Association between payer status and patient-reported outcomes in adult patients with lumbar spinal stenosis treated with decompression surgery

Time to achievement of minimum clinically important difference after lumbar decompression

OBJECTIVE

The objective of this study is to examine factors associated with delayed time to achieve minimum clinically important difference (MCID) in patients undergoing lumbar decompression (LD) for the Patient-Reported Outcomes (PROs) of Oswestry Disability Index (ODI), Visual Analog Scale (VAS) back, and VAS leg pain.

METHODS

Patients undergoing LD with preoperative and postoperative ODI, VAS back, and VAS leg scores were retrospectively reviewed from April 2016 to January 2021. MCID values from previously established studies were utilized to determine MCID achievement. Kaplan-Meier survival analysis determined the time to achieve MCID. Hazard ratios from multivariable Cox regression were utilized to determine the preoperative factors predictive of MCID achievement.

RESULTS

Three-hundred and forty-three patients were identified undergoing LD. Overall MCID achievement rates were 67.4% for ODI, 67.1% for VAS back, and 65.0% for VAS leg. The mean time in weeks for MCID achievement was 22.52 ± 30.48 for ODI, 18.90 ± 27.43 for VAS back, and 20.96 ± 29.81 for VAS leg. Multivariable Cox regression revealed active smoker status, preoperative Patient-Reported Outcomes Measurement Information System Physical Function (PROMIS-PF), ODI, VAS Back, and VAS Leg (HR 1.03-2.14) as predictors of early MCID achievement, whereas an American Society of Anesthesiologist (ASA) classification of 2, Black ethnicity, workers' compensation, private insurance, and diagnosis of foraminal stenosis were predictors of late MCID achievement (HR 0.34-0.58).

CONCLUSION

Most patients undergoing LD achieved MCID within 6 months of surgery. Significant factors for early MCID achievement were active smoking status and baseline PROs. Significant factors for late MCID achievement were ASA = 2, Black ethnicity, type of insurance, and foraminal stenosis diagnosis. These factors may be considered by surgeons in setting patient expectations.

Impact of Social Determinants of Health in Spine Surgery

PURPOSE OF REVIEW

Social determinants of health (SDH) are factors that affect patient health outcomes outside the hospital. SDH are "conditions in the environments where people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risks." Current literature has shown SDH affecting patient reported outcomes in various specialties; however, there is a dearth in research relating spine surgery with SDH. The aim of this review article is to identify connections between SDH and post-operative outcomes in spine surgery. These are important, yet understudied predictors that can impact health outcomes and affect health equity.

RECENT FINDINGS

Few studies have shown associations between SDH pillars (environment, race, healthcare, economic, and education) and spine surgery outcomes. The most notable relationships demonstrate increased disability, return to work time, and pain with lower income, education, environmental locations, healthcare status and/or provider. Despite these findings, there remains a significant lack of understanding between SDH and spine surgery. Our manuscript reviews the available literature comparing SDH with various spine conditions and surgeries. We organized our findings into the following narrative themes: 1) education, 2) geography, 3) race, 4) healthcare access, and 5) economics.

Disparities in the Surgical Treatment of Adult Spine Diseases: A Systematic Review

BACKGROUND

Our goal was to systematically review the literature on racial/ethnic, insurance, and socioeconomic disparities in adult spine surgery in the United States and analyze potential areas for improvement.

METHODS

We conducted a database search of literature published between January 1990 and July 2020 using PRISMA guidelines for all studies investigating a disparity in any aspect of adult spine surgery care analyzed based on race/ethnicity, insurance status/payer, or socioeconomic status (SES).

RESULTS

Of 2679 articles identified through database searching, 775 were identified for full-text independent review by 3 authors, from which a final list of 60 studies were analyzed. Forty-three studies analyzed disparities based on patient race/ethnicity, 32 based on insurance status, and 8 based on SES. Five studies assessed disparities in access to care, 15 examined surgical treatment, 35 investigated in-hospital outcomes, and 25 explored after-discharge outcomes. Minority patients were less likely to undergo surgery but more likely to receive surgery from a low-volume provider and experience postoperative complications. White and privately insured patients generally had shorter hospital length of stay, were more likely to undergo favorable/routine discharge, and had lower rates of in-hospital mortality. After discharge, white patients reported better outcomes than did black patients. Thirty-three studies (55%) reported no disparities within at least 1 examined metric.

CONCLUSIONS

This comprehensive systematic review underscores ongoing potential for health care disparities among adult patients in spinal surgery. We show a need for continued efforts to promote equity and cultural competency within neurologic surgery.

Machine learning-based preoperative predictive analytics for lumbar spinal stenosis

OBJECTIVEPatient-reported outcome measures (PROMs) following decompression surgery for lumbar spinal stenosis (LSS) demonstrate considerable heterogeneity. Individualized prediction tools can provide valuable insights for shared decision-making. The authors aim to evaluate the feasibility of predicting short- and long-term PROMs, reoperations, and perioperative parameters by machine learning (ML) methods.METHODSData were derived from a prospective registry. All patients had undergone single- or multilevel mini-open facet-sparing decompression for LSS. The prediction models were trained using various ML-based algorithms to predict the endpoints of interest. Models were selected by area under the receiver operating characteristic curve (AUC). The endpoints were dichotomized by minimum clinically important difference (MCID) and included 6-week and 12-month numeric rating scales for back pain (NRS-BP) and leg pain (NRS-LP) severity and the Oswestry Disability Index (ODI), as well as prolonged surgery (> 45 minutes), extended length of hospital stay (> 28 hours), and reoperations.RESULTSA total of 635 patients were included. The average age was 62 ± 10 years, and 333 patients (52%) were male. At 6 weeks, MCID was seen in 63%, 76%, and 61% of patients for ODI, NRS-LP, and NRS-BP, respectively. At internal validation, the models predicted MCID in these variables with accuracies of 69%, 76%, and 85%, and with AUCs of 0.75, 0.79, and 0.92. At 12 months, 66%, 63%, and 51% of patients reported MCID; the observed accuracies were 62%, 74%, and 66%, with AUCs of 0.68, 0.72, and 0.79. Reoperations occurred in 60 patients (9.5%), of which 27 (4.3%) occurred at the index level. Overall and index-level reoperations were predicted with 69% and 63% accuracy, respectively, and with AUCs of 0.66 and 0.61. In 15%, a length of surgery greater than 45 minutes was observed and predicted with 78% accuracy and AUC of 0.54. Only 15% of patients were admitted to the hospital for longer than 28 hours. The developed ML-based model enabled prediction of extended hospital stay with an accuracy of 77% and AUC of 0.58.CONCLUSIONSPreoperative prediction of a range of clinically relevant endpoints in decompression surgery for LSS using ML is feasible, and may enable enhanced informed patient consent and personalized shared decision-making. Access to individualized preoperative predictive analytics for outcome and treatment risks may represent a further step in the evolution of surgical care for patients with LSS.