Clinical StudyDiscriminative ability of commonly used indices to predict adverse outcomes after poster lumbar fusion: a comparison of demographics, ASA, the modified Charlson Comorbidity Index, and the modified Frailty Index
Introduction
The incidence of lumbar fusions in the United States has risen significantly over the past decades [1]. Unfortunately, adverse events following these procedures remain an issue. The study of perioperative complications such as general health adverse events, extended length of stay, and discharge to higher level of care have been facilitated by national database studies based on large datasets such as the American College of Surgeons National Surgical Quality Improvement Program database (NSQIP) [2], [3], [4], [5].
Adverse outcomes have received greater attention over time based on rising patient expectations, more rigorous quality improvement programs, and evolving concepts of outcome-based reimbursements. One method of addressing this is defining those that are of greatest risk of such outcomes based on factors such as patient demographics and comorbidities. However, capturing a measure of overall comorbidities makes such analyses complex and has lead to the use of comorbidity indices.
One commonly used index to summarize comorbidities in the orthopedic literature is the Charlson Comorbidity Index (CCI). The CCI was first published in 1987 and is a proven method of classifying comorbid diseases in patients that may alter their mortality risk [6]. Since its inception, it has and continues to undergo modifications, including changes in the number of diseases considered, alterations in the weighted score assigned to each comorbidity, and differing applications of appropriate International Classification of Diseases 9th revision (ICD-9) codes or variables for each disease so that it may be used in varying datasets (leading to a modified version of the CCI [mCCI]) [7], [8], [9], [10]. Surgeons have recognized the power of this tool, and differing versions of the CCI have been demonstrated to be associated with adverse outcomes using regression analysis following a variety of orthopedic procedures, including spine surgery [11], [12], [13], [14].
Another often used system in orthopedic surgery is the American Society of Anesthesiologists (ASA) physical status classification system. The ASA was developed in 1941 and was adopted to a more familiar form in 1962 [15], [16]. The last update was in 1980 when class 6 was added for brain-dead donors present for organ harvest [17]. Its primary purpose is to describe the amount of physiological reserve that a patient has, and it is also used as a method of modifying health-care billing [17]. Similarly to the CCI, the ASA has been often used in orthopedic research and has been shown to be associated with poor patient prognosis through various regressions in many orthopedic interventions, including posterior lumbar fusions (PLFs) [2], [18], [19], [20].
A third comorbidity index that has seen increasing use in orthopedics is the Canadian Study of Health and Aging Frailty Index (FI). It was developed in 2001 to characterize frailty (the decrease in physiological reserves and multisystem impairments separate from chronologic age) and was originally based upon over 90 variables [21]. Since its inception, it has undergone a variety of modifications, including the matching of 11 items to existing variables in national datasets (leading to a modified version of the FI [mFI]) [22]. Over the last several years, the mFI has seen increasing use in orthopedic literature and, through regression analysis, has been demonstrated to associate with poor patient outcomes, including after cervical spine surgery [23], [24], [25].
Although ASA, CCI, and mFI have been shown to be associated with adverse events, a regression analysis alone does not examine the clinical ability of these indices to discriminate between patients who will or will not have an adverse event before the surgery. No known study has compared and contrasted the performance of these classification systems for general health perioperative adverse outcomes following PLF.
In the context of multiple available and used scales for assessing comorbidities and their relation to perioperative adverse outcomes and no current clinical applicability comparison, the purpose of the proposed study is to use NSQIP to evaluate the discriminative ability of the ASA, mCCI, and mFI with the occurrence of postoperative adverse outcomes following PLF. A secondary purpose is to examine the discriminative ability of patient demographic variables (age, body mass index [BMI], and gender) with the same postoperative adverse outcomes. Outcome variables will include patient morbidity or mortality, length of hospital stay, and discharge to higher level of care.
Section snippets
Cohort extraction
A cohort of patients undergoing PLF was extracted from The American College of Surgeon's NSQIP years 2011–2014. NSQIP is a large, national dataset that collects data from over 500 hospitals across the nation to present over 300 patient variables with a 30-day follow-up. The dataset undergoes rigorous inter-rater reliability audits to ensure the data are of the highest quality, with the totality of disagreements being approximately 2% [26]. Patients were included in the cohort based upon Current
Patient demographics
In total, 16,495 patients who underwent elective PLF were extracted from NSQIP data years 2011–2014. The average age was 60.0±13.5 years old (mean±standard deviation), 44.6% were men, and the average BMI was 30.6±6.5 kg/m2. The median ASA score was 2, the median mCCI score was 0, and the median mFI was 0.09. More detailed distributions of the demographic factor and comorbidity indices distributions can be seen in Table 2.
In terms of outcomes, the rates of adverse events identified for this
Discussion
Rising patient expectations, more rigorous quality improvement programs, and the concept of outcomes-based reimbursements, have all led to increased pressures on physicians to optimize risk stratification and surgical outcomes. Adverse events following PLF, such as surgical site infections or venous thromboembolism, can lead to decreased patient satisfaction, complicated recoveries, and increased cost of care [4], [32], [33], [34]. Therefore, the ability to correctly identify the probability of
Conclusion
Overall, prophylactic treatments of postoperative complications following PLF and preoperative risk stratification have been shown to dramatically reduce cost and increase quality of care [35], [41]. The current study showed that easily obtainable information, such as ASA and the patient's age, had equivalent or better predictive power for postoperative adverse outcomes following PLF than formulaic comorbidity indices, such as mCCI and mFI, for which there are logistical barriers to
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FDA device/drug status: Not applicable.
Author disclosures: NTO: Nothing to disclose. DDB: Grants: CSRS (B, Paid directly to institution/employer), MAOA (B, Paid directly to institution/employer), pertaining to the submitted work. PB: Nothing to disclose. RPM: Nothing to disclose. JJC: Nothing to disclose. BNS: Nothing to disclose. AML: Nothing to disclose. JNG: Consulting: Stryker (E), Medtronic (A), Bioventus (D), Novella Clinical (None), Adante Medical Device (B), Vertex (B), ISTO Technologies (C); Grants: Orthopaedic Trauma Association (B, Paid directly to institution/employer), outside the submitted work.
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