Impact of COVID-19 on incidence, clinical presentation, and prognosis of acute chest syndrome in patients with sickle cell disease.

Acute chest syndrome (ACS) is a frequent complication of sickle cell disease (SCD). Because coronavirus disease 2019 (COVID-19) increases mortality and morbidity in many diseases, we retrospectively analyzed the impact of SARS-CoV-2 infection on the incidence, the clinical presentation, and the prognosis of ACS in patients with SCD by comparing ACS episode before and during COVID-19 pandemic. Ninety-nine episodes of ACS were registered over 24 months before pandemic versus 81 episodes over 24 months during the pandemic period. The number of ACS episodes varies among children regarding the two period of time: 26 episodes (26%) for the pre-pandemic period versus 11 episodes (13%) for the pandemic period (p = 0.03). Comparisons between adults and children showed a higher incidence of initial VOC (45% vs. 24%; p = 0.04) in adults, and a higher incidence of initial pneumonia (35% vs. 15%; p = 0.01) and documented infection (35% vs. 7%; p < 0.001) in children. One patient died during the pandemic period but without any relationship with ACS or COVID-19. During this pandemic period, 13 episodes of ACS (16%) were found related to coronavirus infection. These ACS episodes did not show any significant differences in terms of outcome when compared to the other ACS episodes observed during this period. Overall, coronavirus infection did not demonstrate a negative impact on incidence, clinical presentation, and outcome of ACS in patients with SCD. Early management, chronic treatment with HU, and exchange transfusions could likely explain the low morbidity and mortality rates.

Association between out-of-bed mobilization during the ICU stay of elderly patients and long-term autonomy: A cohort study.

PURPOSE: Our objective was to estimate the impact of the absence of Out-of-Bed (OoB) mobilization during intensive care unit (ICU) stay among patients ≥70 years on their long-term autonomy. METHODS: We conducted an ancillary study of the multicenter FRAGIREA study, including patients aged over 70 years, admitted to ICU for more than 48 h. We excluded the patients who died before day 180, who were lost to follow-up, and for whom the baseline autonomy (ADL) score was not available. Patients were classified into the OoB or non-OoB-mobilization group, and the impact of OoB mobilization on the decreased 6-month autonomy was estimated. RESULTS: Among the 548 patients of the FRAGIREA cohort, 276 were included in the present study. Among them, 226 had OoB mobilization and 50 did not. 220/276 (80%) patients experienced the transfer to chair as rehabilitation therapy, passive mobilization and transfer to upright sitting were also frequently performed. A decrease in the 6-month autonomy was observed for 63 (23%) patients. After the elimination of potential confounders, non-OoB-mobilization patients had a greater risk of 6-month decreased autonomy (aOR 2.43 [1.18; 4.98]). CONCLUSIONS: Mobilization during the ICU stay of elderly ICU patient survivors was associated with a lower decreased autonomy at 6 months.

Artificial Intelligence for the Characterization of Pulmonary Nodules, Lung Tumors and Mediastinal Nodes on PET/CT.

Lung cancer is the leading cause of cancer related death around the world although early diagnosis remains vital to enabling access to curative treatment options. This article briefly describes the current role of imaging, in particular 2-deoxy-2-[18F]fluoro-D-glucose (FDG) PET/CT, in lung cancer and specifically the role of artificial intelligence with CT followed by a detailed review of the published studies applying artificial intelligence (ie, machine learning and deep learning), on FDG PET or combined PET/CT images with the purpose of early detection and diagnosis of pulmonary nodules, and characterization of lung tumors and mediastinal lymph nodes. A comprehensive search was performed on Pubmed, Embase, and clinical trial databases. The studies were analyzed with a modified version of the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction model Risk Of Bias Assessment Tool (PROBAST) statement. The search resulted in 361 studies; of these 29 were included; all retrospective; none were clinical trials. Twenty-two records evaluated standard machine learning (ML) methods on imaging features (ie, support vector machine), and 7 studies evaluated new ML methods (ie, deep learning) applied directly on PET or PET/CT images. The studies mainly reported positive results regarding the use of ML methods for diagnosing pulmonary nodules, characterizing lung tumors and mediastinal lymph nodes. However, 22 of the 29 studies were lacking a relevant comparator and/or lacking independent testing of the model. Application of ML methods with feature and image input from PET/CT for diagnosing and characterizing lung cancer is a relatively young area of research with great promise. Nevertheless, current published studies are often under-powered and lacking a clinically relevant comparator and/or independent testing.

[The relational withdrawal of the hospitalised child: from a professional project to a research protocol]. / Le retrait relationnel de l'enfant hospitalisé : d'un projet professionnel à un protocole de recherche.

Professional reflection, initiated during childcare studies during a paediatric intensive care internship, can lead to the writing of a research protocol on the relational withdrawal of a hospitalized child. How could the use of the Baby Distress Alarm Scale, a tool for screening and assessing relational withdrawal behaviour, improve the detection of these behaviours in an inpatient child to prevent developmental disorders? Feedback from experience.

Heterogeneity in tumours: Validating the use of radiomic features on 18F-FDG PET/CT scans of lung cancer patients as a prognostic tool.

AIM: The aim was to validate promising radiomic features (RFs)1 on 18F-flourodeoxyglucose positron emission tomography/computed tomography-scans (18F-FDG PET/CT) of non-small cell lung cancer (NSCLC) patients undergoing definitive chemo-radiotherapy. METHODS: 18F-FDG PET/CT scans performed for radiotherapy (RT) planning were retrieved. Auto-segmentation with visual adaption was used to define the primary tumour on PET images. Six pre-selected prognostic and reproducible PET texture -and shape-features were calculated using texture respectively shape analysis. The correlation between these RFs and metabolic active tumour volume (MTV)3, gross tumour volume (GTV)4 and maximum and mean of standardized uptake value (SUV)5 was tested with a Spearman's Rank test. The prognostic value of RFs was tested in a univariate cox regression analysis and a multivariate cox regression analysis with GTV, clinical stage and histology. P-value ≤ 0.05 were considered significant. RESULTS: Image analysis was performed for 233 patients: 145 males and 88 females, mean age of 65.7 and clinical stage II-IV. Mean GTV was 129.87 cm3 (SD 130.30 cm3). Texture and shape-features correlated more strongly to MTV and GTV compared to SUV-measurements. Four RFs predicted PFS in the univariate analysis. No RFs predicted PFS in the multivariate analysis, whereas GTV and clinical stage predicted PFS (p = 0.001 and p = 0.008 respectively). CONCLUSION: The pre-selected RFs were insignificant in predicting PFS in combination with GTV, clinical stage and histology. These results might be due to variations in technical parameters. However, it is relevant to question whether RFs are stable enough to provide clinically useful information.