Risk Severity Scores in Spine Surgery

Richard Menger, MD, MPA

Kevin Wang, MHA

David Polly, MD

Michael Vitale, MD, MPH

OVERVIEW

Despite the best intentions, planning, and skillful execution, no surgeon is immune from complications. In the field of spine surgery, stakes are high, and the consequences potentially catastrophic. Minimizing complications begins with the decision to operate on each patient and efforts to optimize the patient prior to the surgical event. Quantitative, formal risk stratification has emerged as a tool to allow surgeons to make better choices about who to operate on, to focus resources on perioperative management, and to realistically communicate the risks of surgery to patients and their families. Providing tangible, reproducible, and objective information regarding operative risk and outcome can dramatically improve the patient’s understanding and expectations following an operation. Risk stratification is an invaluable objective tool that, when properly standardized, can empower patients and their families in a shared decision-making model.

Here, we discuss that process, as well as the nuance of shared decision-making algorithms, and directly address the limitations and benefits of different risk calculators around spinal surgery.

PERIOPERATIVE PROCESS

A hospital’s perioperative care process is quite complex, involving multiple subprocesses, health care professionals, and systems in support of surgical care. Perioperative care generally refers to the four phases of surgery: preoperative (including optimization of underlying diseases), intraoperative, postoperative, and postdischarge. The perioperative process often is the primary source of hospital admissions, driving hospital margins, and having the greatest impact on total hospital supply costs, which are variable. Hospital programs focused on improving care coordination, and quality throughout the perioperative process can succeed only if they consider the complexity of the health system and of individual surgeon and patient preferences. They must also take into account the dependencies, predictable delays, and potential resource constraints.

The goal of value-based care delivery is to improve clinical and patient experience outcomes while holding the line on costs. A cohesive approach to patient care should be defined around a specific patient’s planned surgical procedure. Care
delivery models such as the Perioperative Surgical Home and Enhanced Recovery after Surgery include many of these components, and their effectiveness is supported by data published in the literature.

Once the patient and the surgeon have agreed on the need for surgery, a crucial next step is for them to agree on what constitutes a successful outcome. Physician leaders can take the lead in educating their colleagues on the need for such conversations. Once the physician and patient have reached a consensus on the outcome of the surgery, the work can begin to optimize the patient for surgery by addressing underlying diseases, deploying prehabilitation activities, and completing a risk assessment.

Conceptually, a patient can be cleared for surgery but not optimized for surgery. For example, a patient can undergo surgery with mild anemia and be administered a blood transfusion in the operating room (OR), which can be associated with a longer length of stay, higher costs, and higher readmissions rates. This patient is merely “cleared for surgery.” On the other side, a similar patient’s surgery can be postponed and the anemia be treated before the patient undergoes the surgical procedure. This patient is “optimized for surgery.” Ensuring that chronic disease conditions and other clinical characteristics, such as diabetes and hypertension, are optimized (ie, the patient receives all of the appropriate care for these conditions and is in the best possible state) can have a positive effect on postoperative complications, length of stay and readmission rates, and ultimately costs.

Prehabilitation processes such as home physical therapy, smoking cessation, diet optimization, and exercise can have a profound impact on postoperative clinical and economic outcomes. Identifying the appropriate resources and deploying them in advance of the procedure is a critical step that is often overlooked. By identifying and stratifying patient risks during the preoperative phase, providers can best tailor the deployment of resources during the operative and postoperative phases to ensure that the procedure results in the best possible outcomes. The tools to identify high-risk patients exist but are underutilized, creating an approach that treats every patient the same and leaves gaps in care that could result in less-than-ideal outcomes. The more risk factors that a patient has, the less likely that the patient is going to have a successful outcome. This patient is also at greater risk for readmission or for entering a postacute facility instead of going home.

A clearer understanding of preoperative risk is a necessary prerequisite and first step in optimizing care. As surgeons, we need help understanding “when to say no,” when to “slow the machine” and focus resources on preoperative optimization, and when to consider more “limited goals surgery.” Clearer and more accurate risk adjustment is also critical as big data are increasingly available in the public domain. Unless such data accurately adjusts for risk, this reporting will have the unintended consequence of hindering the access of more complex patients to appropriate care.¹

Understanding risk can allow for potential intervention and change the surgical plan in order to optimize the possibility of outperforming expectations. Outcomes are not just dependent on technical skill. There is a layered approach involving team, planning, infrastructure, and technique (Fig. 1). Some issues may be due to sharp edge errors of execution but much, much more likely, patients are hurt because we do not have the optimal teams, culture, communication, and strategies in place. There are common principles that are recognized in many high
reliability industries such as aviation, nuclear energy production, and the U.S. Navy including:

Figure 1. Illustrative concept of the aggregate impact of team, planning/infrastructure, and technical skill on patient outcomes.

Reducing variability.
Creating infrastructure to minimize a chaotic work environment.
Making better decisions as a group.
Using data to move the bell curve in a desirable direction.

Risk stratification has the potential to leverage these tactics toward the benefit of the patient.

SPINE SURGERY OUTCOMES

Spine surgery, especially spinal deformity surgery, can have a large burden or complication, and understanding the factors that increase risk protocol is vital to managing this complex patient cohort. The Scoi-RISK-1 trial noted that 22.18% of 273 patients undergoing complex adult spinal deformity surgery had a decrease in their lower motor strength scores.² Vertebral column resections in pediatric patients for severe spinal deformity produce a 59% complication rate.³ Of nearly 76 000 patients undergoing surgery for adolescent idiopathic scoliosis, only 0.9% suffered a neurologic complication. However, targeted analysis illustrated that older patients (15-19 years old), smokers, those with higher medical comorbidities, those with preoperative sensory or motor deficits, and those undergoing surgery in a Southern hospital are more likely to have a complication.⁴

Properly investigating, understanding, and adequately explaining these risk factors can help the surgical decision-making process, manage patient expectations, and aid in shared decision-making. Most importantly, properly implemented risk stratification has the potential to reduce complications and improve outcomes.

Developing Risk Calculators

Although administrative data can be used to provide some limited information regarding host factors, we must remember that most of these data sources were not created for this purpose and will have limited granularity in this regard. Surgeons must carefully seek a balance between what is easily measured and what is important to measure, as Donabedian cautioned in his classic 1966 paper.¹,⁵ Moreover, risk stratification needs to extend beyond a focus on preoperative host factors and ask hard questions regarding variability in surgical intervention. Surgeons still challenged to quantify how much the relative risk of infection is driven by host factors like diabetes and obesity, systems factors like appropriate antibiotic administration and use of preoperative chlorhexidine gluconate, or technical factors such as time in the operating theater and blood loss, which likely serve as proxies of surgical skill.¹

Prior research efforts have found that hospital effects accounted for 8.8% and surgeon effects account for 14.4% of variability in complications.¹,⁶ Surgeon factors accounted for 54.5% of variation in hospital reoperation rates and 47.2% of variation in hospital complication rates. Prior studies have indicated a wide variability in pediatric spine surgeon practices to prevent surgical site infection and associations in variation of surgical outcomes and compliance with safety practices.¹ Additionally, correlations in hospital volume and operative mortality for high-risk surgery, leading to policy changes to eliminate low-volume surgery, vary greatly. As a result, structural measures such as intensive care unit staffing and National Quality Forum-Endorsed Safe Practices have been developed by organizations like the Leapfrog Group to attempt to account for some of these differences when rating hospital quality.¹,⁷ However, further efforts are needed to determine whether these practices, quality improvement interventions in particular, impact risk stratification and the ability to predict health outcomes for patients.

INPUTS FOR RISK CALCULATION

Once the diagnosis of the underlying pathology is made and the decision is that surgical intervention should be considered, a series of questions arise. The first encompasses the magnitude of the intervention, the patient’s ability to tolerate the operation, and the integration of patient factors into a dedicated risk calculation.

Magnitude of the Surgical Intervention

Not all spine surgeries are the same. The surgical invasiveness index is a validated general spine rubric that integrates vertebral levels decompressed and instrumented with the surgical approach.⁸ This is a general spine invasiveness index, which began as a scale to reliably grade levels of spinal degenerative disease. This by-level approach was the foundation of the initial surgical invasiveness score. Then came an assessment of how much was “to be done” at a particular level: decompression alone, fusion, or instrumentation. This was then simply summed and used to look at blood loss and OR time for which there was directional agreement (ie, increased SII score correlated with increased blood loss and increased OR time). Furthermore, the link between surgical complexity and increased surgical site infection has been well documented.⁸

The limitation of the surgical invasiveness index is that it did not specifically account for the correction of spinal deformity by either the magnitude of the
deformity or the osteotomies required to correct the deformity. The integration of specific spinal deformity was done by Neumann and the International Spine Study Group (ISSG) in 2018.⁹ It focused primarily upon sagittal plane correction with surgeon and radiographic parameters. Again, there was a correlation between blood loss and operative theatre time. It is important to recognize that the ISSG is a novel criterion specifically focusing on adult deformity surgical parameters.⁹

Patient’s Ability to Tolerate the Intervention

American Society of Anesthesiologists’ (ASA) preoperative score is one of the earliest evolutions of determining risks to an intervention. It is a subjective assessment of the patient’s overall health, listed from class I (healthy and fit) to class V (patient not expected to live 24 hours with or without surgery).¹⁰ Its markers are largely tied to systemic disease burden and functional limitations. This includes smoking status, alcohol intake, obesity, diabetes, cardiac limitations, vascular pathology, and neurologic pathology. This is a categorical approach to looking at systemic disease that has been widely employed for many years. It has been extensively used by the American College of Surgeons in the National Surgical Quality Improvement database. 11 It has been reproduced in the adult spinal deformity literature as a risk stratification tool by Somani et al. in 2017.¹² Multivariate logistic regression revealed ASA class to be a significant risk factor for mortality, reoperation, length of stay ≥5 days, overall morbidity, wound complications, pulmonary complications, cardiac complications, intra/postoperative red blood cell transfusion, and postoperative sepsis.¹²

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