|PG05 General HPB: Cost Effectiveness (ePoster)
|Selection of ePoster Presentations from Abstract Submissions
|PG05-01 ||Systematic Review of Risk Factors Predicting Surgical Site Infection Following Pancreatic and Liver Resections
Keno Mentor, United Kingdom
K. Mentor1, B. Ratnayake2, G. Alessandri1, S. Robinson1, C. Wilson1, S. White1, J. French1, S. Pandanaboyana1
1HPB Surgery, Newcastle University Trust Hospitals, United Kingdom, 2Surgery, The University of Auckland, New Zealand
Introduction: The risk factors for surgical site infection (SSI) after HPB surgery are poorly reported. This review aimed to identify the risk factor profile for SSI after pancreas and liver resection.
Method: The PUBMED, MEDLINE, EMBASE databases were systematically searched using the PRISMA framework. The primary outcome measure was pooled SSI rates. The secondary outcome measure was risk factor profile determination for SSI.
Results: Seventeen studies including 52,416 patients made the final analysis, of which 39,748 patients underwent pancreatic resection and 12,668 patients liver resection. The overall rate of SSI after pancreatic and liver resection were 25% and 10% respectively (p< 0.001). 32% of pancreaticoduodenectomies (PD) developed SSI vs 28% after distal pancreatectomy (p< 0.001). The rate of incisional SSI in the pancreatic group was 8.8% and organ space SSI 16%. Biliary resection during liver surgery was a risk factor for SSI (25% vs 16%, p=0.004). After liver resection, the incisional SSI rate was 7.6% and organ space SSI rate was 10%. Pancreas specific SSI risk factors were male sex, pre-operative biliary drainage and chemotherapy. Liver specific SSI risk factors were smoking, open resections, significant blood loss and peri-operative blood transfusion.
Conclusions: The risk factors for SSI following pancreatic and liver resections are distinct from each other, with higher SSI rates after pancreatic resection. PD has increased risk of SSI compared to DP. Similarly, biliary resections during liver surgery increase the rates of SSI.
[Rates of surgical site infection (SSI) by HPB surgery type]
|PG05-02 ||Building a Successful Robotic Institute in a Community Hospital
Cataldo Doria, United States
C. Doria1, J. Chung1, D. Visconti1, P. De Deyne1, R. Remstein1, L. Zarifian1, J. Fertel1, S. Kitchko2
1Capital Health, Cancer Center, United States, 2Intuitive, United States
The purpose of this study is to demonstrate that certain community
hospitals are efficient health care delivery organizations.
Health Medical Center (CH) is a licensed acute care community hospital with 221
beds located in Hopewell Township, New Jersey.
The organization competes primarily with the hospitals in the cities of
Philadelphia and New York. In 2015, CH
acquired one daVinci Xi platform that up until the beginning of Q4 of 2018 was
underutilized and almost completely limited to gynecological procedures. An impressive number of patients seeking
robotic surgery were going elsewhere for their procedures. Therefore, a decision was made to redirect the
organization's priorities by implementing a number of initiatives, such as
acquiring an additional robotic platform with plans to add a third one in the following
12 months, hiring 4 new robotic surgeons in different specialties, and pursuing
the Robotic Center of Excellence accreditation.
The details of these initiatives will be discussed in detail at the time
of the presentation.
Results: The success of this new business plan is
clearly demonstrated in the attached bar graph. In 2019 the robotic Hepato-Pancreato-Biliary
(HPB) has grown by more than 400% from Q1 to Q2, and over 600% from Q1 to Q3. After recruitment of a new HPB surgeon, very
complex procedures such as robotic assisted Whipple procedures were also successfully
hospitals with vested financial interest and supportive administration can
rapidly achieve a successful robotic surgery program to serve their patient
|PG05-03 ||Tracking, Prioritising, Streamlining and Auditing the Surgical Pathway with the Swalis Live Audit Model. A Feasibility-pilot Study in the Barts Health HPB Service
Roberto Valente, United Kingdom
R. Valente1,2,3, K. Ryan4, I. Porro5, A. Abraham4, S. Bhattacharya4, N. Mc Donald4, R. Hutchins4
1University College London, United Kingdom, 2HPB Service, Barts and the London School of Medicine and Dentistry, United Kingdom, 3S. Martino University Hospital, Italy, 4Barts and the London School of Medicine and Dentistry, United Kingdom, 5SurgiQ Ltd, Italy
Elective surgery is insufficiently audited, especially pre-admission. Prospective audit is critical to manage capacity and control outcomes. Dynamic tools to monitor and prioritise waiting lists in real-time are essential to generate high-quality data. SWALIS (Valente et al. 2009) is a unique pre-admission management model, live-ordering the queue based on clinical urgency and waiting time, allowing patients' dynamic prioritisation.
This project was run in the London Barts HPB Service. We audited year-2016 waiting lists. The SWALIS prioritisation was simulated on such cohort. We then run a feasibility-pilot study, first assessing the feasibility of the SWALIS prioritisation (May-October 2017), then piloting the model (Nov-January 2017/18) by utilising a bespoke software (SurgiQ), assessing dataavailability on service performance and outcome, by a before-after design. Structured feedback was collected through questionnaires to each of the involved staff categories on specific domains rated on scores 1-5, on data model assessed benefits.
We pilot-tracked 200 patient pathways. The model allowed live information on waiting times, complications, service effectiveness and efficiency, tracking the entire pathway (5/5), with visibility of patient progression against harm (5/5), information for scheduling pre-assessment and theatres (5/5). By monitoring the workup, it reduces risk of inappropriate procedure preparations, postponements and cancellations, and unexpected escalations of level of care (5/5), delivering efficiency increase (+400%) and time/money savings 10%-16%.
The SWALIS model allows improvement in the multispecialty integrated patient management, though requires wide staff and patient involvement. Software integration with clinical systems is essential for a realtime regular use.