion, the SCS, constructed as an independent prognostic factor in a large NSCLC patient population, is validated in another prospective population and appears more informative than the CCI in predicting NSCLC patient outcome.

In this extended validation population, the SCS is more informative than the CCI in predicting NSCLC patient outcome as the former is also more disease specific. Combination of both SCS comorbidity score and LSCC QoL yields a more accurate information that conventional analysis of PS.

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Taking regular walks when living with Parkinson’s disease (PD) has beneficial effects on movement and quality of life. Yet, patients usually show reduced physical activity compared to healthy older adults. Using auditory stimulation such as music can facilitate walking but patients vary significantly in their response. An individualized approach adapting musical tempo to patients’ gait cadence, and capitalizing on these individual differences, is likely to provide a rewarding experience, increasing motivation for walk-in PD. We aim to evaluate the observance, safety, tolerance, usability, and enjoyment of a new smartphone application. It was coupled with wearable sensors (BeatWalk) and delivered individualized musical stimulation for gait auto-rehabilitation at home. Forty-five patients with PD underwent a 1-month, outdoor, uncontrolled gait rehabilitation program, using the BeatWalk application (30 min/day, 5 days/week). The music tempo was being aligned in real-time to patients’ gait cadence in a way that could foster an increase up to +10% of their spontaneous cadence. Open-label evaluation was based on BeatWalk use measures, questionnaires, and a six-minute walk test. Patients used the application 78.8% (±28.2) of the prescribed duration and enjoyed it throughout the program. The application was considered “easy to use” by 75% of the patients. Pain, fatigue, and falls did not increase. Fear of falling decreased and quality of life improved. After the program, patients improved their gait parameters in the six-minute walk test without musical stimulation. BeatWalk is an easy to use, safe, and enjoyable musical application for individualized gait rehabilitation in PD. It increases “walk for exercise” duration thanks to high observance.Clinical Trial Registration: ClinicalTrials.gov Identifier: NCT02647242.

SSM-IBR performed after CT and RT is safe, with an acceptable local morbidity rate. Long-term data are needed to evaluate recurrence rates.

Medical-administrative databases are an important source of big data to assess the epidemiology of diseases and interventions, compare drugs, and investigate rare adverse events. We describe the French national health insurance system databases and the Epidemiology and Safety (EPISAFE) collaboration program in ophthalmology and illustrate the paper with recent studies that used the databases to investigate cataract surgery. The Système national d'information inter-régime de l'assurance maladie (SNIIR-AM) is one of the largest databases of medical and administrative data for a general population, covering nearly 65 million people. The SNIIR-AM database links data for all outpatient-reimbursed health expenditures, hospitalization in all 1,546 French private or public healthcare facilities, and date of death. The use of this database provides information at the scale of the entire country, allowing measurement of the impact of changes in practices and new guidelines. Between 2009 and 2012, a total of 2,717,203 eyes in 1,817,865 patients underwent cataract surgery in France, and the incidence of cataract surgery increased from 9.86 to 11.08/1,000 person-years. The 4-year cumulative risk of retinal detachment was 0.99%, and patients <60 years of age were at higher risk of retinal detachment. The incidence of pseudophakic cystoid macular edema was 0.95%. From 2005 to 2014, from data including more than 6 million procedures, the incidence of endophthalmitis after cataract surgery decreased from 0.15 to 0.05%. The EPISAFE collaboration program encompasses the key issues facing ophthalmology, including public health and public policy concerning disease incidence and prevalence, safety and adverse event monitoring, and clinical guidelines.

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