PreOp® Centers of Excellence Surgery Patient educationhttps://preop.com - http://Store.PreOp.comThese certified medical animations, certified medical scripts...

Background Cardiovascular diseases (CVDs) are difficult to diagnose early and have risk factors that are easy to overlook. Early prediction and personalization of treatment through the use of artificial intelligence (AI) may help clinicians and patients manage CVDs more effectively. However, to apply AI approaches to CVDs data, it is necessary to establish and curate a specialized database based on electronic health records (EHRs) and include pre-processed unstructured data. Methods To build a suitable database (CardioNet) for CVDs that can utilize AI technology, contributing to the overall care of patients with CVDs. First, we collected the anonymized records of 748,474 patients who had visited the Asan Medical Center (AMC) or Ulsan University Hospital (UUH) because of CVDs. Second, we set clinically plausible criteria to remove errors and duplication. Third, we integrated unstructured data such as readings of medical examinations with structured data sourced from EHRs to create the CardioNet. We subsequently performed natural language processing to structuralize the significant variables associated with CVDs because most results of the principal CVD-related medical examinations are free-text readings. Additionally, to ensure interoperability for convergent multi-center research, we standardized the data using several codes that correspond to the common data model. Finally, we created the descriptive table (i.e., dictionary of the CardioNet) to simplify access and utilization of data for clinicians and engineers and continuously validated the data to ensure reliability. Results CardioNet is a comprehensive database that can serve as a training set for AI models and assist in all aspects of clinical management of CVDs. It comprises information extracted from EHRs and results of readings of CVD-related digital tests. It consists of 27 tables, a code-master table, and a descriptive table. Conclusions CardioNet database specialized in CVDs was established, with continuing data collection. We are actively supporting multi-center research, which may require further data processing, depending on the subject of the study. CardioNet will serve as the fundamental database for future CVD-related research projects.

Background Cardiovascular diseases (CVDs) are difficult to diagnose early and have risk factors that are easy to overlook. Early prediction and personalization of treatment through the use of artificial intelligence (AI) may help clinicians and patients manage CVDs more effectively. However, to apply AI approaches to CVDs data, it is necessary to establish and curate a specialized database based on electronic health records (EHRs) and include pre-processed unstructured data. Methods To build a suitable database (CardioNet) for CVDs that can utilize AI technology, contributing to the overall care of patients with CVDs. First, we collected the anonymized records of 748,474 patients who had visited the Asan Medical Center (AMC) or Ulsan University Hospital (UUH) because of CVDs. Second, we set clinically plausible criteria to remove errors and duplication. Third, we integrated unstructured data such as readings of medical examinations with structured data sourced from EHRs to create the CardioNet. We subsequently performed natural language processing to structuralize the significant variables associated with CVDs because most results of the principal CVD-related medical examinations are free-text readings. Additionally, to ensure interoperability for convergent multi-center research, we standardized the data using several codes that correspond to the common data model. Finally, we created the descriptive table (i.e., dictionary of the CardioNet) to simplify access and utilization of data for clinicians and engineers and continuously validated the data to ensure reliability. Results CardioNet is a comprehensive database that can serve as a training set for AI models and assist in all aspects of clinical management of CVDs. It comprises information extracted from EHRs and results of readings of CVD-related digital tests. It consists of 27 tables, a code-master table, and a descriptive table. Conclusions CardioNet database specialized in CVDs was established, with continuing data collection. We are actively supporting multi-center research, which may require further data processing, depending on the subject of the study. CardioNet will serve as the fundamental database for future CVD-related research projects.

Detector-based spectral CT offers the possibility of obtaining spectral information from which discrete acquisitions at different energy levels can be derived, yielding so-called virtual monoenergetic images (VMI). In this study, we aimed to develop a jointly optimized deep-learning framework based on dual-energy CT pulmonary angiography (DE-CTPA) data to generate synthetic monoenergetic images (SMI) for improving automatic pulmonary embolism (PE) detection in single-energy CTPA scans. For this purpose, we used two datasets: our institutional DE-CTPA dataset D1, comprising polyenergetic arterial series and the corresponding VMI at low-energy levels (40 keV) with 7892 image pairs, and a 10% subset of the 2020 RSNA Pulmonary Embolism CT Dataset D2, which consisted of 161,253 polyenergetic images with dichotomous slice-wise annotations (PE/no PE). We trained a fully convolutional encoder-decoder on D1 to generate SMI from single-energy CTPA scans of D2, which were then fed into a ResNet50 network for training of the downstream PE classification task. The quantitative results on the reconstruction ability of our framework revealed high-quality visual SMI predictions with reconstruction results of 0.984 ± 0.002 (structural similarity) and 41.706 ± 0.547 dB (peak signal-to-noise ratio). PE classification resulted in an AUC of 0.84 for our model, which achieved improved performance compared to other naïve approaches with AUCs up to 0.81. Our study stresses the role of using joint optimization strategies for deep-learning algorithms to improve automatic PE detection. The proposed pipeline may prove to be beneficial for computer-aided detection systems and could help rescue CTPA studies with suboptimal opacification of the pulmonary arteries from single-energy CT scanners.

Detector-based spectral CT offers the possibility of obtaining spectral information from which discrete acquisitions at different energy levels can be derived, yielding so-called virtual monoenergetic images (VMI). In this study, we aimed to develop a jointly optimized deep-learning framework based on dual-energy CT pulmonary angiography (DE-CTPA) data to generate synthetic monoenergetic images (SMI) for improving automatic pulmonary embolism (PE) detection in single-energy CTPA scans. For this purpose, we used two datasets: our institutional DE-CTPA dataset D1, comprising polyenergetic arterial series and the corresponding VMI at low-energy levels (40 keV) with 7892 image pairs, and a 10% subset of the 2020 RSNA Pulmonary Embolism CT Dataset D2, which consisted of 161,253 polyenergetic images with dichotomous slice-wise annotations (PE/no PE). We trained a fully convolutional encoder-decoder on D1 to generate SMI from single-energy CTPA scans of D2, which were then fed into a ResNet50 network for training of the downstream PE classification task. The quantitative results on the reconstruction ability of our framework revealed high-quality visual SMI predictions with reconstruction results of 0.984 ± 0.002 (structural similarity) and 41.706 ± 0.547 dB (peak signal-to-noise ratio). PE classification resulted in an AUC of 0.84 for our model, which achieved improved performance compared to other naïve approaches with AUCs up to 0.81. Our study stresses the role of using joint optimization strategies for deep-learning algorithms to improve automatic PE detection. The proposed pipeline may prove to be beneficial for computer-aided detection systems and could help rescue CTPA studies with suboptimal opacification of the pulmonary arteries from single-energy CT scanners.

How to prepare for your upcoming procedure with JourneyLite Physicians

Northwest ENT and Allergy Center provides videos to help give you an idea of what to expect for your upcoming surgery.

If you have citation software installed, you can download article citation data to the citation manager of your choice

Download Table | Comparison of the components of RCRI: CAIS vs original (4): The occurrence of major cardiac complications (MCCs) postoperatively  from publication: The Revised Cardiac Risk Index in the new millennium: A single-centre prospective cohort re-evaluation of the original variables in 9,519 consecutive elective surgical patients | Cardiac complications following non-cardiac surgery are major causes of morbidity and mortality. The Revised Cardiac Risk Index (RCRI) has become a standard for predicting post-surgical cardiac complications. This study re-examined the original six risk factors to confirm... | Cohort, Auditing and Analytical Ultracentrifugation (AUC) | ResearchGate, the professional network for scientists.

npj Digital Medicine - A foundational vision transformer improves diagnostic performance for electrocardiograms

מחקר חדש שפורסם ב-The Lancet Digital Health הראה שאלגוריתם למידה עמוקה יכול לזהות מטופלים בסיכון גבוה למות לאחר ניתוח או פרוצדורות, טוב יותר מאשר מחשבון סיכונים קונבנציונלי

Purpose: to determine whether pre-surgical treatment using long-acting somatostatin analogues (SSAs) may improve surgical outcomes in acromegaly. Methods: retrospective study of 48 patients with acromegaly operated by endoscopic transsphenoidal approach and for first time. Surgical remission was evaluated based on the 2010 criteria. Results: most patients, 83.3% (n = 40), harbored macroadenomas and 31.3% (n = 15) invasive pituitary adenomas. In this case, 14 patients were treated with lanreotide LAR and 6 with octreotide LAR, median monthly doses of 97.5 [range 60–120] and 20 [range 20–30] mg, respectively, for at least 3 months preoperatively. Presurgical variables were comparable between pre-treated and untreated patients (p > 0.05). Surgical remission was more frequent in those pre-treated with monthly doses ≥90 mg of lanreotide or ≥30 mg of octreotide than in untreated or pre-treated with lower doses (OR = 4.64, p = 0.025). However, no differences were found between pre-treated and untreated patients when lower doses were included or between those treated for longer than 6 months compared to those untreated or pre-treated for shorter than 6 months. Similarly, no differences were found either in terms of surgical or endocrine complications (OR = 0.65, p = 0.570), independently of the doses and the duration of SSA treatment (p > 0.05). Conclusions: the dose of SSAs is a key factor during pre-surgical treatment, since the beneficial effects in surgical remission were observed with monthly doses equal or higher than 90 mg of lanreotide and 30 mg of octreotide, but not with lower doses.

This study aims to predict endoleak after endovascular aneurysm repair (EVAR) using machine learning (ML) integration of patient characteristics, sten…

Background Pre-operative risk assessment can help clinicians prepare patients for surgery, reducing the risk of perioperative complications, length of hospital stay, readmission and mortality. Further, it can facilitate collaborative decision-making and operational planning. Objective To develop effective pre-operative risk assessment algorithms (referred to as Patient Optimizer or POP) using Machine Learning (ML) that predicts the development of post-operative complications and provides pilot data to inform the design of a larger prospective study. Methods After institutional ethics approval, we developed a baseline model that encapsulates the standard manual approach of combining patient-risk and procedure-risk. In an automated process, additional variables were included and tested with 10-fold cross-validation, and the best performing features were selected. The models were evaluated and confidence intervals calculated using bootstrapping. Clinical expertise was used to restrict the cardinality of categorical variables (e.g. pathology results) by including the most clinically relevant values. The models were created with extreme gradient-boosted trees using XGBoost [[1][1]]. We evaluated performance using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). Data was obtained from a metropolitan university teaching hospital from January 2015 to July 2020. Data collection was restricted to adult patients undergoing elective surgery. Results A total of 11,475 adult admissions were included. For predicting the risk of any postoperative complication, kidney failure and length-of-stay (LOS), POP achieved an AUROC (95%CI) of 0.755 (0.744, 0.767), 0.869 (0.846, 0.891) and 0.841 (0.833, 0.847) respectively and AUPRC of 0.651 (0.632, 0.669), 0.326 (0.293, 0.359) and 0.741 (0.729, 0.753) respectively. For 30-day readmission and in-patient mortality, POP achieved an AUROC (95%CI) of 0.61 (0.587, 0.635) and 0.866 (0.777, 0.943) respectively and AUPRC of 0.116 (0.104, 0.132) and 0.031 (0.015, 0.072) respectively. Conclusion The POP algorithms effectively predicted any post-operative complications, kidney failure and LOS in the sample population. A larger study is justified to improve the algorithm to better predict complications and length of hospital stay. A larger dataset may also improve the prediction of additional specific complications, readmissions and mortality. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The research was supported by a grant from the Victorian Medical Research Acceleration Fund, Round 4 (March 2020). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: A waiver of ethical approval was received from Austin Health Office for Research (approval number: 38679, approval date: 23rd March 2020). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The datasets generated and/or analysed during the current study are not publicly available as there was no data sharing as part of the ethics approval (and raw data is potentially re-identifiable) but are available from the corresponding author on reasonable request. [1]: #ref-1

On January 5, 2011, OPNET introduced AppMapper Xpert, a solution that can effectively discover and map, in near real-time -- application ecosystems, including both application components and the under

PreOpNet is an ECG-waveform based architecture for prediction of post-operative mortality and MACE - ecg-net/PreOpNet

A novel deep learning algorithm applied to a single preoperative ECG improved risk prediction for death after cardiac surgery and other inpatient procedures for a large cohort, researchers reported.Compared with a widely used standard perioperative risk assessment tool and alternative ECG assessment tools, the deep learning algorithm, called PreOpNet, was able to more effectively identify

Preop assessments using MyPreOp, winner of the 2019 AAGBI Innovation Award. Patient driven preoperative assessment software solution.

Comprehensive, point-of-care resource for performing preoperative evaluations