Genetic analysis on formalin‐fixed paraffin‐embedded (FFPE) tissue specimens has become a mainstream method, from conventional direct sequencing to comprehensive analysis using next‐generation sequencing (NGS). In this study, we evaluated the quality of DNA and RNA extracted from FFPE sections, derived from surgical specimens of different tumor types. Electrophoresis was performed using a 4200 TapeStation to evaluate DNA and RNA fragmentation. DNA Ct values were higher and significantly increased over a period of 4 years compared with those from cell lines or frozen tissues. The RNA integrity number equivalent (RIN) ranged from 1 to 4.1 and DV200 ranged from 7.3 to 81%. Twelve of the 108 cases were analyzed by NGS using the AmpliSeq Cancer HotSpot Panel v2 on a Miniseq system. A sufficient number of reads and coverage were obtained in all cases. Our results revealed that NGS analysis was sufficient for FFPE‐derived DNA within 4 years of preservation. Conversely, approximately 20% of the RNA derived from FFPE within 4 years from the collection could be inappropriate for gene analysis based on RIN and DV200. It was suggested that FFPE would be adequate for genetic analysis, although it is desirable to store frozen specimens for the tumor tissues to be subjected to genetic analysis.
Mesothelioma is a malignancy with poor prognosis. It is chiefly caused by asbestos exposure and its symptoms can occur about 30-50 yr after the initial exposure. This study aims to predict the future trends in mesothelioma mortality in Japan using a method that is an alternative to the age-cohort model. Our approach is based on a risk function that links mesothelioma mortality combined with data pertaining to the population, size of the labor force, and quantity of asbestos imports. We projected the number of deaths occurring in individuals aged 50-89 for yr 2003-2050 using risk functions. Our results have indicated that mesothelioma mortality among Japanese people aged 50-89 yr will continue to increase until 2027 and reach a maximum of 66,327 deaths in the years 2003-2050. Our estimate has also suggested that the number of mesothelioma deaths could be significantly reduced if there were adequate compliance with the administrative level guidelines for occupational asbestos exposure.
Aims/Introduction
Although the epidemiological relationship between hypoglycemia and increased risk of acute coronary syndrome (ACS) has been well established, the time period for increased risk of ACS after a severe hypoglycemic episode remains unknown. The present study aimed to determine the ACS risk after a severe hypoglycemic episode.
Materials and Methods
We carried out a retrospective population‐based cohort study based on national claims data in Japan. We retrieved data of diabetes patients aged ≥35 years collected from April 2014 to March 2016. The absolute risk of ACS was defined as the occurrence of an emergency percutaneous coronary intervention after a severe hypoglycemic episode.
Results
In total, data of 7,909,626 patients were included in the analysis. The absolute risk of ACS was 2.9 out of 1,000 person‐years in all patients. ACS risk in patients with severe hypoglycemic episodes was 3.0 out of 1,000 person‐years. Severe hypoglycemic episodes increased the absolute risk of ACS in patients aged ≥70 years, but not in patients aged <70 years. The absolute risk of ACS was 10.6 out of 1,000 person‐years within 10 days of a severe hypoglycemic episode. There was a significant trend between shorter duration after an episode and higher ACS risk.
Conclusions
Severe hypoglycemia was associated with an increased risk of ACS in elderly diabetes patients. ACS risk increased with a shorter period after a severe hypoglycemic episode, suggesting that severe hypoglycemia leads to an increased risk of ACS in diabetes patients. These findings show that it is important to avoid severe hypoglycemia while treating diabetes, particularly in elderly patients.
Aims/Introduction
The purpose of the present study was to quantify errors in the diagnosis of diabetes for use in the national database, using a sufficient population size.
Materials and methods
A claims database constructed by the JMDC (Tokyo, Japan), using standardized disease classifications and anonymous record linkage, was used in this validation study. We included patients with health insurance claims data from April 2005 to March 2019 in the JMDC claims database. We excluded patients without a record of specific health checkups in Japan. Sample size calculation was based on a 5% prevalence of diabetes and 0.4% absolute accuracy (i.e., 1,250,000 individuals), to calculate the sensitivity, specificity, positive predictive value and negative predictive value.
Results
In total, 2,999,152 patients were included in this study, of which 165,515 were classified as having diabetes based on specific health checkups (validation cohort prevalence of 5.5%). The newly devised algorithm had three elements – the diagnosis‐related codes for diabetes without suspected flag, the medication codes for diabetes and then these two codes on the same record – and yielded a sensitivity of 74.6%, positive predictive value of 88.4% and Kappa Index of 0.80 (the highest values).
Conclusions
In future claims database studies, our validated algorithms will be useful as diagnostic criteria for diabetes.
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