Improving the quality of care for patients at risk for venous thromboembolism

2010 ◽  
Vol 67 (10_Supplement_6) ◽  
pp. S3-S8
Author(s):  
Stuart T. Haines
Author(s):  
Aaron Dora‐Laskey ◽  
Joan Kellenberg ◽  
Chin Hwa Dahlem ◽  
Elizabeth English ◽  
Monica Gonzalez Walker ◽  
...  

2001 ◽  
Vol 161 (12) ◽  
pp. 1549 ◽  
Author(s):  
Courtney H. Lyder ◽  
Jeanette Preston ◽  
Jacqueline N. Grady ◽  
Jeanne Scinto ◽  
Richard Allman ◽  
...  

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.


2019 ◽  
Vol 8 (7) ◽  
pp. 1065 ◽  
Author(s):  
Emilie Reber ◽  
Filomena Gomes ◽  
Maria F. Vasiloglou ◽  
Philipp Schuetz ◽  
Zeno Stanga

Malnutrition is an independent risk factor that negatively influences patients’ clinical outcomes, quality of life, body function, and autonomy. Early identification of patients at risk of malnutrition or who are malnourished is crucial in order to start a timely and adequate nutritional support. Nutritional risk screening, a simple and rapid first-line tool to detect patients at risk of malnutrition, should be performed systematically in patients at hospital admission. Patients with nutritional risk should subsequently undergo a more detailed nutritional assessment to identify and quantify specific nutritional problems. Such an assessment includes subjective and objective parameters such as medical history, current and past dietary intake (including energy and protein balance), physical examination and anthropometric measurements, functional and mental assessment, quality of life, medications, and laboratory values. Nutritional care plans should be developed in a multidisciplinary approach, and implemented to maintain and improve patients’ nutritional condition. Standardized nutritional management including systematic risk screening and assessment may also contribute to reduced healthcare costs. Adequate and timely implementation of nutritional support has been linked with favorable outcomes such as a decrease in length of hospital stay, reduced mortality, and reductions in the rate of severe complications, as well as improvements in quality of life and functional status. The aim of this review article is to provide a comprehensive overview of nutritional screening and assessment methods that can contribute to an effective and well-structured nutritional management (process cascade) of hospitalized patients.


2020 ◽  
Vol 191 ◽  
pp. S31-S36
Author(s):  
Anton Ilich ◽  
Vaibhav Kumar ◽  
Michael Henderson ◽  
Ranjeeta Mallick ◽  
Philip Wells ◽  
...  

PLoS ONE ◽  
2011 ◽  
Vol 6 (12) ◽  
pp. e29334 ◽  
Author(s):  
Sabine Ludt ◽  
Michel Wensing ◽  
Joachim Szecsenyi ◽  
Jan van Lieshout ◽  
Justine Rochon ◽  
...  

2016 ◽  
Vol 140 ◽  
pp. S172-S173 ◽  
Author(s):  
A. Young ◽  
J. Phillips ◽  
H. Hancocks ◽  
C. Hill ◽  
N. Joshi ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document