Recent analyses of series of small-cell lung cancer (SCLC) patients included in clinical trials have shown improved survival over time, but it has been impossible to determine whether this was due to selection biases, stage migration, or true therapeutic improvement. To determine if there has been a true improvement of survival over time, we reviewed the medical records of all consecutive patients diagnosed with SCLC between 1981 and 1994 in the Bas-Rhin in France. Among the 787 patients (median age 63), there was no significant period effect for sex, age, or stage. Staging work-ups became increasingly thorough (significant period effect). The mean number of investigations and of tumour sites detected correlated significantly. The chemotherapy rate increased (from 76.4% in 1981-1983 to 91.7% in 1993-1994, P = 10(-5)) and mediastinal irradiation decreased (to roughly 25% of patients after 1983). Median survival time increased for the overall population from 6.6 months in 1981-1983 to 11.3 months in 1993-1994 (P = 10(-5)), for patients with limited disease (LD) from 9.2 (P = 0.002) months to 14.0 months, and for those with extensive (ED) disease from 3.5 months to 9.6 months (P = 10(-5)). Significant independent prognostic factors were disease extent, clinical trial participation, period, type of chemotherapy, and mediastinal irradiation in LD. Survival time has truly improved as 'state of the art' management of SCLC has changed.
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http://dx.doi.org/10.1054/bjoc.2001.1955 | DOI Listing |
Cancer Med
January 2025
Division of Gastroenterology and Nephrology, Department of Multidisciplinary Internal Medicine, Faculty of Medicine, Tottori University, Yonago, Japan.
Background And Aim: In recent years, there has been a rise in cryptogenic hepatocellular carcinoma (c-HCC) cases in Japan, posing a detection challenge due to an unknown etiology. This study aims to enhance diagnostic strategies for c-HCC by analyzing its characteristics and exploring current opportunities for detection.
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JMIR Form Res
January 2025
ICMR-National Institute for Research in Digital Health and Data Science, Ansari Nagar, New Delhi, 110029, India, 91 7840870009.
Background: Verbal autopsy (VA) has been a crucial tool in ascertaining population-level cause of death (COD) estimates, specifically in countries where medical certification of COD is relatively limited. The World Health Organization has released an updated instrument (Verbal Autopsy Instrument 2022) that supports electronic data collection methods along with analytical software for assigning COD. This questionnaire encompasses the primary signs and symptoms associated with prevalent diseases across all age groups.
View Article and Find Full Text PDFCirc Genom Precis Med
January 2025
Department of Medicine, Division of Cardiology (M.P., N.J.P., N.P.S.), Duke University, Durham, NC.
Background: Established risk models may not be applicable to patients at higher cardiovascular risk with a measured Lp(a) (lipoprotein[a]) level, a causal risk factor for atherosclerotic cardiovascular disease.
Methods: This was a model development study. The data source was the Nashville Biosciences Lp(a) data set, which includes clinical data from the Vanderbilt University Health System.
Rev Med Suisse
January 2025
Service de néphrologie, Département de médecine, Hôpitaux universitaires de Genève, Genève 14.
Certain molecules, such as GLP-1 agonists and endothelin antagonists, possess nephroprotective properties. When treating IgA nephropathy, endothelin antagonists and sibeprenlimab have shown effectiveness in slowing the progression of chronic kidney isease. Additionally, the infusion of amino acids can reduce the incidence of mild acute kidney injury following cardiac surgery.
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January 2025
Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
Aims: Aortic stenosis (AS) is a common and progressive disease, which, if left untreated, results in increased morbidity and mortality. Monitoring and follow-up care can be challenging due to significant variability in disease progression. This study aimed to develop machine learning models to predict the risks of disease progression and mortality in patients with mild AS.
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