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BMC Surgery

, 19:163 | Cite as

Spine surgeon specialty differences in single-level percutaneous kyphoplasty

  • Kejia HuEmail author
  • Motao Liu
  • Amy J. Wang
  • Gexin Zhao
  • Yuhao Sun
  • Chaoqun Yang
  • Yiwang Zhang
  • Matthew M. Hutter
  • Dehong FengEmail author
  • Bomin SunEmail author
  • Ziv Williams
Open Access
Research article
Part of the following topical collections:
  1. Trauma care and orthopedic surgery

Abstract

Background

Percutaneous kyphoplasty (PKP) is a procedure performed by a spine surgeon who undergoes either orthopedic or neurosurgical training. The relationship between short-term adverse outcomes and spine specialty is presently unknown. To compare short-term adverse outcomes of single-level PKP when performed by neurosurgeons and orthopedic surgeons in order to develop more concretely preventive strategies for patients under consideration for single-level PKP.

Methods

We evaluated patients who underwent single-level PKP from 2012 to 2014 through the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP). We used univariate analysis and multivariate logistic regression to assess the association between spine surgeon specialty and short-term adverse events, including postoperative complication and unplanned readmission, and to identify different independent risk predictors between two specialties.

Results

Of 2248 patients who underwent single-level PKP procedure, 1229 patients (54.7%) had their operations completed by a neurosurgeon. There were no significant differences in the development of the majority of postoperative complications and the occurrence of unplanned readmission between the neurosurgical cohort (NC) and the orthopedic cohort (OC). A difference in the postoperative blood transfusion rate (0.7% NS vs. 1.7% OC, P = 0.039) was noted and may due to the differences in comorbidities between patients. Multivariate regression analysis revealed different independent predictors of postoperative adverse events for the two spine specialties.

Conclusions

By comparing a large range of demographic feature, preoperative comorbidities, and intraoperative factors, we find that short-term adverse events in single-level PKP patients does not affect by spine surgeon specialty, except that the OC had higher postoperative blood transfusion rate. In addition, the different perioperative predictors of postoperative complications and unplanned readmissions were identified between the two specialties. These findings can lead to better evidence-based patient counseling and provide valuable information for medical evaluation and potentially devise methods to reduce patients’ risk.

Keywords

Percutaneous kyphoplasty Surgeon specialty Postoperative complications Unplanned readmission Risk predictors ACS NSQIP 

Abbreviations

ASA

American Society of Anesthesiologists

BMI

Body mass index

NC

Neurosurgery cohort

OC

Orthopedic cohort

PKP

Percutaneous kyphoplasty

SD

Standard deviation

Background

Percutaneous kyphoplasty (PKP) has become a common surgical technique for the treatment of vertebral compression fractures, including osteoporotic, traumatic, and metastatic compression fractures [1, 2, 3]. With the goal of alleviating pain and preventing further loss of vertebral body height or progression of kyphotic deformity, PKP is a modification of percutaneous vertebroplasty (PVP), in which a balloon tamp is inflated in the vertebral body to create a cavity for injection of cement and minimization of extravasation. Although there are several different indications for PKP and PVP, previous studies have suggested that both procedures are effective treatments, and noted an overall trend of increasing utilization of percutaneous vertebral augmentations with a significantly greater use of PKP compared to PVP [4, 5, 6, 7].

Despite PKP being a ubiquitous minimally invasive percutaneous technique, adverse events may occur. Understanding risk factors for complications remains a central focus because short-term perioperative complications and hospital readmission is a major burden on the healthcare system [8, 9, 10]. Most of the previous studies examining perioperative complications have gravitated towards studying therapeutic approaches, devices, and pharmaceuticals. Using elderly patients (65 years or older) who underwent vertebral augmentation in the the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database from 2011 to 2012, a study by Toy et al. [11] identified ASA class 4 and inpatient status as risk factors for postoperative complication. History of pulmonary disease and inpatient status were found to be risk factors for unplanned readmission. However, surgeon specialty or practice area as a provider-side factor must also be considered.

Like many other spinal procedures, PKP is often performed by surgeons of two different specialties. When vertebral compression fractures patients are considering having a PKP procedure, one of the most common questions that they or their primary care providers may ask is, “A neurosurgeon or an orthopedic surgeon?” Prior studies have demonstrated that surgeons with training or practice in separate specialties might achieve different outcomes when performing the same operation [12, 13]. The current studies of PKP have all yielded.

mixed data between the two specialties. However, several spine-related studies have argued that past specialty training is not a risk factor for short-term adverse outcomes for spine procedures [14, 15, 16, 17]. However, to our knowledge, no report has investigated differences in short-term perioperative outcomes by surgeon specialty for PKP or identified and distinguished risk factors related to outcomes in these patients.

To help address this gap in the reported literature, we utilize the ACS NSQIP database, in which spine surgeon specialty is recorded, for the purpose of discovering whether different adverse 30-day short-term outcome rates in patients following single-level PKP exist between a neurosurgical cohort (NC) and an orthopedic surgery cohort (OC). In addition, we attempt to identify different independent predictors of adverse outcomes for NC and OC, which may enhance evidence-based preoperative strategies and the implementation of accurate risk-reduction decision making.

Methods

Data source

The ACS NSQIP database was used and PKP patients from 2012 to 2014 were included. Patients’ data of 121 participating United States hospitals was recorded by the ACS-NSQIP database in 2006 and the number of hospitals has since grown to over 500 by 2014. The data included in the database were identified and collected prospectively by trained professionals from appropriate hospitals of many kind, both academic and private centers, as well as urban and rural, in a prospective systematical and random way. We collected data from patients undergoing major surgical procedures in 30 days after operation, and extracted over 150 patient variables. For example, medical records, operative reports, and patient interviews conducted preoperatively, intraoperatively and postoperatively were include in our analysis [18, 19] We used Current Procedural Terminology (CPT) codes 22,523 and 22,524 (Thoracic/Lumbar Percutaneous vertebral augmentation, including cavity creation, fracture reduction and bone biopsy included when performed, using mechanical device, 1 vertebral body, unilateral or bilateral cannulation, inclusive of all imaging guidance) to capture all the patients following single-level spine PKP. Because the data were Health Insurance Portability and Accountability Act (HIPAA)-compliant, this study received an exempt determination from the Massachusetts General Hospital Investigational Review Board.

Variable selection

We estimated patient-based demographic predictors including hospital status (whether surgery was performed in an inpatient or outpatient setting), age (categorized as ≤64, 65 to 84 and ≥ 85 years old), sex, race, body mass index (BMI, calculated as weight in kilograms divided by the square of height in meters; BMI ≤29.9 kg/m2, not obese; ≥30 kg/m2, obese) [20], functional status (independent versus dependent), transfer status (admitted from home vs. admitted from acute care/nursing home/outside emergency department/other), diagnosis, and medical comorbidities (anemia, diabetes, pulmonary disease, cardiovascular disorders, bleeding disorders, blood transfusion≥1 unit packed/whole RBCs within 72 h of operation before surgery, and other system disorders such as disseminated cancer, dialysis use, and steroid use for a chronic condition). We also evaluated surgical risk factors such as the anatomic level anatomic level of operation (thoracic/lumbar region), the American Society of Anesthesiologists (ASA) classification, the type of anesthesia used (spinal/epidural/regional/others versus general) and the operative time.

Outcomes measure

The primary outcomes we measured were the differences of the development of adverse postoperative events between the two spine specialties cohort, such as systemic complications (occurrence of bleeding transfusion ≥1 unit of packed/whole RBCs given within 72 h post-operatively, deep vein thrombosis, thrombophlebitis, pulmonary embolism, urinary tract infection, pneumonia, unplanned intubation, acute kidney injury, cerebrovascular accident,, sepsis, septic shock, unplanned reoperation and mortality), surgical site wound complications (superficial and deep) within 30-day perioperative duration. We also took unplanned hospital readmission as a second outcome.

Statistical analysis

The differences in patient and procedure characteristics according to spine surgeon specialty were tested using the χ2, Fisher exact, or Student’s t-test, as appropriate. We used multivariate logistic regression to evaluate risk factors or early predictors and maintained frequencies greater than 10 and also had P values < 0.2 in the initial univariate testing [21]. Variables that were missing in more than 20% of the cohort were excluded to avoid model distortion [22]. Odds ratios (OR) and 95% confidence intervals (CI) were reported for both the univariate and multivariate analyses. The variables with P values < 0.05 with OR and 95% CI exclusive of 1.0 in the multivariate test were regarded as significant independent predictor [23]. We measured the discriminative power and the goodness of fit of the predictive model using the C-statistic and the Hosmer and Lemeshow test, respectively. All analyses were performed using Stata version 14.0 MP (StataCorp LP, College Station, TX).

Results

Participants and descriptive data

A total of 2248 patients who received single level PKP procedures were included and characteristics of the population according to the specialty are provided (Table 1). One thousand two hundred twenty-nine patients (54.7%) were operated on by neurosurgeons. Although there were no significant differences in most of the demographics, preoperative and surgical characteristics, practice patterns differed between NC and OC in several ways. The NC had a larger proportion of Caucasians and African Americans, but a smaller proportion of Asians (P < 0.001). Neurosurgeons also performed more PKP operations in emergency cases (P = 0.005) and in an inpatient setting (P < 0.001). Compared with those in OC, patients in NC were younger (P < 0.001), more likely to have independent status (P < 0.001), and more likely to have preoperative comorbidities of disseminated cancer (P < 0.001) and hypertension requiring medicating (P = 0.03).
Table 1

Demographics, preoperative, and surgical characteristics of patients undergoing single-level spine percutaneous kyphoplasty

Variable

Orthopedic Surgeon

Neurosurgeon

P value †

Available Number (n = 2248)

n = 1019 (45.3%)

n = 1229 (54.7%)

Demographics

 Hospital status

  Outpatient

653 (64.1)

695 (56.6)

< 0.001

  Inpatient

366 (35.9)

534 (43.4)

 Age Range, year

  64 or less

128 (12.6)

238 (19.4)

< 0.001

  65–84

629 (61.7)

721 (58.7)

  85 or greater

262 (25.7)

270 (21.9)

 Sex

  Male

295 (29.0)

369 (30.0)

0.61

  Female

724 (71.0)

860 (70.0)

 Body mass index, kg/m2 (n = 2233)

  BMI ≤ 29.9

780 (77.5)

909 (74.0)

0.071

  BMI ≥30

228 (22.5)

318 (25.8)

 Race (n = 2132)

  Caucasian

887 (90.4)

1097 (89.3)

< 0.001

  African American

19 (1.9)

32 (2.6)

 Asian

70 (7.1)

18 (1.5)

  Others

5 (0.5)

4 (0.3)

 Functional status (n = 2191)

  Independent

828 (81.2)

1090 (88.7)

< 0.001

  Dependent

160 (15.7)

113 (9.2)

 Transfer status (n = 2245)

  Not transferred (admitted from home)

955 (93.8)

1156 (94.1)

0.689

  Transfer from other

63 (6.2)

71 (5.8)

 Diagnosis

  Pathologic fracture

584 (57.3)

618 (50.3)

< 0.001

  Traumatic fracture

364 (35.7)

517 (42.1)

  Others

111 (10.9)

94 (7.6)

Preoperative Comorbidities

 Anemia

385 (37.8)

509 (41.4)

0.08

 Recent weight loss (> 10% of body weight in last 6 months)

13 (1.3)

19 (1.5)

0.59

 All diabetes mellitus

  Insulin-dependent diabetes

74 (7.3)

101 (8.2)

0.441

  Non-insulin-dependent diabetes

94 (9.2)

127 (10.3)

 Pulmonary

  Smoking (current smoker within 1 year)

121 (11.9)

157 (12.8)

0.519

  Dyspnea

121 (11.9)

163 (13.3)

0.069

  Chronic obstructive pulmonary disease

150 (14.7)

176 (14.3)

0.789

 Cardiovascular

  Hypertension requiring medicating

692 (67.9)

781 (63.5)

0.03

  Congestive heart failure

27 (2.6)

27 (2.2)

0.485

 Urinary system

  Acute renal failure

12 (1.2)

7 (0.6)

0.117

  Dialysis use

3 (0.3)

2 (0.2)

0.509

 Other system disease

  Disseminated cancer

29 (2.8)

86 (7)

< 0.001

  Bleeding disorder

86 (8.4)

88 (7.2)

0.258

  Preoperative blood transfusion of ≥1 unit of packed/whole RBCs within 72 h of operation

11 (1.1)

10 (0.8)

0.514

  Steroid use for chronic condition

128 (12.6)

150 (12.2)

0.798

  Systemic sepsis

26 (2.6)

30 (2.4)

0.867

Operative variables

 Anatomic Level of Operation

  Thoracic Spine

476 (46.7)

611 (49.7)

0.156

  Lumbar Spine

543 (53.3)

618 (50.3)

 Wound classification

  Clean

1017 (99.8)

1227 (99.8)

0.851

  Clean/contaminated

2 (0.2)

1 (0.1)

  Contaminated

0

0

  Dirty/infected

0

1 (0.1)

 ASA classification (n = 2244)

  1-No Disturb

8 (0.8)

7 (0.6)

0.097

  2-Mild Disturb

264 (25.9)

287 (23.4)

  3-Severe Disturb

661 (64.9)

796 (64.8)

  4-Life Threat

84 (8.2)

136 (11.1)

  5-Moribund

0

1 (0.1)

 Type of anesthesia (n = 2247)

  General

921 (90.4)

1082 (88)

0.085

  All others

98 (9.6)

146 (11.9)

  Emergency case

18 (1.8)

46 (3.7)

0.005

  Operative time, mina

33.94 ± 40.99

35.95 ± 34.03

0.2041

 Discharge Destination (n = 2232)

  Home

823 (80.1)

1016 (82.7)

0.637

  Facility Which Not Home

176 (17.2)

206 (16.8)

  Days from Operation to Discharge, daya

1.30 ± 2.91

1.61 ± 4.55

0.06

Data are reported as number (percentage) of patients except where noted. For variables with missing data, the number of available observations is given in brackets

ASA American Society of Anesthesiologists, N number of patients, SD standard deviation, BMI body mass index

aValues are presented as the mean and the standard deviation

†Calculated using ANOVA for continuous variables and chi-square tests for categorical variables, differences were considered significant at p < 0.05, and these values are listed in bold type

Postoperative adverse events

A total of 230 patients (10.2%) had documented one or more postoperative complications while 214 patients (9.5%) had unplanned readmissions within the 30-day postoperative period. NC and OC had similar results for the majority of the postoperative adverse outcomes (Table 2). There was no difference in overall complications (OC 10.6% vs. NC 10.0%, P = 0.601), including mortality (OC 2.0% vs. NC 1.5%, P = 0.121), systemic complications (OC 6.2% vs. NC 5.9%, P = 0.81) or wound complications (OC 0.1% vs. NC 0.2%, P = 0.676). The only exception being that OC had a higher occurrence of bleeding requiring transfusions within 72 h postoperatively (OC 1.7% vs. NC 0.7%, P = 0.039). Unplanned reoperation rates (OC 3.5% vs. NC 3.7%, P = 0.791) were similar, but neurosurgeons performed more reoperation cases that related to the principal PKP procedure (OC 0.4% vs. NC 1.1%, P = 0.048) and also had significantly shorter time window between the principal procedure and reoperation (OC 21.54 ± 8.03 day vs. NC 18.0 ± 8.29 day, P < 0.001). Unplanned readmission rates (OC 9.2% vs. NC 9.8%, P = 0.664) between the two specialties showed no difference. In addition, the NC had shorter times from the principal operation to unplanned readmissions than the OC (OC 14.95 ± 8.55 day vs. NC 13.84 ± 7.60 day, P < 0.001).
Table 2

Frequency of postoperative complications and outcomes of single-level spine percutaneous kyphoplasty by specialty

Characteristic

Total

OC (N = 1019)

NC (n = 1229)

P value†

Overall Complications

230 (10.2)

108 (10.6)

122 (10.0)

0.601

Mortality in 30d

42 (1.9)

24 (2.0)

18 (1.5)

0.121

Systemic Complications

136 (6.0)

63 (6.2)

73 (5.9)

0.81

Occurrence of bleeding transfusions (≥1 unit of packed/whole RBCs given within 72 h post-operatively)

26 (1.2)

17 (1.7)

9 (0.7)

0.039

Pneumonia

25 (1.1)

13 (1.3)

12 (1)

0.5

Pulmonary embolism

15 (0.7)

7 (0.7)

8 (0.7)

0.917

Unplanned intubation

15 (0.7)

7 (0.7)

8 (0.7)

0.917

Ventilator > 48 h

7 (0.3)

3 (0.3)

4 (0.3)

0.895

Stroke/cerebrovascular accident

3 (0.1)

2 (0.2)

1 (0.1)

0.593

Cardiac arrest requiring CPR

5 (0.2)

4 (0.4)

1 (0.1)

0.183

Myocardial Infarction

7 (0.3)

4 (0.4)

3 (0.2)

0.708

Deep venous thrombosis requiring therapy

11 (0.5)

5 (0.5)

6 (0.5)

0.993

Acute renal failure

5 (0.2)

1 (0.1)

4 (0.3)

0.385

Progressive renal insufficiency

5 (0.2)

2 (0.2)

3 (0.2)

0.811

Urinary tract infection

50 (2.2)

19 (1.9)

31 (2.5)

0.292

Sepsis/septic shock

18 (0.8)

8 (0.8)

10 (0.8)

0.94

Wound related Complication

3 (0.1)

1 (0.1)

2 (0.2)

0.676

Unplanned reoperation

82 (3.7)

36 (3.5)

46 (3.7)

0.791

Reoperation related to principal operative procedure

18 (0.8)

4 (0.4)

14 (1.1)

0.048

Days from principal operative procedure to Unplanned Reoperationa

 

21.54 ± 8.03

18 ± 8.29

< 0.001

Unplanned readmission

214 (9.5)

94 (9.2)

120 (9.8)

0.664

Unplanned Readmission likely related to the principal procedure

74 (3.3)

33 (3.2)

41 (3.3)

0.897

Days from principal operative procedure to Unplanned Readmissiona

 

14.95 ± 8.55

13.84 ± 7.60

0.001

CPR cardiopulmonary resuscitation, OR operation room, N number of patients, NC neurosurgical cohort, OC orthopedic cohort

aValues are presented as the mean and the standard deviation

†Calculated using ANOVA for continuous variables and chi-square tests for categorical variables, differences were considered significant at p < 0.05, and these values are listed in bold type

Univariate analysis

Overall postoperative complication

For both specialties, patients with postoperative complications were more likely to be associated with inpatient status for their procedures, have dependent functional status, have disseminated cancer, use steroids for a chronic condition, have systemic sepsis, or be ASA class level 3 or 4. However, transferred status, recent weight loss> 10%, anemia, preoperative anemia, and bleeding disorders were specific for NC. In addition, for OC, development of complications was also associated with black race, cardiovascular disorder, and in those who experienced longer operative durations (Fig. 1).
Fig. 1

Univariate analysis of factors associated with 30-day overall postoperative complications after single-level percutaneous kyphoplasty between neurosurgical cohort and orthopedic cohort. * presented as same factors between two cohorts

Postoperative blood transfusions

PKP patients with preoperative anemia and longer operative durations had significantly higher chances for requiring postoperative blood transfusions in both specialties. For the NC, transfusions were also associated with having been transferred from another facility not including home, having a preoperative pulmonary disorder, or having disseminated cancer. Interestingly, lumbar PKP had a lower transfusion rate than thoracic PKP. For OC, African Americans had higher transfusion rates compared with Caucasians, in addition those with preoperative diabetes and systemic sepsis. Interestingly, patients aged 65 to 84 were less likely to require a transfusion compared with those aged 64 or less (Fig. 2).
Fig. 2

Univariate analysis of factors associated with 30-day Postoperative blood transfusions after single-level percutaneous kyphoplasty between neurosurgical cohort and orthopedic cohort. * presented as same factors between two cohorts

Unplanned readmission

Patients with inpatients procedure status, dependent functional status, preoperative pulmonary disorder, disseminated cancer, steroid use for a chronic condition, and ASA class level 3 or 4 all had significantly higher unplanned readmission rates in both specialties. For NC, the additional associated risk factors were being aged 85 or greater, having preoperative anemia, having recent weight loss> 10%, and requiring a preoperative blood transfusion. For OC, having a urinary system disorder also increased the unplanned readmission rates. However, operations in the lumbar region decreased the readmission rates compared with those in the thoracic spine (Fig. 3).
Fig. 3

Univariate analysis of factors associated with 30-day unplanned readmission after single-level percutaneous kyphoplasty between neurosurgical cohort and orthopedic cohort. * presented as same factors between two cohorts

Multivariate regression models

While controlling for all exposures of interest and despite the cohorts being relatively small conducting a multivariate regression for postoperative transfusion, several factors were independently associated with overall postoperative complication and unplanned readmission rates in OC and NC were identified (Table 3).
Table 3

Independent predictors of overall postoperative complications and unplanned readmissions following single-level spine percutaneous kyphoplasty treated by neurosurgeons and orthopedic surgeons

Surgeon Specialty

Independent Predictors

Odds ratio

95% Confidence Interval

P value*

C-statistic

Overall Postoperative Complication

NC

Inpatients vs. Outpatients

1.874

1.216

2.889

0.004

0.7005

Dependent vs. Independent

2.418

1.424

4.107

0.001

Disseminated cancer

2.743

1.511

4.979

0.001

Weight loss > 10% in 6 months

3.867

1.387

10.780

0.01

OC

Inpatients vs. Outpatients

3.187

1.968

5.159

< 0.001

0.7559

Systemic sepsis

3.688

1.365

9.964

0.01

Operative duration

1.010

1.004

1.016

0.001

Unplanned Readmission

NC

Anemia

1.993

1.298

3.058

0.002

0.7109

Disseminated cancer

2.160

1.168

3.994

0.014

Preoperative blood transfusion

7.919

1.922

32.632

0.004

Steroid use for chronic disorder

2.203

1.352

3.589

0.002

OC

Inpatients vs. Outpatients

2.187

1.356

3.527

0.001

0.7175

Urinary system disorder

3.728

1.061

13.094

0.04

Lumbar vs. Thoracic Region

0.594

0.370

0.953

0.031

ASA 3 or 4 vs. ASA 1 or 2

2.139

1.020

4.486

0.044

NC neurosurgical cohort, OC orthopedic cohort, ASA American Society of Anesthesiologists class

*Significance, P ≤ 0.05

Neurosurgeon PKP cases

For the NC, PKP patients who had inpatient status (OR = 1.87, P = 0.004), dependent functional status (OR = 2.42, P = 0.001), had preoperative disseminated cancer (OR = 2.74, P = 0.001), or who had recent weight loss (> 10% of body weight in last 6 months) (OR = 3.87, P < 0.001) were at increased odds of postoperative complications compared with controls. Preoperative anemia (OR = 1.99, P = 0.002), disseminated cancer (OR = 2.16, P = 0.014), steroid use for chronic disorder (OR = 2.20, P = 0.002) and blood transfusion (OR = 19.69, P < 0.001) showed a strong trend toward predicting unplanned readmission.

Orthopedic surgeon PKP cases

For the OC, longer operative durations (OR = 1.01, P = 0.001), preoperative systemic sepsis (OR = 3.69, P = 0.01), and inpatient status (OR = 1.87, P = 0.004) in PKP patients were all significantly associated with overall postoperative complication. The independent predictors of unplanned readmissions were inpatient status (OR = 2.19, P = 0.001), preoperative kidney disorder (OR = 3.73, P = 0.04), and ASA 3 or 4 (OR = 2.14, P = 0.044), but operations in the lumbar region (OR = 0.59, P = 0.03) showed a decreased odds of unplanned readmission comparing with those in the thoracic spine.

The C-statistics for the final regression models (0.7005, 0.7559, 0.7109 and 0.7175, respectively) indicated good discriminative capacities, and the goodness-of-fit tests showed no statistically significant lack of fit, demonstrating good calibrations between all the models and the source data.

Discussion

In the current health care environment, quality outcomes are critical metrics of patients’ satisfaction. Identification of risk predictors of short-term adverse events is important for risk stratification and tailored therapy for patients. Toy et al. [11] reported an overall postoperative complication rate of 9.5% in 850 patients, with death occuring in 1.5% of patients and 10.8% of patients requiring readmission. Our study, while being specific to single-level PKP and being derived from a different period data set from ACS NSQIP database, demonstrated similar results, Our study was also able to distinguish several significant differences in the baseline clinical demographics of PKP patients between the OC and the NC. Considering these baseline differences, it would be natural to hypothesize that risk factors for adverse events would also be different. However, to date, there has been a lack of studies primarily focusing on the difference of risk predictors among spine patients according to surgeon specialty. To the best of our knowledge, this is the first report identifying different risk predictors for short-term adverse events following PKP by spine specialty. Our study confirmed that postoperative complications and unplanned readmissions between the two specialties most often performing single-level PKP were almost indistinguishable, which suggests that spine surgeon specialty may not affect the majority of short-term adverse events in PKP patients. While there is no significant difference in mortality between the NC and the OC according to previous studies, 30-day perioperative mortality has been reported as low as 0.13% in kyphoplasty [24] and 0.57% in vertebroplasty [25] for osteoporotic vertebral fractures, both of which are dramatically lower than in our study. This high mortality rate following PKP may be due to the older population comprising majority of the surgical cohort (83.7%) and the high rates (approximately 50%) of preoperative co-morbidities in this population. Our study suggests that the 30-day mortality (1.9%) following PKP was similar to other spine surgical procedures, when comparing with the NSQIP dataset source [17] and other dataset sources (including those from USA (1.4%) [26] or Europe (2.7%) [27]), despite the minimally invasive nature of PKP.

The only difference in postoperative complications between the OC and the NC is the occurrence of bleeding requiring transfusion. Several past articles comparing neurosurgeon and orthopedic surgeons in spine surgery have also reported this postoperative transfusion difference [14, 15, 16, 17]. PKP was developed as a minimally invasive procedure to avoid blood loss compared with open surgery; it is interesting that similar to other spine procedures, the difference persists between the two specialties. For specific hematopoietic system disorders, there were no significant differences in preoperative anemia, bleeding disorders and those who received preoperative blood transfusion between the OC and the NC. Using univariate analysis, we found that only preoperative anemia was associated with postoperative transfusion for both specialties. Although there are an insufficient number of cases to do a multivariate regression analysis to find independent predictors of transfusion requirements for each specialty, several reasons may be cited for the discordance in postoperative transfusion rates between the OC and the NC. First, it may be reflective of the comorbidity burden in these patients and not the specialty itself. We identified some differences in comorbidities between patients being treated by these two specialties, but it remains hard to quantify which cohorts overall condition is worse. For example, the NC had a higher proportion of inpatients, patients with disseminated cancer, and emergency cases, while the OC had higher rates of patients with dependent status and hypertension requiring medication. A second explanation may be the differences in the frequency of underlying spine pathologies. For instance, the OC had a higher percentage of pathologic fractures while the NC had higher rates of disseminated cancer. However, pathologic vertebral fractures due to degenerative disease such as osteoporosis may also have different bleeding tendencies compared with tumor-related metastatic malignant lesions, traumatic fractures have widely varying bleeding rates depending on the extent of bony injury, which increase the complexity of analysis. Given the limitations of the NSQIP database, it is difficult to determine the relationship between these two. A final reason it may be difficult to elucidate the relationship between post-operative transfusions in these two cohorts is that each specialty may have different thresholds and criteria for which a blood transfusion occurs. At present there are no clear universal transfusion protocols and the degree to which postoperative anemia can be tolerated may differ for both specialties. In addion, unnecessary transfusions may increase other complications and contribute to wasting limited medical resources [28], making comparisons of blood-preservation strategies between the two specialties challenging.

With multivariate regression analyses, our study showed the noteworthy finding that between the two specialties, almost all the independent risk factors associated with overall postoperative complication and unplanned readmission after PKP are different. Inpatient status is the only risk factor for postoperative complications shared by both specialties, which is concordant with a previous vertebral augmentation study [11], and may help further signify its role in risk stratification. In addition, anemia and obtaining a preoperative blood transfusion were independent risk factors for unplanned readmission in the NC but not the OC. The variations in independent predictors for postoperative adverse events between the OC and the NC may reflect specialty differences in patient management and may have implications for perioperative management of PKP patients. Understanding these risk factors may not only enhance spine surgeons’ knowledge but also lead to better evidence-based patient counseling regarding expected adverse events during admission and what may occur after discharge. In addition, simple yet effective measures including patient education regarding the risks and types of adverse events to expect, rigorous early in-person or telephone follow-up, monitoring of surgical sites, and coordinated post-discharge care via mid-level providers may reduce the incidence of these complications and readmission rates, subsequently leading to decreased healthcare expenditures [29].

Similar to other NSQIP database studies, several limitations of our study deserve mention. First, the postoperative complications tracked by NSQIP database are not specific to PKP procedure or spine surgery but are instead generalized across all surgical specialties. Moreover, several PKP procedure-specific adverse events are not available in the database, because they are either too specific or not evident at 30 days, such as cement leakage or extravasation, cement embolisms, allergic reactions to cement, hematoma, vertebral fractures at adjacent levels and neurological compromise. These cannot be clarified with the NSQIP database, which may skew the data towards a higher postoperative complication incidence. Furthermore, in many parts of the world interventional radiologists also perform PKP procedures, which were not compared here. Second, the NSQIP dataset does not have exact and precise information on the standardized comorbidity scores. Here, we attempted to control for the presence of comorbid disease by using the ASA class and the total number of different system comorbidities as covariates. Third, there are likely factors that were not investigated in NSQIP database, such as insurance status and other socioeconomic factors which may also be factors that influence readmission [30]. Fourth, the presence of missing data has the potential to influence conclusions substantially, so we employed similar practices that others have used to handle NSQIP dataset including the complete case analysis, which involves analysis of only those patients for whom data exist for all predictors of interest and all other cases are excluded from evaluation. However, the importance of using missing values carefully still needs to be highlighted [22]. Finally, although the final model was well calibrated to the data, the number of patients who had postoperative adverse events was comparatively small; therefore, it is difficult to build a stable multivariate regression model for the risk factors in the subgroup of each specific complication, so prospective studies with PKP metrics are needed to fully understand the scope of the postoperative adverse events. Nonetheless, the ACS NSQIP database still provides some important advantages that shed light on the short-term perioperative setting and its specific relationship to single-level PKP procedures.

Conclusion

Using a nationwide cohort across a large range of demographic features, preoperative comorbidities, and intraoperative factors, our findings suggest that spine surgeon specialty does not affect short-term adverse events in single-level PKP patients, except that the OC had higher postoperative blood transfusion rates. In addition, the different perioperative predictors of postoperative complications and unplanned readmissions were identified between the two specialties. These findings can help improve our understanding of these potential perioperative issues that can arise in single-level PKP patient population treated by different spine surgeons. With early recognition, this information can assist spine surgeons and related healthcare providers in determining which patients may merit further preoperative medical evaluation while striving for minimizing the chances of postoperative adverse events and triaging PKP patients at discharge for optimal follow-up.

Notes

Acknowledgements

Not applicable

Authors’ contributions

KH, DF, BS and ZW designed and conceptualized the study. KH, ML and MMH collected the data. KH, ML and AJW analyzed and interpreted the data. KH wrote the manuscript. KH, GZ, YS, CY YZ, MMH, DF, BS and ZW provided critical revisions that were important for the intellectual content. All authors read and approved the final version of the manuscript.

Funding

This work was supported by Shanghai Pujiang Program (KH, 19PJ1407500), Medical and Engineering Cross Research Fund from Shanghai Jiao Tong University (KH, YG2019QNA31), Shanghai Municipal Health Commission Clinical Study Special Fund (KH, 20194Y0067).

Ethics approval and consent to participate

This study received an exempt determination from the Massachusetts General Hospital Investigational Review Board, as the data are Health Insurance Portability and Accountability Act (HIPAA)-compliant.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

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Copyright information

© The Author(s). 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  1. 1.Center of Functional NeurosurgeryRuijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
  2. 2.Department of NeurosurgeryMassachusetts General Hospital, Harvard Medical SchoolBostonUSA
  3. 3.Department of OrthopedicsWuxi People’s Hospital, Nanjing Medical UniversityWuxiChina
  4. 4.Department of Biomedical EngineeringColumbia UniversityNew YorkUSA
  5. 5.State Key Laboratory of Oncology in South ChinaSun Yat-sen University Cancer CenterGuangzhouChina
  6. 6.Department of Hand SurgeryHuashan Hospital, Fudan UniversityShanghaiChina
  7. 7.Department of NeurosurgeryNo. 910th Hospital of The People’s Liberation Army Joint Logistics Support ForceQuanzhouChina
  8. 8.Department of SurgeryMassachusetts General Hospital, Harvard Medical SchoolBostonUSA

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