Reducing postoperative venous thromboembolism in DIEP free flap breast reconstruction: Extended pharmacological thromboprophylaxis within an enhanced recovery programme

Author(s):  
Andrew R McKean ◽  
Jon Knox ◽  
Theodore Nanidis ◽  
Aadil Khan ◽  
Paul Harris ◽  
...  
2017 ◽  
Vol 70 (7) ◽  
pp. 970-972
Author(s):  
Andrew Roy McKean ◽  
Jon Knox ◽  
Paul Harris ◽  
Kelvin Ramsey ◽  
Stuart James ◽  
...  

Author(s):  
Dimitra Kotsougiani-Fischer ◽  
Laura Sieber ◽  
Sebastian Fischer ◽  
Christoph Hirche ◽  
Spyridoula Maraka ◽  
...  

Author(s):  
Nicolas Greige ◽  
Bryce Liu ◽  
David Nash ◽  
Katie E. Weichman ◽  
Joseph A. Ricci

Abstract Background Accurate flap weight estimation is crucial for preoperative planning in microsurgical breast reconstruction; however, current flap weight estimation methods are time consuming. It was our objective to develop a parsimonious and accurate formula for the estimation of abdominal-based free flap weight. Methods Patients who underwent hemi-abdominal-based free tissue transfer for breast reconstruction at a single institution were retrospectively reviewed. Subcutaneous tissue thicknesses were measured on axial computed tomography angiograms at several predetermined points. Multivariable linear regression was used to generate the parsimonious flap weight estimation model. Split-sample validation was used to for internal validation. Results A total of 132 patients (196 flaps) were analyzed, with a mean body mass index of 31.2 ± 4.0 kg/m2 (range: 22.6–40.7). The mean intraoperative flap weight was 990 ± 344 g (range: 368–2,808). The full predictive model (R 2 = 0.68) estimated flap weight using the Eq. 91.3x + 36.4y + 6.2z – 1030.0, where x is subcutaneous tissue thickness (cm) 5 cm lateral to midline at the level of the anterior superior iliac spine (ASIS), y is distance (cm) between the skin overlying each ASIS, and z is patient weight (kg). Two-thirds split-sample validation was performed using 131 flaps to build a model and the remaining 65 flaps for validation. Upon validation, we observed a median percent error of 10.2% (interquartile range [IQR]: 4.5–18.5) and a median absolute error of 108.6 g (IQR: 45.9–170.7). Conclusion We developed and internally validated a simple and accurate formula for the preoperative estimation of hemi-abdominal-based free flap weight for breast reconstruction.


Author(s):  
Nicholas T. Haddock ◽  
Ricardo Garza ◽  
Carolyn E. Boyle ◽  
Sumeet S. Teotia

Abstract Background The Enhanced Recovery After Surgery (ERAS) protocol is a multivariate intervention requiring the help of several departments, including anesthesia, nursing, and surgery. This study seeks to observe ERAS compliance rates and obstacles for its implementation at a single academic institution. Methods This is a retrospective study looking at patients who underwent deep inferior epigastric perforator (DIEP) flap breast reconstruction from January 2016 to September 2019. The ERAS protocol was implemented on select patients early 2017, with patients from 2016 acting as a control. Thirteen points from the protocol were identified and gathered from the patient's electronic medical record (EMR) to evaluate compliance. Results Two hundred and six patients were eligible for the study, with 67 on the control group. An average of 6.97 components were met in the pre-ERAS group. This number rose to 8.33 by the end of 2017. Compliance peaked with 10.53 components met at the beginning of 2019. The interventions most responsible for this increase were administration of preoperative medications, goal-oriented intraoperative fluid management, and administration of scheduled gabapentin postoperatively. The least met criterion was intraoperative ketamine goal of >0.2 mg/kg/h, with a maximum compliance rate of 8.69% of the time. Conclusion The introduction of new protocols can take over a year for full implementation. This is especially true for protocols as complex as an ERAS pathway. Even after years of consistent use, compliance gaps remain. Staff-, patient-, or resource-related issues are responsible for these discrepancies. It is important to identify these issues to address them and optimize patient outcomes.


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