[Morbidity observed in a health area: Impact on professionals and funding]. / Morbilidad observada en un área sanitaria: variabilidad e impacto en profesionales y en financiación.

OBJECTIVE: To analyze morbidity, in the context of a health area, and broken down by health centre, of patients who made contact with healthcare services, in order to propose an adjustment to finance the payment per capita. DESIGN: A descriptive study of morbidity observed in citizens assigned a health area during year 2010. SITE: Health Area 9. Autonomous Community of Madrid. Formed by the municipalities of Fuenlabrada, Humanes, and Moraleja de Enmedio. All levels of health care included. PARTICIPANTS: All citizens with health card assigned to a health center in the area who has maintained contact with the public health service's own area. MEASUREMENTS: Coded contact of patients are grouped using the Population Grouping Clinical Risk 3M TM Software (CRG). Each patient is included in a homogeneous and exclusive group with a numerical morbidity and clinical sense. Through the health card is known primary care centre, physician, age and sex. RESULTS: The distribution of morbidity is obtained by primary care centre, primary care physician, age and sex analyzing differences and combinations. CONCLUSIONS: It was found that the average values of the population morbidity are different in each primary care centre. In order to maintain the principle of equity in health care, it is suggested that an adjustment is made to the per capita payment based on the morbidity rate of the population.

Adjuvant dabrafenib and trametinib for patients with resected BRAF -mutated melanoma: DESCRIBE-AD real-world retrospective observational study.

BRAF and MEK inhibitor, dabrafenib plus trametinib, adjuvant therapy is effective for high-risk resected melanoma patients with BRAF - V600 mutations. However, real-world evidence is limited. We aimed to determine the feasibility of this therapy in routine clinical practice. DESCRIBE-AD, a retrospective observational study, collected real-world data from 25 hospitals in Spain. Histologically confirmed and resected BRAF -mutated melanoma patients aged ≥18 years who were previously treated with dabrafenib plus trametinib adjuvant therapy, were included. The primary objectives were treatment discontinuation rate and time to discontinuation. The secondary objectives included safety and efficacy. From October 2020 to March 2021, 65 patients were included. Dabrafenib and trametinib discontinuation rate due to treatment-related adverse events (TRAEs) of any grade was 9%. Other reasons for discontinuation included patients' decisions (6%), physician decisions (6%), unrelated adverse events (3%), disease progression (5%), and others (5%). The median time to treatment discontinuation was 9 months [95% confidence interval (CI), 5-11]. G3-4 TRAEs occurred in 21.5% of patients, the most common being pyrexia (3%), asthenia (3%), and diarrhoea (3%). Unscheduled hospitalisations and clinical tests occurred in 6 and 22% of patients, respectively. After 20-month median follow-up (95% CI, 18-22), 9% of patients had exitus due to disease progression, with a 12-month relapse-free survival and overall survival rates of 95.3% and 100%, respectively. Dabrafenib and trametinib adjuvant therapy proved effective for melanoma patients in a real-world setting, with a manageable toxicity profile. Toxicity frequencies were low leading to low incidence of unscheduled medical visits, tests, and treatment discontinuations.

SARS-CoV-2 infection in patients with melanoma: results of the Spanish Melanoma Group registry.

BACKGROUND: The Spanish Melanoma Group (GEM) developed a national registry of patients with melanoma infected by SARS-CoV-2 ("GRAVID"). METHODS: The main objective was to describe the COVID-19 fatality rate in patients with melanoma throughout the pandemic, as well as to explore the effect of melanoma treatment and tumor stage on the risk of COVID-19 complications. These are the final data of the register, including cases from February 2020 to September 2021. RESULTS: One hundred-fifty cases were registered. Median age was 68 years (range 6-95), 61 (40%) patients were females, and 63 (42%) patients had stage IV. Thirty-nine (26%) were on treatment with immunotherapy, and 17 (11%) with BRAF-MEK inhibitors. COVID-19 was resolved in 119 cases, including 85 (57%) patients cured, 15 (10%) that died due to melanoma, and 20 (13%) that died due to COVID-19. Only age over 60 years, cardiovascular disorders, and diabetes mellitus increased the risk of death due to COVID-19, but not advanced melanoma stage nor melanoma systemic therapies. Three waves have been covered by the register: February-May 2020, August-November 2020, and December 2020-April 2021. The first wave had the highest number of registered cases and COVID-19 mortality. CONCLUSION: Tumor stage or melanoma treatments are non-significant prognostic factors for COVID-19 mortality. During the pandemic in Spain there was a downward trend in the number of patients registered across the waves, as well as in the severity of the infection. GOV IDENTIFIER: NCT04344002.

Extreme elevation of acute phase reactants and shock secondary to dabrafenib-trametinib.

The emerging role of BRAF and MEK tyrosine-kinase inhibitors has shown new opportunities of treatment for patients with advanced melanoma and BRAF mutations. Its use is associated with some toxicities, as pyrexia, that clinicians may not be familiarized with. We present the case of a patient diagnosed with stage IV melanoma BRAF Val600E mutated who was started on dabrafenib and trametinib and developed three severe episodes of fever, hypotension and acute phase reactants elevation during the first 3 months of therapy, in the absence of microbiological demonstration of infection. The episodes were initially managed as a septic shock with broad-spectrum antibiotics and vasoactive drugs, while treatment with dabrafenib and trametinib was withheld. After two subsequent dose reduction of dabrafenib, the patient did not experience new episodes of fever.

Prognostic value of the TGFß1 rs4803455 single nucleotide polymorphism in small cell lung cancer.

BACKGROUND: Small cell lung cancer (SCLC) is one of the greatest therapeutic challenges of oncology. Potential associations between single nucleotide polymorphisms in heat shock protein ß1 (HSPB1) and transforming growth factor ß1 (TGFß1) and survival have been investigated. METHODS: A prospective multicenter study of 94 patients with SCLC treated between 2013 and 2016 was conducted. Clinical, tumour-related, therapeutic, and genetic (9 SNPs of TGFß1 gene and 5 of HSPB1 gene) variables were analyzed. RESULTS: The cohort included 77 men and 17 women with a median age of 61 years. Eighty percent presented with limited stage at diagnosis and received thoracic radiation with a median dose of 45 Gy (twice-daily radiation in 42%). Forty-seven percent received concurrent platinum-based chemotherapy and 57% received prophylactic cranial irradiation (PCI). Overall survival (OS) was 34% at 2 years and 16% at 3 years. In multivariate analysis, the rs4803455:CA genotype of the TGFß1 gene showed a statistically significant association with lower disease-free survival (DFS; hazard ratio [HR] 3.13; confidence interval [CI] 1.19-8.17; p = 0.020) and higher local recurrence (HR 3.80; CI 1.37-10.5; p = 0.048), and a marginal association with lower OS (HR 1.94; CI 0.98-3.83; p = 0.057). A combined analysis showed that patients receiving PCI and carrying the rs4803455:CA genotype had statistically significant lower OS (p < 0.001) and DFS (p < 0.001) than patients receiving PCI and carrying the rs4803455:AA genotype. CONCLUSIONS: Genetic analysis showed the CA genotype of TGFß1 SNP rs4803455 was associated with worse prognosis in patients with SCLC and could be considered as a potential biomarker.

Prediction of patient evolution in terms of Clinical Risk Groups form routinely collected data using machine learning.

Chronicity is a problem that is affecting quality of life and increasing healthcare costs worldwide. Predictive tools can help mitigate these effects by encouraging the patients' and healthcare system's proactivity. This research work uses supervised learning techniques to build a predictive model of the healthcare status of a chronic patient, using Clinical Risk Groups (CRGs) as a measure of chronicity and prescription and diagnosis data as predictors. The model is addressed to the whole population in our healthcare system regardless of the disease, as data used are widely available in a consistent way for all patients. We explore different ways to encode data that are appropriate for machine learning. Results suggest that these data alone can be used to build accurate models, and show that, in our set, prescription information has a higher predictive value than diagnosis.