Double Channel Neural Non Invasive Blood Pressure Prediction

Cardiovascular Diseases represent the leading cause of deaths in the world. Arterial Blood Pressure (ABP) is an important physiological parameter that should be properly monitored for the purposes of prevention. This work applies the neural network output-error (NNOE) model to ABP forecasting. Three input configurations are proposed based on ECG and PPG for estimating both systolic and diastolic blood pressures. The double channel configuration is the best performing one by means of the mean absolute error w.r.t the corresponding invasive blood pressure signal (IBP); indeed, it is also proven to be compliant with the ANSI/AAMI/ISO 81060-2:2013 regulation for non invasive ABP techniques. Both ECG and PPG correlations to IBP signal are further analyzed using Spearman’s correlation coefficient. Despite it suggests PPG is more closely related to ABP, its regression performance is worse than ECG input configuration one. However, this behavior can be explained looking to human biology and ABP computation, which is based on peaks (systoles) and valleys (diastoles) extraction

Development and Validation of an Algorithm for the Digitization of ECG Paper Images

The electrocardiogram (ECG) signal describes the heart’s electrical activity, allowing it to detect several health conditions, including cardiac system abnormalities and dysfunctions. Nowadays, most patient medical records are still paper-based, especially those made in past decades. The importance of collecting digitized ECGs is twofold: firstly, all medical applications can be easily implemented with an engineering approach if the ECGs are treated as signals; secondly, paper ECGs can deteriorate over time, therefore a correct evaluation of the patient’s clinical evolution is not always guaranteed. The goal of this paper is the realization of an automatic conversion algorithm from paper-based ECGs (images) to digital ECG signals. The algorithm involves a digitization process tested on an image set of 16 subjects, also with pathologies. The quantitative analysis of the digitization method is carried out by evaluating the repeatability and reproducibility of the algorithm. The digitization accuracy is evaluated both on the entire signal and on six ECG time parameters (R-R peak distance, QRS complex duration, QT interval, PQ interval, P-wave duration, and heart rate). Results demonstrate the algorithm efficiency has an average Pearson correlation coefficient of 0.94 and measurement errors of the ECG time parameters are always less than 1 mm. Due to the promising experimental results, the algorithm could be embedded into a graphical interface, becoming a measurement and collection tool for cardiologists

A Comparison of Deep Learning Techniques for Arterial Blood Pressure Prediction

Continuous vital signal monitoring is becoming more relevant in preventing diseases that afflict a large part of the world’s population; for this reason, healthcare equipment should be easy to wear and simple to use. Non-intrusive and non-invasive detection methods are a basic requirement for wearable medical devices, especially when these are used in sports applications or by the elderly for self-monitoring. Arterial blood pressure (ABP) is an essential physiological parameter for health monitoring. Most blood pressure measurement devices determine the systolic and diastolic arterial blood pressure through the inflation and the deflation of a cuff. This technique is uncomfortable for the user and may result in anxiety, and consequently affect the blood pressure and its measurement. The purpose of this paper is the continuous measurement of the ABP through a cuffless, non-intrusive approach. The approach of this paper is based on deep learning techniques where several neural networks are used to infer ABP, starting from photoplethysmogram (PPG) and electrocardiogram (ECG) signals. The ABP was predicted first by utilizing only PPG and then by using both PPG and ECG. Convolutional neural networks (ResNet and WaveNet) and recurrent neural networks (LSTM) were compared and analyzed for the regression task. Results show that the use of the ECG has resulted in improved performance for every proposed configuration. The best performing configuration was obtained with a ResNet followed by three LSTM layers: this led to a mean absolute error (MAE) of 4.118 mmHg on and 2.228 mmHg on systolic and diastolic blood pressures, respectively. The results comply with the American National Standards of the Association for the Advancement of Medical Instrumentation. ECG, PPG, and ABP measurements were extracted from the MIMIC database, which contains clinical signal data reflecting real measurements. The results were validated on a custom dataset created at Neuronica Lab, Politecnico di Torino

External validation of a deep-learning model to predict severe acute kidney injury based on urine output changes in critically ill patients

Objectives: The purpose of this study was to externally validate algorithms (previously developed and trained in two United States populations) aimed at early detection of severe oliguric AKI (stage 2/3 KDIGO) in intensive care units patients. Methods: The independent cohort was composed of 10'596 patients from the university hospital ICU of Amsterdam (the “AmsterdamUMC database”) admitted to their intensive care units. In this cohort, we analysed the accuracy of algorithms based on logistic regression and deep learning methods. The accuracy of investigated algorithms had previously been tested with electronic intensive care unit (eICU) and MIMIC-III patients. Results: The deep learning model had an area under the ROC curve (AUC) of 0,907 (± 0,007SE) with a sensitivity and specificity of 80% and 89%, respectively, for identifying oliguric AKI episodes. Logistic regression models had an AUC of 0,877 (± 0,005SE) with a sensitivity and specificity of 80% and 81%, respectively. These results were comparable to those obtained in the two US populations upon which the algorithms were previously developed and trained. Conclusion: External validation on the European sample confirmed the accuracy of the algorithms, previously investigated in the US population. The models show high accuracy in both the European and the American databases even though the two cohorts differ in a range of demographic and clinical characteristics, further underlining the validity and the generalizability of the two analytical approaches. Graphical abstract: [Figure not available: see fulltext.

Advanced management protocol of transanal irrigation in order to improve the outcome of pediatric patients with fecal incontinence

Background: Transanal irrigation (TAI) is employed for children with fecal incontinence, but it can present several problems which require a study of their outcomes among different patholo-gies and without a tailored work up. The aim of our study was to evaluate the effectiveness of an advanced protocol in order to tailor TAI, prevent complications, and evaluate outcomes. Methods: We included 70 patients (14 anorectal malformation, 12 Hirschsprung’s disease, 24 neurological impairment, 20 functional incontinence) submitted to a comprehensive protocol with Peristeen®: fecal score, volumetric enema, rectal ultrasound, anorectal 3D manometry, and diary for testing and parameter adjustment. Results: Among the patients, 62.9% needed adaptations to the parameters, mainly volume of irrigated water and number of puffs of balloon. These adaptations were positively correlated with pre-treatment manometric and enema data. In each group, the improvement of score was statistically significant in all cases (p 0.000); the main factor influencing the efficacy was the rate of sphincter anomalies. The ARM group had slower improvement than other groups, whereas functional patients had the best response. Conclusions: Our results showed that TAI should not be standardized for all patients, because each one has different peculiarities; evaluation of patients before TAI with rectal ultrasound, enema, and manometry allowed us to tailor the treatment, highlighting different outcomes among various pathologies, thus improving the efficacy

Outcomes of Advanced Hodgkin Lymphoma after Umbilical Cord Blood Transplantation: A Eurocord and EBMT Lymphoma and Cellular Therapy & Immunobiology Working Party Study

Allogeneic stem cell transplantation is an alternative for patients with relapsed or refractory Hodgkin lymphoma (HL), but only limited data on unrelated umbilical cord blood transplantation (UCBT) are available. We analyzed 131 adults with HL who underwent UCBT in European Society for Blood and Marrow Transplantation centers from 2003 to 2015. Disease status at UCBT was complete remission (CR) in 59 patients (47%), and almost all patients had received a previous autologous stem cell transplantation. The 4-year progression-free survival (PFS) and overall survival (OS) were 26% (95% confidence interval [CI], 19% to 34%) and 46% (95% CI, 37% to 55%), respectively. Relapse incidence was 44% (95% CI, 36% to 54%), and nonrelapse mortality (NRM) was 31% (95% CI, 23% to 40%) at 4 years. In multivariate analysis refractory/relapsed disease status at UCBT was associated with increased relapse incidence (hazard ratio [HR], 3.14 [95% CI, 1.41 to 7.00], P = .005) and NRM (HR, 3.61 [95% CI, 1.58 to 8.27], P = .002) and lower PFS (HR, 3.45 [95% CI, 1.95 to 6.10], P < .001) and OS (HR, 3.10 [95% CI, 1.60 to 5.99], P = .001). Conditioning regimen with cyclophosphamide + fludarabine + 2 Gy total body irradiation (Cy+Flu+2GyTBI) was associated with decreased risk of NRM (HR, .26 [95% CI, .10 to .64], P = .004). Moreover, Cy+Flu+2GyTBI conditioning regimen was associated with a better OS (HR, .25 [95% CI, .12 to .50], P < .001) and PFS (HR, .51 [95% CI, .27 to .96], P = .04). UCBT is feasible in heavily pretreated patients with HL. The reduced-intensity conditioning regimen with Cy+Flu+2GyTBI is associated with a better OS and NRM. However, outcomes are poor in patients not in CR at UCBT

[Some behavioral characteristics of physicians desired by ambulatory patients. A pilot survey].

We must pay attention to character formation of Medical Doctors because it could build a good or bad relationship with colleagues and patients: it is not a merely "humanistic" goal but a necessary component of professional excellence. The first endpoint of this study is to identify how to improve the quality of the outpatient visit.We tested a user-friendly questionnaire, distributed to 100 patients.The most important behavioral characteristics desired by patients from physicians are: 1. to have the physician's attention without feeling hurried (such as without the physician answering a phone call during the office visit); 2. to have continuity of care even in the ambulatory setting; 3. to find a relationship of empathy, participation and sharing; 4. to have a peaceful relationship of collaboration with the nurses and other health care personnel; 5. to find the physician appropriately groomed and dressed; 6. to receive the full diagnosis with clarity and at the most appropriate moment of communication

Alexithymia and irony comprehension and their relations with depression, anxiety, general symptomatology and personality disorders: A comparison between clinical and non-clinical participants

Poor awareness of one's own emotions and theory of mind appears to be a feature of many adult psychiatric conditions. In this study, we explored whether alexithymia and poor understanding of irony, both elements of the metacognitive system, were impaired in a clinical sample (n = 20) when compared to a non-clinical group (n = 35). We expected that both elements were impaired in the clinical group and explored whether they were correlated with personality disorder traits, global symptomatology, depression and anxiety. Finally, we sought to investigate whether alexithymia and difficulties in irony comprehension were related to each other. Results partially supported our hypotheses. Both emotional awareness and irony comprehension were impaired in the clinical vs. non-clinical sample. Alexithymia was related to personality disorder