By Dr Gajan Srikanthan, Director of Clinical Pathways, Lumeon
In medical practice, it is the clinician’s responsibility to assess the risk of any procedure or medication and compare it to the potential benefits.
Increasingly, surgical risk assessment tools allow comparison of surgical outcomes to help guide decision-making. These tools are based on data that stratifies patients according to risk of adverse events, which allows a clinician to determine the risks of a surgical procedure that are specific to each patient.
An ideal clinical prediction tool would allow better comparison of estimated future prognosis with or without surgical intervention. It would also allow patient-centered discussion and decisions, and better distribution of healthcare resources based on likely outcomes.
However, the sheer number of available tools makes it difficult to choose which risk assessment tool to use. Many of the current multi-variable risk stratification tools rely on postoperative data that is not available when consenting or assessing a patient for surgery, while other tools rely on laboratory and clinical values that aren’t routinely collected.
Additionally, the current abundance of risk assessment tools that apply to small populations has created an overwhelming number of scoring systems leading to few being used consistently in clinical practice. And low awareness and lack of guidance around appropriate use all result in reduced uptake and implementation.
Going forward, the goal has to be real-time, universal, preoperative assessment of individual patient risks, shared with the patient and family. Risk assessment tools will need to:
• Be able to cover all patients undergoing surgery
• Use a small number of risk variables that cover a broad range of outcomes and operations
• Electronically abstract the majority of required risk variables from the EHR, compute the risks for adverse outcomes, and incorporate the results into the preoperative record
• Use data preoperatively to prospectively inform patients/families of risks and aid the surgical team to optimize care of the patient
• Identify high-risk patients preoperatively for enhanced informed consent and implementation of care optimization processes in order to reduce adverse outcomes
Existing technology puts us well on our way to realizing this comprehensive vision. Overall, three tools for risk assessment are most promising:
- For general surgical procedures, the ACS NSQIP PMP is a relatively easily administered tool with good predictive ability that can be adjusted based on a surgeon’s clinical experience and intuition. This mature and tested tool was developed to permit pre-operative risk assessment for common surgical procedures based on ACS NSQIP data. The PMP uses 16 objective pre-operative variables and has been validated for open pancreatic and laparoscopic/open colorectal, gall bladder, and hernia surgery. Analysis of PMP found it to be 93 percent accurate at predicting death.
- The Surgical Risk Preoperative Assessment System (SURPAS) is a new internally validated risk assessment score based on NSQIP data. It is focused on the nine most common surgical specialties (general, vascular, orthopedic, thoracic, plastic, urologic, otolaryngologic, gynaecologic, and neurosurgery). It has the potential to be a useful tool for multiple surgical specialties given its use of only eight pre-operative variables, ability to adjust risk for emergent procedures, and strong predictive strength (c statistic 0.928).
- The 9-point S-MPM (Surgical Mortality Probability Model) 30-day mortality risk index is relatively simple to use and has excellent predictive capability. It was derived empirically and includes three risk factors: ASA (American Society of Anaesthesiologists) physical status, emergency status, and surgery risk class. Despite its simplicity and ease of application, the risk score exhibits excellent statistical performance and can be used preoperatively S- MPM exhibited excellent discrimination (c statistic, 0.897).
Together with functional scoring tools such as the Duke Activity Status Index and cardiac risk assessment models like the Revised Cardiac Risk Index, surgical mortality predictive tools will be increasingly used in the pre-operative environment to identify high-risk patients requiring greater clinical input. Meanwhile, these tools mean patients at low risk can be managed digitally with minimal real-time interaction with care teams before their day of surgery.
Such targeted assessment and prediction allow clinicians to approach decision-making with an evidence-based understanding of an individual’s level of risk. Such acuity has the potential to systematically optimize outcomes for surgical intervention.