IH incidence was determined across specialty. Colorectal (7.7%), vascular (5.2%), bariatric (4.8%), and transplant (4.5%) specialties demonstrated the highest incidence of surgically repaired hernia (Supplemental 2, http://links.lww.com/SLA/B700). In terms of SSI occurrence, colorectal (4.9%), hepatobiliary (3.0%), and vascular (3.0) were the most likely. The most common occurring risk factors among the 8 index operations that increased the likelihood of developing IH were history of AS (87.5%), smoking history (75%), and history of a recent acute infection (75%). The 8 individual models showed excellent reliability with C-statistic between 0.77 and 0.89: high—bariatric (C = 0.89) and gastrectomy (C = 0.89); low—urologic (C = 0.77) and vascular (C = 0.77). The individual risk model parameters were incorporated into specialty-specific modules and implemented into an accessible web-based platform (http://www.pennherniariskcalc.com) and an iOS/Android compatible mobile app, entitled “Penn Hernia Risk Calculator” (Fig. 3).
IH has plagued abdominal surgeons for decades. One of the main barriers to implementing effective strategies to reduce incidence IH and costs and the associated treatment morbidity is the rapid and precise identification of at-risk patients. With the advent of the EMR and utilization of bioinformatics, we have the ability to automate data acquisition and understand risk. In this study of nearly 5 years of follow-up, across 3 major hospitals, analyzing 558 variables from 164,014 encounters belonging to 78,030 patients, we characterize the incidence of operational IH, demonstrate risk variation IH across treating specialties, and integrate several models into a single unifying risk-prediction app. We expect this app to permit the identification of at-risk patients and advance the science of hernia prevention.
This study demonstrates a 3.8% incidence of operative IH among 29,739 patients undergoing AS after an average follow-up of 57.9 months. It characterizes the morbidity and costs of IH and emphasizes the economic value of hernia prevention among the largest longitudinal cohort of patients described to date. Important risk factors predisposing all patients who undergo AS to IH included emergent surgery, prior AS, open approach, and high comorbidity burden. In contrast, nonobese individuals and those undergoing laparoscopic procedures were at lower risk for developing IH. An internally validated risk stratification model predicted IH risk ranging from 0.5% to 26.2% and identified patients at greater risk for readmission and higher healthcare resource utilization after AS. Furthermore, by using a hybrid of robust augmented claims data through the EMR and a bioinformatics approach, we were able to accurately predict operative IH in multiple surgery-specific models (C-statistic = 0.76–0.89). We have converted these models into a singular surgeon-facing interface that encourages patient engagement and risk counseling.
Current literature discussing the evaluation and management of IH has conventionally focused on addressing modifiable risk factors such as obesity and smoking as methods of risk reduction.31 Despite significant advances in technique and available technology, however, IH continues to present a formidable challenge with persistent and unacceptably high rates of recurrence, associated morbidity, and healthcare costs.32 To further accentuate the problem, a major challenge in improving IH outcomes has been the overall low quality of hernia research available to date which may be a byproduct of the necessity for long-term follow-up and sufficiently powered comparisons among diverse hernia subpopulations. Big data analytics has yielded major success for a variety of research questions.33 However, large data sets, such as the NSQIP or HCUP samples, either fail to provide adequate follow-up or lack granularity to be clinically useful for predicting IH.22 Administrative claims, powerful institutional EMR data coupled with chart review, permitted investigation of 558 variables from 164,014 encounters belonging to 78,030 patients across 3 major hospitals. It is robust in sample size, provides long-term follow-up, encompasses information from the full spectrum of healthcare settings, including inpatient, ambulatory, diagnostic labs, and imaging resources, and was able to generate 8 specialty specific predictive models (Fig. 4).
This study is not without limitations that deserve further consideration. We recognize that administrative data codes are dependent on how information is interpreted by those entering the data and may not be representative of what a physician actually documented. Furthermore, although internal validation certainly improves the robustness of analysis and overall model accuracy, external validation should still be pursued to ensure generalizability, ideally with a prospective design. The primary endpoint chosen was surgically treated IH which likely underestimates the true incidence of IH. The retrospective nature of the study along with the lack of standardized criteria informing hernia diagnosis would have led to unreliable results if diagnostic codes were used. Furthermore, the authors acknowledge the IH incidence may have also been underestimated if data regarding diagnosis or surgery of IH were missing, incorrect, or if the patient was lost to follow-up from our healthcare system. Some factors associated with hernia, such as incision type and degree of wound contamination, were excluded from analysis. The authors recognize this limitation and attempt to mitigate confounding by indirectly modeling these via procedure type. In an effort to address this inherent design issue, our analysis was limited to only those patients who had ≥1 year of follow-up. In addition, the development of the app is not without limitations. The functionality and user-friendliness will improve as we obtain feedback from our subscribers. The risk calculator does not capture emergent cases which impacts the development of IH; however, the acuity and urgent nature of those cases limits the application of the decision interface before surgery.
The authors thank the University of Pennsylvania Center for Human Appearance and the Harrison Department of Surgical Research for its generous support in the completion of this study, and also express their gratitude to all abdominal-based surgeons at the University of Pennsylvania health system that treated and cared for the patients included in this study.
Dr Michael Rosen (Cleveland, OH):
First of all, I would like to congratulate Dr Fischer for a great presentation and his group for making another important contribution to our understanding of not only hernia disease, but, in particular, hernia prevention.
In this study, they have looked at a large internal database in their hospital system that has allowed them to perform a very sophisticated analysis to identify an individual patient's risk of developing an IH and requiring surgery for that hernia during several different index operations. They also took it one step further and developed a user-friendly app to allow this information to be readily available, easily calculated, and provide a means for shared decision-making with the patients. I think this type of work is extremely important, and I really do think for hernia surgery, this is where we need to go in trying to come up with ways to prevent this problem.
I do have several questions for Dr Fischer, and they are mainly going to center around 2 issues.
First, although not directly addressed in your study, there are implications of your findings as well as some of your past work that perhaps suggests that there should be the use of prophylactic mesh. I have several concerns about its unmeasured harm in these patients.
My second set of questions will be based on what do we do with this data and what have you done?
Number one, with regard to the potential harm that the suggestion of prophylactic mesh brings up, I have several questions. Your entire population actually had a fairly low rate of IH formation at only 3.8% which I think was probably relatively surprising to you as well. I think there is no doubt, as you mentioned, some of this is underreporting because you only looked at hernias that actually did require an operation, but if you look at your most extreme high-risk group, the rate of hernia formation was only 23%, and whether you are a glass half full or half empty depends on how you look at that because three-quarters of the patients in that group did not actually go on to develop a hernia. And if prophylactic mesh was applied to that group of patients, three-quarters of them would be getting a mesh that they did not need, and certainly some of them are going to suffer complications. Furthermore, in that specifically high-risk group, when you look at all those variables, those are the patients who are also most likely to suffer mesh-related complications.
So I am curious what you will do with your app to allow that information to be portrayed to patients. Will there be a rate of mesh infection, rate of reoperation due to complications, a rate of chronic pain that will adequately inform patients as a tool when doing this as the potential downsides of mesh?
You also reported that there was $62 million spent on the care of hernias as a result of this 3.8% of patients. But I also wonder what the number would look like if everyone at your hospital started to use prophylactic mesh or, say, an absorbable synthetic or a biologic mesh that could cost several thousand dollars per piece, and ultimately who is going to be paying for that mesh?
My final set of questions are really related to what is next? What have you done with this data at your institution? Have you been able to identify a specific risk level based on your calculator that justifies the placement of prophylactic mesh or some other intervention other than just good surgical care? Really, what has been the buy-in even at your institution? Are vascular surgeons, gynecologists, urologists, colorectal surgeons now putting in mesh prophylactically or doing something different? If so, how did you roll this out? Did you set up workshops to not only teach people who do not typically put in mesh how to place that mesh? Did you also teach them how to potentially close fascia with the 4:1 suture-to-wound ratio? Because that has been shown in multiple randomized controlled trials to reduce recurrence.
And in summary, I think one of the dangers of this type of research is that we might be simply promoting the addition of another medical device, in this case potentially mesh, to overcome poor surgical technique.
Again, I look forward to your responses. I appreciate the opportunity to discuss this paper, and I congratulate you on a very impressive study. Thank you very much.
Dr John P. Fischer (Philadelphia, PA):
Dr Rosen, thank you for those fantastic comments and great questions. I will answer them in order.
I think the first fundamental question really relates to the use of mesh to support a fascial closure and potentially prevent incisional hernia. I think that more data are certainly needed. I think that in our study we had a lower incidence of incisional hernia. I think it is important, and I think it really does relate to how we defined this outcome. The rigor of this study, I think, is derived from the fact that our outcome was a surgical intervention, that is, a repair of an incisional hernia, which I think is very important both to the accuracy of the data and how it should be interpreted because all of these patients developed a complication after the index abdominal surgery that required a second surgery, that is, hernia repair.
I think that the comment about the risk of IH is very important, and I wanted to share with the group that one slide of the stratification with the patients in the extreme risk group and having a hernia incidence of 24% is for a cohort or group. Certainly, if you do point risk estimation for many of these surgical specialties within this app, the risk goes well into 30% and 40% ranges. So I think that individualized risk assessment is important, and I think that if we can point estimate risk, we can certainly get a better sense as to what a patient's risk of a complication is. I think linearly it does go up above 23%. But your point about potentially doing a prophylactic or preventive surgical intervention which carries potential morbidity and risk in a group of patients in whom they are all not going to get the outcome is critically important and, therefore, has to be weighed carefully.
I would take it one step further and say that there is a near direct linear correlation between surgical site infection and postoperative hernia. I think this fundamentally relates to the fact that IH is a wound healing issue. The overlap between those postoperative conditions, surgical site infections and hernia make it very important that if a piece of mesh is put inside a patient, you may have a postoperative wound event and potentially a mesh infection. Certainly, the choice of anatomic plane and type of mesh would have to be considered carefully and this additional prophylactic procedure carefully and thoroughly discussed with each patient. I think that is got to be carefully evaluated. And my hope with that initial slide in which I tried to characterize how we can avoid hernia, I think it has to be a multipronged approach. It is not just about putting a piece of mesh in someone. I think it is, as you alluded to, education, improved technique, surgical site infection reduction, maybe a different surgical approach. And the STICH trial, although a fantastic study, I think has limitations. I mean, the patient population was not obese. They used 2 0 Maxon and a short stitch technique and compared it to kind of 1 cm number one Maxon close, and I do not think it has been replicated in the United States. I think certainly that is an area of opportunity.
In the PRIMA study, which showed very favorable results, randomized controlled trial, onlay and retromuscular prophylactic mesh compared with primary suture closure, and a big statistically significant difference in hernia occurrence with no difference in other outcomes and no mesh infections reported in the study, the only complications really of interest were seromas, frankly.
So I think it is very, very important, and I think that as you look at this data and the associated costs and the context of the potential costs of putting in resorbable mesh or biologic mesh, the picture may be very different. But I would just suggest that we do not have enough long-term data to even truly understand the impact of mesh on hernia repair, frankly.
So I think that this is an area of significant opportunity for us to advance the understanding of this disease and improve outcomes for patients. But the intention of this app is not a decision tool to tell someone to put in prophylactic mesh. I think it is to create awareness and potentially to be even used in clinical trials. I think the cost of mesh certainly would be important, but this app is not intended to suggest the use of mesh, although it may do it unintentionally, as you indicate.
Hopefully, I got all the questions you asked me. What to do with this app, again, this is just kind of happening, and the app has just been released. We are hoping that people use it, that people give us feedback about it. We are collaborating with the department of surgery to implement this, and I think that successful implementation hinges upon several key things. First, the data should be prospectively validated both within our health system and externally to really rigorously evaluate how accurate it is. I think second is to engage stakeholders. I think that those stakeholders are broad and diverse, as I alluded to at the beginning of this talk, various different surgical specialties.
So I think that that process has to begin. We have to educate. We have to engage, and we have to understand, and I think that through that, and kind of through this, and as I have worked in this area, I have definitely engaged many different specialists, including urologists, gynecologists, and many different general surgery specialties with interest from them to both improve the way we evaluate and talk to patients but potentially to be involved in clinical trials.
I think that at this point, we are not doing something different. We are not just putting in prophylactic mesh or modifying our suturing technique, but we are trying to bring this to the point of care to see how it is going to impact our conversations and our interactions with our patients.
I think more to come on that. I think the next step for this is going to be prospective validation to really make sure that this is rigorous enough to accurately predict what we are trying to, and hopefully have an impact someday in the way we take care of patients. Thank you for your questions.
Dr Selwyn Vickers (Birmingham, AL):
Two quick questions. One in relationship to the app and some of the fixed risk factors and modifiable risk factors. Will the app have the potential of actually, in elective cases, to allow the physician to know where prehabilitation and modifiable risk factors may impact the cost and the risk of hernia recurrence? So when someone is having elective surgery, not for cancer, but for an elective procedure, and you have a significant number of risk factors, what is the role and opportunity of prehabilitation in changing the outcome?
And then the final question, the CMS and the Affordable Care Act now is requiring institutions to deal with cost transparency. You have listed a lot of numbers around the cost of hernia repair. What role does cost play, and is it a part of your app to allow a surgeon to understand the cost differential as well as the patient of a procedure?
Dr John P. Fischer (Philadelphia, PA):
Thank you for those questions, Dr Vickers. To respond to the first question, which really focuses on can we identify risk factors in elective surgery? The answer is yes. And I think that the intention of this app—and I did not actually show all features—you can rerun the simulation and actually uncheck risk factors. The 2 most common you could do would be smoking and reducing obesity which would, in turn, change the predicted risk. I think that giving the surgeon the ability to show this or demonstrate this to a patient is a very, very powerful thing that the visual messaging behind changing someone's risk of a potential outcome at the bedside I think could really impact care. More specifically, your question about prehabilitation and eventual hernia, I think there is a lot of good data. A randomized controlled trial in Annals of Surgery by Michael Lee Yang showed it reduced complications in patients undergoing hernia repair. I think that there is a role here. I think we try to think very carefully about the ultimate use of this app and how it would work in clinical practice to really optimize its design. We take it one step further to say that the predicted risk can be e-mailed to the physician or the patient through this interface, and we are working on trying to find a way to communicate, have the app communicate with the electronic health record.
I think lastly, as it relates to cost transparency, we fit our models around the IH outcome, and it did correlate nicely with cost. We’re currently not displaying cost because I think that cost is a very complicated thing. Is it institution dependent? Is it charges? Is it direct cost? How long is the time horizon? So we do not necessarily plan on having costs be part of this because it is so institution dependent. But I think in the end, trying to find a way to more indirectly capture healthcare expenditures related to this issue, I think, would be very, very important to display inside the app. Thank you for your questions.
Dr Kamal Itani (Boston, MA):
Congratulations. A very nice study, very well presented. I have 2 concerns. The first one is regarding that you are probably underestimating the number of ventral hernias that occurred in your database because as Dr Rosen mentioned, you are only capturing those patients who had surgery after the hernia was detected. You are probably missing those who were not operated on.
The second concern is the short time line of your database. You do not have long-term follow-up on those patients, and we know that incisional hernias are long-lived problems that can occur after a long time. So you are also underestimating the number of hernias that occur over time.
For these 2 concerns you are probably, in your calculator, underestimating the number of incisonal hernias.
Dr John P. Fischer (Philadelphia, PA):
Thank you for the comments and questions. I think those are all spot on. Full disclosure, I think that the decision between picking an outcome that we know happened versus picking an outcome that we are unsure of, I think for the purposes of precision risk prediction, in our mind, made more sense to go with the surgically treated incisional hernias. I would tell you that the app actually does display the incidence of predicted IH operative repair and occurrence. It's about twice as high with just a diagnosis or occurrence. I think you are absolutely right. Not only did we underestimate it by calculating it for operative hernia, we underestimated all the patients that went to different hospitals to have their hernias fixed. I think that the ideal situation would be following all patients for as long as possible across all health systems. We did do that with the HCUP project, and it allowed us to track patients longitudinally over 4 years. I think what is most interesting is you look at the low incidence in that group of 560,000 patients, the incidence of hernia, primary hernia, primary IH was 3%. But as time elapses, those patients that were treated develop a high incidence of recurrence, almost exponential over time.
I would say—and I may have not been clear here—the follow-up average in this cohort of 29,000 patients was 58 months, so that is 5 years.
Dr Kamal Itani (Boston, MA):
One other quick question. In your model, or in the example that you showed, the incidence was 22%. Was this over a lifetime or was it over 5 years?
Dr John P. Fischer (Philadelphia, PA):
It is over the average follow-up for the cohort. And in the colorectal cohort, I do not recall offhand. I think it was 50-plus months. It individualizes the estimation of risk for the average amount of follow-up in that group. So it is surgery subspecialty specific across 8 different modules.
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