Enrico Gianluca Caiani
@polimi.it
Electronics, Information and Biomedical Engineering
Politecnico di Milano
RESEARCH INTERESTS
digital health, health geomatics, space physiology, cardiac imaging, data processing, IT tools for regulatory science
Scopus Publications
Scopus Publications
Amruta Umakant Mahakalkar, Lorenzo Gianquintieri, Lorenzo Amici, Maria Antonia Brovelli, and Enrico Gianluca Caiani
Elsevier BV
Lorenzo Gianquintieri, Daniele Oxoli, Enrico Gianluca Caiani, and Maria Antonia Brovelli
Elsevier BV
Lorenzo Gianquintieri, Daniele Oxoli, Enrico Gianluca Caiani, and Maria Antonia Brovelli
Springer Science and Business Media LLC
AbstractAir pollution is the one of the most significant environmental risks to health worldwide. An accurate assessment of population exposure would require a continuous distribution of measuring ground-stations, which is not feasible. Therefore, significant efforts are spent in implementing air-quality models. However, a complex scenario emerges, with the spread of many different solutions, and a consequent struggle in comparison, evaluation and replication, hindering the definition of the state-of-art. Accordingly, aim of this scoping review was to analyze the latest scientific research on air-quality modelling, focusing on particulate matter, identifying the most widespread solutions and trying to compare them. The review was mainly focused, but not limited to, machine learning applications. An initial set of 940 results published in 2022 were returned by search engines, 142 of which resulted significant and were analyzed. Three main modelling scopes were identified: correlation analysis, interpolation and forecast. Most of the studies were relevant to east and south-east Asia. The majority of models were multivariate, including (besides ground stations) meteorological information, satellite data, land use and/or topography, and more. 232 different algorithms were tested across studies (either as single-blocks or within ensemble architectures), of which only 60 were tested more than once. A performance comparison showed stronger evidence towards the use of Random Forest modelling, in particular when included in ensemble architectures. However, it must be noticed that results varied significantly according to the experimental set-up, indicating that no overall best solution can be identified, and a case-specific assessment is necessary.
Stefano Spina, Lorenzo Gianquintieri, Francesco Marrazzo, Maurizio Migliari, Giuseppe Maria Sechi, Maurizio Migliori, Andrea Pagliosa, Rodolfo Bonora, Thomas Langer, Enrico Gianluca Caiani,et al.
BMJ
BackgroundThe regional emergency medical service (EMS) in Lombardy (Italy) developed clinical algorithms based on operator-based interviews to detect patients with COVID-19 and refer them to the most appropriate hospitals. Machine learning (ML)-based models using additional clinical and geospatial epidemiological data may improve the identification of infected patients and guide EMS in detecting COVID-19 cases before confirmation with SARS-CoV-2 reverse transcriptase PCR (rtPCR).MethodsThis was an observational, retrospective cohort study using data from October 2020 to July 2021 (training set) and October 2021 to December 2021 (validation set) from patients who underwent a SARS-CoV-2 rtPCR test within 7 days of an EMS call. The performance of an operator-based interview using close contact history and signs/symptoms of COVID-19 was assessed in the training set for its ability to determine which patients had an rtPCR in the 7 days before or after the call. The interview accuracy was compared with four supervised ML models to predict positivity for SARS-CoV-2 within 7 days using readily available prehospital data retrieved from both training and validation sets.ResultsThe training set includes 264 976 patients, median age 74 (IQR 55–84). Test characteristics for the detection of COVID-19-positive patients of the operator-based interview were: sensitivity 85.5%, specificity 58.7%, positive predictive value (PPV) 37.5% and negative predictive value (NPV) 93.3%. Contact history, fever and cough showed the highest association with SARS-CoV-2 infection. In the validation set (103 336 patients, median age 73 (IQR 50–84)), the best-performing ML model had an AUC of 0.85 (95% CI 0.84 to 0.86), sensitivity 91.4% (95 CI% 0.91 to 0.92), specificity 44.2% (95% CI 0.44 to 0.45) and accuracy 85% (95% CI 0.84 to 0.85). PPV and NPV were 13.3% (95% CI 0.13 to 0.14) and 98.2% (95% CI 0.98 to 0.98), respectively. Contact history, fever, call geographical distribution and cough were the most important variables in determining the outcome.ConclusionML-based models might help EMS identify patients with SARS-CoV-2 infection, and in guiding EMS allocation of hospital resources based on prespecified criteria.
Emma Svennberg, Enrico G Caiani, Nico Bruining, Lien Desteghe, Janet K Han, Sanjiv M Narayan, Frank E Rademakers, Prashanthan Sanders, and David Duncker
Oxford University Press (OUP)
Abstract Aims Over the past 25 years there has been a substantial development in the field of digital electrophysiology (EP) and in parallel a substantial increase in publications on digital cardiology. In this celebratory paper, we provide an overview of the digital field by highlighting publications from the field focusing on the EP Europace journal. Results In this journey across the past quarter of a century we follow the development of digital tools commonly used in the clinic spanning from the initiation of digital clinics through the early days of telemonitoring, to wearables, mobile applications, and the use of fully virtual clinics. We then provide a chronicle of the field of artificial intelligence, a regulatory perspective, and at the end of our journey provide a future outlook for digital EP. Conclusion Over the past 25 years Europace has published a substantial number of papers on digital EP, with a marked expansion in digital publications in recent years.
Yijun Ren, Michele Bertoldi, Alan G. Fraser, and Enrico Gianluca Caiani
Springer Science and Business Media LLC
Abstract Introduction The EU Medical Device Regulation 2017/745 defines new rules for the certification and post-market surveillance of medical devices (MD), including an additional review by Expert Panels of clinical evaluation data for high-risk MD if reports and alerts suggest possibly associated increased risks. Within the EU-funded CORE-MD project, our aim was to develop a tool to support such process in which web-accessible safety notices (SN) are automatically retrieved and aggregated based on their specific MD categories and the European Medical Device Nomenclature (EMDN) classification by applying an Entity Resolution (ER) approach to enrich data integrating different sources. The performance of such approach was tested through a pilot study on the Italian data. Methods Information relevant to 7622 SN from 2009 to 2021 was retrieved from the Italian Ministry of Health website by Web scraping. For incomplete EMDN data (68%), the MD best match was searched within a list of about 1.5 M MD on the Italian market, using Natural Language Processing techniques and pairwise ER. The performance of this approach was tested on the 2440 SN (32%) already provided with the EMDN code as reference standard. Results The implemented ER method was able to correctly assign the correct manufacturer to the MD in each SN in 99% of the cases. Moreover, the correct EMDN code at level 1 was assigned in 2382 SN (97.62%), at level 2 in 2366 SN (96.97%) and at level 3 in 2329 SN (95.45%). Conclusion The proposed approach was able to cope with the incompleteness of the publicly available data in the SN. In this way, grouping of SN relevant to a specific MD category/group/type could be used as possible sentinel for increased rates in reported serious incidents in high-risk MD.
Sara Caramaschi, Gabriele B. Papini, and Enrico G. Caiani
MDPI AG
Tracking a person’s activities is relevant in a variety of contexts, from health and group-specific assessments, such as elderly care, to fitness tracking and human–computer interaction. In a clinical context, sensor-based activity tracking could help monitor patients’ progress or deterioration during their hospitalization time. However, during routine hospital care, devices could face displacements in their position and orientation caused by incorrect device application, patients’ physical peculiarities, or patients’ day-to-day free movement. These aspects can significantly reduce algorithms’ performances. In this work, we investigated how shifts in orientation could impact Human Activity Recognition (HAR) classification. To reach this purpose, we propose an HAR model based on a single three-axis accelerometer that can be located anywhere on the participant’s trunk, capable of recognizing activities from multiple movement patterns, and, thanks to data augmentation, can deal with device displacement. Developed models were trained and validated using acceleration measurements acquired in fifteen participants, and tested on twenty-four participants, of which twenty were from a different study protocol for external validation. The obtained results highlight the impact of changes in device orientation on a HAR algorithm and the potential of simple wearable sensor data augmentation for tackling this challenge. When applying small rotations (<20 degrees), the error of the baseline non-augmented model steeply increased. On the contrary, even when considering rotations ranging from 0 to 180 along the frontal axis, our model reached a f1-score of 0.85±0.11 against a baseline model f1-score equal to 0.49±0.12.
Gianfranco Parati, Grzegorz Bilo, Anastasios Kollias, Martino Pengo, Juan Eugenio Ochoa, Paolo Castiglioni, George S. Stergiou, Giuseppe Mancia, Kei Asayama, Roland Asmar,et al.
Ovid Technologies (Wolters Kluwer Health)
Blood pressure is not a static parameter, but rather undergoes continuous fluctuations over time, as a result of the interaction between environmental and behavioural factors on one side and intrinsic cardiovascular regulatory mechanisms on the other side. Increased blood pressure variability (BPV) may indicate an impaired cardiovascular regulation and may represent a cardiovascular risk factor itself, having been associated with increased all-cause and cardiovascular mortality, stroke, coronary artery disease, heart failure, end-stage renal disease, and dementia incidence. Nonetheless, BPV was considered only a research issue in previous hypertension management guidelines, because the available evidence on its clinical relevance presents several gaps and is based on heterogeneous studies with limited standardization of methods for BPV assessment. The aim of this position paper, with contributions from members of the European Society of Hypertension Working Group on Blood Pressure Monitoring and Cardiovascular Variability and from a number of international experts, is to summarize the available evidence in the field of BPV assessment methodology and clinical applications and to provide practical indications on how to measure and interpret BPV in research and clinical settings based on currently available data. Pending issues and clinical and methodological recommendations supported by available evidence are also reported. The information provided by this paper should contribute to a better standardization of future studies on BPV, but should also provide clinicians with some indications on how BPV can be managed based on currently available data.
Marco Penso, Sara Moccia, Enrico G. Caiani, Gloria Caredda, Maria Luisa Lampus, Maria Ludovica Carerj, Mario Babbaro, Mauro Pepi, Mattia Chiesa, and Gianluca Pontone
Elsevier BV
Amruta Umakant Mahakalkar, Eugenio Morello, Farah Makki, Ahmed Hazem Eldesoky, and Enrico Caiani
IOP Publishing
Abstract The quality of our habitat strongly determines the well-being of both our society and us as individuals. The Urban Health (UH) index is an emerging tool for decision-makers to bridge the disparities in the quality of life in cities. Our study assesses the quality of the built environment as a proxy for urban health and proposes a theoretical framework for constructing a UH index. We first conducted a literature review and statistical analyses to select and screen a comprehensive array of urban health indicators, and then used Principal Component Analysis (PCA) to obtain the indicators’ weights and build the UH index. On applying the framework on the city of Ferrara, Italy, we obtained promising results with four interpretable principal components explaining the contextual conditions. The autocorrelation of the UH index (Moran’s I = 0. 795) demonstrated strong clustering, with very healthy urban census tracts located within the city centre and decreasing overall urban health in peripheral census tracts.
Yijun Ren and Enrico Gianluca Caiani
IEEE
Under the European Union Medical Device (MD) Regulation 2017/745, expert panel’s decision on providing a scientific opinion on the Clinical Evaluation Assessment Report for high-risk MD is required, as part of the conformity assessment procedure. To this aim, the perceived risk of similar MDs already on the market, based on the European Medical Device Nomenclature (EMDN), could help. To generate such information, we propose a generalized framework to automatically collect and display in an aggregated way the publicly available safety notices (SNs), even when characterized by partial unstructuredness and incompleteness. This novel approach was tested on the Dutch data, consisting of 3618 SNs from 2015 to 2022, retrieved from the official government website by Web scraping. After the identification of named entities, the best match MD was searched within the Italian and Portuguese datasets of devices using Natural Language Processing techniques. Algorithm performance was tested on potentially equal SNs (472) published by both the Dutch and Italian authorities: assignment of the same EMDN code at level 1 was present in 454 out of 472 (96.19%) SNs, at level 2 in 447 (94.70%) SNs, at level 3 in 433 (91.74%) SNs. The proposed approach was able to cope with public data unavailability and incompleteness, thus providing structured data with appropriate EMDN usable for aggregation and safety signal detection.
Julia Nawaro, Lorenzo Gianquintieri, Andrea Pagliosa, Giuseppe M. Sechi, and Enrico Gianluca Caiani
Frontiers Media SA
Objectives: We aimed to analyze recent literature on heat effects on cardiovascular morbidity and mortality, focusing on the adopted heat definitions and their eventual impact on the results of the analysis.Methods: The search was performed on PubMed, ScienceDirect, and Scopus databases: 54 articles, published between January 2018 and September 2022, were selected as relevant.Results: In total, 21 different combinations of criteria were found for defining heat, 12 of which were based on air temperature, while the others combined it with other meteorological factors. By a simulation study, we showed how such complex indices could result in different values at reference conditions depending on temperature. Heat thresholds, mostly set using percentile or absolute values of the index, were applied to compare the risk of a cardiovascular health event in heat days with the respective risk in non-heat days. The larger threshold’s deviation from the mean annual temperature, as well as higher temperature thresholds within the same study location, led to stronger negative effects.Conclusion: To better analyze trends in the characteristics of heatwaves, and their impact on cardiovascular health, an international harmonization effort to define a common standard is recommendable.
Marco Penso, Mario Babbaro, Sara Moccia, Andrea Baggiano, Maria Ludovica Carerj, Marco Guglielmo, Laura Fusini, Saima Mushtaq, Daniele Andreini, Mauro Pepi,et al.
Frontiers Media SA
AimsDiagnosis of myocardial fibrosis is commonly performed with late gadolinium contrast-enhanced (CE) cardiac magnetic resonance (CMR), which might be contraindicated or unavailable. Coronary computed tomography (CCT) is emerging as an alternative to CMR. We sought to evaluate whether a deep learning (DL) model could allow identification of myocardial fibrosis from routine early CE-CCT images.Methods and resultsFifty consecutive patients with known left ventricular (LV) dysfunction (LVD) underwent both CE-CMR and (early and late) CE-CCT. According to the CE-CMR patterns, patients were classified as ischemic (n = 15, 30%) or non-ischemic (n = 35, 70%) LVD. Delayed enhancement regions were manually traced on late CE-CCT using CE-CMR as reference. On early CE-CCT images, the myocardial sectors were extracted according to AHA 16-segment model and labeled as with scar or not, based on the late CE-CCT manual tracing. A DL model was developed to classify each segment. A total of 44,187 LV segments were analyzed, resulting in accuracy of 71% and area under the ROC curve of 76% (95% CI: 72%−81%), while, with the bull’s eye segmental comparison of CE-CMR and respective early CE-CCT findings, an 89% agreement was achieved.ConclusionsDL on early CE-CCT acquisition may allow detection of LV sectors affected with myocardial fibrosis, thus without additional contrast-agent administration or radiational dose. Such tool might reduce the user interaction and visual inspection with benefit in both efforts and time.
Alan G Fraser, Elisabetta Biasin, Bart Bijnens, Nico Bruining, Enrico G Caiani, Koen Cobbaert, Rhodri H Davies, Stephen H Gilbert, Leo Hovestadt, Erik Kamenjasevic,et al.
Informa UK Limited
ABSTRACT Introduction Artificial intelligence (AI) encompasses a wide range of algorithms with risks when used to support decisions about diagnosis or treatment, so professional and regulatory bodies are recommending how they should be managed. Areas covered AI systems may qualify as standalone medical device software (MDSW) or be embedded within a medical device. Within the European Union (EU) AI software must undergo a conformity assessment procedure to be approved as a medical device. The draft EU Regulation on AI proposes rules that will apply across industry sectors, while for devices the Medical Device Regulation also applies. In the CORE-MD project (Coordinating Research and Evidence for Medical Devices), we have surveyed definitions and summarize initiatives made by professional consensus groups, regulators, and standardization bodies. Expert opinion The level of clinical evidence required should be determined according to each application and to legal and methodological factors that contribute to risk, including accountability, transparency, and interpretability. EU guidance for MDSW based on international recommendations does not yet describe the clinical evidence needed for medical AI software. Regulators, notified bodies, manufacturers, clinicians and patients would all benefit from common standards for the clinical evaluation of high-risk AI applications and transparency of their evidence and performance.
Gianfranco Parati, Alexandra Goncalves, David Soergel, Rosa Maria Bruno, Enrico Gianluca Caiani, Eva Gerdts, Felix Mahfoud, Lorenzo Mantovani, Richard J McManus, Paola Santalucia,et al.
Oxford University Press (OUP)
Abstract Hypertension is the most common and preventable risk factor for cardiovascular disease (CVD), accounting for 20% of deaths worldwide. However, 2/3 of people with hypertension are undiagnosed, untreated, or under treated. A multi-pronged approach is needed to improve hypertension management. Elevated blood pressure (BP) in childhood is a predictor of hypertension and CVD in adulthood; therefore, screening and education programmes should start early and continue throughout the lifespan. Home BP monitoring can be used to engage patients and improve BP control rates. Progress in imaging technology allows for the detection of preclinical disease, which may help identify patients who are at greatest risk of CV events. There is a need to optimize the use of current BP control strategies including lifestyle modifications, antihypertensive agents, and devices. Reducing the complexity of pharmacological therapy using single-pill combinations can improve patient adherence and BP control and may reduce physician inertia. Other strategies that can improve patient adherence include education and reassurance to address misconceptions, engaging patients in management decisions, and using digital tools. Strategies to improve physician therapeutic inertia, such as reminders, education, physician–peer visits, and task-sharing may improve BP control rates. Digital health technologies, such as telemonitoring, wearables, and other mobile health platforms, are becoming frequently adopted tools in hypertension management, particularly those that have undergone regulatory approval. Finally, to fight the consequences of hypertension on a global scale, healthcare system approaches to cardiovascular risk factor management are needed. Government policies should promote routine BP screening, salt-, sugar-, and alcohol reduction programmes, encourage physical activity, and target obesity control.
Marco Penso, Mario Babbaro, Sara Moccia, Marco Guglielmo, Maria Ludovica Carerj, Carlo Maria Giacari, Mattia Chiesa, Riccardo Maragna, Mark G. Rabbat, Andrea Barison,et al.
Springer Science and Business Media LLC
Abstract Background Segmentation of cardiovascular magnetic resonance (CMR) images is an essential step for evaluating dimensional and functional ventricular parameters as ejection fraction (EF) but may be limited by artifacts, which represent the major challenge to automatically derive clinical information. The aim of this study is to investigate the accuracy of a deep learning (DL) approach for automatic segmentation of cardiac structures from CMR images characterized by magnetic susceptibility artifact in patient with cardiac implanted electronic devices (CIED). Methods In this retrospective study, 230 patients (100 with CIED) who underwent clinically indicated CMR were used to developed and test a DL model. A novel convolutional neural network was proposed to extract the left ventricle (LV) and right (RV) ventricle endocardium and LV epicardium. In order to perform a successful segmentation, it is important the network learns to identify salient image regions even during local magnetic field inhomogeneities. The proposed network takes advantage from a spatial attention module to selectively process the most relevant information and focus on the structures of interest. To improve segmentation, especially for images with artifacts, multiple loss functions were minimized in unison. Segmentation results were assessed against manual tracings and commercial CMR analysis software cvi42(Circle Cardiovascular Imaging, Calgary, Alberta, Canada). An external dataset of 56 patients with CIED was used to assess model generalizability. Results In the internal datasets, on image with artifacts, the median Dice coefficients for end-diastolic LV cavity, LV myocardium and RV cavity, were 0.93, 0.77 and 0.87 and 0.91, 0.82, and 0.83 in end-systole, respectively. The proposed method reached higher segmentation accuracy than commercial software, with performance comparable to expert inter-observer variability (bias ± 95%LoA): LVEF 1 ± 8% vs 3 ± 9%, RVEF − 2 ± 15% vs 3 ± 21%. In the external cohort, EF well correlated with manual tracing (intraclass correlation coefficient: LVEF 0.98, RVEF 0.93). The automatic approach was significant faster than manual segmentation in providing cardiac parameters (approximately 1.5 s vs 450 s). Conclusions Experimental results show that the proposed method reached promising performance in cardiac segmentation from CMR images with susceptibility artifacts and alleviates time consuming expert physician contour segmentation.
Jeremy Rabineau, Margot Issertine, Fabian Hoffmann, Darius Gerlach, Enrico G. Caiani, Benoit Haut, Philippe van de Borne, Jens Tank, and Pierre-François Migeotte
Frontiers Media SA
Microgravity has deleterious effects on the cardiovascular system. We evaluated some parameters of blood flow and vascular stiffness during 60 days of simulated microgravity in head-down tilt (HDT) bed rest. We also tested the hypothesis that daily exposure to 30 min of artificial gravity (1 g) would mitigate these adaptations. 24 healthy subjects (8 women) were evenly distributed in three groups: continuous artificial gravity, intermittent artificial gravity, or control. 4D flow cardiac MRI was acquired in horizontal position before (−9 days), during (5, 21, and 56 days), and after (+4 days) the HDT period. The false discovery rate was set at 0.05. The results are presented as median (first quartile; third quartile). No group or group × time differences were observed so the groups were combined. At the end of the HDT phase, we reported a decrease in the stroke volume allocated to the lower body (−30% [−35%; −22%]) and the upper body (−20% [−30%; +11%]), but in different proportions, reflected by an increased share of blood flow towards the upper body. The aortic pulse wave velocity increased (+16% [+9%; +25%]), and so did other markers of arterial stiffness (CAVI; CAVI0). In males, the time-averaged wall shear stress decreased (−13% [−17%; −5%]) and the relative residence time increased (+14% [+5%; +21%]), while these changes were not observed among females. Most of these parameters tended to or returned to baseline after 4 days of recovery. The effects of the artificial gravity countermeasure were not visible. We recommend increasing the load factor, the time of exposure, or combining it with physical exercise. The changes in blood flow confirmed the different adaptations occurring in the upper and lower body, with a larger share of blood volume dedicated to the upper body during (simulated) microgravity. The aorta appeared stiffer during the HDT phase, however all the changes remained subclinical and probably the sole consequence of reversible functional changes caused by reduced blood flow. Interestingly, some wall shear stress markers were more stable in females than in males. No permanent cardiovascular adaptations following 60 days of HDT bed rest were observed.
Polychronis E Dilaveris, Christos Konstantinos Antoniou, Enrico G Caiani, Ruben Casado-Arroyo, Andreu Μ Climent, Matthijs Cluitmans, Martin R Cowie, Wolfram Doehner, Federico Guerra, Magnus T Jensen,et al.
Oxford University Press (OUP)
Abstract The role of subclinical atrial fibrillation as a cause of cryptogenic stroke is unambiguously established. Long-term electrocardiogram (ECG) monitoring remains the sole method for determining its presence following a negative initial workup. This position paper of the European Society of Cardiology Working Group on e-Cardiology first presents the definition, epidemiology, and clinical impact of cryptogenic ischaemic stroke, as well as its aetiopathogenic association with occult atrial fibrillation. Then, classification methods for ischaemic stroke will be discussed, along with their value in providing meaningful guidance for further diagnostic efforts, given disappointing findings of studies based on the embolic stroke of unknown significance construct. Patient selection criteria for long-term ECG monitoring, crucial for determining pre-test probability of subclinical atrial fibrillation, will also be discussed. Subsequently, the two major classes of long-term ECG monitoring tools (non-invasive and invasive) will be presented, with a discussion of each method’s pitfalls and related algorithms to improve diagnostic yield and accuracy. Although novel mobile health (mHealth) devices, including smartphones and smartwatches, have dramatically increased atrial fibrillation detection post ischaemic stroke, the latest evidence appears to favour implantable cardiac monitors as the modality of choice; however, the answer to whether they should constitute the initial diagnostic choice for all cryptogenic stroke patients remains elusive. Finally, institutional and organizational issues, such as reimbursement, responsibility for patient management, data ownership, and handling will be briefly touched upon, despite the fact that guidance remains scarce and widespread clinical application and experience are the most likely sources for definite answers.
Narjes Benameur, Ramzi Mahmoudi, Enrico Gianluca Caiani, Younes Arous, Foued Saâdaoui, and Halima Mahjoubi
Elsevier BV
Lorenzo Gianquintieri, Maria Antonia Brovelli, Andrea Pagliosa, Gabriele Dassi, Piero Maria Brambilla, Rodolfo Bonora, Giuseppe Maria Sechi, and Enrico Gianluca Caiani
MDPI AG
The pandemic of COVID-19 has posed unprecedented threats to healthcare systems worldwide. Great efforts were spent to fight the emergency, with the widespread use of cutting-edge technologies, especially big data analytics and AI. In this context, the present study proposes a novel combination of geographical filtering and machine learning (ML) for the development and optimization of a COVID-19 early alert system based on Emergency Medical Services (EMS) data, for the anticipated identification of outbreaks with very high granularity, up to single municipalities. The model, implemented for the region of Lombardy, Italy, showed robust performance, with an overall 80% accuracy in identifying the active spread of the disease. The further post-processing of the output was implemented to classify the territory into five risk classes, resulting in effectively anticipating the demand for interventions by EMS. This model shows state-of-art potentiality for future applications in the early detection of the burden of the impact of COVID-19, or other similar epidemics, on the healthcare system.
Maarten Falter, Martijn Scherrenberg, Karen Driesen, Zoë Pieters, Toshiki Kaihara, Linqi Xu, Enrico Gianluca Caiani, Paolo Castiglioni, Andrea Faini, Gianfranco Parati,et al.
Frontiers Media SA
BackgroundNovel smartwatch-based cuffless blood pressure (BP) measuring devices are coming to market and receive FDA and CE labels. These devices are often insufficiently validated for clinical use. This study aims to investigate a recently CE-cleared smartwatch using cuffless BP measurement in a population with normotensive and hypertensive individuals scheduled for 24-h BP measurement.MethodsPatients that were scheduled for 24-h ambulatory blood pressure monitoring (ABPM) were recruited and received an additional Samsung Galaxy Watch Active 2 smartwatch for simultaneous BP measurement on their opposite arm. After calibration, patients were asked to measure as much as possible in a 24-h period. Manual activation of the smartwatch is necessary to measure the BP. Accuracy was calculated using sensitivity, specificity, positive and negative predictive values and ROC curves. Bland-Altman method and Taffé methods were used for bias and precision assessment. BP variability was calculated using average real variability, standard deviation and coefficient of variation.ResultsForty patients were included. Bland-Altman and Taffé methods demonstrated a proportional bias, in which low systolic BPs are overestimated, and high BPs are underestimated. Diastolic BPs were all overestimated, with increasing bias toward lower BPs. Sensitivity and specificity for detecting systolic and/or diastolic hypertension were 83 and 41%, respectively. ROC curves demonstrate an area under the curve (AUC) of 0.78 for systolic hypertension and of 0.93 for diastolic hypertension. BP variability was systematically higher in the ABPM measurements compared to the smartwatch measurements.ConclusionThis study demonstrates that the BP measurements by the Samsung Galaxy Watch Active 2 show a systematic bias toward a calibration point, overestimating low BPs and underestimating high BPs, when investigated in both normotensive and hypertensive patients. Standards for traditional non-invasive sphygmomanometers are not met, but these standards are not fully applicable to cuffless devices, emphasizing the urgent need for new standards for cuffless devices. The smartwatch-based BP measurement is not yet ready for clinical usage. Future studies are needed to further validate wearable devices, and also to demonstrate new possibilities of non-invasive, high-frequency BP monitoring.
Francesco Blasi, Enrico Gianluca Caiani, Matteo Giuseppe Cereda, Daniela Donetti, Marco Montorsi, Vincenzo Panella, Gaia Panina, Felicia Pelagalli, and Elisabetta Speroni
Frontiers Media SA
The aging of the population, the burden of chronic diseases, possible new pandemics are among the challenges for healthcare in the XXI century. To face them, technological innovations and the national recovery and resilience plan within the European Union can represent opportunities to implement changes and renovate the current healthcare system in Italy, in an effort to guarantee equal access to health services. Considering such scenario, a panel of Italian experts gathered in a multidisciplinary Think Tank to discuss possible design of concepts at the basis of a new healthcare system. These ideas were summarized in a manifesto with six drivers for change: vision, governance, competence, intelligence, humanity and relationship. Each driver was linked to an action to actively move toward a new healthcare system based on trust between science, citizens and institutions.
Sara Moccia, Sarah Solbiati, Mahshad Khornegah, Federica FS Bossi, and Enrico G Caiani
Elsevier BV