AFICILL: a single-cohort, retrospective study on Atrial Fibrillation In Critically ILL patients admitted to a medical sub-intensive care unit: implications for clinical management, outcomes and elaboration of new data-driven models

Introduction: atrial fibrillation (AF) is common among critically-ill patients, who are considered at increased cardioembolic and haemorragic risk. Consequently, anticoagulant therapy might be ineffective or harmful for an excess of haemorragic events which could not be counterbalanced by an adequate reduction of cardioembolic occurrences. Aims: main outcome (MO) was the composite of death or intensive care unit (ICU) transfer in a population of critically-ill subjects admitted to a medical subintensive care unit (sICU); we assessed (i) thromboembolic events (TEE) and major haemorrhages (MH); (ii) current guidelines (GL) adherence and related outcomes; (iii) performance of validated risk scores for TEE and MH; we engineered (iv) new scores adopting machine learning (ML) predicting MO, TEE, MH. Patients and Methods: single-center, retrospective study enrolling all the consecutive AF-affected patients admitted to a sICU for critical illness. Demographic, clinical, therapeutic and laboratoristic data were collected. Performance of CHA2DS2-VASc and HAS-BLED scores was evaluated. GL-adherence and its relationship with outcomes was studied. ML was used to engineer new predictive models. Results: we enrolled 1430 subjects; CHA2DS2-VASc (AUC:0.516;95%CI:0.472-0.560) and HAS-BLED (AUC:0.493;95%CI:0.443-0.543) did not predict TEE or MH; in-hospital warfarin use was associated to increased MO risk (OR:1.73;95%CI:1.06-2.83; p<0.05); low-molecular-weight-heparin use was not associated to an increased MO risk; antiplatelet drugs use was associated to MO risk reduction (OR:0.51;95%CI:0.34-0.78;p<0.002). GL-adherent treatment was associated to TEE risk reduction and MH and MO risk increase; ML identified specific features for MO, TEE, MH: ML-based classifiers outperformed CHA2DS2-VASc (AUC: from 0.516 to 0.90, p<0.0001) and HAS-BLED (AUC: from 0.493 to 0.82, p<0.0001). Discussion: AF-related outcomes cannot be predicted in critically-ill patients with currently validated methods. GL-adherence is associated to a significant TEE reduction, but also to MH and MO increase. ML algorithms can identify the most important features and shape specific scores able to outperform the classical models

Towards personalized diagnosis of Glioblastoma in Fluid-attenuated inversion recovery (FLAIR) by topological interpretable machine learning

Glioblastoma multiforme (GBM) is a fast-growing and highly invasive brain tumour, it tends to occur in adults between the ages of 45 and 70 and it accounts for 52 percent of all primary brain tumours. Usually, GBMs are detected by magnetic resonance images (MRI). Among MRI, Fluid-attenuated inversion recovery (FLAIR) sequence produces high quality digital tumour representation. Fast detection and segmentation techniques are needed for overcoming subjective medical doctors (MDs) judgment. In the present investigation, we intend to demonstrate by means of numerical experiments that topological features combined with textural features can be enrolled for GBM analysis and morphological characterization on FLAIR. To this extent, we have performed three numerical experiments. In the first experiment, Topological Data Analysis (TDA) of a simplified 2D tumour growth mathematical model had allowed to understand the bio-chemical conditions that facilitate tumour growth: the higher the concentration of chemical nutrients the more virulent the process. In the second experiment topological data analysis was used for evaluating GBM temporal progression on FLAIR recorded within 90 days following treatment (e.g., chemo-radiation therapy - CRT) completion and at progression. The experiment had confirmed that persistent entropy is a viable statistics for monitoring GBM evolution during the follow-up period. In the third experiment we had developed a novel methodology based on topological and textural features and automatic interpretable machine learning for automatic GBM classification on FLAIR. The algorithm reached a classification accuracy up to the 97%.Comment: 22 pages; 16 figure

Using Topological Data Analysis for diagnosis pulmonary embolism

Pulmonary Embolism (PE) is a common and potentially lethal condition. Most patients die within the first few hours from the event. Despite diagnostic advances, delays and underdiagnosis in PE are common.To increase the diagnostic performance in PE, current diagnostic work-up of patients with suspected acute pulmonary embolism usually starts with the assessment of clinical pretest probability using plasma d-Dimer measurement and clinical prediction rules. The most validated and widely used clinical decision rules are the Wells and Geneva Revised scores. We aimed to develop a new clinical prediction rule (CPR) for PE based on topological data analysis and artificial neural network. Filter or wrapper methods for features reduction cannot be applied to our dataset: the application of these algorithms can only be performed on datasets without missing data. Instead, we applied Topological data analysis (TDA) to overcome the hurdle of processing datasets with null values missing data. A topological network was developed using the Iris software (Ayasdi, Inc., Palo Alto). The PE patient topology identified two ares in the pathological group and hence two distinct clusters of PE patient populations. Additionally, the topological netowrk detected several sub-groups among healthy patients that likely are affected with non-PE diseases. TDA was further utilized to identify key features which are best associated as diagnostic factors for PE and used this information to define the input space for a back-propagation artificial neural network (BP-ANN). It is shown that the area under curve (AUC) of BP-ANN is greater than the AUCs of the scores (Wells and revised Geneva) used among physicians. The results demonstrate topological data analysis and the BP-ANN, when used in combination, can produce better predictive models than Wells or revised Geneva scores system for the analyzed cohortComment: 18 pages, 5 figures, 6 tables. arXiv admin note: text overlap with arXiv:cs/0308031 by other authors without attributio

Sleep actigraphic patterns and cognitive status

none9noWe performed an actigraphic assessment of sleep characteristics in healthy subjects and patients with cognitive impairment. Thirty subjects were included and classified into controls (10 subjects), mild cognitive impairment (10 patients) and mild-to-moderate Alzheimer's disease (10 patients). Sleep quality was assessed using the Pittsburgh Sleep Quality Index. Participants had a 7-day actigraphic record. Sleep parameters collected were time in bed, total sleep time, sleep efficiency, sleep latency, wakefulness after sleep onset, number of awakenings, and mean motor activity. Significant differences between mild cognitive impairment and controls patients were found for sleep latency (p = 0.05); Alzheimer's disease patients had significantly worse scores for Pittsburgh Sleep Quality Index (p = 0.01), time in bed (p = 0.001), total sleep time (p = 0.04), sleep latency, sleep efficiency, motor activity (p = 0.0001) and wakefulness after sleep onset (p = 0.001) compared to controls. When comparing Alzheimer's disease and mild cognitive impairment, differences were significant for sleep latency (p = 0.01), wakefulness after sleep onset (p = 0.004), sleep efficiency, number of awakenings and motor activity (p = 0.0001). In addition to showing a high prevalence of sleep alterations in subjects with cognitive impairment, our data suggest that they are evident from the earliest stages of cognitive decline. Further studies are needed to assess whether early correction of sleep alterations can positively influence the evolution of cognitive impairment. The opportunity to provide clinically meaningful information with a simple assessment of sleep characteristics based on actigraphy suggests that wider use of the approach in patients with cognitive decline should be considered.openBuratti, Laura; Camilletti, Roberta; Pulcini, Alessandra; Rocchi, Chiara; Viticchi, Giovanna; Falsetti, Lorenzo; Baldinelli, Sara; Fiori, Chiara; Silvestrini, MauroBuratti, Laura; Camilletti, Roberta; Pulcini, Alessandra; Rocchi, Chiara; Viticchi, Giovanna; Falsetti, Lorenzo; Baldinelli, Sara; Fiori, Chiara; Silvestrini, Maur

Ipsilateral and contralateral carotid stenosis contribute to the outcome of reperfusion treatment for ischemic stroke

IntroductionIpsilateral and contralateral carotid stenosis (ICS, CCS) influence acute ischemic stroke (AIS) severity and prognosis. Few data are available about their impact on reperfusion therapies efficacy. Aim of this study was to evaluate the impact of ICS and CCS on the effect of intravenous thrombolysis (IT), mechanical thrombectomy (MT) or both and of antiplatelet therapy (AT).MethodsWe enrolled all the consecutive patients admitted for AIS to our stroke unit and submitted to IT, MT, IT+MT, or AT. We established the presence of a significant ICS or CCS (≥70%) by ultrasound examination or brain angio-CT, or MRI. Clinical and instrumental information were collected; delta National Institutes of Health Stroke Scale (NIHSS) from pre-treatment to patients' discharge was employed as the main outcome measure.ResultsIn total, 460 subjects were enrolled, 86 with ICS and 38 with CCS. We observed a significant linear trend of delta (NIHSS) between carotid stenosis categories for patients undergoing IT (p = 0.011), MT (p = 0.046), and MT+IT (p = 0.040), but no significant trend among subjects receiving no reperfusion treatments was observed (p = 0.174).DiscussionAccording to our findings, ICS and CCS negatively influence AIS patients' outcome treated by interventional therapies. ICS might exert an unfavorable effect both by cerebral hypoperfusion and by continuous microembolization toward ischemic area, while CCS is probable involved in reducing the collateral circles effectiveness. The importance of early carotid stenosis detection and treatment should then be reevaluated not only to manage the prevention approaches but also to obtain insights about post-stroke treatment strategies efficacy

Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation

Critically ill patients affected by atrial fibrillation are at high risk of adverse events: however, the actual risk stratification models for haemorrhagic and thrombotic events are not validated in a critical care setting. With this paper we aimed to identify, adopting topological data analysis, the risk factors for therapeutic failure (in-hospital death or intensive care unit transfer), the in-hospital occurrence of stroke/TIA and major bleeding in a cohort of critically ill patients with pre-existing atrial fibrillation admitted to a stepdown unit; to engineer newer prediction models based on machine learning in the same cohort. We selected all medical patients admitted for critical illness and a history of pre-existing atrial fibrillation in the timeframe 01/01/2002-03/08/2007. All data regarding patients' medical history, comorbidities, drugs adopted, vital parameters and outcomes (therapeutic failure, stroke/TIA and major bleeding) were acquired from electronic medical records. Risk factors for each outcome were analyzed adopting topological data analysis. Machine learning was used to generate three different predictive models. We were able to identify specific risk factors and to engineer dedicated clinical prediction models for therapeutic failure (AUC: 0.974, 95%CI: 0.934-0.975), stroke/TIA (AUC: 0.931, 95%CI: 0.896-0.940; Brier score: 0.13) and major bleeding (AUC: 0.930:0.911-0.939; Brier score: 0.09) in critically-ill patients, which were able to predict accurately their respective clinical outcomes. Topological data analysis and machine learning techniques represent a concrete viewpoint for the physician to predict the risk at the patients' level, aiding the selection of the best therapeutic strategy in critically ill patients affected by pre-existing atrial fibrillation

Neural hypernetwork approach for pulmonary embolism diagnosis

Background Hypernetworks are based on topological simplicial complexes and generalize the concept of two-body relation to many-body relation. Furthermore, Hypernetworks provide a significant generalization of network theory, enabling the integration of relational structure, logic and analytic dynamics. A pulmonary embolism is a blockage of the main artery of the lung or one of its branches, frequently fatal. Results Our study uses data on 28 diagnostic features of 1427 people considered to be at risk of pulmonary embolism enrolled in the Department of Internal and Subintensive Medicine of an Italian National Hospital “Ospedali Riuniti di Ancona”. Patients arrived in the department after a first screening executed by the emergency room. The resulting neural hypernetwork correctly recognized 94 % of those developing pulmonary embolism. This is better than previous results obtained with other methods (statistical selection of features, partial least squares regression, topological data analysis in a metric space). Conclusion In this work we successfully derived a new integrative approach for the analysis of partial and incomplete datasets that is based on Q-analysis with machine learning. The new approach, called Neural Hypernetwork, has been applied to a case study of pulmonary embolism diagnosis. The novelty of this method is that it does not use clinical parameters extracted by imaging analysis

The association between insight and depressive symptoms in schizophrenia: Undirected and Bayesian network analyses

Background. Greater levels of insight may be linked with depressive symptoms among patients with schizophrenia, however, it would be useful to characterize this association at symptom-level, in order to inform research on interventions. Methods. Data on depressive symptoms (Calgary Depression Scale for Schizophrenia) and insight (G12 item from the Positive and Negative Syndrome Scale) were obtained from 921 community-dwelling, clinically-stable individuals with a DSM-IV diagnosis of schizophrenia, recruited in a nationwide multicenter study. Network analysis was used to explore the most relevant connections between insight and depressive symptoms, including potential confounders in the model (neurocognitive and social-cognitive functioning, positive, negative and disorganization symptoms, extrapyramidal symptoms, hostility, internalized stigma, and perceived discrimination). Bayesian network analysis was used to estimate a directed acyclic graph (DAG) while investigating the most likely direction of the putative causal association between insight and depression. Results. After adjusting for confounders, better levels of insight were associated with greater self-depreciation, pathological guilt, morning depression and suicidal ideation. No difference in global network structure was detected for socioeconomic status, service engagement or illness severity. The DAG confirmed the presence of an association between greater insight and self-depreciation, suggesting the more probable causal direction was from insight to depressive symptoms. Conclusions. In schizophrenia, better levels of insight may cause self-depreciation and, possibly, other depressive symptoms. Person-centered and narrative psychotherapeutic approaches may be particularly fit to improve patient insight without dampening self-esteem

Serum Albumin Is Inversely Associated With Portal Vein Thrombosis in Cirrhosis

We analyzed whether serum albumin is independently associated with portal vein thrombosis (PVT) in liver cirrhosis (LC) and if a biologic plausibility exists. This study was divided into three parts. In part 1 (retrospective analysis), 753 consecutive patients with LC with ultrasound-detected PVT were retrospectively analyzed. In part 2, 112 patients with LC and 56 matched controls were entered in the cross-sectional study. In part 3, 5 patients with cirrhosis were entered in the in vivo study and 4 healthy subjects (HSs) were entered in the in vitro study to explore if albumin may affect platelet activation by modulating oxidative stress. In the 753 patients with LC, the prevalence of PVT was 16.7%; logistic analysis showed that only age (odds ratio [OR], 1.024; P = 0.012) and serum albumin (OR, -0.422; P = 0.0001) significantly predicted patients with PVT. Analyzing the 112 patients with LC and controls, soluble clusters of differentiation (CD)40-ligand (P = 0.0238), soluble Nox2-derived peptide (sNox2-dp; P &lt; 0.0001), and urinary excretion of isoprostanes (P = 0.0078) were higher in patients with LC. In LC, albumin was correlated with sCD4OL (Spearman's rank correlation coefficient [r(s)], -0.33; P &lt; 0.001), sNox2-dp (r(s), -0.57; P &lt; 0.0001), and urinary excretion of isoprostanes (r(s), -0.48; P &lt; 0.0001) levels. The in vivo study showed a progressive decrease in platelet aggregation, sNox2-dp, and urinary 8-iso prostaglandin F2 alpha-III formation 2 hours and 3 days after albumin infusion. Finally, platelet aggregation, sNox2-dp, and isoprostane formation significantly decreased in platelets from HSs incubated with scalar concentrations of albumin. Conclusion: Low serum albumin in LC is associated with PVT, suggesting that albumin could be a modulator of the hemostatic system through interference with mechanisms regulating platelet activation