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Catheterization

Catheterization is a medical procedure where a thin, flexible tube (catheter) is inserted into the body to diagnose or treat a variety of conditions.
This process is commonly used to access the urinary bladder, blood vessels, or other body cavities.
Catheterization can help drain urine, deliver medications, or monitor bodily functions.
It plays a crucial role in various medical specialties, including cardiology, urology, and radiology.
Proper catheter placement and management are essential to prevent complications and ensure patient safety.
Researchers can streamline catheterization studies using innovative AI-powered platforms like PubCompare.ai, which optimize protocols and identify the best products for their research.

Most cited protocols related to «Catheterization»

1
Comparative Analysis of Blood Pressure Measurement Methods
Cited 46 times
Several BP measurement methods are now available. The main methods include catheterization, auscultation, oscillometry, volume clamping, and tonometry.
Catheterization is the gold standard method [6 (link)]. This method measures instantaneous BP by placing a strain gauge in fluid contact with blood at any arterial site (e.g., radial artery, aorta). However, the method is invasive.
Auscultation, oscillometry, and volume clamping are noninvasive methods. These methods employ an inflatable cuff.
Auscultation is the standard clinical method [7 (link)]. This method measures systolic and diastolic BP by occluding an artery with a cuff and detecting the Korotkoff sounds using a stethoscope and manometer during cuff deflation. The first sound indicates the initiation of turbulent flow and thus systolic BP, while the fifth sound is silent and indicates the renewal of laminar flow and thus diastolic BP.
Oscillometry is the most popular non-invasive, automatic method [8 (link), 9 (link)]. This method measures mean, diastolic, and systolic BP by also using a cuff but with a pressure sensor inside it. The measured cuff pressure not only rises and falls with cuff inflation and deflation but also shows tiny oscillations indicating the pulsatile blood volume in the artery. The amplitude of these oscillations varies with the applied cuff pressure, as the arterial elasticity is nonlinear. The BP values are estimated from the varying oscillation amplitudes using the empirical fixed-ratios principle. When evaluated against auscultation using an Association for the Advancement of Medical Instrumentation (AAMI) protocol, some oscillometric devices achieve BP errors within the AAMI limits of 5 mmHg bias and 8 mmHg precision [10 ]. However, oscillometry is unreliable in subjects with certain conditions such as atrial fibrillation, stiff arteries, and pre-eclampsia [11 ].
Volume clamping is a non-invasive, automatic method used in research [12 (link), 13 ]. This method measures instantaneous (finger) BP by using a cuff and a photoplethysmography (PPG) sensor to measure the blood volume (see Section V.A). The blood volume at zero transmural pressure is estimated via oscillometry. The cuff pressure is then continually varied to maintain this blood volume throughout the cardiac cycle via a fast servo-control system. The applied cuff pressure may thus equal BP. Volume clamping devices also achieve BP errors within AAMI limits when evaluated against auscultation and near AAMI limits when evaluated against radial artery catheterization [14 (link)].
However, cuff use has several drawbacks. In particular, cuffs are cumbersome and time consuming to use, disruptive during ambulatory monitoring, especially while sleeping, and do not readily extend to low resources settings.
Tonometry is another non-invasive method used in research that, in theory, does not require an inflatable cuff [15 , 16 ]. This method measures instantaneous BP by pressing a manometer-tipped probe on an artery. The probe must flatten or applanate the artery so that its wall tension is perpendicular to the probe. However, manual and automatic applanation have proven difficult. As a result, in practice, the measured waveform has been routinely calibrated with cuff BP whenever a BP change is anticipated [17 (link)].
In sum, the existing BP measurement methods are invasive, manual, or require a cuff. So, none are suitable for ubiquitous (i.e., ultra-convenient, unobtrusive, and low cost) monitoring.
Mukkamala R., Hahn J.O., Inan O.T., Mestha L.K., Kim C.S., Töreyin H, & Kyal S. (2015). Towards Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice. IEEE transactions on bio-medical engineering, 62(8), 1879-1901.
Publication 2015
Aorta Arteries Arteries, Radial Atrial Fibrillation Auscultation BLOOD Blood Pressure Blood Volume Cardiac Volume Catheterization Clinical Protocols Diastole Elasticity Fingers Gold Manometry Medical Devices Oscillometry Photoplethysmography Pre-Eclampsia Pressure Pressure, Diastolic Sound Stethoscopes Strains Systole Systolic Pressure Tonometry
2
Predicting Pediatric PICU Mortality
Cited 41 times
This investigation was performed in the CPCCRN of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (8 (link)). Detailed methods for the TOPICC data collection have been previously described (6 (link)). There were seven sites, and one was composed of two institutions. In brief, patients from newborn to less than 18 years were randomly selected and stratified by hospital from December 4, 2011, to April 7, 2013. Patients from both general/medical and cardiac/cardiovascular PICUs were included. Moribund patients (vital signs incompatible with life for the first 2 hr after PICU admission) were excluded. Only the first PICU admission during hospitalization was included. The protocol was approved by all participating institutional review boards. Other analyses using this database have been published (6 (link), 7 (link), 9 (link), 10 (link)).
Data included descriptive and demographic information (Table 1). Interventions included both surgery and interventional catheterization. Cardiac arrest included closed chest massage within 24 hours before hospitalization or after hospital admission but before PICU admission. Admission source was classified as emergency department, inpatient unit, postintervention unit, or admission from another institution. Diagnosis was classified by the system of primary dysfunction based on the reason for PICU admission; cardiovascular conditions were classified as congenital or acquired.
The primary outcome in this analysis was hospital survival versus death.
Physiologic status was measured using the PRISM physiologic variables (5 (link)) with a shortened time interval (2 hr before PICU admission to 4 hr after admission for laboratory data and the first 4 hr of PICU care for other physiologic variables). For model building, the PRISM components were separated into cardiovascular (heart rate, systolic blood pressure, and temperature), neurologic (pupillary reactivity and mental status), respiratory (arterial Po2, pH, Pco2, and total bicarbonate), chemical (glucose, potassium, blood urea nitrogen, and creatinine), and hematologic (WBC count, platelet count, prothrombin, and partial thromboplastin time) components, and the total PRISM was also separated into neurologic and non-neurologic categories.
The time interval for assessing PRISM data was modified for cardiac patients under 91 days old because some institutions admit infants to the PICU before a cardiac intervention to “optimize” the clinical status but not for intensive care; in these cases, the postintervention period more accurately reflects intensive care. However, in other infants for whom the cardiac intervention is delayed after PICU admission or the intervention is a therapy required because of failed medical management of the acute condition, the routine PRISM data collection time interval is an appropriate reflection of critical illness. Therefore, we identified infants for whom it would be more appropriate to use data from the 4 hours after the cardiac intervention (postintervention time interval) and those for whom using the admission time interval was more appropriate. We operationalized this decision on the conditions likely to present within the first 90 days, the time period when the vast majority of these conditions present (Table 2).
Statistical analyses used SAS 9.4 (SAS Institute Inc., Cary, NC) for descriptive statistics, model development, and fit assessment and R 3.1.1 (The R Foundation for Statistical Computing, Vienna, Austria; http://www.wu.ac.at/statmath) for evaluation of predictive ability. Patient characteristics were descriptively compared and evaluated across sites using the Kruskal-Wallis test for continuous variables and the Pearson chi-square test for categorical variables. The statistical analysis was under the direction of R.H.
The dataset was randomly divided into a derivation set (75%) for model building and a validation set (25%) stratified by the study site. Univariate mortality odds ratios were computed, and variables with a significance level of less than 0.1 were considered candidate predictors for the final model. As was the case for the previously published trichotomous (death, survival with significant new morbidity, and intact survival) model construction, a nonautomated (examined by biostatistician and clinician at each step) backward stepwise selection approach was used to select factors. Multicategorical factors (e.g., diagnostic categories) had factors combined when appropriate per statistical and clinical criteria. Clinician input was included (and paramount) in this process to ensure that the model fit was relevant and consistent with clinical information. Construction of a clinically relevant, sufficiently predictive model using predictors readily available to the clinician took precedence over inclusion based solely on statistical significance. We were cognizant of the existing trichotomous outcome model and attempted, when statistically justified, to create a compatible two-outcome model that could aid in a smooth transition to using the three-outcome approach.
Final candidate models were evaluated based on 2D receiver operating characteristic (ROC) curves (discrimination) and the Hosmer-Lemeshow goodness of fit (calibration). For the entire dataset, goodness of fit with respect to key subgroups was assessed by examining SMRs for descriptive and diagnostic categories not used in the final model. Only categories with at least 10 outcomes in observed and expected cells were used.
Pollack M.M., Holubkov R., Funai T., Dean J.M., Berger J.T., Wessel D.L., Meert K., Berg R.A., Newth C.J., Harrison R.E., Carcillo J., Dalton H., Shanley T., Jenkins T.L, & Tamburro R. (2016). The Pediatric Risk of Mortality Score: Update 2015. Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies, 17(1), 2-9.
Publication 2016
Activated Partial Thromboplastin Time Arteries Bicarbonates Cardiac Arrest Cardiovascular Diseases Cardiovascular System Catheterization Cells Chest Creatinine Critical Illness Diagnosis Discrimination, Psychology Disease Management Ethics Committees, Research Glucose Heart Hospitalization Infant Infant, Newborn Inpatient Intensive Care Massage Operative Surgical Procedures Patients physiology Platelet Counts, Blood Potassium prisma Prothrombin Rate, Heart Reflex Respiratory Diaphragm Respiratory Rate Signs, Vital Systems, Nervous Systolic Pressure Therapeutics Urea Nitrogen, Blood
3
ISCHEMIA Trial: Invasive vs. Conservative Strategy
Cited 28 times

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Publication 2018
Angina, Unstable Angina Pectoris BLOOD Cardiac Arrest Cardiovascular System Catheterization Catheterizations, Cardiac Cerebrovascular Accident Clinical Trials Data Monitoring Committees Congestive Heart Failure Heart Hospitalization Hypersensitivity Ischemia Lung TimeLine
4
Functional Status Outcomes in Pediatric Critical Care
Cited 26 times
The current investigation was performed at the 7 sites (8 PICUS) in the CPCCRN. These sites have approximately 17,000 PICU admissions per year.10 (link) The details of patient selection and data collection have been published.9 ,11 (link) In brief, only the first PICU admission was included. Patients ranging in ages from newborn to less than 18 years were randomly selected from both the General/Medical PICUs and Cardiac/Cardiovascular PICUs. There were no separate general surgical or neurological PICUs. This report includes the initial 5017 patients from a larger data collection and included all enrolled patients from the first day of the study (December 4, 2011) to the day when the 5000th patient was enrolled (August 2, 2012). The protocol was approved by the Institutional Review Boards at all participating institutions.
Data for this analysis included diagnostic and demographic data and FSS scores determined at PICU admission to assess baseline (pre-hospital admission) status and status at PICU and hospital discharge. Baseline FSS status was determined from the medical records supplemented by caretaker knowledge as needed to reflect chronic functional status prior to the acute illness. Researchers, research coordinators, and research assistants were trained in data collection with in-person training on multiple occasions and conducted bi-weekly teleconference calls. Diagnoses were classified by the system of dysfunction accounting for the primary reason for PICU admission. Since a previous publication on this sample,9 we are able to better categorize some of the miscellaneous classifications resulting in small changes in the numbers of diagnoses. Operative status included both operating room and interventional catheterization procedures but not diagnostic catheterization procedures.
The FSS was developed to provide assessment of functional status suitable for large studies. It is composed of 6 domains (mental status, sensory, communication, motor function, feeding, respiratory) with domain scores ranging from 1 (normal) to 5 (very severe dysfunction). Therefore, total scores may range from 6 to 30 with lower scores indicating better function. The operational definitions and manual for the classifications have been published.8 (link) The FSS validation consisted of comparison to the ABAS II, a validated measure of pediatric adaptive behavior, and comparison to the pediatric performance scales, the Pediatric Cerebral and Overall Performance Categories (PCPC/POPC).8 (link),9 For this analysis, we categorized FSS scores of 6–7 as good, 8–9 as mildly abnormal, 10–15 as moderately abnormal, 16–21 as severely abnormal, and > 21 as very severely abnormal. These category ranges were chosen based on the dysfunction reflected in the score and to be the approximately equivalent FSS score range that corresponded to the POPC categories.9 Newborns who had never achieved a stable baseline of function were assigned an FSS = 6; this was operationalized by assigning a baseline FSS score of 6 to all infant admissions from 0–2 days of age and to transfers from another facility for infants from 3–6 days of age. Significant, new morbidity was defined as worsening of FSS of 3 or greater from baseline to hospital discharge. This definition was based on a consensus perception of the importance of the change(s), and this was the change in mean FSS scores between the normal and moderate disability categories of the POPC.9 Since this was the initial use of the FSS to define new morbidities, we evaluated the change in individual FSS domains and the magnitude of that change for both patients with a worsening FSS of 3 or greater and 2 or less.
Data are expressed as mean +/− standard deviation. Comparison of data across categories utilized the Pearson chi-square test and the Mantel-Haenszel chi-square test. The assessment of association between morbidity and mortality rates utilized the Pearson correlation.
Pollack M.M., Holubkov R., Funai T., Clark A., Berger J.T., Meert K., Newth C.J., Shanley T., Moler F., Carcillo J., Berg R.A., Dalton H., Wessel D.L., Harrison R.E., Doctor A., Dean J.M, & Jenkins T.L. (2014). Pediatric Intensive Care Outcomes: Development of New Morbidities During Pediatric Critical Care. Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies, 15(9), 821-827.
Publication 2014
Cardiovascular System Catheterization Conditioning, Psychology Diagnosis Disabled Persons Disease, Chronic Ethics Committees, Research Heart Infant Infant, Newborn Patient Discharge Patients Respiratory Diaphragm Respiratory Rate
5
Pediatric Critical Care Research Network Study
Cited 16 times
This investigation was performed in the Collaborative Pediatric Critical Care Research Network (CPCCRN) of the Eunice Kennedy Shriver National Institute of Child Health and Human Development.29 (link) Patients from newborn to less than 18 years were randomly selected and stratified by hospital from December 4, 2011 to April 7, 2013. The study had daily limits on the number of patients enrolled at each center. To ensure that patients enrolled in TOPICC were randomly selected from all eligible PICU admissions, a random number sequence was generated by the Data Coordinating Center for each calendar day. During enrollment days when a site had more eligible patients than the daily limit, this number sequence was used to randomly select those patients to be enrolled, based on the trailing digits of their medical record number. Patients from both general/medical and cardiac/cardiovascular PICUs were included. There were no separate general surgical or neurological PICUs. Moribund patients (vital signs incompatible with life for the first two hours after PICU admission) were excluded. Only the first PICU admission during a hospitalization was included. Researchers, research coordinators, and research assistants were trained in data collection in-person during quarterly network meetings and during biweekly conference calls. All sites had electronic medical records. Data were collected daily although information available in the medical records may have been accessed retrospectively. The protocol was approved by all Institutional Review Boards. Descriptive publications on partial samples have occurred.20 (link),21 (link),30 (link)Data included descriptive and demographic information (Table 1). Interventions included surgery and interventional catheterization. Cardiac arrest included closed chest massage within 24 hours prior to hospitalization or after hospital admission but prior to PICU admission. Admission source was classified as emergency department, inpatient unit, or post intervention unit from the same hospital or another institution. Diagnosis was classified by system of primary dysfunction based on the reason for PICU admission; cardiovascular conditions were classified as congenital or acquired. Potential predictors of morbidity and/or mortality were identified a priori and included gender, age, admission source, admission status (elective vs. emergency), post-intervention status and type of intervention, cardiac arrest, diagnosis, baseline functional status, and physiological status.
Pollack M.M., Holubkov R., Funai T., Berger J.T., Clark A.E., Meert K., Berg R.A., Carcillo J., Wessel D.L., Moler F., Dalton H., Newth C.J., Shanley T., Harrison R.E., Doctor A., Jenkins T.L., Tamburro R, & Dean J.M. (2015). Simultaneous Prediction of New Morbidity, Mortality, and Survival without New Morbidity from Pediatric Intensive Care: A New Paradigm for Outcomes Assessment. Critical care medicine, 43(8), 1699-1709.
Publication 2015
Cardiac Arrest Cardiovascular Diseases Cardiovascular System Catheterization Chest Conditioning, Psychology Conferences Critical Care Diagnosis Emergencies Ethics Committees, Research Fingers Gender Heart Hospitalization Infant, Newborn Inpatient Massage Operative Surgical Procedures Patients physiology Signs, Vital

Most recents protocols related to «Catheterization»

1
Invasive LV Pressure and CAD Evaluation
A total of 42 patients, who underwent LV catheterization for coronary angiography, were prospectively included. The invasive LV pressure was recorded. The LV dp/dt min, tau and LVEDP were averaged over 3–6 cardiac cycles. An LVEDP value of > 16 mmHg was defined as an elevated LV filling pressure [23 (link)]. The invasive values were measured by two researchers, who were blinded to the results of the MW measurements. All patients underwent coronary angiography with multiple projections. CAD was defined when the lumen was stenotic for more than 50% in one or more major epicardial coronary arteries [24 (link)].
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.
Full text: Click here
Publication 2023
Angiography Artery, Coronary Catheterization Catheterizations, Cardiac Coronary Angiography Heart Patients Pressure Stenosis
2
Predicting Kidney Failure Outcomes in Surgery
We identified candidate predictors from the literature and input from clinicians with expertise in kidney failure and perioperative medicine. The final list of variables included demographics of age and sex. Surgeries were categorized into 11 surgery types based on CCI codes, including categories that are specific to people with kidney failure (kidney transplant, peritoneal dialysis catheter insertion, and AV fistula creation). Surgery setting was classified using the administrative data as ambulatory elective, inpatient elective, or inpatient urgent/emergent. We considered comorbidities of previous AMI, cancer, chronic pulmonary disease, dementia, diabetes, heart failure, hypertension, liver disease, obesity, peripheral vascular disease, and stroke. These were defined using validated algorithms of International Statistical Classification of Diseases and Related Health Problems Ninth and Tenth Revision (ICD-9-CM and ICD-10-CA) codes [17 (link)] with an unrestricted lookback period for permanent conditions and 3 years for temporary conditions (Supplementary Tables 3 and 4). Kidney failure treatment modality was categorized as non-dialysis, hemodialysis, or peritoneal dialysis. Preoperative outpatient serum albumin (in g/L) and serum hemoglobin (in g/L) within the year before surgery were included as candidates. There were no missing values for variables except for albumin (15%) and hemoglobin (0.2%), which were imputed using multivariable normal regression with an iterative Markov chain Monte Carlo method.
Harrison T.G., Hemmelgarn B.R., James M.T., Sawhney S., Manns B.J., Tonelli M., Ruzycki S.M., Zarnke K.B., Wilson T.A., McCaughey D, & Ronksley P.E. (2023). Prediction of major postoperative events after non-cardiac surgery for people with kidney failure: derivation and internal validation of risk models. BMC Nephrology, 24, 49.
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Publication 2023
Albumins Catheterization Cerebrovascular Accident Congestive Heart Failure Dementia Diabetes Mellitus Disease, Chronic Fistula, Arteriovenous Hemodialysis Hemoglobin High Blood Pressures Inpatient Kidney Kidney Failure Kidney Transplantation Liver Diseases Lung Lung Diseases Malignant Neoplasms Menstruation Disturbances Obesity Operative Surgical Procedures Outpatients Peripheral Vascular Diseases Peritoneal Dialysis Serum Serum Albumin
3
Preoperative Kidney Failure and Surgical Outcomes
We included all adults (≥ 18 years) with an inpatient or ambulatory surgery performed between April 1 2005 and February 28 2019 in Alberta, Canada. Surgeries were identified using the Canadian Classification of Health Interventions (CCI) coding [14 ], which is a standardized coding system for procedures. Radiologic or non-surgical procedures were excluded (e.g., endoscopy, hemodialysis catheter insertion, arteriovenous [AV] fistulogram, etc.). Further, we included only those with preoperative kidney failure, defined as an eGFR < 15 mL/min/1.73m2 or receiving hemodialysis or peritoneal dialysis for at least 90 days as an outpatient before the index surgical procedure. For non-dialysis participants, at least two outpatient measures of serum creatinine between 7–365 days were necessary prior to surgery to avoid misclassification of people with preoperative acute kidney injury, per a validated algorithm [15 (link)]. We estimated eGFR using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation without including the Black race coefficient [16 (link)]. We excluded people that left Alberta within 30 days of their surgery, and those without available demographic data.
Harrison T.G., Hemmelgarn B.R., James M.T., Sawhney S., Manns B.J., Tonelli M., Ruzycki S.M., Zarnke K.B., Wilson T.A., McCaughey D, & Ronksley P.E. (2023). Prediction of major postoperative events after non-cardiac surgery for people with kidney failure: derivation and internal validation of risk models. BMC Nephrology, 24, 49.
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Publication 2023
Adult Ambulatory Surgical Procedures Catheterization Creatinine Dialysis EGFR protein, human Endoscopy Hemodialysis Inpatient Kidney Failure Kidney Injury, Acute Negroes Operative Surgical Procedures Outpatients Peritoneal Dialysis Serum
4
Validation of AI Model for Predicting Heart Failure
We develop and validate our model using datasets from two different hospitals. Our first dataset consists of 7121 records from 3767 unique patients who underwent cardiac catheterization at Massachusetts General Hospital (Hospital 1). All patients had a diagnosis of HF (according to ICD 9/10 codes in their medical record) within the 1 year prior to their catheterization date.
This dataset is split into an 80% development set, used to train predictive models, and a 20% internal holdout test set, used for model evaluation. Datasets are constructed such that no data from a single patient appears in different data splits; i.e., all data splits are done on a per-patient basis. We further split the development set on a per-patient level using an 80–20 split into training and “dev” sets. The training set is used to train the model and the dev set is used to determine when training is completed.
Our second dataset consists of 2725 records from 1249 unique patients who underwent cardiac catheterization at the Brigham and Women’s Hospital (Hospital 2). As with data from MGH, these patients all had a diagnosis of heart failure (according to ICD 9/10 codes in their medical record) within the 1 year prior to their catheterization date. We used this entire dataset as an external validation set for model evaluation.
Each record in the datasets consists of: the mean Pulmonary Capillary Wedge Pressure (as measured by cardiac catheterization), a 10-s, 12-lead ECG recorded by the same system (GE Healthcare MUSE) on the same day as the catheterization procedure, and basic demographic information (age/sex). Dataset details are summarized in Table 2.

Model performance (AUROC) on test data. HFNet significantly outperforms the baseline logistic regression (LR) model.

ModelAUROC
Internal test setExternal holdout set
LR0.71 + − 0.010.67 + − 0.01
HFNet0.82 + − 0.01 *0.81 + − 0.01 *

Significant values are in bold.

Key: *: p value < 1e − 10.

Raghu A., Schlesinger D., Pomerantsev E., Devireddy S., Shah P., Garasic J., Guttag J, & Stultz C.M. (2023). ECG-guided non-invasive estimation of pulmonary congestion in patients with heart failure. Scientific Reports, 13, 3923.
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Publication 2023
Catheterization Catheterizations, Cardiac Diagnosis Electrocardiography, 12-Lead Heart Failure Muse Patients Pulmonary Wedge Pressure Woman
5
Deep Learning Model for Estimating Elevated Cardiac Filling Pressures
We developed a deep learning model, HFNet, which estimates whether a patient’s mPCWP is over 18 mmHg, using the 10-s 12-lead ECG, age, and sex as input features. We included age and sex in the model since we hypothesized that that these features encode some prior information about filling pressures; e.g., older age and male sex are associated with a higher prevalence of cardiac disease. The model combines a convolutional encoder for the ECG and a fully connected network encoder for the demographic features. The model’s weights and biases are initialized as previously described17 (link). The model is then trained to minimize the binary cross-entropy loss between its output, corresponding to mPCWP > 18 mmHg, and the binary label of whether the pressure measured during catheterization was elevated or not. Training proceeds for at most 50 epochs using the Adam optimizer with a learning rate of 1e-3, however, we use early stopping based on the dev set AUROC score to prevent model overfitting. Full architectural details of the model are in the Supplementary Material.
Raghu A., Schlesinger D., Pomerantsev E., Devireddy S., Shah P., Garasic J., Guttag J, & Stultz C.M. (2023). ECG-guided non-invasive estimation of pulmonary congestion in patients with heart failure. Scientific Reports, 13, 3923.
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Publication 2023
ADAM 3 Catheterization Electrocardiography, 12-Lead Entropy EPOCH protocol Heart Diseases Males Patients Pressure

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More about "Catheterization"

Catheterization is a common medical procedure involving the insertion of a flexible tube, or catheter, into the body to diagnose or treat various conditions.
This process is frequently used to access the urinary bladder, blood vessels, or other body cavities.
Catheterization can serve multiple purposes, such as draining urine, delivering medications, or monitoring bodily functions.
It plays a crucial role in numerous medical specialties, including cardiology, urology, and radiology.
Proper catheter placement and management are essential to prevent complications and ensure patient safety.
Researchers studying catheterization can streamline their efforts using innovative AI-powered platforms like PubCompare.ai, which optimize protocols and identify the best products for their research.
This platform can help locate protocols from literature, pre-prints, and patents, and utilize intelligent comparisons to determine the most suitable protocols and products.
Lipiodol, Progreat, PowerLab, SPR-839, Rompun, Vivid 7, Visipaque, Vevo 2100, PowerLab 8/30, and PVAN3.2 are some related terms and products that may be relevant to catheterization research.
By incorporating these synonyms, related terms, and key subtopics, researchers can enhance their understanding and optimize their catheterization studies.
The use of AI-powered tools like PubCompare.ai can help streamline the research process and lead to more efficient and effective catheterization studies.