Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia

In a pandemic with a novel disease, disease-specific prognosis models are available only with a delay. To bridge the critical early phase, models built for similar diseases might be applied. To test the accuracy of such a knowledge transfer, we investigated how precise lethal courses in critically ill COVID-19 patients can be predicted by a model trained on critically ill non-COVID-19 viral pneumonia patients. We trained gradient boosted decision tree models on 718 (245 deceased) non-COVID-19 viral pneumonia patients to predict individual ICU mortality and applied it to 1054 (369 deceased) COVID-19 patients. Our model showed a significantly better predictive performance (AUROC 0.86 [95% CI 0.86-0.87]) than the clinical scores APACHE2 (0.63 [95% CI 0.61-0.65]), SAPS2 (0.72 [95% CI 0.71-0.74]) and SOFA (0.76 [95% CI 0.75-0.77]), the COVID-19-specific mortality prediction models of Zhou (0.76 [95% CI 0.73-0.78]) and Wang (laboratory: 0.62 [95% CI 0.59-0.65]; clinical: 0.56 [95% CI 0.55-0.58]) and the 4C COVID-19 Mortality score (0.71 [95% CI 0.70-0.72]). We conclude that lethal courses in critically ill COVID-19 patients can be predicted by a machine learning model trained on non-COVID-19 patients. Our results suggest that in a pandemic with a novel disease, prognosis models built for similar diseases can be applied, even when the diseases differ in time courses and in rates of critical and lethal courses

Australian clinical practice guidelines for the diagnosis and management of Barrett's esophagus and early esophageal adenocarcinoma

Author version made available following 12 month embargo from date of publication according to publisher copyright policy.Barrett's esophagus (BE), a common condition, is the only known precursor to esophageal adenocarcinoma (EAC). There is uncertainty about the best way to manage BE as most people with BE never develop EAC and most patients diagnosed with EAC have no preceding diagnosis of BE. Moreover, there have been recent advances in knowledge and practice about the management of BE and early EAC. To aid clinical decision making in this rapidly moving field, Cancer Council Australia convened an expert working party to identify pertinent clinical questions. The questions covered a wide range of topics including endoscopic and histological definitions of BE and early EAC; prevalence, incidence, natural history, and risk factors for BE; and methods for managing BE and early EAC. The latter considered modification of lifestyle factors; screening and surveillance strategies; and medical, endoscopic, and surgical interventions. To answer each question, the working party systematically reviewed the literature and developed a set of recommendations through consensus. Evidence underpinning each recommendation was rated according to quality and applicability

Automated Detection of Refactorings in Evolving Components

One of the costs of reusing software components is migrating applications to use the new version of the components. Migrating an application can be error-prone, tedious, and disruptive of the development process. Our previous work shows that more than 80% of the disruptive changes in four different components were caused by refactorings. If the refactorings that happened between two versions of a component could be automatically detected, a refactoring tool could replay them on applications. We present an algorithm that detects refactorings performed during component evolution. Our algorithm uses a combination of a fast syntactic analysis to detect refactoring candidates and a more expensive semantic analysis to refine the results. The experiments on codebases ranging from 17 KLOC to 350 KLOC show that our algorithm detects refactorings in real-world components with accuracy over 85%

Vorhersage von Langzeitergebnissen nach Hüftprothesenimplantation anhand präoperativer Funktionseinschränkung und akuter postoperativer Schmerzen

General view of living wagon park at Nottingham Goose Fair, photographed 1993

Resting state brain network functional connectivity is not associated with inflammatory markers and blood cell counts in older adults

OBJECTIVE: Systemic inflammation and monocyte counts have previously been associated with changes in resting state functional connectivity (rsFC) in cross-sectional neuroimaging studies. We therefore investigated this association in a longitudinal study of older patients. METHODS: We performed a secondary analysis of longitudinal data from older patients who underwent functional magnet resonance imaging (fMRI) scans before and 3 months after elective surgery. Additionally, serum levels of C-reactive protein and Interleukin-6 as markers of inflammation and leukocyte, lymphocyte and monocyte counts were determined. Correlations between these markers and pre- or postoperative rsFC between regions previously associated with inflammatory markers were investigated using general linear regression models. RESULTS: We found no significant correlations between inflammatory markers or blood cell counts and mean connectivity within four resting state networks (RSNs), neither preoperatively nor postoperatively. Significant inter-region rsFC was found within these RSNs between a few regions either pre- or postoperatively, but no inter-region connections were consistently observed in both pre- and postoperative fMRI scans. CONCLUSIONS: Inflammatory markers and monocyte counts were not associated with rsFC in our study, contrasting previous results. SIGNIFICANCE: Multiple measurements in the same individuals, as performed here, provide a way to reduce the high risk of false positive results in fMRI studies. TRIAL REGISTRATION: Clinicaltrials.gov (registration number NCT02265263)

Effects of propofol anesthesia on the processing of noxious stimuli in the spinal cord and the brain

Drug-induced unconsciousness is an essential component of general anesthesia, commonly attributed to attenuation of higher-order processing of external stimuli and a resulting loss of information integration capabilities of the brain. In this study, we investigated how the hypnotic drug propofol at doses comparable to those in clinical practice influences the processing of somatosensory stimuli in the spinal cord and in primary and higher-order cortices. Using nociceptive reflexes, somatosensory evoked potentials and functional magnet resonance imaging (fMRI), we found that propofol abolishes the processing of innocuous and moderate noxious stimuli at low to medium concentration levels, but that intense noxious stimuli evoked spinal and cerebral responses even during deep propofol anesthesia that caused profound electroencephalogram (EEG) burst suppression. While nociceptive reflexes and somatosensory potentials were affected only in a minor way by further increasing doses of propofol after the loss of consciousness, fMRI showed that increasing propofol concentration abolished processing of intense noxious stimuli in the insula and secondary somatosensory cortex and vastly increased processing in the frontal cortex. As the fMRI functional connectivity showed congruent changes with increasing doses of propofol – namely the temporal brain areas decreasing their connectivity with the bilateral pre-/postcentral gyri and the supplementary motor area, while connectivity of the latter with frontal areas is increased – we conclude that the changes in processing of noxious stimuli during propofol anesthesia might be related to changes in functional connectivity