scholarly journals Combining Systems Pharmacology Modeling with Machine Learning to Identify Sub-Populations at Risk of Arrhythmia

2019 ◽  
Vol 116 (3) ◽  
pp. 230a
Author(s):  
Meera Varshneya ◽  
Xueyan Mei ◽  
Eric A. Sobie
2021 ◽  
Vol 35 (2) ◽  
pp. 301-311 ◽  
Author(s):  
Mark É. Czeisler ◽  
Mark E. Howard ◽  
Shantha M. W. Rajaratnam

Author(s):  
Andrew J. Paul ◽  
Christopher L. Cahill ◽  
Laura MacPherson ◽  
Michael G. Sullivan ◽  
Myles R. Brown

2021 ◽  
pp. 219256822110193
Author(s):  
Kevin Y. Wang ◽  
Ijezie Ikwuezunma ◽  
Varun Puvanesarajah ◽  
Jacob Babu ◽  
Adam Margalit ◽  
...  

Study Design: Retrospective review. Objective: To use predictive modeling and machine learning to identify patients at risk for venous thromboembolism (VTE) following posterior lumbar fusion (PLF) for degenerative spinal pathology. Methods: Patients undergoing single-level PLF in the inpatient setting were identified in the National Surgical Quality Improvement Program database. Our outcome measure of VTE included all patients who experienced a pulmonary embolism and/or deep venous thrombosis within 30-days of surgery. Two different methodologies were used to identify VTE risk: 1) a novel predictive model derived from multivariable logistic regression of significant risk factors, and 2) a tree-based extreme gradient boosting (XGBoost) algorithm using preoperative variables. The methods were compared against legacy risk-stratification measures: ASA and Charlson Comorbidity Index (CCI) using area-under-the-curve (AUC) statistic. Results: 13, 500 patients who underwent single-level PLF met the study criteria. Of these, 0.95% had a VTE within 30-days of surgery. The 5 clinical variables found to be significant in the multivariable predictive model were: age > 65, obesity grade II or above, coronary artery disease, functional status, and prolonged operative time. The predictive model exhibited an AUC of 0.716, which was significantly higher than the AUCs of ASA and CCI (all, P < 0.001), and comparable to that of the XGBoost algorithm ( P > 0.05). Conclusion: Predictive analytics and machine learning can be leveraged to aid in identification of patients at risk of VTE following PLF. Surgeons and perioperative teams may find these tools useful to augment clinical decision making risk stratification tool.


1980 ◽  
Vol 73 (1) ◽  
pp. 25-27 ◽  
Author(s):  
GEORGE A. NORTON ◽  
RAYMOND W. POSTLETHWAIT ◽  
WILLIAM M. THOMPSON

2012 ◽  
Vol 37 (3) ◽  
pp. 274-298 ◽  
Author(s):  
Daniel Stahl ◽  
Andrew Pickles ◽  
Mayada Elsabbagh ◽  
Mark H. Johnson ◽  
The BASIS Team

Critical Care ◽  
2019 ◽  
Vol 23 (1) ◽  
Author(s):  
Juan Gonzalez del Castillo ◽  
◽  
Darius Cameron Wilson ◽  
Carlota Clemente-Callejo ◽  
Francisco Román ◽  
...  

Abstract Background The performance of blood biomarkers (mid-regional proadrenomedullin (MR-proADM), procalcitonin (PCT), C-reactive protein (CRP), and lactate) and clinical scores (Sequential Organ Failure Assessment (SOFA), National Early Warning Score (NEWS), and quick SOFA) was compared to identify patient populations at risk of delayed treatment initiation and disease progression after presenting to the emergency department (ED) with a suspected infection. Methods A prospective observational study across three EDs. Biomarker and clinical score values were calculated upon presentation and 72 h, and logistic and Cox regression used to assess the strength of association. Primary outcomes comprised of 28-day mortality prediction and delayed antibiotic administration or intensive care (ICU) admission, whilst secondary outcomes identified subsequent disease progression. Results Six hundred eighty-four patients were enrolled with hospitalisation, ICU admission, and infection-related 28-day mortality rates of 72.8%, 3.4%, and 4.4%, respectively. MR-proADM and NEWS had the strongest association with hospitalisation and the requirement for antibiotic administration, whereas MR-proADM alone had the strongest association with ICU admission (OR [95% CI]: 5.8 [3.1 - 10.8]) and mortality (HR [95% CI]: 3.8 [2.2 - 6.5]). Patient subgroups with high MR-proADM concentrations (≥ 1.77 nmol/L) and low NEWS (< 5 points) values had significantly higher rates of ICU admission (8.1% vs 1.6%; p < 0.001), hospital readmission (18.9% vs. 5.9%; p < 0.001), infection-related mortality (13.5% vs. 0.2%; p < 0.001), and disease progression (29.7% vs. 4.9%; p < 0.001) than corresponding patients with low MR-proADM concentrations. ICU admission was delayed by 1.5 [0.25 – 5.0] days in patients with high MR-proADM and low NEWS values compared to corresponding patients with high NEWS values, despite similar 28-day mortality rates (13.5% vs. 16.5%). Antibiotics were withheld in 17.4% of patients with high MR-proADM and low NEWS values, with higher subsequent rates of ICU admission (27.3% vs. 4.8%) and infection-related hospital readmission (54.5% vs. 14.3%) compared to those administered antibiotics during ED treatment. Conclusions Patients with low severity signs of infection but high MR-proADM concentrations had an increased likelihood of subsequent disease progression, delayed antibiotic administration or ICU admission. Appropriate triage decisions and the rapid use of antibiotics in patients with high MR-proADM concentrations may constitute initial steps in escalating or intensifying early treatment strategies.


2010 ◽  
Vol 27 (5) ◽  
pp. 418-424 ◽  
Author(s):  
Hayley Mark ◽  
Susan G. Sherman ◽  
Joy Nanda ◽  
Tracey Chambers-Thomas ◽  
Mathilda Barnes ◽  
...  

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