Research

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

Preoperative testing (e.g., chest radiography, electrocardiography, laboratory testing, urinalysis) is often performed before surgical procedures. These investigations can be helpful to stratify risk, direct anesthetic choices, and guide postoperative management, but often are obtained because of protocol rather than medical necessity. The decision to order preoperative tests should be guided by the patient’s clinical history, comorbidities, and physical examination findings. Patients with signs or symptoms of active cardiovascular disease should be evaluated with appropriate testing, regardless of their preoperative status. Electrocardiography is recommended for patients undergoing high-risk surgery and those undergoing intermediate-risk surgery who have additional risk factors. Patients undergoing low-risk surgery do not require electrocardiography. Chest radiography is reasonable for patients at risk of postoperative pulmonary complications if the results would change perioperative management. Preoperative urinalysis is recommended for patients undergoing invasive urologic procedures and those undergoing implantation of foreign material. Electrolyte and creatinine testing should be performed in patients with underlying chronic disease and those taking medications that predispose them to electrolyte abnormalities or renal failure. Random glucose testing should be performed in patients at high risk of undiagnosed diabetes mellitus. In patients with diagnosed diabetes, A1C testing is recommended only if the result would change perioperative management. A complete blood count is indicated for patients with diseases that increase the risk of anemia or patients in whom significant perioperative blood loss is anticipated. Coagulation studies are reserved for patients with a history of bleeding or medical conditions that predispose them to bleeding, and for those taking anticoagulants. Patients in their usual state of health who are undergoing cataract surgery do not require preoperative testing.

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

Holds membership information for SHC issuers that are part of the VCI (https://vci.org/) Directory. - the-commons-project/vci-directory

Cedars-Sinai Medical Center Inc. was sued by a former employee who says the Los Angeles hospital’s $2.1 billion retirement plan is plagued by excessive administrative fees and pricey, poorly performing funds.

This research describes findings of sequencing and phylogenetic analyses of SARS-CoV-2 isolates from symptomatic patients cared for at Cedar-Sinai Medical Center in November-December 2020 during a regional surge in cases and hospitalizations.

Since 2013, the IRS has released data culled from over 1.8 million nonprofit tax filings. Use this database to find organizations and see details like their executive compensation, revenue and expenses, as well as download tax filings going back as far as 2001.

by John Weeks, Publisher/Editor of The Integrator Blog News and Reports Editor’s note: This analysis article is not edited and the authors are solely responsible for the content. The views a

Cardinal Santos Medical Center (CSMC) recently launched the Hospital on Wheels, their newest healthcare innovation and brand-new expansion

Cohort identification with the Biomedical Informatics Program at UCLA CTSI