Background Traditionally, dengue surveillance is based on case reporting to a central health agency. However, the delay between a case and its notification can limit the system responsiveness. Machine learning methods have been developed to reduce the reporting delays and to predict outbreaks, based on non-traditional and non-clinical data sources. The aim of this systematic review was to identify studies that used real-world data, Big Data and/or machine learning methods to monitor and predict dengue-related outcomes. Methodology/Principal findings We performed a search in PubMed, Scopus, Web of Science and grey literature between January 1, 2000 and August 31, 2020. The review (ID: CRD42020172472) focused on data-driven studies. Reviews, randomized control trials and descriptive studies were not included. Among the 119 studies included, 67% were published between 2016 and 2020, and 39% used at least one novel data stream. The aim of the included studies was to predict a dengue-related outcome (55%), assess the validity of data sources for dengue surveillance (23%), or both (22%). Most studies (60%) used a machine learning approach. Studies on dengue prediction compared different prediction models, or identified significant predictors among several covariates in a model. The most significant predictors were rainfall (43%), temperature (41%), and humidity (25%). The two models with the highest performances were Neural Networks and Decision Trees (52%), followed by Support Vector Machine (17%). We cannot rule out a selection bias in our study because of our two main limitations: we did not include preprints and could not obtain the opinion of other international experts. Conclusions/Significance Combining real-world data and Big Data with machine learning methods is a promising approach to improve dengue prediction and monitoring. Future studies should focus on how to better integrate all available data sources and methods to improve the response and dengue management by stakeholders.

We reinvestigate the relationship between axillary lymph node involvement in breast cancer and the overall risk of death. Patients were women from the Surveillance, Epidemiology, and End Results (SEER) program, aged between 50 and 65 years, presenting a first primary T1-T2 (tumor size ≤5 cm), node-positive, non-metastasized unilateral breast carcinoma, diagnosed from 1988 to 1997, treated with mastectomy without radiotherapy. Hazard ratios (HRs) were computed at each percentage of involved nodes using the proportional hazards model, adjusting for the patient's demographic and tumor characteristics. The pattern of the hazard ratios was examined using serial correlations. Significance testing used the "portmanteau" test. Based on 4,387 records available for analysis, the relation between adjusted mortality and axillary lymph node involvement was modeled as H t-H t-1 = μ + a t , where t is the percentage of involved nodes, H t is the mortality hazard ratio at the percentage t, μ is a constant, and a t is white noise. The constant μ was estimated at 0.020, corresponding to a 2% increment in the mortality hazard ratio per 1% increase in the percentage of positive nodes. The model was considered acceptable by the "portmanteau" test (P=0.205). We conclude that the effect of the tumor burden might be expressed as a random walk difference model, relating the mortality hazard ratio with the percentage of involved nodes. We will use the model to explore how treatments affect the course of the disease.

In randomized clinical trials where the times to event of two treatment groups are compared under a proportional hazards assumption, it has been established that omitting prognostic factors from the model entails an underestimation of the hazards ratio. Heterogeneity due to unobserved covariates in cancer patient populations is a concern since genomic investigations have revealed molecular and clinical heterogeneity in these populations. In HIV prevention trials, heterogeneity is unavoidable and has been shown to decrease the treatment effect over time. This article assesses the influence of trial duration on the bias of the estimated hazards ratio resulting from omitting covariates from the Cox analysis. The true model is defined by including an unobserved random frailty term in the individual hazard that reflects the omitted covariate. Three frailty distributions are investigated: gamma, log-normal, and binary, and the asymptotic bias of the hazards ratio estimator is calculated. We show that the attenuation of the treatment effect resulting from unobserved heterogeneity strongly increases with trial duration, especially for continuous frailties that are likely to reflect omitted covariates, as they are often encountered in practice. The possibility of interpreting the long-term decrease in treatment effects as a bias induced by heterogeneity and trial duration is illustrated by a trial in oncology where adjuvant chemotherapy in stage 1B NSCLC was investigated.

No abstract

AIMTo investigate rates of distant metastases (DM) detected with [18]fluorodeoxyglucose-positron emission tomography/computed tomography (18FDG-PET/CT) in early stage invasive breast cancer.METHODSWe searched the English language literature databases of PubMed, EMBASE, ISI Web of Knowledge, Web of Science and Google Scholar, for publications on DM detected in patients who had 18FDG-PET/CT scans as part of the staging for early stages of breast cancer (stage I and II), prior to or immediately following surgery. Reports published between 2011 and 2017 were considered. The systematic review was conducted according to the PRISMA guidelines.RESULTSAmong the 18 total studies included in the analysis, the risk of DM ranged from 0% to 8.3% and 0% to 12.9% for stage I and II invasive breast cancer, respectively. Among the patients with clinical stage II, the rate of occult metastases diagnosed by 18FDG-PET/CT was 7.2% (range, 0%-19.6%) for stage IIA and 15.8% (range, 0%-40.8%) for stage IIB. In young patients (< 40-year-old), 18FDG-PET/CT demonstrated a higher prevalence of DM at the time of diagnosis for those with aggressive histology (i.e., triple-negative receptors and poorly differentiated grade).CONCLUSIONYoung patients with poorly differentiated tumors and stage IIB triple-negative breast cancer may benefit from 18FDG-PET/CT at initial staging to detect occult DM prior to surgery.

TomoBreast hypothesized that hypofractionated 15 fractions/3 weeks image-guided radiation therapy (H-IGRT) can reduce lung-heart toxicity, as compared with normofractionated 25-33 fractions/5-7 weeks conventional radiation therapy (CRT). 123 women with stage I-II operated breast cancer were randomized to receive CRT (N=64) or H-IGRT (N=59). The primary endpoint used a four-items measure of the time to 10% alteration in any of patient self-reported measure, physician clinical evaluation, echocardiography or lung function tests, analyzed by intention-to-treat without exclusion. Results found comparable survivals, but H-IGRT significantly reduced the toxicity measured by lung diffusion capacity and alveolar volume as compared with CRT, G1 in 53% (31/59) versus 72% (44/61) patients, P=0.006; G2, 29% versus 48%, P=0.020. H-IGRT significantly reduced the risk of composite cardio-pulmonary alteration at 5 years, 10.2% versus 26.7%, P=0.024. In conclusion, TomoBreast is a proof-of-concept that image-guided radiation-therapy can improve the overall balance of lung-heart outcomes in breast cancer adjuvant therapy. Furthermore, the significance of the findings supports the efficacy of a small trial size design, which can be critical when clinical research resources are limited.

Background Traditionally, dengue prevention and control rely on vector control programs and reporting of symptomatic cases to a central health agency. However, case reporting is often delayed, and the true burden of dengue disease is often underestimated. Moreover, some countries do not have routine control measures for vector control. Therefore, researchers are constantly assessing novel data sources to improve traditional surveillance systems. These studies are mostly carried out in big territories and rarely in smaller endemic regions, such as Martinique and the Lesser Antilles. Objective The aim of this study was to determine whether heterogeneous real-world data sources could help reduce reporting delays and improve dengue monitoring in Martinique island, a small endemic region. Methods Heterogenous data sources (hospitalization data, entomological data, and Google Trends) and dengue surveillance reports for the last 14 years (January 2007 to February 2021) were analyzed to identify associations with dengue outbreaks and their time lags. Results The dengue hospitalization rate was the variable most strongly correlated with the increase in dengue positivity rate by real-time reverse transcription polymerase chain reaction (Pearson correlation coefficient=0.70) with a time lag of −3 weeks. Weekly entomological interventions were also correlated with the increase in dengue positivity rate by real-time reverse transcription polymerase chain reaction (Pearson correlation coefficient=0.59) with a time lag of −2 weeks. The most correlated query from Google Trends was the “Dengue” topic restricted to the Martinique region (Pearson correlation coefficient=0.637) with a time lag of −3 weeks. Conclusions Real-word data are valuable data sources for dengue surveillance in smaller territories. Many of these sources precede the increase in dengue cases by several weeks, and therefore can help to improve the ability of traditional surveillance systems to provide an early response in dengue outbreaks. All these sources should be better integrated to improve the early response to dengue outbreaks and vector-borne diseases in smaller endemic territories.

A worldwide pandemic of viral infection due to SARS-CoV-2 (and its resultant disease, COVID-19) has been ongoing since 2019. Martinique was affected by a major wave in summer 2021, with saturation of the health system forcing the implementation of home care management. We conducted a retrospective, observational study that included patients treated in the KOVIDHOM 972 program. We included adult patients with SARS-CoV2 hypoxemic pneumonia and requiring 4 L per minute or less of oxygen. In total, 418 were discharged to home with oxygen therapy after hospitalization for SARS-CoV-2 hypoxemic acute pneumonia, and 416 were analyzed. Half (50.2%) were women. Mean age was 58.8 ± 13.0 years. Time from onset of symptoms to hospitalization was 9.1 ± 3.5 days, and average length of stay was 10.5 ± 7.4 days. Maximum oxygen flow during hospitalization was 6.9 ± 4.5 L/min in patients who did not require intensive care. Average oxygen flow at discharge was 1.8 ± 07 L/min. At 30 days after discharge, the readmission rate was 0.5% (95% CI: 0–1.18), and the death rate was 0.5% (95% CI 0–1.18). Our study shows a very low rate of readmission or death in COVID-19 patients discharged to home with oxygen therapy. These results highlight the possibility of safe home care in carefully selected patients. Such programs could be useful in pandemic or wide-scale emergency situations.