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> Physiology > Organ or Tissue Function > Systole

Systole

Systole is the contraction phase of the cardiac cycle, during which the ventricles of the heart pump blood into the pulmonary and systemic circulatory systems.
It occurs between the opening and closing of the aortic and pulmonary valves, and is followed by diastole, the relaxation phase of the heart.
Systole plays a crucial role in maintaining the circulatry flow of blood throughout the body.
Understanding and analyzing systolic function is an important aspect of cardiovascular physiology and clinical cardiology.

Most cited protocols related to «Systole»

1
Design and Development of Metabochip
Cited 106 times
The Metabochip was designed by representatives of the Body Fat Percentage [9] (link), CARDIoGRAM (coronary artery disease and myocardial infarction) [10] (link), DIAGRAM (type 2 diabetes) [11] (link), GIANT (anthropometric traits) [3] (link), [12] (link), [13] (link), Global Lipids Genetics (lipids) [4] (link), HaemGen (hematological measures) [14] (link), ICBP (blood pressure) [15] (link), MAGIC (glucose and insulin) [16] (link)–[18] (link), and QT-IGC (QT interval) [19] (link), [20] (link) GWAS meta-analysis consortia. The array is comprised of SNPs selected across two tiers of traits (Table 1). Tier 1 is comprised of eleven traits deemed to be of primary interest: type 2 diabetes (T2D), fasting glucose, coronary artery disease and myocardial infarction (CAD/MI), low density lipoprotein (LDL) cholesterol, high density lipoprotein (HDL) cholesterol, triglycerides, body mass index (BMI), systolic and diastolic blood pressure, QT interval, and waist-to-hip ratio adjusted for BMI (WHR). Tier 2 is comprised of twelve traits of secondary interest: fasting insulin, 2-hour glucose, glycated hemoglobin (HbA1c), T2D age of diagnosis, early onset T2D (diagnosis age<45 years), waist circumference adjusted for BMI, height, body fat percentage, total cholesterol, platelet count, mean platelet volume, and white blood cell count.
We included three design classes of SNPs on the Metabochip (Table 2):
In total, 217,695 SNPs were chosen for the array (Table 2). 20,970 SNPs (9.6%) failed during the assay manufacturing process, resulting in 196,725 SNPs available for genotyping. A summary file annotating each Metabochip SNP with ascertainment criteria, SNP assay, a list of unintended duplicate SNPs (Supplementary Table S4), and reference strand orientation for alleles is provided at http://www.sph.umich.edu/csg/kang/MetaboChip/.
Voight B.F., Kang H.M., Ding J., Palmer C.D., Sidore C., Chines P.S., Burtt N.P., Fuchsberger C., Li Y., Erdmann J., Frayling T.M., Heid I.M., Jackson A.U., Johnson T., Kilpeläinen T.O., Lindgren C.M., Morris A.P., Prokopenko I., Randall J.C., Saxena R., Soranzo N., Speliotes E.K., Teslovich T.M., Wheeler E., Maguire J., Parkin M., Potter S., Rayner N.W., Robertson N., Stirrups K., Winckler W., Sanna S., Mulas A., Nagaraja R., Cucca F., Barroso I., Deloukas P., Loos R.J., Kathiresan S., Munroe P.B., Newton-Cheh C., Pfeufer A., Samani N.J., Schunkert H., Hirschhorn J.N., Altshuler D., McCarthy M.I., Abecasis G.R, & Boehnke M. (2012). The Metabochip, a Custom Genotyping Array for Genetic Studies of Metabolic, Cardiovascular, and Anthropometric Traits. PLoS Genetics, 8(8), e1002793.
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Publication 2012
ADCAD1 Alleles Biological Assay Blood Pressure Body Fat Cholesterol Diabetes Mellitus, Non-Insulin-Dependent Diagnosis Genome-Wide Association Study Gigantism Glucose Hemoglobin, Glycosylated High Density Lipoprotein Cholesterol Index, Body Mass Insulin Leukocyte Count Lipids Low-Density Lipoproteins Platelet Counts, Blood Pressure, Diastolic Single Nucleotide Polymorphism Systole Triglycerides Volumes, Mean Platelet Waist-Hip Ratio Waist Circumference
2
Cardiometabolic Traits Genetic Association
Cited 64 times
To explore the relationship between BMI and an array of cardiometabolic traits and diseases, association results for the 97 BMI index SNPs were requested from 13 GWAS meta-analysis consortia: DIAGRAM (type 2 diabetes)56 (link), CARDIoGRAM-C4D (CAD)57 (link), ICBP (systolic and diastolic blood pressure (SBP, DBP))58 (link), GIANT (waist-to-hip ratio, hip circumference, and waist circumference, each unadjusted and adjusted for BMI)13 (link),59 , GLGC (HDL, low density lipoprotein cholesterol, triglycerides, and total cholesterol)60 (link), MAGIC (fasting glucose, fasting insulin, fasting insulin adjusted for BMI, and two-hour glucose)61 (link)–63 (link), ADIPOGen (BMI-adjusted adiponectin)64 (link), CKDgen (urine albumin-to-creatinine ratio (UACR), estimated glomerular filtration rate, and overall CKD)65 (link),66 (link), ReproGen (age at menarche, age at menopause)67 (link),68 (link), GENIE (diabetic nephropathy)69 (link),70 (link). Proxies (r2 > 0.8 in CEU) were used when an index SNP was unavailable.
Locke A.E., Kahali B., Berndt S.I., Justice A.E., Pers T.H., Day F.R., Powell C., Vedantam S., Buchkovich M.L., Yang J., Croteau-Chonka D.C., Esko T., Fall T., Ferreira T., Gustafsson S., Kutalik Z., Luan J., Mägi R., Randall J.C., Winkler T.W., Wood A.R., Workalemahu T., Faul J.D., Smith J.A., Zhao J.H., Zhao W., Chen J., Fehrmann R., Hedman Å.K., Karjalainen J., Schmidt E.M., Absher D., Amin N., Anderson D., Beekman M., Bolton J.L., Bragg-Gresham J.L., Buyske S., Demirkan A., Deng G., Ehret G.B., Feenstra B., Feitosa M.F., Fischer K., Goel A., Gong J., Jackson A.U., Kanoni S., Kleber M.E., Kristiansson K., Lim U., Lotay V., Mangino M., Leach I.M., Medina-Gomez C., Medland S.E., Nalls M.A., Palmer C.D., Pasko D., Pechlivanis S., Peters M.J., Prokopenko I., Shungin D., Stančáková A., Strawbridge R.J., Sung Y.J., Tanaka T., Teumer A., Trompet S., van der Laan S.W., van Setten J., Van Vliet-Ostaptchouk J.V., Wang Z., Yengo L., Zhang W., Isaacs A., Albrecht E., Ärnlöv J., Arscott G.M., Attwood A.P., Bandinelli S., Barrett A., Bas I.N., Bellis C., Bennett A.J., Berne C., Blagieva R., Blüher M., Böhringer S., Bonnycastle L.L., Böttcher Y., Boyd H.A., Bruinenberg M., Caspersen I.H., Chen Y.D., Clarke R., Daw E.W., de Craen A.J., Delgado G., Dimitriou M., Doney A.S., Eklund N., Estrada K., Eury E., Folkersen L., Fraser R.M., Garcia M.E., Geller F., Giedraitis V., Gigante B., Go A.S., Golay A., Goodall A.H., Gordon S.D., Gorski M., Grabe H.J., Grallert H., Grammer T.B., Gräßler J., Grönberg H., Groves C.J., Gusto G., Haessler J., Hall P., Haller T., Hallmans G., Hartman C.A., Hassinen M., Hayward C., Heard-Costa N.L., Helmer Q., Hengstenberg C., Holmen O., Hottenga J.J., James A.L., Jeff J.M., Johansson Å., Jolley J., Juliusdottir T., Kinnunen L., Koenig W., Koskenvuo M., Kratzer W., Laitinen J., Lamina C., Leander K., Lee N.R., Lichtner P., Lind L., Lindström J., Lo K.S., Lobbens S., Lorbeer R., Lu Y., Mach F., Magnusson P.K., Mahajan A., McArdle W.L., McLachlan S., Menni C., Merger S., Mihailov E., Milani L., Moayyeri A., Monda K.L., Morken M.A., Mulas A., Müller G., Müller-Nurasyid M., Musk A.W., Nagaraja R., Nöthen M.M., Nolte I.M., Pilz S., Rayner N.W., Renstrom F., Rettig R., Ried J.S., Ripke S., Robertson N.R., Rose L.M., Sanna S., Scharnagl H., Scholtens S., Schumacher F.R., Scott W.R., Seufferlein T., Shi J., Smith A.V., Smolonska J., Stanton A.V., Steinthorsdottir V., Stirrups K., Stringham H.M., Sundström J., Swertz M.A., Swift A.J., Syvänen A.C., Tan S.T., Tayo B.O., Thorand B., Thorleifsson G., Tyrer J.P., Uh H.W., Vandenput L., Verhulst F.C., Vermeulen S.H., Verweij N., Vonk J.M., Waite L.L., Warren H.R., Waterworth D., Weedon M.N., Wilkens L.R., Willenborg C., Wilsgaard T., Wojczynski M.K., Wong A., Wright A.F., Zhang Q., Brennan E.P., Choi M., Dastani Z., Drong A.W., Eriksson P., Franco-Cereceda A., Gådin J.R., Gharavi A.G., Goddard M.E., Handsaker R.E., Huang J., Karpe F., Kathiresan S., Keildson S., Kiryluk K., Kubo M., Lee J.Y., Liang L., Lifton R.P., Ma B., McCarroll S.A., McKnight A.J., Min J.L., Moffatt M.F., Montgomery G.W., Murabito J.M., Nicholson G., Nyholt D.R., Okada Y., Perry J.R., Dorajoo R., Reinmaa E., Salem R.M., Sandholm N., Scott R.A., Stolk L., Takahashi A., Tanaka T., van ’t Hooft F.M., Vinkhuyzen A.A., Westra H.J., Zheng W., Zondervan K.T., Heath A.C., Arveiler D., Bakker S.J., Beilby J., Bergman R.N., Blangero J., Bovet P., Campbell H., Caulfield M.J., Cesana G., Chakravarti A., Chasman D.I., Chines P.S., Collins F.S., Crawford D.C., Cupples L.A., Cusi D., Danesh J., de Faire U., den Ruijter H.M., Dominiczak A.F., Erbel R., Erdmann J., Eriksson J.G., Farrall M., Felix S.B., Ferrannini E., Ferrières J., Ford I., Forouhi N.G., Forrester T., Franco O.H., Gansevoort R.T., Gejman P.V., Gieger C., Gottesman O., Gudnason V., Gyllensten U., Hall A.S., Harris T.B., Hattersley A.T., Hicks A.A., Hindorff L.A., Hingorani A.D., Hofman A., Homuth G., Hovingh G.K., Humphries S.E., Hunt S.C., Hyppönen E., Illig T., Jacobs K.B., Jarvelin M.R., Jöckel K.H., Johansen B., Jousilahti P., Jukema J.W., Jula A.M., Kaprio J., Kastelein J.J., Keinanen-Kiukaanniemi S.M., Kiemeney L.A., Knekt P., Kooner J.S., Kooperberg C., Kovacs P., Kraja A.T., Kumari M., Kuusisto J., Lakka T.A., Langenberg C., Marchand L.L., Lehtimäki T., Lyssenko V., Männistö S., Marette A., Matise T.C., McKenzie C.A., McKnight B., Moll F.L., Morris A.D., Morris A.P., Murray J.C., Nelis M., Ohlsson C., Oldehinkel A.J., Ong K.K., Madden P.A., Pasterkamp G., Peden J.F., Peters A., Postma D.S., Pramstaller P.P., Price J.F., Qi L., Raitakari O.T., Rankinen T., Rao D.C., Rice T.K., Ridker P.M., Rioux J.D., Ritchie M.D., Rudan I., Salomaa V., Samani N.J., Saramies J., Sarzynski M.A., Schunkert H., Schwarz P.E., Sever P., Shuldiner A.R., Sinisalo J., Stolk R.P., Strauch K., Tönjes A., Trégouët D.A., Tremblay A., Tremoli E., Virtamo J., Vohl M.C., Völker U., Waeber G., Willemsen G., Witteman J.C., Zillikens M.C., Adair L.S., Amouyel P., Asselbergs F.W., Assimes T.L., Bochud M., Boehm B.O., Boerwinkle E., Bornstein S.R., Bottinger E.P., Bouchard C., Cauchi S., Chambers J.C., Chanock S.J., Cooper R.S., de Bakker P.I., Dedoussis G., Ferrucci L., Franks P.W., Froguel P., Groop L.C., Haiman C.A., Hamsten A., Hui J., Hunter D.J., Hveem K., Kaplan R.C., Kivimaki M., Kuh D., Laakso M., Liu Y., Martin N.G., März W., Melbye M., Metspalu A., Moebus S., Munroe P.B., Njølstad I., Oostra B.A., Palmer C.N., Pedersen N.L., Perola M., Pérusse L., Peters U., Power C., Quertermous T., Rauramaa R., Rivadeneira F., Saaristo T.E., Saleheen D., Sattar N., Schadt E.E., Schlessinger D., Slagboom P.E., Snieder H., Spector T.D., Thorsteinsdottir U., Stumvoll M., Tuomilehto J., Uitterlinden A.G., Uusitupa M., van der Harst P., Walker M., Wallaschofski H., Wareham N.J., Watkins H., Weir D.R., Wichmann H.E., Wilson J.F., Zanen P., Borecki I.B., Deloukas P., Fox C.S., Heid I.M., O’Connell J.R., Strachan D.P., Stefansson K., van Duijn C.M., Abecasis G.R., Franke L., Frayling T.M., McCarthy M.I., Visscher P.M., Scherag A., Willer C.J., Boehnke M., Mohlke K.L., Lindgren C.M., Beckmann J.S., Barroso I., North K.E., Ingelsson E., Hirschhorn J.N., Loos R.J, & Speliotes E.K. (2015). Genetic studies of body mass index yield new insights for obesity biology. Nature, 518(7538), 197-206.
Publication 2015
ADIPOQ protein, human Albumins Cholesterol Cholesterol, beta-Lipoprotein Creatinine Diabetes Mellitus, Non-Insulin-Dependent Diabetic Nephropathy Genie Genome-Wide Association Study Gigantism Glomerular Filtration Rate Glucose Insulin Menarche Menopause Pressure, Diastolic Systole Triglycerides Urine Waist-Hip Ratio Waist Circumference
3
Predictive Model for Atrial Fibrillation
Cited 51 times
Means and standard deviation and frequency distribution of relevant covariates were calculated by cohort and race. We initially ran cohort‐ and race‐specific Cox proportional hazard models to assess individual predictors of AF after age‐ and sex‐adjustment in each cohort up to 7 years of follow‐up. Variables considered included age, sex, height, weight, current smoking, systolic and diastolic blood pressure, use of antihypertensive medication, history of diabetes, fasting blood glucose, estimated glomerular filtration rate (eGFR) <60 mL/kg per m2,20 (link) total blood cholesterol, HDL cholesterol, triglycerides, heart rate, electrocardiographic‐derived left ventricular hypertrophy, PR interval, history of coronary artery bypass graft (CABG), history of heart failure, history of myocardial infarction, and history of stroke. We selected as candidate predictors for our pooled model any variable significantly associated with AF (P<0.05) in at least 2 of the 3 cohorts, and ran the final Cox proportional hazards model on our participant‐specific pooled data using backward selection of variables (P<0.05 to remain in the model). Age, sex, and race interactions were tested, as was the assumption of proportional hazards. Model‐based individual 5‐year risk of AF was calculated. We evaluated model performance using the C‐statistic,21 (link) discrimination slopes,22 (link) and Nam and D'Agostino's modified Hosmer‐Lemeshow chi‐square statistic for survival analysis.23 To facilitate the use of our score in those clinical settings with limited access to electrocardiograms or blood tests, we first developed a predictive model that did not require information from electrocardiogram and blood tests (which we labeled “simple model”). We then developed a more complex model adding electrocardiographic variables and blood tests (labeled “augmented model”). Variables were retained in the models if they were significantly associated with AF incidence (P<0.05). We calculated the added predicted value of the augmented model versus the simple model with the increment in the C‐statistic and the categorical net reclassification improvement (NRI) using the following risk categories: <2.5%, 2.5% to 5%, >5%.22 (link)
Alonso A., Krijthe B.P., Aspelund T., Stepas K.A., Pencina M.J., Moser C.B., Sinner M.F., Sotoodehnia N., Fontes J.D., Janssens A.C., Kronmal R.A., Magnani J.W., Witteman J.C., Chamberlain A.M., Lubitz S.A., Schnabel R.B., Agarwal S.K., McManus D.D., Ellinor P.T., Larson M.G., Burke G.L., Launer L.J., Hofman A., Levy D., Gottdiener J.S., Kääb S., Couper D., Harris T.B., Soliman E.Z., Stricker B.H., Gudnason V., Heckbert S.R, & Benjamin E.J. (2013). Simple Risk Model Predicts Incidence of Atrial Fibrillation in a Racially and Geographically Diverse Population: the CHARGE‐AF Consortium. Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, 2(2), e000102.
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Publication 2013
Antihypertensive Agents BLOOD Blood Glucose Cerebrovascular Accident Cholesterol Congestive Heart Failure Coronary Artery Bypass Surgery Diabetes Mellitus Discrimination, Psychology Electrocardiogram Electrocardiography Glomerular Filtration Rate Hematologic Tests High Density Lipoprotein Cholesterol Left Ventricular Hypertrophy Myocardial Infarction Pressure, Diastolic Rate, Heart Systole Triglycerides
4
Validating Heart Failure Algorithms in Claims Data
Cited 50 times
The methods and details for Mini-Sentinel systematic reviews have been described elsewhere. (Carnahan, 2011) The specific search strategy for the HF review can be found in the full report which can be found at http://mini-sentinel.org/foundational_activities/related_projects/default.aspx. Briefly, PubMed and Iowa Drug Information Service (IDIS) searches were performed to identify studies published between 1990 and June, 2010 that evaluated the validity of algorithms for identifying HF in administrative and claims data. Certain search terms related to administrative and claims data are described in detail by Carnahan (2011) and were included in all Mini-Sentinel systematic review searches. In addition to these key words, the following PubMed search terms were used for the HF report: “Heart Failure” [Mesh]. In addition, the IDIS search included specification of the following terms: 428. (NOTE: 428. includes: FAILURE, HEART NEC; FAILURE, HEART, CONGESTIVE; FAILURE, HEART, LEFT; FAILURE, HEART, SYSTOLIC; and FAILURE, HEART, DIASTOLIC). Mini-Sentinel collaborators were requested to identify any published or unpublished work that validated an algorithm to identify HF in administrative and claims data.
Two Mini-Sentinel investigators reviewed all abstracts identified through the initial PubMed and IDIS searches, identifying potentially relevant articles based on predefined criteria. Articles were excluded from full text review if they did not study heart failure, were not based on an administrative or claims dataset, or included a data source outside of the U.S. or Canada. Articles identified for full review by either investigator were retrieved and reviewed by two investigators. In the event of disagreement between reviewers, the full article was reviewed.
Selected articles were reviewed with the goal of identifying validated algorithms for identifying HF in administrative and claims data. Investigators also identified citations from the article’s reference sections if they were cited as studies validating an algorithm for HF or were otherwise deemed to be potentially relevant. Articles identified through reference sections were reviewed in a similar manner. A single investigator abstracted information for each study which included the following: database, coding system (e.g., ICD-9 codes), study population (including information on inpatient and outpatient composition of the sample), time period, incident or prevalent case, specific algorithm used to identify cases of HF, adjudication criteria (e.g., Framingham criteria), validation process (e.g., medical record review), and validation statistics. The second reviewer confirmed the accuracy of abstracted information.
Cohen’s kappa for agreement was calculated between reviewers for the inclusion versus exclusion of abstracts and full-text text articles.
Saczynski J.S., Andrade S.E., Harrold L.R., Tjia J., Cutrona S.L., Dodd K.S., Goldberg R.J, & Gurwitz J.H. (2012). Mini-Sentinel Systematic Evaluation of Health Outcome of Interest Definitions for Studies Using Administrative and Claims Data: Heart Failure. Pharmacoepidemiology and drug safety, 21(0 1), 10.1002/pds.2313.
Publication 2012
Congestive Heart Failure Diastole Drug Information Services Heart Inpatient Outpatients Systole
5
Validating Heart Failure-Related Health Status
Cited 48 times
The 23-item KCCQ quantifies 7 domains of patients’ HF-related health status: Physical Limitation (6 items), Symptom Stability (1 item), Symptom Frequency (4 items), Symptom Burden (3 items), Self-Efficacy (2 items), Quality of Life (3 items), and Social Limitations (4 items). Item responses are coded sequentially (1, 2, 3, etc.) from worst to best status. Scores are generated for each domain and scaled from 0 to 100, with 0 denoting the worst and 100 the best possible status. In addition, several summary scores are calculated: a Total Symptom score (average of Symptom Frequency and Symptom Burden), a Clinical Summary score (average of Physical Limitation and Total Symptoms), and an Overall Summary score (average of Physical Limitation, Total Symptoms, Quality of Life, and Social Limitation).2 (link) The KCCQ has been shown to be valid, reproducible, and sensitive to clinical change in patients with systolic dysfunction, HF with preserved ejection fraction, and valvular heart disease.2 (link),13 (link),21 (link),22 (link) Moreover, patients’ KCCQ scores are independently prognostic of survival, HF admissions, and costs.9 (link)–12 (link)
Spertus J.A, & Jones P.G. (2015). Development and Validation of a Short Version of the Kansas City Cardiomyopathy Questionnaire. Circulation. Cardiovascular Quality and Outcomes, 8(5), 469-476.
Publication 2015
Patients Physical Examination Systole Valve Disease, Heart

Most recents protocols related to «Systole»

1
Catheter-Based Renal Neuromodulation for Hypertension
Not available on PMC !

Example 2

Example 2 describes the outcome of catheter-based renal neuromodulation on human patients diagnosed with hypertension. Patients selected having a baseline systolic blood pressure of 160 mm Hg or more (≥150 mm Hg for patients with type 2 diabetes) and taking three or more antihypertensive drugs, were randomly allocated into two groups: 51 assessed in a control group (antihypertensive drugs only) and 49 assessed in a treated group (undergone renal neuromodulation and antihypertensive drugs).

Patients in both groups were assessed at 6 months. Office-based blood pressure measurements in the treated group were reduced by 32/12 mm Hg (SD 23/11, baseline of 178/96 mm Hg, p<0.0001), whereas they did not differ from baseline in the control group (change of I/O mm Hg, baseline of 178/97 mm Hg, p=0.77 systolic and p=0.83 diastolic). Between-group differences in blood pressure at 6 months were 33/11 mm Hg (p<0.0001). At 6 months, 41 (84%) of 49 patients who underwent renal neuromodulation had a reduction in systolic blood pressure of 10 mm Hg or more, compared with 18 (35%) of 51 control patients (p<0.0001).

US11865343B2. Methods for treating post-traumatic stress disorder in patients via renal neuromodulation (2024-01-09). Medtronic Ireland Manufacturing Unlimited Company [IE]. Inventors: Marcia Gallagher [IE], Douglas A. Hettrick [US].
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Patent 2024
Antihypertensive Agents BLOOD Blood Pressure Catheters Determination, Blood Pressure Diabetes Mellitus, Non-Insulin-Dependent Diastole High Blood Pressures Homo sapiens Kidney Patients Post-Traumatic Stress Disorder Pressure Systole Systolic Pressure
2
Comprehensive Health Evaluation of Postmenopausal Women
A structured and detailed survey designed by professional physicians was used to collect the demographic and clinical parameters of the study subjects including self-reported illness and the currently used medications. The number of subjects in the smoking and alcohol consumption groups were low among early postmenopausal women, and were therefore excluded from analysis. Systolic and diastolic blood pressure was measured using an electronic brachial sphygmomanometer (T30J, OMRON, Japan). Anthropometric parameters including height, weight, waist circumference, and hip circumference were measured using standard procedures by well-trained nurses. Blood samples (8–10 mL) were collected from the antecubital vein after at least 8 h of overnight fasting and evaluated in the laboratory center within 24 h. Metabolic biomarkers and liver function parameters including fasting blood glucose (FBG), triglycerides (TGs), total cholesterol (TC), low-density-lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), serum uric acid (UA), serum aspartate aminotransferase (AST), and serum alanine aminotransferase (ALT) levels were measured. Furthermore, the blood counts of white blood cells (WBC) and neutrophils (NE) were also analyzed. Abdominal ultrasonography was performed using the SIEMENS ACUSON S2000 ABVS ultrasound scanner (Siemens Healthineers, Erlangen, Germany), and was operated by experienced ultra-sonographers. The data was recorded in the electronic medical system of the Health Examination Center.
Lu Y., Ge L., Yang H., He Y, & Wang Y. (2023). Chinese Visceral Adipose Index Shows Superior Diagnostic Performance in Predicting the Risk of Metabolic Dysfunction Associated Fatty Liver Disease in Early Postmenopausal Chinese Women. Diabetes, Metabolic Syndrome and Obesity, 16, 607-617.
Publication 2023
Abdomen Alanine Transaminase Aspartate Transaminase Biological Markers BLOOD Blood Glucose Cholesterol Cholesterol, beta-Lipoprotein High Density Lipoprotein Cholesterol Leukocytes Liver Neutrophil Nurses Pharmaceutical Preparations Physicians Pressure, Diastolic Serum Sphygmomanometers Systole Triglycerides Ultrasonography Uric Acid Veins Waist Circumference Woman
3
Myocardial Work Analysis During IVR
The MW parameters for ventricular systole were derived by entering the timings of the mitral valve closure and aortic valve closure (defined from the Doppler trace at the aortic valve). Global systolic constructive work (GSCW) was defined as the MW during shortening in systole, and global systolic wasted work (GSWW) was defined as the MW during lengthening in systole. The MW parameters specific for IVR were calculated through deduction: MCWIVR (myocardial constructive work during IVR, the myocardial work performed for lengthening during IVR) = GCW—GSCW; MWWIVR (myocardial wasted work during IVR, the myocardial work performed for shortening during IVR) = GWW—GSWW. The total myocardial work during IVR (MWIVR) was obtained from the sum of MCWIVR and MWWIVR. Myocardial work efficiency during IVR (MWEIVR) was calculated, as follows: MCWIVR / (MCWIVR + MWWIVR) × 100%. The MWIVR parameters were normalized by dividing these by the corresponding IVRT.
Guo Y., Wang X., Yang C.G., Meng X.Y., Li Y., Xia C.X., Xu T., Weng S.X., Zhong Y., Zhang R.S, & Wang F. (2023). Noninvasive assessment of myocardial work during left ventricular isovolumic relaxation in patients with diastolic dysfunction. BMC Cardiovascular Disorders, 23, 129.
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Publication 2023
Heart Ventricle Mitral Valve Myocardium Systole Valves, Aortic
4
Noninvasive Myocardial Work Assessment via Echocardiography
In the EchoPAC software, the MW parameters were obtained through the pressure-strain loop (PSL) area module constructed from the curves for noninvasively estimated LV pressures and LV strains. The peak LV systolic pressure was assumed to be equal to the brachial cuff systolic BP measured during the echocardiographic study. This noninvasive method was validated by various research teams [1 (link), 3 (link), 4 (link), 21 , 22 (link)]. The myocardial work was calculated as the integral of power between mitral valve closure and mitral valve opening. The timings for the valvular events were defined on Doppler spectrums before entering the automated function imaging (AFI). The global work index (GWI) was defined as the total MW within the PSL area, from mitral valve closure to mitral valve opening. Global constructive work (GCW) was defined as the MW performed for shortening during ventricular systole and lengthening during IVR. Global wasted work (GWW) was defined as the MW performed for lengthening during ventricular systole and shortening during IVR. Global work efficiency (GWE) was calculated as the percentage of myocardial constructive work in the total MW (GCW / [GCW + GWW] × 100).
Guo Y., Wang X., Yang C.G., Meng X.Y., Li Y., Xia C.X., Xu T., Weng S.X., Zhong Y., Zhang R.S, & Wang F. (2023). Noninvasive assessment of myocardial work during left ventricular isovolumic relaxation in patients with diastolic dysfunction. BMC Cardiovascular Disorders, 23, 129.
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Publication 2023
Echocardiography Heart Ventricle Mitral Valve Myocardium Pressure Strains Systole Systolic Pressure
5
Doppler Phonolyser: Innovative Heart Sound Analysis
The Doppler Phonolyser AD0302 (Bu-Ali Research Institute, Mashhad, Iran, www.phonolyser.com) is a “smart heart sound analyzer based on the Doppler Effect” used to diagnose congenital and structural diseases of the heart (Fig. 1). Doppler, sound and electrocardiogram signals are displayed on the monitor of the device online synchronously. By this technique, the physician can determine the time of the murmurs. This device separates normal sound from a murmur by analyzing the heart sound. The physician can determine the time of the murmurs using the synchronization of the Doppler signal and ECG. The lack of influence of ambient noise and use of the Doppler Effect make the device more efficient in detecting cardiac murmurs. The Doppler Phonolyser overcomes two of the following technical issues that have long been of interest to physicians:

The Doppler Phonolyser shows a low gradient difference that cannot be heard through a stethoscope.

Cases such as ambient sounds, patient breathing, obesity and slimming and transpiration do not affect Doppler Phonolyser's function.

Doppler Phonolyser device

Phonolyser’s software is an AI-based software that detects abnormalities in the heart’s blood flow. It shows 3 graphs (Fig. 2).

Doppler Phonolyser shows 3 graphs. the top graph shows the electrocardiogram signal, the middle graph shows the sound of the heart, and the bottom graph shows Doppler results

The top graph is ECG that is used to find the systole and diastole time of the heart. The middle one shows the sound of the heart, and the bottom graph shows doppler results. If the heart has a normal structure the Doppler graph will be green and if due to congenital heart diseases the blood flow has any turbulence, the graph’s color will be changed to red.
The technical parameters are shown in Table 5.

Technical parameters of Doppler Phonolyser

ParametersDescription
ProbUltrasound 2 MHz
Device diameters275 × 204x97mm
power

100–240 V

50–60 Hz

single-phase supply

LCD

5 inches

resolution: 800 × 480

Printer

Thermal printer

Paper width: 57.5 ± 0.5 mm

paper roll diameter: 50 mm Max

Method(s) of sterilizationBy methods validated and described by the manufacture
Suitability for use in an oxygen rich environmentNon-inclusion
Mode of operationContinuous operation
Temperature + 10^C–+ 40^C
Humidity < 80%
Pressure86 kPa–106 kPa
Protection against harmful ingress of water or particulate matterNo production (IP00)
Khalilian M.R., Safari M., Hajipour M., Rahmani K., Safari M., Ahmadpour M.H, & Tahouri T. (2023). Evaluation of the heart sounds in children using a Doppler Phonolyser. BioMedical Engineering OnLine, 22, 24.
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Publication 2023
Blood Circulation Congenital Heart Defects Diagnosis Diastole Doppler Effect Hearing Heart Heart Diseases Heart Sounds Medical Devices Obesity Oxygen Patients Physicians Sound Stethoscopes Systole

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