Decline and incomplete recovery in cancer diagnoses during the COVID-19 pandemic in Belgium: a year-long, population-level analysis.

BACKGROUND: Oncological care was considerably impacted by the COVID-19 pandemic. Worrisome declines in diagnostic procedures and cancer diagnoses in 2020 have been reported; however, nationwide, population-based evidence is limited. Quantification of the magnitude and distribution of the remaining outstanding diagnoses is likewise lacking. METHODS: Using accelerated delivery of data from pathology laboratories to the Belgian Cancer Registry, we compared the nationwide rates of new diagnoses of invasive cancers in 2020 to 2019. RESULTS: We observed a 44% reduction in total diagnoses of invasive cancers in April 2020 compared with April 2019, coinciding with the first wave of the COVID-19 pandemic. The reduction was largest in older patients and for skin cancers (melanoma and nonmelanoma). Reductions in diagnosis were less pronounced among children and adolescents (0-19 years). A smaller decline was observed for most cancers with typically poorer prognosis or obvious symptoms, including some hematological malignancies, lung, and pancreatic cancer. Suspension of organized population screening programs was reflected in a strong decline in diagnosis in the screening age groups for female breast cancer (56%) and for colorectal cancer in both men (49%) and women (60%). The number of diagnoses began to increase from the end of April and stabilized at the beginning of June at or just above 2019 levels. There has yet to be a complete recovery in cancer diagnoses, with an estimated 6%, or ∼4000 diagnoses, still outstanding for all of 2020. Among solid tumors, head and neck cancers have the largest remaining year-over-year decrease in diagnoses at 14%. CONCLUSION: These results add to the evidence of a profound impact of the COVID-19 pandemic on oncological care and identify groups at risk for continuing diagnostic delays. These data should stimulate health care providers worldwide to facilitate targeted, accessible, and efficient procedures for detection of cancers affected by this delay.

Electrical stimulation of the bed nucleus of the stria terminalis reduces anxiety in a rat model.

We recently showed that deep brain stimulation (DBS) in the bed nucleus of the stria terminalis (BST) reduces obsessions, compulsions and associated anxiety in patients suffering from severe, treatment-refractory obsessive-compulsive disorder. Here, we investigated the anxiolytic effects of electrical BST stimulation in a rat model of conditioned anxiety, unrelated to obsessions or compulsions. Two sets of stimulation parameters were evaluated. Using fixed settings at 100 Hz, 40 µs and 300 µA (Set A), we observed elevated freezing and startle levels, whereas stimulation at 130 Hz, 220 µs and individually tailored amplitudes (Set B) appeared to reduce freezing. In a follow-up experiment, we evaluated the anxiolytic potential of Set B more extensively, by adding a lesion group and an additional day of stimulation. We found that electrical stimulation significantly reduced freezing, but not to the same extent as lesions. Neither lesions nor stimulation of the BST affected motor behavior or unconditioned anxiety in an open-field test. In summary, electrical stimulation of the BST was successful in reducing contextual anxiety in a rat model, without eliciting unwanted motor effects. Our findings underline the therapeutic potential of DBS in the BST for disorders that are hallmarked by pathological anxiety. Further research will be necessary to assess the translatability of these findings to the clinic.

Heart rate time series characteristics for early detection of infections in critically ill patients.

It is difficult to make a distinction between inflammation and infection. Therefore, new strategies are required to allow accurate detection of infection. Here, we hypothesize that we can distinguish infected from non-infected ICU patients based on dynamic features of serum cytokine concentrations and heart rate time series. Serum cytokine profiles and heart rate time series of 39 patients were available for this study. The serum concentration of ten cytokines were measured using blood sampled every 10 min between 2100 and 0600 hours. Heart rate was recorded every minute. Ten metrics were used to extract features from these time series to obtain an accurate classification of infected patients. The predictive power of the metrics derived from the heart rate time series was investigated using decision tree analysis. Finally, logistic regression methods were used to examine whether classification performance improved with inclusion of features derived from the cytokine time series. The AUC of a decision tree based on two heart rate features was 0.88. The model had good calibration with 0.09 Hosmer-Lemeshow p value. There was no significant additional value of adding static cytokine levels or cytokine time series information to the generated decision tree model. The results suggest that heart rate is a better marker for infection than information captured by cytokine time series when the exact stage of infection is not known. The predictive value of (expensive) biomarkers should always be weighed against the routinely monitored data, and such biomarkers have to demonstrate added value.