ObjectivesDelirium is a serious condition, which poses treatment challenges during hospitalisation for COVID-19. Improvements in testing, vaccination and treatment might have changed patient... Show moreObjectivesDelirium is a serious condition, which poses treatment challenges during hospitalisation for COVID-19. Improvements in testing, vaccination and treatment might have changed patient characteristics and outcomes through the pandemic. We evaluated whether the prevalence and risk factors for delirium, and the association of delirium with in-hospital mortality changed through the pandemic.MethodsThis study was part of the COVID-OLD study in 19 Dutch hospitals including patients ≥70 years in the first (spring 2020), second (autumn 2020) and third wave (autumn 2021). Multivariable logistic regression models were used to study risk factors for delirium, and in-hospital mortality. Differences in effect sizes between waves were studied by including interaction terms between wave and risk factor in logistic regression models.Results1540, 884 and 370 patients were included in the first, second and third wave, respectively. Prevalence of delirium in the third wave (12.7%) was significantly lower compared to the first (22.5%) and second wave (23.5%). In multivariable-adjusted analyses, pre-existing memory problems was a consistent risk factor for delirium across waves. Previous delirium was a risk factor for delirium in the first wave (OR 4.02), but not in the second (OR 1.61) and third wave (OR 2.59, p-value interaction-term 0.028). In multivariable-adjusted analyses, delirium was not associated with in-hospital mortality in all waves.ConclusionDelirium prevalence declined in the third wave, which might be the result of vaccination and improved treatment strategies. Risk factors for delirium remained consistent across waves, although some attenuation was seen in the second wave. Show less
Minnema, J.; Ernst, A.; Eijnatten, M. van; Pauwels, R.; Forouzanfar, T.; Batenburg, K.J.; Wolff, J. 2022
Computer-assisted surgery (CAS) allows clinicians to personalize treatments and surgical interventions and has therefore become an increasingly popular treatment modality in maxil-lofacial surgery.... Show moreComputer-assisted surgery (CAS) allows clinicians to personalize treatments and surgical interventions and has therefore become an increasingly popular treatment modality in maxil-lofacial surgery. The current maxillofacial CAS consists of three main steps: (1) CT image reconstruction, (2) bone segmentation, and (3) surgical planning. However, each of these three steps can introduce errors that can heavily affect the treatment outcome. As a consequence, tedious and time-consuming manual post-processing is often necessary to ensure that each step is performed adequately. One way to overcome this issue is by developing and implementing neural networks (NNs) within the maxillofacial CAS workflow. These learning algorithms can be trained to perform specific tasks without the need for explicitly defined rules. In recent years, an extremely large number of novel NN approaches have been proposed for a wide variety of applications, which makes it a difficult task to keep up with all relevant developments. This study therefore aimed to summarize and review all relevant NN approaches applied for CT image reconstruction, bone segmentation, and surgical planning. After full text screening, 76 publications were identified: 32 focusing on CT image reconstruction, 33 focusing on bone segmentation and 11 focusing on surgical planning. Generally, convolutional NNs were most widely used in the identified studies, although the multilayer perceptron was most commonly applied in surgical planning tasks. Moreover, the drawbacks of current approaches and prom-ising research avenues are discussed.Dentomaxillofacial Radiology (2022) 51, 20210437. doi: 10.1259/dmfr.20210437 Show less
