This work is supported by the Instituto de Salud Carlos III of the Spanish Government and the European Regional Development Fund (ERDF) through the PI14/02161 and the DTS15/00153 research projects and Xunta de Galicia, Centro singular de investigaci&#243;n de Galicia accreditation 2016-2019 Ref. ED431G/01; and Grupos de Referencia Competitiva, Ref. ED431C 2016-047.

All procedures were conducted under institutionally approved protocols with patient consent.
1. Patient and Checkup Registration
Note: See Figure 1.
<ol>
	<li>Go to http://www.varpa.es/Hydra/&#160;using any modern web browser.</li>
	<li>Use an existing account associated with a doctor to Log In to the Hydra web tool.</li>
	<li>Fill in the patient registration form including patient code, date of birth, gender and ethnicity to register a new patient. Click on the include button to fill in the family background of premature CVDs. Click on the Next button to move forward to introduce the first checkup. 
	Note: These global parameters are included in patient enrollment and the information added from here relates to a specific checkup.</li>
	<li>Add a new checkup. 
	Note: The input data is organized in thematic blocks. Each block includes the option to be hidden or visible. If all the information of a block is unknown, use the hidden option. Keep the option NR/DK (no response/do not know) in the fields that do not match any case. See Figure 2.
	<ol>
		<li>Fill in the checkup date; the current date used by default.</li>
		<li>Fill in the block corresponding to patient habits such as smoking, exercise, diet, etc.</li>
		<li>Fill in the block corresponding to precedents of cardiovascular illness such as cardiopathy, acute aortic dissection, strokes, etc.</li>
		<li>Fill in the block corresponding to concomitant illnesses such as diabetes, obesity, nephropathy, etc.</li>
		<li>Fill in the block corresponding to urological records with the information related to erectile malfunction, prostatic hyperplasia, etc. 
		Note: This block is enabled in the checkup form when the gender of the patient is male.</li>
		<li>Fill in the block corresponding to gynecological records with the information related to hypertension in pregnancy, menopause, the age of menopause, surgical menopause, etc. 
		Note: This block is enabled in the checkup form when the gender of the patient is female.</li>
		<li>Fill in the block corresponding to anti-hypertensive treatment taken before the checkup date including the treatment type, the schedule, and the dose.</li>
		<li>Fill in the block corresponding to treatments that can alter blood pressure such as vasoconstrictors, oral contraceptives, corticosteroids, etc.</li>
		<li>Fill in the block corresponding to any other treatments such as fibrates, statins, insulin, etc. 
		Note: The options to input the timetable, dose or type are only enabled when each specific treatment is selected.</li>
		<li>Click on the Next button to move forward to the second checkup form relating to the physical examination and clinical analysis. 
		Note: See Figure 3.</li>
		<li>Fill in the block corresponding to the physical examination with the information related to height, weight, circumference of the dominant arm, etc. 
		Note: The body mass index and the waist height index are automatically computed from the previous data.</li>
		<li>Fill in the block corresponding to blood pressure recordings such as systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse, including 1 measurement standing and 3 measurements sitting. Check the boxes related to abdominal murmurs, carotid murmur, etc. 
		Note: The mean of the 3 repetitions of sitting blood pressure measurements is automatically computed.</li>
		<li>Fill in the block corresponding to the ABPM recording. Upload the ABPM file and complete the information related to the time during which the patient wore the monitor such as the hours and quality of sleep, the time of going to bed and waking up, etc. 
		Note: The ABPM upload is mandatory for the block related to ABPM information to be included. If there is no ABPM file available, click on the hide button.</li>
		<li>Fill in the block corresponding to biological/analytical recordings with the information related to blood analysis such as glycemia, creatinine, ferritin, microalbuminuria, etc. 
		Note: The standard units for the parameters are indicated on the checkup form, as reference.</li>
		<li>Fill in the block corresponding to the electrocardiogram recording with the information related to the different wave intensities, arrhythmia, ischemic cardiopathy, etc.</li>
		<li>Fill in the block corresponding to the echocardiogram recording with the information related to the interventricular septum, left ventricle diameter in systole, posterior wall of the left ventricle, etc.</li>
		<li>Fill in the block corresponding to other measurements such as pulse wave velocity, carotid stenosis, ankle-arm index, etc.</li>
		<li>Click on the End button to record the checkup associated with the corresponding patient on the platform. 
		Note: The platform moves forward to the checkup page that includes all the introduced data. See Figure 4.</li>
		<li>Click on the Edit button to add new information or update the introduced data throughout the checkup forms. Click on the End button to go back to the checkup page.</li>
		<li>Click on the Implemented treatment button to move forward to the form and prescribe any specific treatment.</li>
		<li>Fill in the block corresponding to anti-hypertensive treatment including the dose, the schedule and the type of the prescribed treatment.</li>
		<li>Fill in the block corresponding to treatments that can affect blood pressure such as vasoconstrictors, oral contraceptives, corticosteroids, etc.</li>
		<li>Fill in the block corresponding to other treatments such as fibrates, statins, insulin, etc.</li>
		<li>Click on the Definitive report button to generate the final report. It proceeds to the checkup report including all the introduced data and the prescribed treatment.</li>
	</ol>
	</li>
	<li>Update patient profile and perform checkup management.
	<ol>
		<li>Click on the Find patient link on the main menu bar and insert the patient code, or press the List patients link and select the patient code to proceed to the profile of a registered patient.</li>
		<li>Click on the Update button to revise and update any patient information (except the patient code, which is fixed) in the patient profile.</li>
		<li>Click on the Revise checkup link to access the checkup report (see step 4.2).</li>
		<li>Click on the Smart report link to access a brief checkup overview (see step 4.3).</li>
		<li>Click on the ABPM report link to access an overview of the ABPM results (see step 4.4).</li>
		<li>Click on the Edit link to add new information or modify the information introduced in the checkup. 
		Note: This option is available only before the generation of the definitive report.</li>
		<li>Click on the New checkup button to add a new checkup for the patient. 
		Note: Consecutive checkups are automatically prefilled with the information details that were included in the previous revision. See Figure 5.</li>
	</ol>
	</li>
</ol>
2. Risk Stratification Tables
Note: The risk stratification service provides an automatic computation of the CV risk factor based upon various risk stratification tables that are recommended in the guidelines of the European Society of Hypertension/European Society of Cardiology (ESH/ESC)22. For each of the tables, the CV risk factor is computed and recorded based upon various parameters that are uploaded in the patient profile throughout the steps of the checkup data input. The higher or lower importance of each of the tables in the analysis is provided by the specialist while ensuring that each designed stratification table pays special attention to the specific conditions of the patient.
<ol>
	<li>Click on the Find patient link and insert the patient code or click on the List patients link and select the patient code for a patient with existing registered checkups.</li>
	<li>From the list of checkups, click on the Revise checkup link to access the checkup report and go to the block of risk stratification tables.</li>
	<li>Click on the ESH/ESC table link to access the table page. Check the highlighted cell to get the qualitative level of cardiovascular risk. Check the recommendations and possible antihypertensive treatment related to the resulting risk. Click on the Go back link to access the general checkup report. 
	Note: This decision table uses the SBP and DBP measurements together with several risk factors and diseases (age, abdominal obesity, dyslipidemia, metabolic syndrome, etc.) to provide the CV risk factor as well as recommendations or treatment23.</li>
	<li>Click on the MS table link to access the Metabolic Syndrome (MS) table page. Check the presence of MS on the basis of the Adult Treatment Panel (ATP) III criterion. Check the presence of MS on the basis of the International Diabetes Fund (IDF) 2005 criterion. Click on the Go back link to access the general checkup report. 
	Note: This table uses the information related to abdominal obesity, triglyceridemia, c-HDL, BP and fasting glucose24. ATP III criterion indicates the presence of MS if 3 of the above measures are outside the tolerance levels. According to IDF 2005 criterion, MS is determined by the presence of abdominal obesity together with 2 of the other measures outside the tolerance levels.</li>
	<li>Click on the Score table link to access the Systematic Coronary Risk Evaluation (SCORE) table page. Check the highlighted cell to obtain the 10-year risk of fatal cardiovascular events. Check the color of the highlighted cell in the legend to obtain the qualitative level related to the risk. Click on the Go back link to access the general checkup report. 
	Note: This table uses information related to age, gender, SBP, smoking, and cholesterol25.</li>
	<li>Click on the Framingham table link to access the table page. Check the highlighted cell in the last table to obtain the 10-year risk of suffering a coronary event (angina, heart attack, with or without symptoms, fatal or not). Click on the Go back link to access the general checkup report. 
	Note: This table uses information related to the parameters of age, gender, smoking, diabetes, cholesterol, cholesterol HDL, and BP26. The highlighted cells indicate the contribution of each category to the final risk.</li>
</ol>
3. ABPM Analysis
Note: ABPM is a common test that allows the monitoring of the patient&#8217;s blood pressure throughout their daytime/nocturnal routine9. The device selected for recording ABPM measurements (see the Table of Materials) is among the few BP monitors that are officially validated by international organizations such as the British Hypertension Society (BHS) or the ESH.
<ol>
	<li>Put the BP monitor on the patient and check that it is operating properly by taking an initial manual measurement. Instruct the patient on how to obtain the measurements manually before going to sleep and on waking up in order to delimit the day and night recordings.</li>
	<li>After the recording period, remove the BP monitor from the patient and retrieve the ABPM file. Upload the ABMP file to the block of ABPM measurements of an existing or a new checkup related to the patient (step 1.4.13). 
	Note: The monitor remains connected throughout a period of time (usually 24h or 48h) and the measurements are regularly recorded at predefined intervals (typically 15 or 30 minutes).</li>
	<li>Perform ABPM analysis.
	<ol>
		<li>Click on the Find patient link and insert the patient code, or click on the List patients link and select the patient code of a patient with any registered checkup containing ABPM data.</li>
		<li>Click on the Revise checkup link to access the checkup report and go to the block of ABPM analysis.</li>
		<li>Click on the ABPM link to access the ABPM information display.</li>
		<li>Check the rate of valid records in the general information section to ensure that the results extracted from the ABPM file are reliable.</li>
		<li>Check the information regarding the period of time during which the patient wore the monitor such as the number of hours and quality of sleep, the time of going to sleep and waking up, etc.</li>
		<li>Check the ABPM map including the graphical representation of all the recordings, such as the SBP, DBP and pulse as well as the areas under or over the maximum normal levels for each measurement.</li>
		<li>Click on the buttons for 48h, 24h (i) or 24h (ii) to change the visualization mode for the 48 h ABPM files.</li>
		<li>Click on the means button to switch the visualization mode to an even representation of the measurements. 
		Note: This visualization mode computes each point from the average among consecutive measurements in the raw data.</li>
		<li>Check the additional information extracted from the original data such as the means and standard deviations of SBP, DBP, pulse rate and pulse pressure (the difference between SBP and DBP).</li>
		<li>Check the parameters regarding BP evolution that were automatically computed by the tool: nocturnal BP drop, sleep thought, pre-waking morning surge and day/night quotient for SBP and DBP measures.</li>
		<li>Check the summary table that contains the automatically computed areas under or over the maximum clinically defined thresholds for the nocturnal and diurnal measurements of SBP, DBP, and pulse pressure. 
		Note: Furthermore, the ABPM service also calculates the circadian profile from the relation between the daytime and nocturnal BP&#178;&#179;. This profile allows cardiovascular risks to be determined by analyzing the presence of BP deviations. The smart report (step 4.4) and the ABPM report (step 4.5) contain the information related to the circadian profile.</li>
	</ol>
	</li>
</ol>
4. Clinical Reports
Note: The report service provides a set of reports that gather all the relevant information to support the clinical decision-making process, helping physicians in their clinical practice and promoting collaboration among experts.
<ol>
	<li>Click on the Find patient link and insert the patient code, or click on the List patients link and select the code of a patient with any registered checkup.</li>
	<li>Click on the Revise checkup link to access the full checkup report which contains all the data recorded during the checkup registration process grouped into the various categories.</li>
	<li>Click on the ABPM link to access the data extracted from the ABPM analysis. Click on the specific link of each risk stratification table to review all the information regarding the computation of the risk value. Click on the Go back link to return to the patient page. 
	Note: The parameters that are outside their normal levels are shown in red in order to facilitate their identification. In the same way, the Yes/No fields are also marked with green or red icons for an intuitive visualization of the normal and pathological cases, respectively.</li>
	<li>Click on the Smart report link to access a brief overview of the checkup which only contains essential information.
	<ol>
		<li>Check the summary of the risk stratification tables, containing the results that were extracted from each table. Check the ABPM graph included in the final report and click on the ABPM map link for further information. Click on the Go back link to return to the patient page.</li>
	</ol>
	</li>
	<li>Click on the ABPM report link to access an additional smart report, in this case focused on the ABPM information and results. Check the information corresponding to the ABPM recording such as the statistical measurements extracted from the SBP, DBP, and pulse, the areas over and under the normal values, the circadian profile, etc. Click on the Go back link to return to the patient page. 
	Note: The report service provides the option to print the reports or export to standard formats, such as PDF, making it easier to present the report to the patient or use it for discussions with other clinicians.</li>
</ol>

<ol><li>World Health Organization. Cardiovascular Diseases (CVDs). World Health Organization. , Available from:
 <a target="_blank" href="http://www.who.int/cardiovascular_diseases/en/">http://www.who.int/cardiovascular_diseases/en/</a> (2018).</li>
<li>World Health Organization. Hearts: technical package for cardiovascular disease management in primary health care. World Health Organization, Technical Documents. , Available from:
 <a target="_blank" href="http://www.who.int/iris/handle/10665/252661">http://www.who.int/iris/handle/10665/252661</a> (2016).</li>
<li>World Health Organization. A global brief on hypertension: silent killer, global public health crisis: World Health Day 2013. World Health Organization, Technical Documents. , Available from:
 <a target="_blank" href="http://www.who.int/iris/handle/10665/79059">http://www.who.int/iris/handle/10665/79059</a> (2013).</li>
<li>Stamler, J., Stamler, R., Neaton, J. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Blood+pressure%2C+systolic+and+diastolic%2C+and+cardiovascular+risks%3A+US+population+data%5BTitle%5D)+AND+%22Archives+of+Internal+Medicine%22%5BJournal%5D)">Blood pressure, systolic and diastolic, and cardiovascular risks: US population data.</a> Archives of Internal Medicine. 153 (5), 598-615 (1993).</li>
<li>Kannel, W., Wilson, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((An+update+on+coronary+risk+factors%5BTitle%5D)+AND+%22Medical+Clinics+of+North+America%22%5BJournal%5D)">An update on coronary risk factors.</a> Medical Clinics of North America. 79 (5), 951-971 (1995).</li>
<li>Tarride, J. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+review+of+the+cost+of+cardiovascular+disease%5BTitle%5D)+AND+%22The+Canadian+Journal+of+Cardiology%22%5BJournal%5D)">A review of the cost of cardiovascular disease.</a> The Canadian Journal of Cardiology. 25 (6), 195-202 (2009).</li>
<li>Wolf-Maier, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Hypertension+prevalence+and+blood+pressure+levels+in+6+European+countries%2C+Canada%2C+and+the+United+States%5BTitle%5D)+AND+%22The+Journal+of+American+Medical+Association%22%5BJournal%5D)">Hypertension prevalence and blood pressure levels in 6 European countries, Canada, and the United States.</a> The Journal of American Medical Association. 289 (18), 2363-2369 (2003).</li>
<li>Kearney, P., Whelton, M., Reynolds, K., Muntner, P., Whelton, P., He, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Global+burden+of+hypertension%3A+analysis+of+worldwide+data%5BTitle%5D)+AND+%22The+Lancet%22%5BJournal%5D)">Global burden of hypertension: analysis of worldwide data.</a> The Lancet. 365 (9455), 217-223 (2005).</li>
<li>Hermida, R., Smolensky, H., Ayala, E., Portaluppi, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Ambulatory+Blood+Pressure+Monitoring+%28ABPM%29+as+the+reference+standard+for+diagnosis+of+hypertension+and+assessment+of+vascular+risk+in+adults%5BTitle%5D)+AND+%22Chronobiology+International%22%5BJournal%5D)">Ambulatory Blood Pressure Monitoring (ABPM) as the reference standard for diagnosis of hypertension and assessment of vascular risk in adults.</a> Chronobiology International. 32 (10), 1329-1342 (2015).</li>
<li><a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=%22Telemedicine%3A+A+Guide+to+Assessing+Telecommunications+in+Health+Care%22">Telemedicine: A Guide to Assessing Telecommunications in Health Care</a>. Field, M. , National Academy Press. (1996).</li>
<li>Charles, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Telemedicine+can+lower+costs+and+improve+access%5BTitle%5D)+AND+%22Healthcare+Financical+Management%22%5BJournal%5D)">Telemedicine can lower costs and improve access.</a> Healthcare Financical Management. 54 (4), 66-69 (2000).</li>
<li>Lundberg, T., Westman, G., Hellstrom, S., Sandstrom, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Digital+imaging+and+telemedicine+as+a+tool+for+studying+inflammatory+conditions+in+the+middle+ear+-+evaluation+of+image+quality+and+agreement+between+examiners%5BTitle%5D)+AND+%22International+Journal+of+Pediatric+Otorhinolaryngoly%22%5BJournal%5D)">Digital imaging and telemedicine as a tool for studying inflammatory conditions in the middle ear - evaluation of image quality and agreement between examiners.</a> International Journal of Pediatric Otorhinolaryngoly. 72 (1), 73-79 (2008).</li>
<li>Ortega, M., Barreira, N., Novo, J., Penedo, M., Pose-Reino, A., Gómez-Ulla, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Sirius%3A+a+web-based+system+for+retinal+image+analysis%5BTitle%5D)+AND+%22International+Journal+of+Medical+Informatics%22%5BJournal%5D)">Sirius: a web-based system for retinal image analysis.</a> International Journal of Medical Informatics. 79 (10), 722-732 (2010).</li>
<li>Novo, J., Rouco, J., Barreira, N., Ortega, M., Penedo, M. G., Campilho, A. <a target="_blank" href="https://link-springer-com.remotexs.ntu.edu.sg/article/10.1007/s40846-017-0256-y">Wivern: a Web-Based System Enabling Computer-Aided Diagnosis and Interdisciplinary Expert Collaboration for Vascular Research.</a> Journal of Medical and Biological Engineering. 37 (6), 920-935 (2017).</li>
<li>Paredes, S., Rocha, T., de Carvalho, P., Henriques, J., Morais, J. <a target="_blank" href="https://ieeexplore.ieee.org/document/6703067/">Matlab tool for cardiovascular disease risk prediction.</a> Experiment@ International Conference (exp.at' 13). , 190-191 (2013).</li>
<li>Goff, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((2013+ACC%2FAHA+Guideline+on+the+Assessment+of+Cardiovascular+Risk%5BTitle%5D)+AND+%22Circulation%22%5BJournal%5D)">2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk.</a> Circulation. 137 (11), (2013).</li>
<li>Sufi, F., Khalil, I., Tari, Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+cardiod+based+technique+to+identify+cardiovascular+diseases+using+mobile+phones+and+body+sensors%5BTitle%5D)+AND+%22Conference+Proceedings+IEEE+Engineering+in+Medicine+and+Biology+Society%22%5BJournal%5D)">A cardiod based technique to identify cardiovascular diseases using mobile phones and body sensors.</a> Conference Proceedings IEEE Engineering in Medicine and Biology Society. 2010, 5500-5503 (2010).</li>
<li>Lin, C. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((An+intelligent+telecardiology+system+using+a+wearable+and+wireless+ECG+to+detect+atrial+fibrillation%5BTitle%5D)+AND+%22IEEE+Transactions+on+Information+Technology+in+Biomedicine%22%5BJournal%5D)">An intelligent telecardiology system using a wearable and wireless ECG to detect atrial fibrillation.</a> IEEE Transactions on Information Technology in Biomedicine. 14 (3), 726-733 (2010).</li>
<li>Lee, H., Wang, W., Lu, S., Wu, B., Ko, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Home-based+mobile+cardio-pulmonary+rehabilitation+consultant+system%5BTitle%5D)+AND+%22Conference+Proceedings+IEEE+Engineering+in+Medicine+and+Biology+Society%22%5BJournal%5D)">Home-based mobile cardio-pulmonary rehabilitation consultant system.</a> Conference Proceedings IEEE Engineering in Medicine and Biology Society. , 989-992 (2011).</li>
<li>Kang, H., Park, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Development+of+hypertension+management+mobile+application+based+on+clinical+practice+guidelines%5BTitle%5D)+AND+%22Studies+in+Health+Technology+and+Informatics%22%5BJournal%5D)">Development of hypertension management mobile application based on clinical practice guidelines.</a> Studies in Health Technology and Informatics. 210, 602-606 (2015).</li>
<li>Novo, J., Hermida, A., Ortega, M., Barreira, N., Penedo, M. G., López, J. E., Calvo, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Hydra%3A+A+web-based+system+for+cardiovascular+analysis%2C+diagnosis+and+treatment%5BTitle%5D)+AND+%22Computer+methods+and+programs+in+biomedicina%22%5BJournal%5D)">Hydra: A web-based system for cardiovascular analysis, diagnosis and treatment.</a> Computer methods and programs in biomedicina. 139, 61-81 (2017).</li>
<li>Janes, H., Pepe, M., Gu, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Assessing+the+value+of+risk+predictions+by+using+risk+stratification+tables%5BTitle%5D)+AND+%22Annals+of+Internal+Medicine%22%5BJournal%5D)">Assessing the value of risk predictions by using risk stratification tables.</a> Annals of Internal Medicine. 149 (10), 751-760 (2008).</li>
<li>Mancia, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((2007+Guidelines+for+the+management+of+arterial+hypertension%3A+the+Task+Force+for+the+Management+of+Arterial+Hypertension+of+the+European+Society+of+Hypertension+%28ESH%29+and+of+the+European+Society+of+Cardiology+%28ESC%29%5BTitle%5D)+AND+%22Journal+of+Hypertension%22%5BJournal%5D)">2007 Guidelines for the management of arterial hypertension: the Task Force for the Management of Arterial Hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC).</a> Journal of Hypertension. 25 (6), 1105-1187 (2007).</li>
<li>Grundy, S., Brewer, H., Cleeman, J., Smith, S., Lenfant, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Definition+of+metabolic+syndrome%3A+report+of+the+National+Heart%2C+Lung%2C+and+Blood+Institute%2FAmerican+Heart+Association+Conference+on+scientific+issues+related+to+definition%5BTitle%5D)+AND+%22Circulation%22%5BJournal%5D)">Definition of metabolic syndrome: report of the National Heart, Lung, and Blood Institute/American Heart Association Conference on scientific issues related to definition.</a> Circulation. 109 (3), 433-438 (2004).</li>
<li>Conroy, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Estimation+of+ten-year+risk+of+fatal+cardiovascular+disease+in+Europe%3A+the+SCORE+project%5BTitle%5D)+AND+%22European+Heart+Journal%22%5BJournal%5D)">Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project.</a> European Heart Journal. 24 (11), 987-1003 (2003).</li>
<li>Kannel, W., McGee, D., Gordon, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+general+cardiovascular+risk+profile%3A+the+Framingham+study%5BTitle%5D)+AND+%22American+Journal+of+Cardiology%22%5BJournal%5D)">A general cardiovascular risk profile: the Framingham study.</a> American Journal of Cardiology. 38 (1), 46-51 (1976).</li>
</ol>

The authors have nothing to disclose.

The early identification and monitoring of various cardiovascular risk factors together with an appropriate treatment are critical for the prevention of cardiovascular diseases and premature deaths. In the daily clinical routine, clinicians have to handle large amounts of diverse information to check all the different variables and parameters that affect the circulatory system. Hence, it is a tedious and time-consuming task that complicates diagnosis and treatment prescription.
The proposed protocols allow a complete analysis of the cardiovascular system. These protocols include the input of all data related to cardiovascular analysis which are recorded in a full patient profile and organized into different checkups throughout time. The centralized management of these data together with the various services provided by the platform facilitates the clinical decision-making process as well as information interchange between experts. The various services included on the platform were designed and implemented considering the needs and preferences of expert clinicians in order to incorporate all the necessary tools in the best possible way for a comfortable professional use. This way, the checkup service allows the recording of comprehensive patient data, organized into different checkups to track the evolution over time. From the raw data, the platform automatically analyzes and extracts all the properties that are needed for diagnostic and treatment purposes resulting in considerable reduction in time and effort. Here, various risk stratification tables of reference are incorporated into the platform for automatic CV risk computation. Furthermore, the ABPM service allows the tracking of blood pressure over a period of time, allowing the extraction of additional, valuable information. Finally, the report services allow an efficient review of the summarized relevant data.
Therefore, the proposed platform collects a large quantity of diverse, relevant parameters and gathers them using different standard protocols according to ESH/ESC guidelines22 in order to support the decision-making process. The limitation of this protocol is the availability of the large amount of data involved since it comprises an exhaustive anamnesis, a physical examination, recordings of several measurements, biological data extracted from the blood test, knowledge about family precedents, etc. Each of the tools/services combines multiple medical parameters to compute the cardiovascular risk factors in such a way that these calculations cannot be performed when no data are available. However, even if the patient profile is not complete, the availability of partial data allows the computation of some of the risk factors providing relevant results to support the clinical decision-making process. Each service details the data involved in its computation and the results are incremental on the basis of the available data.
There are a number of tools available for CV assessment which are mainly focused on satisfying patient demand in specific scenarios. However, the proposed protocol is fully oriented to medical specialists, covering all the services to support the decision-making process in their daily routine. Regarding BP monitoring, there are several commercial systems that are mostly focused on the performance of the measurements, the compatibility with other operative systems, the ease of use, etc. These devices do not analyze recorded data whereas the ABPM service, given a recording file, will analyze all the information from the measurements and automatically extract valuable parameters relevant for clinical practice. Moreover, it provides a graphical representation incorporating additional data that facilitates visualization and analysis. Finally, the reporting service allows an efficient review of the summarized data containing all the relevant information with the aim of helping clinicians in their clinical practice. Therefore, the proposed protocol allows a complete and reliable analysis of the cardiovascular system to support the clinical decision-making process via a set of functionalities and services that are required by physicians for risk evaluation, early diagnosis, treatment prescription, and tracking over time. This leads to a qualitative improvement in health-care services and a reduction in time and effort, facilitating the work of clinicians in their daily practice.
The large amount of medical data involved together with the possibility of discussions among experts provides an adequate environment for clinical research. Future work in this field will include the analysis of the impact of the different CV risk factors and the correlation between various medical parameters in order to extract additional information relevant to clinical practice. The gathering and storage of significant volumes of clinical data can also serve as a basis for computational analysis of big data with the objective of data dimensionality reduction; this can also serve as a complementary source of information for the clinical users of the platform. Furthermore, future work will involve the inclusion of specific questionnaires &#8212; for a more exhaustive analysis of some factors (e.g., stress, diet or exercise) &#8212; and internalization in the form of support for more languages and reference units. Graphical improvements are also planned, e.g., the integration of cosinor analysis for blood pressure times series which can facilitate the inspection of blood pressure characteristics and tendencies.

Cardiovascular diseases (CVDs) are a group of disorders of the circulatory system that constitute the leading cause of disability and premature death throughout the world1,2. According to the World Health Organization (WHO), an estimated 17.7 million people died from CVDs in 2015, representing 31% of all global deaths1,2. There are many risk factors for CVDs, including behavioral factors such as tobacco use, an unhealthy diet, harmful use of alcohol and inadequate physical activity as well as physiological factors, including raised blood pressure (hypertension), high cholesterol or elevated blood glucose, among others2,3. Hypertension represents a major risk factor for premature cardiovascular disease, being responsible for a high level of cardiovascular morbidity and mortality4,5. Furthermore, it is estimated that the incidence of hypertension among adults in developed countries is almost 40%6,7,8. However, it remains widely undetected, undertreated and poorly controlled3,4.
CVD is a major public health problem which imposes a significant economic burden on any given health-care system6. Early identification of the highest cardiovascular risks and appropriate treatment can prevent clinical events and premature deaths4,5. Hence, there are noticeable health and economic gains attached to comprehensively and thoroughly tracking all these factors. The total risk of developing a CVD is determined by the combined effect of cardiovascular risk factors2,4,5, which commonly coexist and act multiplicatively. Therefore, a total-risk approach is advisable for early detection, as well as for clinical decision-making on the intensity of preventive interventions. Thus, morbidity, early mortality and disability could be reduced and the quality of life could be improved in individuals with an elevated total CVD risk2.
The diagnosis of CVDs is determined by the analysis of a wide range of parameters that are gathered by different procedures used by physicians in their clinical practice. The assessment of these parameters allows the computation of a total CV risk factor which is useful for diagnostic and treatment purposes2,4,5. In addition to the stratification of CV risks, ambulatory blood pressure monitoring (ABPM)9 also provides valuable information. The ABPM test allows the tracking of the patient&#39;s blood pressure (BP) during their daily routine, avoiding the influence of the clinical setting (white coat syndrome). Thus, a reliable set of measurements is obtained, allowing the extraction of additional information that supports the clinical decision-making process.
Therefore, the analysis of the cardiovascular system involves a large amount of data, entailing a tedious and time-consuming task that complicates diagnosis and treatment prescription. In this regard, the availability of a patient&#39;s full profile that gathers all the required data together with a set of automated services to extract the necessary information would be a significant improvement to guide clinicians in their decision-making process. Apart from this, the availability of an accessible platform that centralizes all patient information not only enables collaboration among different specialists from different locations but also allows discussion of debatable cases and provides reliable diagnoses.
In recent years, the use of computer-based applications and telemedicine has increased considerably, playing an important role in improving public health and welfare in all sectors of the population. This is due to their ability to extract relevant and useful information for the early diagnosis and treatment of several diseases10. The use of these tools improves the quality of health-care services, thus conveniently and reliably satisfying patient demand as well as reducing costs11. As a reference, the number of global imaging-based procedures has risen considerably, given the increasing availability of medical equipment and more sophisticated capture devices. Therefore, Lundberg et al.12 proposed a telemedicine tool to assess digital image quality and agreement between examiners in the field of the otorhinolaryngology. Ortega et al.13 developed SIRIUS, a computer-aided diagnosis framework for the analysis of retinal images. Novo et al.14 also presented their platform for the analysis of retinal microcirculation in combination with carotid macrocirculation.
With regard to CV assessment, there has been a steady increase in the number of tools available throughout the years. Some of the utilities are designed to predict cardiovascular disease risk &#8212; such as the tool proposed by Paredes et al.15 &#8212; or to calculate risk online by implementing the algorithm proposed by Goff et al.16 according to a guideline on the assessment of cardiovascular risk to calculate the 10-year risk of heart disease. Other systems are designed to be used with mobile phones, such as the proposal of Sufi et al.17 that identifies diseases from body sensors, the device designed by Lin et al.18 for tracking the electrocardiogram in order to detect the presence of abnormal rhythms and send an alarm, the app from Lee et al.19 for monitoring breathing and heart rate values while a person exercising or the application implemented by Kang and Park20 to manage raised blood pressure on the basis of clinical guidelines.
The available utilities are mainly designed to satisfy patient demand in specific scenarios. On the other hand, this article describes a protocol based on Hydra21, a platform focused on the analysis of the cardiovascular system, that is designed entirely to support specialists in their clinical decision-making process. This tool incorporates a set of functionalities and services that physicians require for reliable cardiovascular analysis including risk assessment, early diagnosis, treatment prescription and the monitoring of patients over time. Therefore, there is a tool for the input and management of patient data recorded in different checkups. Then, a risk stratification tool automatically provides a CV risk factor based on different risk stratification tables of reference. In addition to this, the ABPM analysis tool allows the extraction of valuable information from the analysis of blood pressure recordings over a specific period of time. Finally, the most relevant information is summarized in a set of reports that guide clinicians in diagnosis and proper treatment prescription. In this way, the described protocol leads to an improvement in complete cardiovascular analysis supporting a reliable diagnosis and proper treatment. Furthermore, the presented platform allows collaboration among experts, thereby promoting clinical research.

<table><tbody><tr><td>Computer with color screen</td><td>N/A</td><td>N/A</td><td></td></tr><tr><td>Internet connection</td><td>N/A</td><td>N/A</td><td></td></tr><tr><td>Modern web broser</td><td>N/A</td><td>N/A</td><td>Google Chrome, Internet Explorer, Safari, Fierfox, etc.</td></tr><tr><td>Blood pressure monitor</td><td>Spacelabs</td><td>N/A</td><td>Spacelabs 90217</td></tr></tbody></table>

hydra, a computer-based platform for aiding clinicians in cardiovascular analysis and diagnosis

Cardiovascular diseases (CVDs) are the leading cause of death throughout the world. The total risk of developing CVD is determined by the combined effect of different cardiovascular risk factors (e.g., diabetes, raised blood pressure, unhealthy diet, tobacco use, stress, etc.) that commonly coexist and act multiplicatively. Most CVDs can be prevented by an early identification of the highest risk factors and an appropriate treatment. The stratification of cardiovascular risk factors involves a wide range of parameters and tests that specialists use in their clinical practice. In addition to cardiovascular (CV) risk stratification, ambulatory blood pressure monitoring (ABPM) also provides relevant information for diagnostic and treatment purposes. This work presents a list of protocols based on the Hydra platform, a web-based system for clinical decision support which incorporates a set of functionalities and services that are required for complete cardiovascular analysis, risk assessment, early diagnosis, treatment and monitoring of patients over time. The program includes tools for inputting and managing comprehensive patient data, organized into different checkups to track the evolution over time. It also has a risk stratification tool to compute a CV risk factor based upon several risk stratification tables of reference. Additionally, the program includes a tool that incorporates ABPM analysis and allows the extraction of valuable information by monitoring blood pressure over a specific period of time. Finally, the reporting service summarizes the most relevant information in a set of reports that aid clinicians in their clinical decision-making process.

The patient registration described in step 1 is carried out by filling in the form presented in Figure 1. Once the user registers a new patient, the application moves forward to introduce the first checkup, which allows the input of comprehensive patient data. Figure 2 shows a screenshot of the first form of the checkup information. Once the Next button is clicked, the application moves forward to the second checkup form presented in Figure 3. After clicking on the End button, the checkup is recorded by the system (assigned to the patient). Hydra (referred to as the &#39;platform&#39;) moves forward to the register checkup page, including all the introduced data. From this page, the user can edit the introduced data or access the implemented treatment form shown in Figure 4, in order to prescribe the patient a specific treatment. Once the checkup registration process is complete, the platform moves forward to the patient page shown in Figure 5, including general data and a list of the submitted checkups.
Besides the centralized management of all the patient data, the platform also provides an automatic computation of the total cardiovascular risk factor based on different risk stratification tables recommended in the standard guidelines of the ESC/ESH. Figure 6 shows an example of CV risk calculation on the basis of the antihypertensive treatment decision table. In this case, the computed risk appears highlighted in the table, and below, the recommendations and treatment related to this risk are shown. Moreover, the different factors that have contributed to the computation of the risk are listed below. An example of the metabolic syndrome (MS) table is shown in Figure 7 including the risk that is obtained using two different criteria of reference and the factors involved in these computations. Figure 8 shows an example of the systematic coronary risk evaluation (SCORE) table indicating the 10-year risk of suffering a coronary event and the list of relevant parameters. Finally, Figure 9 shows an example of the Framingham table that calculates the risk of severe CVD or a hard event and the contribution of each category to the final risk. This way, the service of risk stratification allows an automatic computation of the CV risk on the basis of different tables of reference as well as the involvement of the different parameters that have contributed to reaching the related risk, for a more detailed analysis by the expert clinician.
In addition to risk stratification, the ABPM also provides valuable information to support the clinical decision-making process. Therefore, given an ABPM file containing recordings over a period of time, the tool can provide automatic computation of additional relevant parameters such as the mean and standard deviation of the different measurements (SBP, DBP and pulse), the area of the records over and under the thresholds that represent the maximum normal values, the circadian profile, etc. Figure 10 shows a graphical representation of the ABPM map and a table containing the information automatically computed by the ABPM tool.
Finally, the reporting service provides summarized reports that gather all the relevant, available information to help clinicians in their decision-making process and promotes collaboration among experts. An example of some representative parts of a full report is shown in Figure 11. Similarly, Figure 12 and Figure 13 show examples of a smart report and an ABPM report respectively. All the services the platform has to offer result in improved quality of health-care, while helping physicians perform complete cardiovascular analysis.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58132/58132fig1.jpg" /> 
Figure 1. Patient registration form. The form is used to register a new patient and includes various global parameters related to patient enrolment. The block of family precedents of premature cardiovascular illness can be hidden. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58132/58132fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58132/58132fig2v2.jpg" /> 
Figure 2. First form for checkup registration. This includes information about habits, pathologies and previous treatments grouped into different blocks. All the different blocks have the option to keep them hidden or visible. If all the information of a block is unknown, the user should use the hide the block. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58132/58132fig2v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58132/58132fig3v2.jpg" /> 
Figure 3. Second form for checkup registration. This covers the physical and clinical analyses, grouped in different blocks. All the different blocks include the option to be hidden or visible. If all the information of a block is unknown, the user should use the hidden option. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58132/58132fig3v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/58132/58132fig4.jpg" /> 
Figure 4. Implemented treatment form to prescribe the patient any specific treatment. This includes blocks for anti-hypertensive treatment, treatments that can affect blood pressure and other treatments. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58132/58132fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/58132/58132fig5.jpg" /> 
Figure 5. The patient profile page. This includes general data and a list of submitted checkups. From this list, it is possible to access the different reports of each checkup. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58132/58132fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/58132/58132fig6v2.jpg" /> 
Figure 6. Example of an antihypertensive treatment decision table. The highlighted cell represents the computed CV risk and the &#34;Risk/treatment&#34; field details the recommendations related to this risk. Moreover, the contribution of the different factors to the final result is listed below. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58132/58132fig6v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/58132/58132fig7.jpg" /> 
Figure 7. Example of an MS risk stratification table on the basis of two different criteria. The conditions that are true for each criterion are highlighted in red. The results for each criterion and the factors involved in these computations are shown on the right-hand side. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58132/58132fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/58132/58132fig8.jpg" /> 
Figure 8. Example of a SCORE risk stratification table. The highlighted cell corresponds to the 10-year risk of CVD and the list of risk factors summarizing the parameters that lead to this result. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58132/58132fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/58132/58132fig9.jpg" /> 
Figure 9. Example of a Framingham risk stratification table. In each table, the contribution of each category is highlighted in red. The computed risk of severe CV or hard events is shown below the tables as well as the risk factors involved in the calculation. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58132/58132fig9large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 10" class="xfigimg" src="/files/ftp_upload/58132/58132fig10.jpg" /> 
Figure 10. Example of an ABPM map. This includes the graphical representation and complementary measurements of a 48 h monitor register. Green dots represent manual measurements. The red and blue lines are related to the maximum levels for systolic and diastolic blood pressure, respectively. The filled areas correspond to the intervals that exceed these maximum levels during the day and night. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58132/58132fig10large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 11" class="xfigimg" src="/files/ftp_upload/58132/58132fig11.jpg" /> 
Figure 11. Example of a full report. This report lists all the introduced data for a specific checkup. Some representative portions are included. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58132/58132fig11large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 12" class="xfigimg" src="/files/ftp_upload/58132/58132fig12.jpg" /> 
Figure 12. Example of a smart report. This report includes essential information to support the clinical decision-making process. It includes the results of the risk stratification tables, the ABPM map and a list of relevant parameters for diagnosis and treatment. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58132/58132fig12large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 13" class="xfigimg" src="/files/ftp_upload/58132/58132fig13.jpg" /> 
Figure 13. Example of an ABPM report. It includes the ABPM map and all the information extracted from the ABPM service. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58132/58132fig13large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis at JoVE.com

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

This article presents a protocol based on Hydra &#8212; a web-based system for clinical decision support that integrates a full and detailed set of functionalities and services required by physicians for complete cardiovascular analysis, risk assessment, early diagnosis, treatment, and monitoring over time.

Cardiovascular diseases (CVDs) are the leading cause of death throughout the world. The total risk of developing CVD is determined by the combined effect ...

hydra-computer-based-platform-for-aiding-clinicians-cardiovascular

Nanyang Technological University

Research

JoVE Journal

Medicine

7.5K Views.  University of A Coruña. Deze methode kan helpen bij het beantwoorden van belangrijke vragen in het klinische besluitvormingsproces voor de preventie van hart- en vaatziekten en vroegtijdige sterfgevallen. De methode die ik wil kiezen voor dit platform is dat het een betrouwbare en objectieve analyse van het cardiovasculaire systeem mogelijk maakt, inclusief risicobeoordeling, vroegtijdige diagnose, termijn en recept, en monitoring in de tijd. Het geven van het gebruiksvriendelijke ontwerp en volledige functionalitei...

Video: Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Preparing a Microfluidics-Based Vascularized Human Micro-Tumor Model

Establishing a Physiologic Human Vascularized Micro-Tumor Model for Cancer Research

A lack of validated cancer models that recapitulate the tumor microenvironment of solid cancers in vitro remains a significant bottleneck for preclinical cancer research and therapeutic development.&#160;To overcome this problem, we have developed the vascularized microtumor (VMT), or tumor chip, a microphysiological system that realistically models the complex human tumor microenvironment. The VMT forms de novo within a microfluidic platform by co-culture of multiple human cell types under dynamic, physiological flow conditions. This tissue-engineered micro-tumor construct incorporates a living perfused vascular network that supports the growing tumor mass just as newly formed vessels do in vivo. Importantly, drugs and immune cells must cross the endothelial layer to reach the tumor, modeling in vivo physiological barriers to therapeutic delivery and efficacy. Since the VMT platform is optically transparent, high-resolution imaging of dynamic processes such as immune cell extravasation and metastasis can be achieved with direct visualization of fluorescently labeled cells within the tissue. Further, the VMT retains in vivo tumor heterogeneity, gene expression signatures, and drug responses. Virtually any tumor type can be adapted to the platform, and primary cells from fresh surgical tissues grow and respond to drug treatment in the VMT, paving the way toward truly personalized medicine. Here, the methods for establishing the VMT and utilizing it for oncology research are outlined. This innovative approach opens new possibilities for studying tumors and drug responses, providing researchers with a powerful tool to advance cancer research.

Author Spotlight: Creating Human Vascularized Micro-Tumors as Models for Translational Cancer Research 

This protocol presents a physiologically relevant tumor-on-a-chip model to perform high-throughput basic and translational human cancer research, advancing drug screening, disease modeling, and personalized medicine approaches with a description of loading, maintenance, and evaluation procedures.

A lack of validated cancer models that recapitulate the tumor microenvironment of solid cancers in vitro remains a significant bottleneck for preclinical ...

Cancer Research

 IVUS-Based Computational Method to Quantify Coronary Mechanical Properties

Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery

Quantifying the mechanical properties of coronary arterial walls could provide meaningful information for the diagnosis, management, and treatment of coronary artery diseases. Since patient-specific coronary samples are not available for patients requiring continuous monitoring, direct experimental testing of vessel material properties becomes impossible. Current coronary models typically use material parameters from available literature, leading to significant mechanical stress/strain calculation errors. Here, we would introduce a finite element model-based updating approach (FEMBUA) to quantify patient-specific in vivo material properties of coronary arteries based on medical images. In vivo cine intravascular ultrasound (IVUS) and virtual histology (VH)-IVUS images of coronary arteries were acquired from a patient with coronary artery disease. Cine IVUS images showing the vascular movement over one cardiac cycle were segmented, and two IVUS frames with maximum and minimum lumen circumferences were selected to represent the coronary geometry under systolic and diastolic pressure conditions, respectively. VH-IVUS image was also segmented to obtain the vessel contours, and a layer thickness of 0.05 cm was added to the VH-IVUS contours to reconstruct the coronary geometry. A computational finite element model was created with an anisotropic Mooney-Rivlin material model used to describe the vessel's mechanical properties and pulsatile blood pressure conditions prescribed to the coronary luminal surface to make it contract and expand. Then, an iterative updating approach was employed to determine the material parameters of the anisotropic Mooney-Rivlin model by matching minimum and maximum lumen circumferences from the computational finite element model with those from cine IVUS images. This image-based finite element model-based updating approach could be successfully extended to determine the material properties of arterial walls in various vascular beds and holds the potential for risk assessment of cardiovascular diseases.

In vivo cine intravascular ultrasound images show the coronary cross-sectional movement corresponding to different pressure loading conditions. Based on a finite element model, an iterative scheme was employed to determine the patient-specific mechanical properties of coronary arteries in vivo by matching coronary motion from the computational model and medical images.

Quantifying the mechanical properties of coronary arterial walls could provide meaningful information for the diagnosis, management, and treatment of ...

Engineering

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training

Three dimensional&#160;models can be a valuable tool for surgeons as they develop surgical plans and medical fellows as they learn about complex cases. In particular, 3D models can play an important role in the field of cardiology, where complex congenital heart diseases occur. While many 3D printers can provide anatomically correct and detailed models, existing 3D printing materials fail to replicate myocardial tissue properties and can be extremely costly. This protocol aims to develop a process for the creation of patient-specific models of complex congenital heart defects using a low-cost silicone that more closely matches cardiac muscle properties. With improved model fidelity, actual surgical procedural training could occur in advance of the procedure. Successful creation of cardiac models begins with the segmentation of radiologic images to generate a virtual blood pool (blood that fills the chambers of the heart) and myocardial tissue mold. The blood pool and myocardial mold are 3D printed in acrylonitrile butadiene styrene (ABS), a plastic dissolvable in acetone. The mold is assembled around the blood pool, creating a negative space simulating the myocardium. Silicone with a shore hardness of 2A is poured into the negative space and allowed to cure. The myocardial mold is removed, and the remaining silicone/blood pool model is submerged in acetone. The described process results in a physical model in which all cardiac features, including intra-cardiac defects, are represented with more realistic tissue properties and are more closely approximated than a direct 3D printing approach. The successful surgical correction of a model with a ventricular septal defect (VSD) using a GORE-TEX patch (standard surgical intervention for defect) demonstrates the utility of the method.

Patient-specific models improve surgeon and fellow confidence when developing or learning surgical plans. Three-dimensional (3D) printers generate adequate detail for surgical preparation, but fail to replicate tissue haptic fidelity. A protocol is presented detailing the creation of patient-specific, silicone cardiac models, combining 3D printing precision with simulated silicone tissue.

Three dimensional&#160;models can be a valuable tool for surgeons as they develop surgical plans and medical fellows as they learn about complex cases. In ...

Bioengineering

In Silico Clinical Trials for Cardiovascular Disease

The SILICOFCM project mainly aims to develop a computational platform for in silico clinical trials of familial cardiomyopathies (FCMs). The unique characteristic of the platform is the integration of patient-specific biological, genetic, and clinical imaging data. The platform allows the testing and optimization of medical treatment to maximize positive therapeutic outcomes. Thus, adverse effects and drug interactions can be avoided, sudden cardiac death can be prevented, and the time between the commencement of drug treatment and the desired result can be shortened. This article presents a parametric model of the left ventricle automatically generated from patient-specific ultrasound images by applying an electromechanical model of the heart. Drug effects were prescribed through specific boundary conditions for inlet and outlet flow, ECG measurements, and calcium function for heart muscle properties. Genetic data from patients were incorporated through the material property of the ventricle wall. Apical view analysis involves segmenting the left ventricle using a previously trained U-net framework and calculating the bordering rectangle based on the length of the left ventricle in the diastolic and systolic cycle. M-mode view analysis includes bordering of the characteristic areas of the left ventricle in the M-mode view. After extracting the dimensions of the left ventricle, a finite elements mesh was generated based on mesh options, and a finite element analysis simulation was run with user-provided inlet and outlet velocities. Users can directly visualize on the platform various simulation results such as pressure-volume, pressure-strain, and myocardial work-time diagrams, as well as animations of different fields such as displacements, pressures, velocity, and shear stresses.

In Silico Clinical Trials for Cardiovascular Disease

This protocol demonstrates the workflow of the SILICOFCM platform for automatically generating a parametric model of the left ventricle from patient-specific ultrasound images by applying a multi-scale electromechanical model of the heart. This platform enables in silico clinical trials intended to reduce real clinical trials and maximize positive therapeutic outcomes.

The SILICOFCM project mainly aims to develop a computational platform for in silico clinical trials of familial cardiomyopathies (FCMs). The unique ...

Designing a Bioreactor to Improve Data Acquisition and Model Throughput of Engineered Cardiac Tissues

Heart failure remains the leading cause of death worldwide, creating a pressing need for better preclinical models of the human heart. Tissue engineering is crucial for basic science cardiac research; in vitro human cell culture eliminates the interspecies differences of animal models, while a more tissue-like 3D environment (e.g., with extracellular matrix and heterocellular coupling) simulates in vivo conditions to a greater extent than traditional two-dimensional culture on plastic Petri dishes. However, each model system requires specialized equipment, for example, custom-designed bioreactors and functional assessment devices. Additionally, these protocols are often complicated, labor-intensive, and plagued by the failure of the small, delicate tissues.
This paper describes a process for generating a robust human engineered cardiac tissue (hECT) model system using induced pluripotent stem-cell-derived cardiomyocytes for the longitudinal measurement of tissue function. Six hECTs with linear strip geometry are cultured in parallel, with each hECT&#160;suspended from a pair of force-sensing polydimethylsiloxane (PDMS) posts attached to PDMS racks. Each post is capped with a black PDMS stable post tracker (SPoT), a new feature that improves the ease of use, throughput, tissue retention, and data quality. The shape allows for the reliable optical tracking of post deflections, yielding improved twitch force tracings with absolute active and passive tension. The cap geometry eliminates tissue failure due to hECTs slipping off the posts, and as they involve a second step after PDMS rack fabrication, the SPoTs can be added to existing PDMS post-based designs without major changes to the bioreactor fabrication process.
The system is used to demonstrate the importance of measuring hECT function at physiological temperatures and shows stable tissue function during data acquisition. In summary, we describe a state-of-the-art model system that reproduces key physiological conditions to advance the biofidelity, efficiency, and rigor of engineered cardiac tissues for in vitro applications.

Three-dimensional cardiac tissues bioengineered using stem-cell-derived cardiomyocytes have emerged as promising models for studying healthy and diseased human myocardium in vitro while recapitulating key aspects of the native cardiac niche. This manuscript describes a protocol for fabricating and analyzing high-content engineered cardiac tissues generated from human induced pluripotent stem-cell-derived cardiomyocytes.

Heart failure remains the leading cause of death worldwide, creating a pressing need for better preclinical models of the human heart. Tissue engineering ...

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

Catheter-based interventions are standard treatment options for cardiovascular pathologies. Therefore, patient-specific models could help training physicians&#39; wire-skills as well as improving planning of interventional procedures. The aim of this study was to develop a manufacturing process of patient-specific 3D-printed models for cardiovascular interventions.
To create a 3D-printed elastic phantom, different 3D-printing materials were compared to porcine biological tissues (i.e., aortic tissue) in terms of mechanical characteristics. A fitting material was selected based on comparative tensile tests and specific material thicknesses were defined. Anonymized contrast-enhanced CT-datasets were collected retrospectively. Patient-specific volumetric models were extracted from these datasets and subsequently 3D-printed. A pulsatile flow loop was constructed to simulate the intraluminal blood flow during interventions. Models&#39; suitability for clinical imaging was assessed by x-ray imaging, CT, 4D-MRI and (Doppler) ultrasonography. Contrast medium was used to enhance visibility in x-ray-based imaging. Different catheterization techniques were applied to evaluate the 3D-printed phantoms in physicians&#39; training as well as for pre-interventional therapy planning.
Printed models showed a high printing resolution (~30 &#181;m) and mechanical properties of the chosen material were comparable to physiological biomechanics. Physical and digital models showed high anatomical accuracy when compared to the underlying radiological dataset. Printed models were suitable for ultrasonic imaging as well as standard x-rays. Doppler ultrasonography and 4D-MRI displayed flow patterns and landmark characteristics (i.e., turbulence, wall shear stress) matching native data. In a catheter-based laboratory setting, patient-specific phantoms were easy to catheterize. Therapy planning and training of interventional procedures on challenging anatomies (e.g., congenital heart disease (CHD)) was possible.
Flexible patient-specific cardiovascular phantoms were 3D-printed, and the application of common clinical imaging techniques was possible. This new process is ideal as a training tool for catheter-based (electrophysiological) interventions and can be used in patient-specific therapy planning.

Here we present development of a mock circulation setup for multimodal therapy evaluation, pre-interventional planning, and physician-training on cardiovascular anatomies. With the application of patient-specific tomographic scans, this setup is ideal for therapeutic approaches, training, and education in individualized medicine.

Catheter-based interventions are standard treatment options for cardiovascular pathologies. Therefore, patient-specific models could help training ...

Bridging the Technology Divide in the COVID-19 Era: Using Virtual Outreach to Expose Middle and High School Students to Imaging Technology

Increasing the diversity of students choosing careers in science, technology, engineering, and mathematics (STEM) fields is an area of intense focus across the USA, especially in kindergarten through 12th grade (K-12)-focused pipeline programs in medical schools. A diverse STEM workforce contributes to better problem-solving and equity in health care. Two of the many major barriers for rural students are the lack of sufficient STEM role models and limited access to technology in the classroom. Medical schools often serve as an important resource for students in the local community who can easily gain access to STEM professionals and modern technology through on-campus, sponsored events and STEM outreach to the local classrooms. However, underrepresented minority (URM) students often live in socioeconomically distressed parts of rural states such as Arkansas, where access to STEM role models and technology is limited. Virtual learning in the COVID-19 era has proven that the imaging technology resources of a medical school can be harnessed to reach a wider audience, especially students living in rural areas far from the medical school campus.

This article presents an overview of how synchronous web-based virtual outreach can be used to expose 6th-12th grade students to advanced imaging technologies such as ultrasound, computerized tomography, and electroencephalography. The paper discusses the methods and equipment needed to livestream integrated educational sessions for effective student engagement in STEM.

Increasing the diversity of students choosing careers in science, technology, engineering, and mathematics (STEM) fields is an area of intense focus ...

POCUS Protocol for IVC Assessment in Management of Heart Failure

Workflow and Framework for Collecting and Implementing Point-of-Care Ultrasound Data in the Management of Heart Failure Patients

Management and prevention of acute decompensated heart failure remain highly prevalent and challenging medical conditions. Incorporation of Point-of-Care Ultrasound (POCUS) as an adjunctive tool for assessing volume status and treatment response has shown significant promise. POCUS can be used for imaging internal anatomic structures serially and capturing these images for comparison and measurement over time. This protocol describes a scalable and standardized methodology for the serial assessment of the inferior vena cava (IVC). The methodology includes serial image collection, measurement, and presentation in the electronic medical record. A workflow for POCUS-acquired images of the IVC was created to capture the images and measure the diameter in a discrete data field for direct comparison over time and in response to clinical management. The protocol also includes the assessment of the presence or absence of pleural effusion as discrete data in the standardized workflow. By integrating POCUS into heart failure management, clinicians can improve patient outcomes through more precise and timely adjustments in treatment.

Author Spotlight: Workflow for Integrating POCUS Data into EHR for Managing Heart Failure Patients

This protocol presents a scalable workflow for the use of point-of-care ultrasound in the management of hospitalized and outpatient heart failure patients.

Management and prevention of acute decompensated heart failure remain highly prevalent and challenging medical conditions. Incorporation of Point-of-Care ...

Standardizing PoC TCCD Ultrasound Protocols for Critical Care in Adults

Point of Care Transcranial Color-Coded Duplex Ultrasound of the Middle Cerebral Artery

In the assessment and management of many clinical problems, point-of-care (PoC) ultrasound is an emerging bedside tool. Transcranial color-coded duplex (TCCD) ultrasound can be valuable in multiple situations, including for patients who are unconscious or have an equivocal neurologic examination, as it helps rule in specific intracranial pathologies. Despite the known diagnostic value of transcranial ultrasound, its use in critical care medicine remains variable. This variability is partly due to inconsistent training across hospitals, stemming from a lack of standardized education and training. Additionally, the brain has often been overlooked in many critical care protocols, such as RUSH (Rapid Ultrasound for Shock and Hypotension) and FAST (Focused Assessment with Sonography in Trauma) exams. To address these gaps, this article proposes a protocol for PoC TCCD image acquisition in adults, detailing indications, limitations, transducer selection, placement, sequence acquisition, and image optimization. Furthermore, the use of PoC TCCD is discussed as a means of screening for three conditions: vasospasm, raised intracranial pressure, and progression of cerebral circulatory arrest.

Transcranial ultrasound is an essential tool for monitoring patients with various neurological conditions. Although it is commonly used in a protocolized fashion in consultative studies, the brain has been overlooked in many protocols utilizing point-of-care ultrasound (PoCUS). This study proposes a PoCUS image acquisition protocol.

In the assessment and management of many clinical problems, point-of-care (PoC) ultrasound is an emerging bedside tool. Transcranial color-coded duplex ...

Echocardiographic Assessment Using Subxiphoid-Only Examination for Hypotensive Patients

Point-of-care ultrasound, or POCUS, has gathered significant interest as a tool to augment clinical decision-making in the care of acutely ill patients. Patients with hemodynamic instability, in particular, require immediate intervention and are at high risk for further cardiovascular and/or respiratory decompensation. POCUS examination of these patients, therefore, requires a protocol that is sufficient to answer clinicians' questions while still retaining adequate brevity to be feasible in a short period of time at the bedside. Here, we demonstrate a protocol for obtaining such information by the Echocardiographic Assessment using Subxiphoid-Only - Mean Arterial Pressure, or EASy MAP exam, a focused ultrasonographic assessment of the cardiovascular and respiratory systems. The cardiovascular portion of the examination utilizes the subcostal window to obtain views of the heart and inferior vena cava (IVC); this is then supplemented by anterior upper lung and posterolateral diaphragmatic pleural views for the respiratory portion. All six views (cardiac, IVC, left and right anterior upper lung, and left and right posterolateral pleural) are obtained using a low-frequency phased array transducer. We discuss some of the common pitfalls encountered in obtaining these images and the basics of interpreting the most common findings; also described is a 12-point scale for rating the quality of the images obtained from the cardiac view and a small number of supplementary views that may be considered when clinically relevant.

The protocol here provides a rapid, phenotype-based approach for focused ultrasound evaluation of the hypotensive patient, using subcostal images of the inferior vena cava and heart, with additional views of the upper lungs and pleural spaces.

Point-of-care ultrasound, or POCUS, has gathered significant interest as a tool to augment clinical decision-making in the care of acutely ill patients. ...

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Samenvatting

Meer video's verkennen