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A Predictive Model of Failure to Rescue After Thoracolumbar Fusion

Article information

Neurospine. 2023;20(4):1337-1345
Publication date (electronic) : 2023 December 31
doi : https://doi.org/10.14245/ns.2346840.420
1Topiwala National Medical College, Mumbai, India
2Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Sandy, UT, USA
3Department of Neurosurgery, University of New Mexico Hospital (UNMH), Albuquerque, NM, USA
4Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
5School of Medicine, Georgetown University, Washington, DC, USA
Corresponding Author Christian A. Bowers Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Sandy, UT 84070, USA Email: christianbowers4@gmail.com
*Joanna M. Roy and Aaron C. Segura contributed equally to this study as co-first authors.
Received 2023 August 15; Revised 2023 September 30; Accepted 2023 October 1.

Abstract

Objective

Although failure to rescue (FTR) has been utilized as a quality-improvement metric in several surgical specialties, its current utilization in spine surgery is limited. Our study aims to identify the patient characteristics that are independent predictors of FTR among thoracolumbar fusion (TLF) patients.

Methods

Patients who underwent TLF were identified using relevant diagnostic and procedural codes from the National Surgical Quality Improvement Program (NSQIP) database from 2011–2020. Frailty was assessed using the risk analysis index (RAI). FTR was defined as death, within 30 days, following a major complication. Univariate and multivariable analyses were used to compare baseline characteristics and early postoperative sequelae across FTR and non-FTR cohorts. Receiver operating characteristic (ROC) curve analysis was used to assess the discriminatory accuracy of the frailty-driven predictive model for FTR.

Results

The study cohort (N = 15,749) had a median age of 66 years (interquartile range, 15 years). Increasing frailty, as measured by the RAI, was associated with an increased likelihood of FTR: odds ratio (95% confidence interval [CI]) is RAI 21–25, 1.3 [0.8–2.2]; RAI 26–30, 4.0 [2.4–6.6]; RAI 31–35, 7.0 [3.8–12.7]; RAI 36–40, 10.0 [4.9–20.2]; RAI 41– 45, 21.5 [9.1–50.6]; RAI ≥ 46, 45.8 [14.8–141.5]. The frailty-driven predictive model for FTR demonstrated outstanding discriminatory accuracy (C-statistic = 0.92; CI, 0.89–0.95).

Conclusion

Baseline frailty, as stratified by type of postoperative complication, predicts FTR with outstanding discriminatory accuracy in TLF patients. This frailty-driven model may inform patients and clinicians of FTR risk following TLF and help guide postoperative care after a major complication.

INTRODUCTION

Rates of spinal fusions have more than doubled over the past 2 decades due to an increasingly older population [1]. Patients undergoing spinal fusion are at risk for a multitude of postoperative complications, which may result in death [2,3]. Mortality resulting from a potentially preventable postoperative complication, or failure to rescue (FTR), has been utilized as a patient safety indicator to track hospital performance and is a frequent target for quality improvement [4,5]. Although FTR prediction could improve patient outcomes and reduce healthcare costs, a reliable risk stratification tool has yet to be established [6,7].

Frailty is a measure of baseline physiological reserve [8]. The risk analysis index (RAI) is a frailty index that demonstrates superior discrimination in predicting adverse outcomes in spine surgery when compared to the 5- and 11-factor modified frailty index, as the RAI accounts for multiple domains of frailty [9-12]. Nevertheless, previous frailty studies predicting FTR after complications in spine surgery are sparse [13].

This study sought to analyze frailty, as measured by RAI, as a potential predictor of FTR in patients undergoing thoracolumbar fusion (TLF) using the American College of Surgeons National Surgery Quality Improvement Program (ACS-NSQIP) database.

MATERIALS AND METHODS

1. Study Design

The present study was designed as a secondary analysis of a quality-controlled, prospectively collected surgical database.

2. Data Source and Setting

Patient cases were acquired from the ACS-NSQIP (2011–2020), a validated database collected from over 700 participating institutions across 11 countries. The quality and consistency of these cases are optimized by ACS-trained data specialists [14]. This study was completed under our institution’s data user agreement with the ACS and is classified as exempt by our Institutional Review Board.

3. Participants

Thoracolumbar spine fixation cases were derived from the ACS-NSQIP using the following Current Procedure Technology codes: 22325, 22326, 22327, 22558, 22533, 22612, 22630, 22633, 22614, 22532, 22556, 22610, 22634, 22614, 22586, 22522, and 22556. The study cohort included adults (age ≥ 18) who experienced a major complication after TLF surgery. All diseases (degenerative/deformity, trauma, infection and tumor) were included. Cases were excluded if they were missing key case details, such as any variable required to calculate the RAI, length of stay, or discharge information.

4. RAI Revised

The revised RAI is a validated, quantitatively robust, frailty metric that has demonstrated superior discrimination across multiple neurosurgical subspecialties [15-18]. The RAI offers utility with its user-friendly calculation, which can be used at the point of care in clinical applications alongside large database studies. A patient’s RAI score is calculated using numerical values assigned to variables, including sex, age, cancer status, weight loss or low appetite, renal failure or use of dialysis, heart failure, shortness of breath, type of residence, and functional status, with final scores ranging from 0 (robust) to 78 (very frail).

5. Variables

Case variables included demographics (e.g., age, sex, race, and ethnicity), preoperative conditions (e.g., diabetes, hypertension, smoker status, and frailty), and postoperative outcomes (e.g., pneumonia, unplanned intubation, and cardiac arrest). Groups were created for renal complications (consisting of acute renal failure and renal insufficiency), and infections (consisting of deep surgical site infection [SSI], organ space SSI, and sepsis). The primary outcome of interest was FTR, defined as mortality within 30 days of surgery following any initial major complication. Major complications included deep SSI, organ space SSI, wound dehiscence, prolonged ventilation ≥ 48 hours, pulmonary embolism, cerebrovascular accident, renal failure, renal insufficiency, myocardial infarction, cardiac arrest, bleeding requiring transfusion, sepsis, septic shock, pneumonia, and unplanned reintubation.

6. Statistical Analysis

Frequency analysis, univariate regression, and multivariable regression were performed using IBM SPSS Statistics ver. 28.0 (IBM Co., Armonk, NY, USA). Receiver operating characteristic (ROC) analysis was performed using MedCalc ver. 20.114 (MedCalc, Ostend, Belgium). Statistical significance was determined a priori as α= 0.05. Whole cohort, FTR, and non-FTR subgroups were analyzed for frequencies of demographic, preoperative, and postoperative variables. Pearson chi-square test and the Mann-Whitney U-test were used to determine the statistical significance of categorical and continuous variables, respectively. Categorical variables were stated as numerical value (% of group), and continuous variables were stated as median (interquartile range [IQR]). A predictive model was created using a stepwise method where only variables with a p-value ≤ 0.05 in univariate regression were selected; these variables were then combined into a multivariable regression and removed if the p-value was not ≤ 0.1. The predictive ability of this multivariable model on FTR was analyzed using ROC curve analysis. A web application was created for user-friendly risk calculation of FTR: https://nsgyfrailtyoutcomeslab.shinyapps.io/FailureToRescueTLfusion/.

RESULTS

1. Participants

The study cohort included 15,749 adult patients who underwent TLF at NSQIP-participating institutions between 2011 and 2020.

2. Preoperative Demographic and Clinical Characteristics

The study cohort had a median age of 66 years (IQR, 15 years), was 57.4% female, and 4.7% Hispanic ethnicity. The racial distribution included 80.5% White, 9.3% Black, 1.8% Asian, and 8.4% all other groups (including Native Hawaiian or Other Pacific Islander, American Indian or Alaska Native, and unspecified race). The most prevalent comorbidities within the study cohort were hypertension (65.2%), diabetes mellitus (22.3%), current smoker status (16.9%), and chronic steroid use (6.7%). The FTR cohort was significantly older, more frail, and consisted of a great proportion of male patients. The entire cohort of FTR, and non-FTR subgroups with preoperative demographics and clinical characteristics, are summarized in Table 1.

Demographics, preoperative characteristics, frailty, and postoperative outcomes in patients who experienced a major complication after undergoing thoracolumbar fusion: whole cohort, FTR, and non-FTR, ACS-NSQIP 2011–2020

3. Surgical Outcomes

A small proportion of the study cohort (n= 129, 0.8%) experienced FTR. The most common postoperative complications were bleeding requiring transfusion (80.5%), septic shock (15.2%), sepsis (5.7%), and pneumonia (5.6%). Compared to the non-FTR subgroup, the FTR subgroup experienced significantly greater proportions of poor postoperative outcomes, including renal complications, thrombosis/embolism complications, pneumonia, unplanned intubation, intubation greater than 48 hours, cardiac arrest, myocardial infarction, and septic shock. The postoperative outcomes of the entire cohort, and both FTR and non-FTR subgroups, are summarized in Table 2.

Risk factors for failure to rescue (vs. rescue) in patients who experienced a major complication after undergoing thoracolumbar fusion, ACS-NSQIP 2011–2020

4. Predictors of FTR

In univariate regression, several preoperative and postoperative variables were significantly associated with FTR, including nonelective surgery status (odds ratio [OR], 4.46; 95% confidence interval [CI], 3.11–6.38), RAI (1.13; 1.11–1.15), chronic steroid use (2.26; 1.37–3.74), bleeding disorders (2.24; 1.13–4.43), renal complications (6.32; 3.60–11.15), thrombosis/embolism complications (2.67; 1.60–4.48), pneumonia (4.35; 2.81–6.72), septic shock (8.76; 4.95–15.51), unplanned intubation (28.03; 19.33–40.65), on ventilator ≥ 48 hours (8.17; 4.84–13.79), cardiac arrest (130.61; 84.64–201.56), and myocardial infarction (6.11; 3.68–10.15). It was observed that each stepwise increase in frailty corresponded with an increasing likelihood of FTR. The results of the univariate regression analysis are displayed in Table 2. A frailty-driven multivariable predictive model was created using RAI alongside chronic steroid use, nonelective surgery status, renal complications, unplanned reintubation, cardiac arrest, and bleeding requiring transfusion (Table 3). On ROC curve analysis, this model demonstrated excellent predictive ability for the primary outcome of FTR, with an area under the ROC (AUROC) of 0.918 (95% CI, 0.888–0.947) (Fig. 1).

Multivariable risk factors for failure to rescue (vs. rescued) in patients who experienced a major complication after undergoing thoracolumbar fusion, ACS-NSQIP 2011–2020

Fig. 1.

Receiver operating characteristic (ROC) curve for the multivariable predictive model on the primary outcome, failure to rescue, American College of Surgeons-National Surgical Quality Improvement Program 2011–2020.

DISCUSSION

1. Key Results

In the analysis of 15,749 TLF patients from a prospective international surgical database, increasing frailty, as measured by the RAI, was associated with an increased likelihood of FTR, or 30-day mortality following a major complication. For example, very frail patients, with an RAI score of ≥ 46, had 45-fold increased odds of FTR when compared to patients with RAI 0–20. A multivariable predictive model was created that predicts FTR with an excellent discriminative ability (AUROC, 0.918).

2. Interpretation

The avoidance of 30-day mortality following a major complication, or FTR, is a hospital quality of care metric [19-21]. Nevertheless, the ability to assess healthcare performance based on FTR has been questioned due to wide variance and inconsistency in FTR rates per individual patient characteristics [22]. A NSQIP analysis of FTR in patients who underwent inpatient general, vascular, thoracic, cardiac, and orthopedic surgery found that patients with an RAI score > 40 had a 44-fold increased odds of FTR [23]. Risk-adjusted FTR metrics in patients undergoing cardiac surgery have demonstrated varying FTR rates according to primary complication type, after adjusting for intraoperative characteristics and individual patient predictors of FTR [24]. The wide variance in FTR rates necessitates that the primary care teams implement individualized protocols according to each complication type [25]. Although escalating care after certain complications has reduced mortality, the association between FTR and type of complication challenges its role as a quality metric [26]. Our results support the need for a similar risk-adjusted metric with respect to frailty in patients undergoing TLF [24].

Previous literature has identified patient predictors of FTR in spine surgery [27]. In a study of 10,841 patients undergoing surgery for cervical spine trauma, patients with chronic liver disease had a 2.86-fold higher odds of FTR [28]. Similarly, in patients undergoing resection of metastatic spine tumors, frailty, measured using the New England Spinal Metastasis Score (NESMS), was utilized to develop a predictive model of FTR [13]. Subsequent model performance characteristics revealed a c-statistic of 0.66. In comparison, our model demonstrated a c-statistic of 0.918. Potential reasons for this wide variation could include differences in frailty indexes that were utilized in both studies. The NESMS measures frailty using 3 components—functional dependence, preoperative albumin levels and cancer burden. Unlike NESMS, RAI is a weighted frailty index that considers comorbid conditions in addition to characteristics such as age and functional dependence.

Our study builds on other surgical specialties’ work by identifying frailty and type of complication as predictors of FTR [23,29,30]. The 45-fold increased odds of FTR in patients with RAI > 46 after TLF surgery demonstrates that these patients are predisposed to FTR, or failure to recover following a major complication due to poor baseline physiological reserve. Our multivariate model showed higher rates of FTR following cardiac arrest or renal failure. These findings have been utilized to create a risk-adjusted metric made available in the form of a user-friendly online calculator: https://nsgyfrailtyoutcomeslab.shinyapps.io/FailureToRescueTLfusion.

3. Clinical Implications

Our results suggest that frailty’s impact on FTR can be used to guide perioperative decision-making. In the preoperative setting, prehabilitation before surgery may help reduce the impact of frailty status on adverse events [31]. When patients develop postoperative complications, the likelihood of 30-day mortality from FTR can be estimated with this model. While the results of our study do not intend to withhold surgical treatment or life-saving interventions in the event of a complication, it can be used to guide goals of care discussions by giving patients and their families a better idea of recovery expectations [32]. The ease of calculating RAI scores allows for it to be utilized as a bedside tool to estimate frailty, and subsequent FTR. Further validation of RAI as a predictor of FTR in patients undergoing TLF prior to its incorporation in clinical decision-making.

4. Generalizability

The present study utilized a large, multinational database with over 700 participating institutions across 11 countries, imparting generalizability across hospital system, geographic, and cultural variability. The excellent discriminatory accuracy of the FTR predictive model (AUROC, 0.918) allows for further reliability and clinical utility of these findings.

5. Limitations

Our study is subject to several limitations. While the NSQIP is a widely used and validated database, reporting, and coding bias may occur. This dataset only captures case data for 30 days following surgery, limiting analysis of long-term outcomes, and the RAI was specifically designed to capture mortality rates at 1 year. Granular, specific, surgical case details, including severity and chronicity of disease, operative details, extent of blood loss, and specific infectious pathogens are not available within the NSQIP. While not analyzed, these factors may have affected rates of FTR [33]. NSQIP-participating institutions are typically large, high-volume hospitals associated with lower FTR rates, which may have resulted in an underestimation of these outcomes within the study cohort [5]. Despite the high AUROC in our predictive model, it is to be noted that we did not control for confounders or perform subsequent internal validation of our model.

CONCLUSION

Knowledge of baseline frailty and the likelihood of FTR following the development of a specific complication may provide patients and clinicians with a more informed prognosis during the decision-making process. This has been incorporated in the form of an open-access, online calculator to facilitate informed decision-making in the event of a complication after TLF surgery: https://nsgyfrailtyoutcomeslab.shinyapps.io/FailureToRescueTLfusion/.

Notes

Conflict of Interest

The authors have nothing to disclose.

Funding/Support

This study received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author Contribution

Conceptualization: JMR; Data curation: ASC, KR; Formal analysis: ASC, KR; Methodology: ASC, KR; Project administration: JMR, CAB; Visualization: CAB; Writing - original draft: JMR, GPS; Writing - review & editing: JMR, ASC, GPS, KR, MMC, CAB.

References

1. Reisener MJ, Pumberger M, Shue J, et al. Trends in lumbar spinal fusion—a literature review. J Spine Surg 2020;6:752–61.
2. Thirumala P, Zhou J, Natarajan P, et al. Perioperative neurologic complications during spinal fusion surgery: incidence and trends. Spine J 2017;17:1611–24.
3. Gephart MG, Zygourakis CC, Arrigo RT, et al. Venous thromboembolism after thoracic/thoracolumbar spinal fusion. World Neurosurg 2012;78:545–52.
4. Silber JH, Williams SV, Krakauer H, et al. Hospital and patient characteristics associated with death after surgery. A study of adverse occurrence and failure to rescue. Med Care 1992;30:615–29.
5. Scali ST, Giles KA, Kubilis P, et al. Impact of hospital volume on patient safety indicators and failure to rescue following open aortic aneurysm repair. J Vasc Surg 2020;71:1135–46.e4.
6. Chen Q, Beal EW, Kimbrough CW, et al. Perioperative complications and the cost of rescue or failure to rescue in hepato-pancreato-biliary surgery. HPB (Oxford) 2018;20:854–64.
7. Lafonte M, Cai J, Lissauer ME. Failure to rescue in the surgical patient: a review. Curr Opin Crit Care 2019;25:706–11.
8. Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci 2001;56:M146–56.
9. Wilson JRF, Badhiwala JH, Moghaddamjou A, et al. Frailty is a better predictor than age of mortality and perioperative complications after surgery for degenerative cervical myelopathy: an analysis of 41,369 patients from the NSQIP database 2010-2018. J Clin Med 2020;9:3491.
10. Chan V, Wilson JRF, Ravinsky R, et al. Frailty adversely affects outcomes of patients undergoing spine surgery: a systematic review. Spine J 2021;21:988–1000.
11. Veronesi F, Borsari V, Martini L, et al. The impact of frailty on spine surgery: systematic review on 10 years clinical studies. Aging Dis 2021;12:625–45.
12. Agarwal N, Goldschmidt E, Taylor T, et al. Impact of frailty on outcomes following spine surgery: a prospective cohort analysis of 668 patients. Neurosurgery 2021;88:552–7.
13. Schoenfeld AJ, Le HV, Marjoua Y, et al. Assessing the utility of a clinical prediction score regarding 30-day morbidity and mortality following metastatic spinal surgery: the New England Spinal Metastasis Score (NESMS). Spine J 2016;16:482–90.
14. Sellers MM, Merkow RP, Halverson A, et al. Validation of new readmission data in the American College of Surgeons National Surgical Quality Improvement Program. J Am Coll Surg 2013;216:420–7.
15. Covell MM, Rumalla K, Kassicieh AJ, et al. Frailty measured by risk analysis index and adverse discharge outcomes after adult spine deformity surgery: analysis of 3104 patients from a prospective surgical registry (2011-2020). Spine J 2023;23:739–45.
16. Conlon M, Thommen R, Kazim SF, et al. Risk analysis index and its recalibrated version predict postoperative outcomes better than 5-factor modified frailty index in traumatic spinal injury. Neurospine 2022;19:1039–48.
17. Kassicieh AJ, Rumalla K, Kazim SF, et al. Preoperative risk model for perioperative stroke after intracranial tumor resection: ACS NSQIP analysis of 30,951 cases. Neurosurg Focus 2022;53:E9.
18. Hall DE, Arya S, Schmid KK, et al. Development and initial validation of the risk analysis index for measuring frailty in surgical populations. JAMA Surg 2017;152:175–82.
19. Malone H, Cloney M, Yang J, et al. Failure to rescue and mortality following resection of intracranial neoplasms. Neurosurgery 2018;83:263–9.
20. Andresen NS, Gourin CG, Stewart CM, et al. Hospital volume and failure to rescue after vestibular schwannoma resection. Laryngoscope 2020;130:1287–93.
21. Tevis SE, Carchman EH, Foley EF, et al. Does anastomotic leak contribute to high failure-to-rescue rates? Ann Surg 2016;263:1148–51.
22. Kuo LE, Kaufman E, Hoffman RL, et al. Failure-to-rescue after injury is associated with preventability: the results of mortality panel review of failure-to-rescue cases in trauma. Surgery 2017;161:782–90.
23. Shah R, Attwood K, Arya S, et al. Association of frailty with failure to rescue after low-risk and high-risk inpatient surgery. JAMA Surg 2018;153e180214.
24. Kurlansky PA, O’Brien SM, Vassileva CM, et al. Failure to rescue: a new society of thoracic surgeons quality metric for cardiac surgery. Ann Thorac Surg 2022;113:1935–42.
25. Mouncey PR, Osborn TM, Power GS, et al. Trial of early, goal-directed resuscitation for septic shock. N Engl J Med 2015;372:1301–11.
26. Portelli Tremont JN, Cook N, Murray LH, et al. Acute traumatic spinal cord injury: implementation of a multidisciplinary care pathway. J Trauma Nurs 2022;29:218–24.
27. Roy JM, Rumalla K, Skandalakis GP, et al. Failure to rescue as a patient safety indicator for neurosurgical patients: are we there yet? A systematic review. Neurosurg Rev 2023;46:227.
28. Bessey JT, Le HV, Leonard DA, et al. The effect of chronic liver disease on acute outcomes following cervical spine trauma. Spine J 2016;16:1194–9.
29. Arya S, Kim SI, Duwayri Y, et al. Frailty increases the risk of 30-day mortality, morbidity, and failure to rescue after elective abdominal aortic aneurysm repair independent of age and comorbidities. J Vasc Surg 2015;61:324–31.
30. Dewan KC, Navale SM, Hirji SA, et al. The role of frailty in failure to rescue after cardiovascular surgery. Ann Thorac Surg 2021;111:472–8.
31. Hanna K, Ditillo M, Joseph B. The role of frailty and prehabilitation in surgery. Curr Opin Crit Care 2019;25:717–22.
32. Ellis DI, Altan D, Chang DC. Failure and rescue in surgery-surgical covenant, palliative care, and reimagining quality. JAMA Surg 2022;Sep. 14. doi: 10.1001/jamasurg.2022.3181. [Epub].
33. Shinall MC Jr, Youk A, Massarweh NN, et al. Association of preoperative frailty and operative stress with mortality after elective vs emergency surgery. JAMA Network Open 2020;3e2010358.

Article information Continued

Fig. 1.

Receiver operating characteristic (ROC) curve for the multivariable predictive model on the primary outcome, failure to rescue, American College of Surgeons-National Surgical Quality Improvement Program 2011–2020.

Table 1.

Demographics, preoperative characteristics, frailty, and postoperative outcomes in patients who experienced a major complication after undergoing thoracolumbar fusion: whole cohort, FTR, and non-FTR, ACS-NSQIP 2011–2020

Variable Total cohort FTR Non-FTR p-value
Total patients 15,749 129 15,620
Age in years, median (IQR) 66 (15) 69 (18.5) 66 (15) < 0.001
Female sex (biological) 9,047 (57.4) 50 (38.8) 8,997 (57.6) < 0.001
Race 0.578
 White 12,675 (80.5) 105 (81.4) 12,570 (80.5)
 Black 1,463 (9.3) 12 (9.3) 1,451 (9.3)
 Asian 285 (1.8) 4 (3.1) 281 (1.8)
 Other 1,326 (8.4) 8 (6.2) 1,318 (8.4)
Hispanic ethnicity 741 (4.7) 3 (2.3) 738 (4.7) 0.200
BMI (kg/m2), median (IQR) 30.0 (4.8) 29.8 (9.5) 30.0 (8.8) 0.134
Nonelective surgery 1,937 (12.3) 49 (38.0) 1,888 (12.1) < 0.001
Preoperative
 RAI, median (IQR) 21 (7) 27 (12) 21 (7) 0.000
 RAI frailty tier < 0.001
  0–20 7,080 (45.0) 28 (21.7) 7,052 (45.1)
  21–25 5,522 (35.1) 29 (22.5) 5,493 (35.2)
  26–30 2,074 (13.2) 32 (24.8) 2,042 (13.1)
  31–35 668 (4.2) 18 (14.0) 650 (4.2)
  36–40 290 (1.8) 11 (8.5) 279 (1.8)
  41–45 89 (0.6) 7 (5.4) 82 (0.5)
  ≥ 46 26 (0.2) 4 (3.1) 22 (0.1)
 Diabetes (insulin or noninsulin dependent) 3,511 (22.3) 38 (29.5) 3,473 (22.2) 0.050
 Current smoker within 1 year 2,668 (16.9) 22 (17.1) 2,646 (16.9) 0.972
 Hypertension 10,269 (65.2) 89 (69.0) 10,180 (65.2) 0.364
 Chronic steroid use 1,063 (6.7) 18 (14.0) 1,045 (6.7) 0.001
 Intubated 69 (0.4) 1 (0.8) 68 (0.4) 0.561
 Bleeding disorder 516 (3.3) 9 (7.0) 507 (3.2) 0.018
 Preoperative transfusion 207 (1.3) 4 (3.1) 203 (1.3) 0.074
Postoperative
 Renal complications 309 (2.0) 14 (10.9) 295 (1.9) < 0.001
  Acute renal failure 103 (0.7) 8 (6.2) 95 (0.6) < 0.001
  Renal insufficiency 208 (1.3) 6 (4.7) 202 (1.3) < 0.001
 Thrombosis/embolism complications 856 (5.4) 17 (13.2) 839 (5.4) < 0.001
  Cerebrovascular accident 170 (1.1) 3 (2.3) 167 (1.1) 0.169
  Pulmonary embolism 695 (4.4) 14 (10.9) 681 (4.4) < 0.001
 Infection complications 1,708 (10.8) 13 (10.1) 1,695 (10.9) 0.778
  Deep SSI 653 (4.1) 4 (3.1) 649 (4.2) 0.550
  Organ space SSI 490 (3.1) 3 (2.3) 487 (3.1) 0.606
  Sepsis 905 (5.7) 8 (6.2) 897 (5.7) 0.824
 Pneumonia 883 (5.6) 26 (20.2) 857 (5.5) < 0.001
 Unplanned intubation 383 (2.4) 49 (38.0) 334 (2.1) < 0.001
 Bleeding requiring transfusion 12,679 (80.5) 61 (47.3) 12,618 (80.8) < 0.001
 On ventilator greater than 48 hours 302 (1.9) 17 (13.2) 285 (1.8) < 0.001
 Cardiac arrest 112 (0.7) 46 (35.7) 66 (0.4) 0.000
 Myocardial infarction 422 (2.7) 18 (14.0) 404 (2.6) < 0.001
 Septic shock 228 (15.2) 14 (10.9) 214 (1.4) < 0.001

Values are presented as number (%) unless otherwise indicated.

FTR, failure to rescue; ACS-NSQIP, American College of Surgeons-National Surgical Quality Improvement Program; IQR, interquartile range; BMI, body mass index; RAI, risk analysis index; SSI, surgical site infection.

Pearson chi-square test; Fisher exact test; Mann-Whitney U-test.

Table 2.

Risk factors for failure to rescue (vs. rescue) in patients who experienced a major complication after undergoing thoracolumbar fusion, ACS-NSQIP 2011–2020

Characteristic OR 95% CI p-value
Age quartile
 1st (18–57) Reference Reference Reference
 2nd (58–66) 1.09 0.63–1.87 0.763
 3rd (67–72) 1.18 0.68–2.07 0.552
 4th (≥ 73) 2.15 1.33–3.48 0.002
Female sex (biological) 0.47 0.33–0.67 < 0.001
 Race
 White Reference Reference Reference
 Black 0.99 0.54–1.80 0.974
 Asian 1.70 0.62–4.66 0.299
 Other 0.73 0.35–1.49 0.385
Hispanic ethnicity 0.48 0.15–1.51 0.210
Body mass index 0.981 0.96–1.00 0.169
Nonelective surgery 4.46 3.11–6.38 < 0.001
Preoperative
 RAI 1.13 1.11–1.15 < 0.001
 RAI frailty tier
  0–20 Reference Reference Reference
  21–25 1.33 0.79–2.24 0.283
  26–30 3.95 2.37–6.57 < 0.001
  31–35 6.98 3.84–12.68 < 0.001
  36–40 9.93 4.89–20.15 < 0.001
  41–45 21.50 9.13–50.62 < 0.001
  ≥ 46 45.79 14.82–141.50 < 0.001
 Diabetes (insulin or noninsulin dependent) 1.46 0.99–2.14 0.051
 Current smoker within 1 year 1.01 0.64–1.60 0.972
 Hypertension 1.19 0.82–1.73 0.365
 Chronic steroid 2.26 1.37–3.74 0.001
 Intubated 1.79 0.25–12.97 0.556
 Bleeding disorder 2.24 1.13–4.43 0.021
 Preoperative transfusion 2.43 0.89–6.64 0.083
Postoperative
 Renal complications 6.32 3.60–11.15 < 0.001
  Renal insufficiency 3.72 1.62–8.55 0.002
  Acute renal failure 10.81 5.14–22.72 < 0.001
 Thrombosis/embolism complications 2.67 1.60–4.48 < 0.001
  Cerebrovascular accident 2.20 0.69–6.99 0.180
  Pulmonary embolism 2.67 1.53–4.68 < 0.001
 Infection complications 0.92 0.52–1.64 0.778
  Deep SSI 0.74 0.27–2.00 0.551
  Organ space SSI 0.74 0.24–2.33 0.607
  Sepsis 1.09 0.53–2.23 0.824
 Pneumonia 4.35 2.81–6.72 < 0.001
 Septic shock 8.76 4.95–15.51 < 0.001
 Unplanned intubation 28.03 19.33–40.65 < 0.001
 Bleeding requiring transfusion 0.21 0.15–0.30 < 0.001
 On ventilator greater than 48 hours 8.17 4.84–13.79 < 0.001
 Cardiac arrest 130.61 84.64–201.56 < 0.001
 Myocardial infarction 6.11 3.68–10.15 < 0.001

ACS-NSQIP, American College of Surgeons-National Surgical Quality Improvement Program; OR, odds ratio; CI, confidence interval; RAI, risk analysis index; SSI, surgical site infection.

Table 3.

Multivariable risk factors for failure to rescue (vs. rescued) in patients who experienced a major complication after undergoing thoracolumbar fusion, ACS-NSQIP 2011–2020

Parameter OR 95% CI p-value
Preoperative
 RAI 1.13 1.10–1.16 < 0.001
 Chronic steroid use 1.95 1.11–3.42 0.020
 Nonelective surgery 2.27 1.44–3.59 < 0.001
Postoperative
 Renal complications 2.59 1.24–5.41 0.011
 Unplanned reintubation 4.99 2.96–8.41 < 0.001
 Cardiac arrest 57.16 32.11–101.74 < 0.001
 Bleeding requiring transfusion 0.48 0.31–0.73 < 0.001

ACS-NSQIP, American College of Surgeons-National Surgical Quality Improvement Program; OR, odds ratio; CI, confidence interval; RAI, risk analysis index.