Citations

Background Perioperative nerve injury (PNI) is a recognized complication of total hip arthroplasty (THA). Regional anesthesia (RA) techniques may increase the risk of neurologic injury. Using a retrospective cohort study, the authors tested the hypothesis that use of RA increases the risk for PNI after elective THA. Methods All adult patients who underwent elective THA at Mayo Clinic during a 20-yr period were included. The primary outcome was the presence of a new PNI within 3 months of surgery. Multivariable logistic regression was used to evaluate patient, surgical, and anesthetic risk factors for PNI. Results Of 12,998 patients undergoing THA, 93 experienced PNI (incidence = 0.72%; 95% CI 0.58-0.88%). PNI was not associated with type of anesthesia (OR = 0.72 for neuraxial-combined vs. general; 95% CI 0.46-1.14) or peripheral nerve blockade (OR = 0.65; 95% CI 0.34-1.21). The risk for PNI was associated with younger age (OR = 0.79 per 10-yr increase; 95% CI 0.69-0.90), female gender (OR = 1.72; 95% CI 1.12-2.64), longer operations (OR = 1.10 per 30-min increase; 95% CI 1.03-1.18) or posterior surgical approach (OR = 1.91 vs. anterior approach; 95% CI 1.22-2.99). Neurologic recovery was not influenced by the use of RA techniques in patients with PNI. Conclusions The risk for PNI after THA was not increased with the use of neuraxial anesthesia or peripheral nerve blockade. Neurologic recovery in patients who experienced PNI was not affected by the use of RA. These results support the use of RA techniques in patients undergoing elective THA given their known functional and clinical benefits.

Objective Robotic-assisted surgery is not widely accepted for general thoracic surgical procedures, and the technical advantages, cost effectiveness, and patient benefit are in question. Few reports have been published to date regarding clinical experience with this technology. We describe our first consecutive case experience with robotic-assisted lung resection. Methods A total of 23 robotic-assisted lung resections were performed from December 1, 2008, to September 30, 2010. Patients were selected on the basis of being candidates for a minimally invasive approach to their lung resection, including criteria such as known or suspected early-stage nonsmall-cell lung cancer, no prior thoracotomy, no neoadjuvant therapy, and a body mass index (BMI) less than 40 kg/cm 2 . Data on patient characteristics and perioperative results were collected retrospectively. Results Overall 90-day mortality was 0%. The total postoperative complication rate was 39%. Conversion of the robotic-assisted procedure to a video-assisted procedure was necessary in four patients (17%), and to a thoracotomy in one patient (4%). We assessed operative time, chest tube duration, and length of hospital stay. Comparison to published outcomes from the Society of Thoracic Surgeons database demonstrated comparable outcomes to standard approaches. Conclusions Robotic-assisted lung resection is safe and feasible, with comparable short-term outcomes to published results from video-assisted or open approaches.

Over the last two decades there has been considerable deliberation, experience, and research in the arena of Health Information Technology (HIT), Electronic Medical Records (EMR), Electronic Health Records (EHR), and more recently, Electronic Personal Health Records (PHR). Despite the challenges involved in adopting these systems and technologies, there is consensus that they bring significant value to the delivery of trusted and affordable healthcare. The investment involved and the impact on customers, clinical and non-clinical staff, and processes are significant and far reaching. This chapter attempts to synthesize the vast amount of information, experience, and implementation perspectives related to Electronic Health Records with the intent of assisting healthcare institutions and key stakeholders make informed choices as they embark on designing, developing, and implementing an EHR. EHR considerations, challenges, opportunities, and future directions are also addressed. The chapter highlights the power of management engineering to facilitate planning, implementation, and sustainability of the EHR, a critical asset for a healthcare organization and the overall healthcare industry.

Discrete-event simulation (DES) is an effective tool to for analyzing and improving healthcare processes. In this chapter we discuss the use of simulation to improve patient flow at an outpatient procedure center (OPC) at Mayo Clinic. The OPC addressed is the Pain Clinic, which was faced with high patient volumes in a new, untested facility. Simulation was particularly useful due to the uncertain patient procedure and recovery times. We discuss the simulation process and show how it helped reduce patient waiting time while ensuring the clinic could meet its target patient volumes.

The outcome of critical illness depends not only on life threatening pathophysiologic disturbances, but also on several complex “system” dimensions: health care providers’ performance, organizational factors, environmental factors, family preferences and the interactions between each component. Systems engineering tools offer a novel approach which can facilitate a “systems understanding” of patient-environment interactions enabling advances in the science of healthcare delivery. Due to the complexity of operations in critical care medicine, certain assumptions are needed in order to understand system behavior. Patient variation and uncertainties underlying these assumptions present a challenge to investigators wishing to model and improve health care delivery processes. In this chapter we present a systems engineering approach to modeling critical care delivery using sepsis resuscitation as an example condition.

Background— Constrictive pericarditis (CP) is a disabling disease, and usually requires pericardiectomy to relieve heart failure. Reversible CP has been described, but there is no known method to predict the reversibility. Pericardial inflammation may be a marker for reversibility. As a pilot study, we assessed whether cardiac magnetic resonance imaging pericardial late gadolinium enhancement (LGE) and inflammatory biomarkers could predict the reversibility of CP after antiinflammatory therapy. Method and Results— Twenty-nine CP patients received antiinflammatory medications after cardiac magnetic resonance imaging. Fourteen patients had resolution of CP, whereas 15 patients had persistent CP after 13 months of follow-up. Baseline LGE pericardial thickness was greater in the group with reversible CP than in the persistent CP group (4±1 versus 2±1 mm, P =0.001). Qualitative intensity of pericardial LGE was moderate or severe in 93% of the group with reversible CP and in 33% of the persistent CP group ( P =0.002). Cardiac magnetic resonance imaging LGE pericardial thickness ≥3 mm had 86% sensitivity and 80% specificity to predict CP reversibility. The group with reversible CP also had higher baseline C-reactive protein and erythrocyte sedimentation rate than the persistent CP group (59±52 versus 12±14 mg/L, P =0.04 and 49±25 versus 15±16 mm/h, P =0.04, respectively). Antiinflammatory therapy was associated with a reduction in C-reactive protein, erythrocyte sedimentation rate, and pericardial LGE in the group with reversible CP but not in the persistent CP group. Conclusions— Reversible CP was associated with pericardial and systemic inflammation. Antiinflammatory therapy was associated with a reduction in pericardial and systemic inflammation and LGE pericardial thickness, with resolution of CP physiology and symptoms. Further studies in a larger number of patients are needed.