Bone metastases from differentiated thyroid carcinoma: heterogenous tumor response to radioactive Iodine therapy and overall survival.

PURPOSE: Bone metastases (BM) from differentiated thyroid carcinoma (DTC) impact negatively the quality of life and the life expectancy of patients. The aim of the study was (a) to evaluate the overall survival (OS) and prognostic factors of OS and (b) to assess predictive factors of complete BM response (C-BM-R) using radioiodine treatment (RAI) either alone or in association with focal treatment modalities. METHODS: A total of 178 consecutive DTC patients harbouring BM, treated between 1989 and 2015, were enrolled in this retrospective study conducted in two tertiary referral centers. OS analysis was performed for the whole cohort, and only the 145 considered non-RAI refractory patients at BM diagnosis were evaluated for C-BM-R following RAI. RESULTS: The median OS from BM diagnosis was 57 months (IQR: 24-93). In multivariate analysis, OS was significantly reduced in the case of T4 stage, 18FDG uptake by the BM and RAI refractory status. Among the 145 DTC considered non-RAI refractory patients at BM diagnosis, 46 patients (31.7%) achieved a C-BM-R following RAI treatment, either alone in 32 (18%) patients or in association with focal BM treatment modalities in 14. The absence of extra-skeletal distant metastasis and of 18FDG uptake in BM were predictive for C-BM-R. CONCLUSIONS: In nearly one-third of DTC patients with RAI avid BM, RAI alone or in combination with BM focal treatment can induce C-BM-R. The presence of 18FDG uptake in BM is associated with an absence of C-BM-R and with a poor OS. 18FDG PET-CT should be performed when BM is suspected.

Link between COVID-19-related in-hospital mortality in continental France administrative areas and weather: an ecological study.

OBJECTIVE: To assess the effect of a weather index on in-hospital COVID-19-linked deaths. DESIGN: Ecological study. SETTING: Continental France administrative areas (départements; henceforth counties). The study period, from 18 March to 30 May 2020, corresponds to the main first outbreak period in France. POPULATION: COVID-19-linked in-hospital deaths. MAIN OUTCOME MEASURES: In-hospital deaths and demographics (population, human density, male sex and population percentage >59 years old) were obtained from national and centralised public databases. County weather indexes were calculated by the French National Meteorological Agency. METHODS: In this observational ecological study, the relationship between in-hospital COVID-19-related mortality and climate zones in continental French counties were analysed, by comparing the cumulative in-hospital death tolls in France by county to other factors (population density, climate, age and sex). The study period lasted from 18 March to 30 May 2020. A multivariate linear-regression analysis of in-hospital mortality included climate zones, population density, population >59 years old and percentages of males as potential predictors. The significance level was set at 5%. RESULTS: Weather indicators and population density were factors independently associated with the COVID-19 death toll. Colder counties had significantly higher mortality rates (p<0.00001). Percentages of males and population >59 years old in counties did not affect COVID-19 in-hospital mortality. CONCLUSIONS: Many parameters influence COVID-19 outbreak-severity indicators. Population density is a strong factor but its exact importance is difficult to discern. Weather (mainly cold winter temperatures) was independently associated with mortality and could help explain outbreak dynamics, which began and were initially more severe in the coldest counties of continental France. Weather partly explains fatality-rate discrepancies observed worldwide.

ICU admissions and in-hospital deaths linked to COVID-19 in the Paris region are correlated with previously observed ambient temperature.

The purpose of this ecological study was to explore the association of weather with severity indicators of coronavirus disease 2019 (COVID-19). Daily COVID-19-related intensive care unit (ICU) admissions and in-hospital deaths in the Paris region and the daily weather characteristics of Paris midtown were correlated with a time lag. We assessed different study periods (41, 45, 50, 55, and 62 days) beginning from 31 March 2020. Daily ICU admissions and in-hospital deaths were strongly and negatively correlated to ambient temperatures (minimal, average, and maximal). The highest Pearson correlation coefficients and statistically significant p values were found 8 days before the occurrence of ICU admissions and 15 days before deaths. Partial correlations with adjustment on days since lockdown showed similar significant results. The study findings show a negative correlation of previously observed ambient temperature with severity indicators of COVID-19 that could partly explain the death toll discrepancies between and within countries.

Improvement of the quality of medical databases: data-mining-based prediction of diagnostic codes from previous patient codes.

INTRODUCTION: Diagnoses and medical procedures collected under the French system of information are recorded in a nationwide database, the "PMSI national database", which is accessible for exploitation. Quality of the data in this database is directly related to the quality of coding, which can be of poor quality. Among the proposed methods for the exploitation of health databases, data mining techniques are particularly interesting. Our objective is to build sequential rules for missing diagnoses prediction by data mining of the PMSI national database. METHOD: Our working sample was constructed from the national database for years 2007 to 2010. The information retained for rules construction were medical diagnoses and medical procedures. The rules were selected using a statistical filter, and selected rules were validated by case review based on medical letters, which enabled to estimate the improvement of diagnoses recoding. RESULTS: The work sample was made of 59,170 inpatient stays. The predicted ICD codes were E11 (non-insulin-dependent diabetes mellitus), I48 (atrial fibrillation and flutter) and I50 (heart failure).We validated three sequential rules with a substantial improvement of positive predictive value: {E11,I10,DZQM006}=>{E11} {E11,I10,I48}=>{E11} {I48,I69}=>{I48} DISCUSSION: We were able to extract by data mining three simple, reliable and effective sequential rules, with a substantial improvement in diagnoses recoding. The results of our study indicate the opportunity to improve the data quality of the national database by data mining methods.