Preoperative risk model for perioperative stroke after intracranial tumor resection: ACS NSQIP analysis of 30,951 cases

Alexander J. KassiciehSchool of Medicine, University of New Mexico, Albuquerque, New Mexico; and

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Kavelin RumallaDepartment of Neurosurgery, University of New Mexico, Albuquerque, New Mexico

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Syed Faraz KazimDepartment of Neurosurgery, University of New Mexico, Albuquerque, New Mexico

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Derek B. AssersonDepartment of Neurosurgery, University of New Mexico, Albuquerque, New Mexico

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Meic H. SchmidtDepartment of Neurosurgery, University of New Mexico, Albuquerque, New Mexico

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Christian A. BowersDepartment of Neurosurgery, University of New Mexico, Albuquerque, New Mexico

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OBJECTIVE

Perioperative and/or postoperative cerebrovascular accidents (PCVAs) after intracranial tumor resection (ITR) are serious complications with devastating effects on quality of life and survival. Here, the authors retrospectively analyzed a prospectively maintained, multicenter surgical registry to design a risk model for PCVA after ITR to support efforts in neurosurgical personalized medicine to risk stratify patients and potentially mitigate poor outcomes.

METHODS

The National Surgical Quality Improvement Program database was queried for ITR cases (2015–2019, n = 30,951). Patients with and without PCVAs were compared on baseline demographics, preoperative clinical characteristics, and outcomes. Frailty (physiological reserve for surgery) was measured by the Revised Risk Analysis Index (RAI-rev). Logistic regression analysis was performed to identify independent associations between preoperative covariates and PCVA occurrence. The ITR-PCVA risk model was generated based on logit effect sizes and assessed in area under the receiver operating characteristic curve (AUROC) analysis.

RESULTS

The rate of PCVA was 1.7% (n = 532). Patients with PCVAs, on average, were older and frailer, and had increased rates of nonelective surgery, interhospital transfer status, diabetes, hypertension, unintentional weight loss, and elevated BUN. PCVA was associated with higher rates of postoperative reintubation, infection, thromboembolic events, prolonged length of stay, readmission, reoperation, nonhome discharge destination, and 30-day mortality (all p < 0.001). In multivariable analysis, predictors of PCVAs included RAI “frail” category (OR 1.7, 95% CI 1.2–2.4; p = 0.006), Black (vs White) race (OR 1.5, 95% CI 1.1–2.1; p = 0.009), nonelective surgery (OR 1.4, 95% CI 1.1–1.7; p = 0.003), diabetes mellitus (OR 1.5, 95% CI 1.1–1.9; p = 0.002), hypertension (OR 1.4, 95% CI 1.1–1.7; p = 0.006), and preoperative elevated blood urea nitrogen (OR 1.4, 95% CI 1.1–1.8; p = 0.014). The ITR-PCVA predictive model was proposed from the resultant multivariable analysis and performed with a modest C-statistic in AUROC analysis of 0.64 (95% CI 0.61–0.66). Multicollinearity diagnostics did not detect any correlation between RAI-rev parameters and other covariates (variance inflation factor = 1).

CONCLUSIONS

The current study proposes a novel preoperative risk model for PCVA in patients undergoing ITR. Patients with poor physiological reserve (measured by frailty), multiple comorbidities, abnormal preoperative laboratory values, and those admitted under high acuity were at highest risk. The ITR-PCVA risk model may support patient-centered counseling striving to respect goals of care and maximize quality of life. Future prospective studies are warranted to validate the ITR-PCVA risk model and evaluate its utility as a bedside clinical tool.

ABBREVIATIONS

ACS = American College of Surgeons; AUROC = area under the receiver operating characteristic curve; BUN = blood urea nitrogen; CPT = Current Procedural Terminology; DVT = deep vein thrombosis; ICD-10 = International Classification of Diseases, Tenth Revision; ITR = intracranial tumor resection; MI = myocardial infarction; NSQIP = National Surgical Quality Improvement Program; PCVA = perioperative and/or postoperative cerebrovascular accident; RAI-rev = Revised Risk Analysis Index; SSI = surgical site infection.

OBJECTIVE

Perioperative and/or postoperative cerebrovascular accidents (PCVAs) after intracranial tumor resection (ITR) are serious complications with devastating effects on quality of life and survival. Here, the authors retrospectively analyzed a prospectively maintained, multicenter surgical registry to design a risk model for PCVA after ITR to support efforts in neurosurgical personalized medicine to risk stratify patients and potentially mitigate poor outcomes.

METHODS

The National Surgical Quality Improvement Program database was queried for ITR cases (2015–2019, n = 30,951). Patients with and without PCVAs were compared on baseline demographics, preoperative clinical characteristics, and outcomes. Frailty (physiological reserve for surgery) was measured by the Revised Risk Analysis Index (RAI-rev). Logistic regression analysis was performed to identify independent associations between preoperative covariates and PCVA occurrence. The ITR-PCVA risk model was generated based on logit effect sizes and assessed in area under the receiver operating characteristic curve (AUROC) analysis.

RESULTS

The rate of PCVA was 1.7% (n = 532). Patients with PCVAs, on average, were older and frailer, and had increased rates of nonelective surgery, interhospital transfer status, diabetes, hypertension, unintentional weight loss, and elevated BUN. PCVA was associated with higher rates of postoperative reintubation, infection, thromboembolic events, prolonged length of stay, readmission, reoperation, nonhome discharge destination, and 30-day mortality (all p < 0.001). In multivariable analysis, predictors of PCVAs included RAI “frail” category (OR 1.7, 95% CI 1.2–2.4; p = 0.006), Black (vs White) race (OR 1.5, 95% CI 1.1–2.1; p = 0.009), nonelective surgery (OR 1.4, 95% CI 1.1–1.7; p = 0.003), diabetes mellitus (OR 1.5, 95% CI 1.1–1.9; p = 0.002), hypertension (OR 1.4, 95% CI 1.1–1.7; p = 0.006), and preoperative elevated blood urea nitrogen (OR 1.4, 95% CI 1.1–1.8; p = 0.014). The ITR-PCVA predictive model was proposed from the resultant multivariable analysis and performed with a modest C-statistic in AUROC analysis of 0.64 (95% CI 0.61–0.66). Multicollinearity diagnostics did not detect any correlation between RAI-rev parameters and other covariates (variance inflation factor = 1).

CONCLUSIONS

The current study proposes a novel preoperative risk model for PCVA in patients undergoing ITR. Patients with poor physiological reserve (measured by frailty), multiple comorbidities, abnormal preoperative laboratory values, and those admitted under high acuity were at highest risk. The ITR-PCVA risk model may support patient-centered counseling striving to respect goals of care and maximize quality of life. Future prospective studies are warranted to validate the ITR-PCVA risk model and evaluate its utility as a bedside clinical tool.

Resection is warranted for many patients with intracranial tumors causing neurological symptoms related to mass effect and tumors with rapid progression concerning for high-grade malignancy. However, cranial surgery is not without risk of complications, including but not limited to perioperative and/or postoperative cerebrovascular accidents (PCVAs). PCVA is a known complication of virtually all surgical interventions and is defined by the American College of Surgeons (ACS) as an “embolic, thrombotic, or hemorrhagic cerebrovascular event with motor, sensory, or cognitive dysfunction that persists for 24 hours or more and occurs within 30 days of operation.”17 The etiology of this event may be due to the pathology of the tumor, injury to structures during surgery, or the physiological changes related to anesthesia and surgery that may compound with preexisting patient comorbidities.811 PCVA is associated with a poor quality of life after surgery and other long-term sequelae, including movement disorders, neuropsychiatric disease, and seizures.1214 The majority of PCVAs in patients with primary brain tumors are related to treatment (surgery or radiotherapy) and are exacerbated by preexisting risk factors, which highlights the importance of appropriate patient counseling and informed consent.10

Rationale

The goal of brain tumor resection is to improve or restore quality of life and/or control disease progression. Unfortunately, the consequences of PCVA, particularly in patients with terminal illness, sometimes render the operation futile. Thus, preoperative risk stratification to select candidates with a favorable benefit/risk ratio and possibly mitigate modifiable factors (e.g., uncontrolled diabetes and atrial fibrillation) is essential. Existing literature has suggested a handful of risk factors associated with PCVA in a neurosurgical context; the current study seeks to build on these findings.1520 While identification of individual-level predictors of PCVA is important, there remains a critical need for the development and validation of survey instruments that integrate into the clinical workflow. Frailty, as measured by the Revised Risk Analysis Index (RAI-rev), is a measure of baseline physiological reserve and is increasingly used for preoperative risk stratification.1619,21,22 However, a frailty-based predictive model for PCVA in patients undergoing intracranial tumor resection (ITR) has not been previously reported.

Objectives

The present study sought to identify and quantify the effects of preoperative characteristics such as frailty that are associated with PCVA in patients undergoing ITR using patient data extracted from the ACS prospectively collected registry, the National Surgical Quality Improvement Program (NSQIP) (specifically for 2015–2019). We strived to develop and validate a clinically translatable predictive model for PCVA for use in this patient population.

Methods

Study Design

The present study was designed as a secondary analysis of a prospectively collected database. The paper was prepared with guidelines from the Equator Network’s Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement.

Data Source and Setting

Data used for this study were gathered from the ACS NSQIP database (2015–2019), a nationally validated database with contributions from more than 700 hospitals, producing more than 1 million deidentified patient cases annually. Patient case data abstracted from medical records included more than 200 variables, including demographics, diagnoses (International Classification of Diseases, Tenth Revision [ICD-10] codes), and procedures (Current Procedural Terminology [CPT]) codes). ACS-trained specialists review these data to ensure validity and organization.23 The study was performed under our data user agreement of the ACS and considered exempt by the institutional review board.

Participants

CPT and ICD-10 codes were used to identify adult (≥ 18 years of age) ITR patient cases from the 2015–2019 NSQIP database.

Revised Risk Analysis Index

The RAI-rev, an adaption of RAI-A, is a quantitatively robust frailty index that is readily captured by clinical surveys.24,25 This score is established by assigning numerical values to sex, age with or without cancer, weight loss, renal failure or dialysis, heart failure, poor appetite, shortness of breath, residence type, and functional status. In the present cohort, there were no missing data within the parameters used to compute the RAI-rev score. The score ranges from 0 (robust) to 78 (most frail). Categorization for this index has been established, with “robust” as RAI-rev scores 0–10, “prefrail” as RAI-rev scores 11–20, “frail” as RAI-rev scores 21–30, and “severely frail” as RAI-rev scores ≥ 31, which is used in the present study.25

Variables

Demographic data including age, sex, race, ethnicity, and BMI were captured. Additional preoperative variables included individual preexisting comorbidities, frailty index (measured by the RAI-rev), preoperative laboratory values, and tumor type (benign, primary malignant, and secondary/metastatic malignant). The primary outcome variable was PCVA. The effect of PCVA on other standardized ACS NSQIP outcomes was considered. Secondary outcomes included unplanned reintubation, sepsis, septic shock, pneumonia, deep vein thrombosis (DVT) or thrombophlebitis, myocardial infarction (MI), superficial surgical site infection (SSI), deep SSI, organ space SSI, wound disruption, cardiac arrest, operative time, length of stay, Clavien-Dindo grade IV, unplanned reoperation, unplanned readmission, 30-day mortality, and discharge to a nonhome destination.

Statistical Analysis

Statistical analysis was performed using IBM SPSS (version 28.0.0.0, IBM Corp.). Statistical significance was determined via an a priori determined alpha of 0.05. Descriptive statistical analysis compared baseline characteristics between PCVA and non-PCVA cohorts. Categorical variables were analyzed with Pearson’s chi-square test with results stated as number (%). Continuous variables were analyzed using the Mann-Whitney U-test with results stated as median (IQR). Univariate and multivariable logistic regression analyses were used to identify preoperative factors associated with PCVA. Baseline clinical parameters with p < 0.1 and a minimum of 10 cases per cell in the univariate model progressed to the multivariable model. The goal of the multivariable model was to create a robust predictive model for PCVA after ITR. Variance inflation factor diagnostics were assessed to ensure no multicollinearity within the model. Discrimination capacity of the model was measured in area under the receiver operating characteristic curve (AUROC) analysis. The ITR-PCVA predictive model was designed as follows: log (Pi/1 − Pi) = β0χ0 + β1χ1 + β2χ2 + βnχn, where Pi = predicted probability, χ = regression parameters, and beta (β) coefficient = effect size of regression parameters.

Results

Participants

A total of 30,951 patients were queried from the ACS NSQIP (2015–2019) database who underwent ITR. The rate of PCVA was 1.7% (532/30,951).

Preoperative Demographic and Clinical Characteristics

The PCVA cohort was chronologically older, on average, than the non-PCVA cohort, with mean (SD) ages of 60 (14) years and 57 (15) years, respectively. The PCVA cohort had a greater proportion of nonelective surgery (46% vs 40%) and interhospital transfer (21% vs 18%) (both p < 0.05). Frailty, measured by the RAI-rev, was higher on average in the PCVA group than in the non-PCVA group (p < 0.001) (Table 1).

TABLE 1.

Comparison of demographics and preoperative clinical characteristics of patients who underwent ITR with and without PCVA in the ACS NSQIP 2015–2019 database

VariableTotal Cohort (n = 30,951)PCVA (n = 532)No PCVA (n = 30,419)p Value
Baseline demographics
 Age, yrs, mean (SD) 57 (15)60 (14)57 (15)<0.001
 Female sex16,162 (52.2)271 (50.9)15,891 (52.2)0.551
 Race<0.001
  White 19,161 (61.9)293 (55.1)18,868 (62.0)
  Black1959 (6.3)50 (9.4)1909 (6.3)
  Other9829 (31.8)189 (35.5)9640 (31.7)
Ethnicity0.727
 Hispanic1857 (6.0)30 (5.6)1827 (6.0)
 Non-Hispanic21,385 (69.1)362 (68.0)21,023 (69.1)
 Unknown7707 (24.9)140 (26.3)7567 (24.9)
BMI, kg/m2, mean (SD)29 (7)28 (9)28 (8)<0.001
Nonelective surgery12,409 (40.1)245 (46.1)12,164 (40.0)0.005
Interhospital transfer5520 (17.8)114 (21.4)5406 (17.8)0.029
RAI-rev score, mean (SD)19.4 (9.2)20 (8)19 (9)0.343
Frailty (RAI-rev)
 Robust5707 (18.4)70 (13.2)5637 (18.5)<0.001
 Prefrail13,544 (43.8)245 (46.1)13,299 (43.7)<0.001
 Frail4945 (16.0)133 (25.0)4812 (15.8)<0.001
 Severely frail6755 (21.8)84 (15.8)6671 (21.9)<0.001
mFI-5, mean (SD)0.6 (0.8)0.8 (0.9)0.6 (0.8)<0.001
Frailty (mFI-5)
 Robust17,319 (56.0)232 (43.6)17,087 (56.2)<0.001
 Prefrail9660 (31.2)192 (36.1)9468 (31.1)<0.001
 Frail3497 (11.3)87 (16.4)3410 (11.2)<0.001
 Severely frail475 (1.5)21 (3.9)454 (1.5)<0.001
Tumor type
 Benign10,094 (32.6)210 (39.5)9884 (32.5)<0.001
 Primary malignant13,708 (44.3)245 (46.1)13,463 (44.3)<0.001
 Secondary malignant 7149 (14.5)77 (14.5)7072 (23.2)<0.001
Comorbidity
 Functionally dependent1153 (3.7)27 (5.1)1126 (3.7)0.097
 Bleeding disorder613 (2.0)16 (3.0)597 (2.0)0.086
 Diabetes mellitus3982 (12.9)108 (20.3)3874 (12.7)<0.001
 COPD1222 (3.9)26 (4.9)1196 (3.9)0.262
 CHF98 (0.3)1 (0.2)97 (0.3)0.594
 Chronic steroid use4331 (14.0)75 (14.1)4256 (14.0)0.944
 Current smoker5453 (17.6)80 (15.0)5373 (17.7)0.115
 Dyspnea1059 (3.4)21 (4.0)1038 (3.4)0.797
 Hypertension11,668 (37.7)271 (50.9)11,397 (37.5)<0.001
 Steroid use4331 (14.0)75 (14.1)4256 (14.0)0.944
 Ventilator dependent246 (0.8)7 (1.3)239 (0.8)0.172
 Weight loss618 (2.0)18 (3.4)600 (2.0)0.021
Preoperative labs
 Anemia999 (3.2)18 (3.4)981 (3.2)0.838
 Hyponatremia3065 (9.9)54 (10.2)3011 (9.9)0.847
 Leukocytosis6970 (22.5)131 (24.6)6839 (22.5)0.241
 Thrombocytopenia 1647 (5.3)31 (5.8)1616 (5.3)0.600
 Coagulopathy372 (1.2)8 (1.5)364 (1.2)0.519
 Hypoalbuminemia16,650 (53.8)289 (54.3)16,361 (53.8)0.805
 Hyperbilirubinemia 627 (2.0)9 (1.7)618 (2.0)0.581
 High SGOT701 (2.3)15 (2.8)686 (2.3)0.386
 High BUN3787 (12.2)94 (17.7)3693 (12.1)<0.001
 High creatinine1017 (3.3)27 (5.1)990 (3.3)0.020

CHF = congestive heart failure; COPD = chronic obstructive pulmonary disease; mFI-5 = modified 5-item frailty index; SGOT = serum glutamic-oxaloacetic transaminase.

Data are presented as the number of patients (%) unless otherwise indicated.

Outcome Data

Patients with PCVA had higher rates of unplanned reintubation (17.1% vs 1.7%), sepsis (5.5% vs 1.2%), septic shock (3.0% vs 0.4%), pneumonia (11.5% vs 1.8%), DVT/thrombophlebitis (8.1% vs 2.0%), pulmonary embolism (4.5% vs 1.3%), MI (0.9% vs 0.2%), deep incisional SSI (0.9% vs 0.4%), organ space SSI (2.4 vs 1.2), wound disruption (0.9 vs 0.2), Clavien-Dindo grade IV complications (31.2% vs 4.9%), unplanned readmission (28.5% vs 12.8%), unplanned reoperation (24.6% vs 5.0%), 30-day mortality (10.0% vs 0.9%), and discharge to nonhome destination (70.8% vs 21.3%) (all p < 0.05). PCVA was associated with prolonged operative time (median 236 vs 181 minutes) and length of stay (median 11 vs 4 days) (both p < 0.001) (Table 2).

TABLE 2.

Comparison of postoperative complications and outcomes of patients who underwent ITR with and without PCVA in the ACS NSQIP 2015–2019 database

VariableNo PCVA (n = 532)No PCVA (n = 30,419)p Value
Postop major complication
 Unplanned reintubation94 (17.7)519 (1.7)<0.001
 Sepsis29 (5.5)351 (1.2)<0.001
 Septic shock16 (3.0)128 (0.4)<0.001
 Pneumonia61 (11.5)550 (1.8)<0.001
 DVT/thrombophlebitis43 (8.1)615 (2.0)<0.001
 PE24 (4.5)390 (1.3)<0.001
 MI5 (0.9)76 (0.2)0.002
 Superficial SSI2 (0.4)198 (0.7)0.433
 Deep incisional SSI5 (0.9)112 (0.4)0.033
 Organ space SSI13 (2.4)368 (1.2)0.011
 Wound disruption5 (0.9)72 (0.2)0.001
 Cardiac arrest requiring CPR4 (0.8)97 (0.3)0.083
Operation time, mins, median (IQR)235.5 (204)181.0 (145)<0.001
Length of stay, days, median (IQR)11 (12)4 (5)<0.001
CD grade IV 166 (31.2)1491 (4.9)<0.001
Unplanned readmission128 (28.5)3179 (12.8)<0.001
Unplanned reoperation131 (24.6)1506 (5.0)<0.001
30-day mortality53 (10.0)280 (0.9)<0.001
Discharge destination
 Home140 (29.2)23,703 (78.7)<0.001
 Nonroutine (including mortality, rehabilitation, SNF, & others)339 (70.8)6401 (21.3)<0.001

CD = Clavien-Dindo classification of surgical complications; CPR = cardiopulmonary resuscitation; PE = pulmonary embolism; SNF = skilled nursing facility. Data are presented as the number of patients (%) unless otherwise indicated.

ITR-PCVA Predictive Model

Univariate analysis screened the following possible risk factors for PCVA: chronological age, RAI-rev frailty, BMI, nonelective surgery, interhospital transfer, tumor type, coagulopathy, diabetes, hypertension, and abnormal preoperative laboratory values (Table 1).

Independent predictors of PCVA in this RAI-driven model were frail (vs robust) status (OR 1.7, 95% CI 1.2–2.4; p = 0.006), Black (vs White) race (OR 1.5, 95% CI 1.1–2.1; p = 0.009), nonelective surgery (OR 1.4, 95% CI 1.1–1.7; p = 0.003), preoperative elevated blood urea nitrogen (BUN) (OR 1.4, 95% CI 1.1–1.8; p = 0.014), diabetes (OR 1.5, 95% CI 1.1–1.9; p = 0.002), and hypertension (OR 1.4, 95% CI 1.1–1.7; p = 0.006) (Table 3). The ITR-PCVA RAI model achieved a discriminatory strength AUROC of 0.64 (95% CI 0.61–0.66) (Fig. 1). The predicted probability (p) of PCVA is estimated by the following equation: Logit (p) = −4.566 + 0.211 × (prefrail = 1 vs 0) + 0.502 × (frail = 1 vs 0) + 0.302 × (severely frail = 1 vs 0) + 0.434 × (Black race = 1 vs 0) + 0.052 × (other race = 1 vs 0) + 0.330 × (hypoalbuminemia = 1 vs 0) + 0.313 × (bleeding disorder = 1 vs 0) + 0.279 × (diabetes = 1 vs 0) + 0.342 × (hypertension = 1 vs 0) + 0.303 × (elevated BUN = 1 vs 0) − 0.182 × (primary malignant tumor = 1 vs 0) − 0.844 × (secondary malignant tumor = 1 vs 0).

TABLE 3.

RAI-rev–driven multivariable logistic regression model for PCVA in patients who underwent ITR in the ACS NSQIP 2015–2019 database

VariableOR95% CIp ValueBeta
Frailty (RAI-rev)0.034
 RobustRefRefRefRef
 Prefrail1.230.90–1.690.1850.211
 Frail1.651.16–2.350.0060.501
 Severely frail1.350.83–2.190.2200.302
Race0.007
 WhiteRefRefRefRef
 Black1.531.11–2.100.0090.424
 Other1.050.84–1.320.6520.052
Nonelective surgery1.361.11–1.660.0030.304
Tumor type<0.001
 BenignRefRefRefRef
 Primary malignant0.830.67–1.040.100−0.182
 Secondary malignant 0.430.28–0.66<0.001−0.844
Preop comorbidity
 Elevated BUN (>25)1.371.07–1.770.0140.316
 Coagulopathy1.370.79–2.360.2600.313
 Diabetes1.461.14–1.870.0020.379
 Hypertension1.351.09–1.670.0060.300
 Hypoalbuminemia (<3)1.390.89–2.180.1490.330
FIG. 1.
FIG. 1.

ROC curve for the ITR-PCVA predictive model (RAI-rev based) with a concordance statistic of 0.64 (95% CI 0.61–0.66).

Discussion

Key Results

To the best of our knowledge, this is the first study to propose a predictive model for PCVA in patients undergoing ITR. The ITR-PCVA risk model based on RAI-rev robustly predicts PCVA with a C-statistic of 0.64. The findings suggest that a risk model created using a combination of frailty score, demographics, comorbidities, type of tumor, and preoperative laboratory values can reliably predict PCVA that is associated with devastating consequences. Of note, factors influencing PCVA rates included frailty, race, nonelective surgery status, preoperative elevated BUN, diabetes, and hypertension.

Interpretation

Neurosurgical intervention is the mainstay treatment for patients with intracranial tumors causing symptoms and/or with high-risk features concerning for rapid disease progression. Particularly in patients with acceptable quality of life preoperatively and nonoperative alternatives available (e.g., radiation therapy), it is critical to anticipate and prevent serious peri- and postoperative complications. Stroke, both ischemic and hemorrhagic, is a known contributor to morbidity and mortality for patients undergoing brain tumor surgery.1214,20,26,27 The incidence of PCVA after intracranial tumor surgery was 1.7% in our cohort of 30,951 cases, which is expectedly higher than rates after hemicolectomy (0.7%), hip replacement (0.2%), and lung resection (0.6%).4 A recent large validated ACS NSQIP study identified a 0.1% risk of perioperative stroke in a large cohort of patients who underwent noncardiac, nonmajor vascular, and nonneurological surgeries.5

PCVA is a distinct pathology associated with unique causes and risk factors compared with strokes in nonsurgical patients.28 However, published data on this topic are sparse and limited to specific tumor and stroke types, reducing the utility of these data in general preoperative decision-making.2932 Previously discovered risk factors for perioperative stroke in neurosurgical patients include diabetes mellitus, ventilator dependence, previous neurological deficits such as hemiparesis and impaired sensorium, and history of stroke.15,29 The present study adds to existing literature by identifying and quantifying independent risk factors for PCVA and proposing a clinically translatable risk model. Frailty was the most robust risk factor, which supports prior literature across the spectrum of neurosurgical patient populations.16,17,19,21,22,33 The current study provides increased data regarding specific aspects of a patient’s demographic.

Generalizability

The present study of 30,951 cases, derived from more than 700 hospitals in the United States, is widely generalizable from a socioeconomic, geographic, and hospital system level. The goal of the present study was to ultimately improve neurosurgical patient outcomes with improved preoperative risk assessment and provide a foundation for future research studies. While the presented predictive model provides a considerable foundation for clinical utility, we recognize the modest AUROC (0.64) of this predictive model, which is comparable to the AUROC generated by the original Spetzler-Martin AVM scoring system (0.66).34 Nonetheless, the current model identifies several independent predictors of PCVA after ITR. We encourage continued reevaluation and prospective critique of the proposed predictive model as the total quantity of PCVA events increase with newer available data sets. Future research will need to address the mechanistic pathways between the predictor variables and outcome of PCVA.

Study Limitations

A nationwide database study using the ACS NSQIP allows for a large sample size and impactful statistical power as well as generalizability. However, there a number of limitations that warrant discussion. Studies of observational design are inherently limited by data quality, observer bias, and granularity of clinical specifics, among other limitations. However, the excellent data quality assurance measures taken by ACS NSQIP mitigate these limitations that are common to other nationwide databases while still providing a large sample size and multicenter perspective. The ACS NSQIP is curated from data entered prospectively by ACS-trained surgical clinical reviewers, which maximizes consistency, reliability, and granularity.

Patient outcomes are tracked for the first 30 days postoperation, and thus long-term outcomes were not reported. PCVA adhered to a standardized definition provided and tracked by the ACS, which promotes generalizability but may not always correlate with clinically relevant stroke.

Furthermore, selection criteria were based on billing codes such as CPT and ICD-10, which hold some inherent bias and inaccuracy because of categorization and institutional coding protocols. More specific patient case details, including family history and disease severity, are not included in the NSQIP and therefore were unable to be analyzed. Lastly, occurrence of PCVA among this study cohort was rare (1.7%), which may have hindered the discriminative power of the predictive model, but frailty and other preoperative factors were nonetheless identified as independent predictors of PCVA.

Conclusions

The current study proposes a novel risk model for PCVA in patients undergoing ITR. The ITR-PCVA risk model supports ongoing efforts in neurosurgical personalized medicine to risk stratify patients and respond appropriately with strategies to mitigate poor outcomes. The present study confirms that PCVA is associated with devastating consequences for patients with intracranial tumors and renders the original operation futile in some cases. Patients at highest risk for PCVA should be counseled appropriately in the preoperative setting in an effort to maximize quality of life and respect their goals of care. Future prospective studies are warranted to validate the ITR-PCVA risk model and evaluate its utility as a bedside clinical tool.

Acknowledgments

We thank Rohini B. McKee, MD, Chief Quality and Safety Officer at the authors’ institution, for her ongoing support in our frailty outcome research initiative including data curation and administrative support.

Disclosures

The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

Author Contributions

Conception and design: Bowers, Rumalla, Kazim. Acquisition of data: Bowers, Rumalla. Analysis and interpretation of data: Kassicieh, Rumalla. Drafting the article: Kassicieh, Rumalla. Critically revising the article: all authors. Reviewed submitted version of manuscript: all authors. Statistical analysis: Kassicieh, Rumalla. Administrative/technical/material support: Bowers, Schmidt. Study supervision: Bowers, Rumalla, Kazim, Schmidt.

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    Wong GY, Warner DO, Schroeder DR, et al. Risk of surgery and anesthesia for ischemic stroke. Anesthesiology. 2000;92(2):425432.

  • 3

    Bucerius J, Gummert JF, Borger MA, et al. Stroke after cardiac surgery: a risk factor analysis of 16,184 consecutive adult patients. Ann Thorac Surg. 2003;75(2):472478.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4

    Bateman BT, Schumacher HC, Wang S, Shaefi S, Berman MF. Perioperative acute ischemic stroke in noncardiac and nonvascular surgery: incidence, risk factors, and outcomes. Anesthesiology. 2009;110(2):231238.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5

    Mashour GA, Shanks AM, Kheterpal S. Perioperative stroke and associated mortality after noncardiac, nonneurologic surgery. Anesthesiology. 2011;114(6):12891296.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 6

    Leary MC, Varade P. Perioperative stroke. Curr Neurol Neurosci Rep. 2020;20(5):12.

  • 7

    Menon U, Kenner M, Kelley RE. Perioperative stroke. Expert Rev Neurother. 2007;7(8):10031011.

  • 8

    Parikh NS, Burch JE, Kamel H, DeAngelis LM, Navi BB. Recurrent thromboembolic events after ischemic stroke in patients with primary brain tumors. J Stroke Cerebrovasc Dis. 2017;26(10):23962403.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9

    Kamiya-Matsuoka C, Cachia D, Yust-Katz S, et al. Ischemic stroke in patients with gliomas at The University of Texas-M.D. Anderson Cancer Center. J Neurooncol. 2015;125(1):143148.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 10

    Kreisl TN, Toothaker T, Karimi S, DeAngelis LM. Ischemic stroke in patients with primary brain tumors. Neurology. 2008;70(24):23142320.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 11

    Seidel C, Hentschel B, Simon M, et al. A comprehensive analysis of vascular complications in 3,889 glioma patients from the German Glioma Network. J Neurol. 2013;260(3):847855.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 12

    Suri R, Rodriguez-Porcel F, Donohue K, et al. Post-stroke movement disorders: the clinical, neuroanatomic, and demographic portrait of 284 published cases. J Stroke Cerebrovasc Dis. 2018;27(9):23882397.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 13

    Ferro JM, Caeiro L, Figueira ML. Neuropsychiatric sequelae of stroke. Nat Rev Neurol. 2016;12(5):269280.

  • 14

    Doria JW, Forgacs PB. Incidence, implications, and management of seizures following ischemic and hemorrhagic stroke. Curr Neurol Neurosci Rep. 2019;19(7):37.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 15

    Larsen AMG, Cote DJ, Karhade AV, Smith TR. Predictors of stroke and coma after neurosurgery: an ACS-NSQIP analysis. World Neurosurg. 2016;93:299305.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 16

    Wilson JRF, Badhiwala JH, Moghaddamjou A, Yee A, Wilson JR, Fehlings MG. 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(11):E3491.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 17

    Theriault BC, Pazniokas J, Adkoli AS, et al. Frailty predicts worse outcomes after intracranial meningioma surgery irrespective of existing prognostic factors. Neurosurg Focus. 2020;49(4):E16.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 18

    Henry RK, Reeves RA, Wackym PA, Ahmed OH, Hanft SJ, Kwong KM. Frailty as a predictor of postoperative complications following skull base surgery. Laryngoscope. 2021;131(9):19771984.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 19

    Huq S, Khalafallah AM, Jimenez AE, et al. Predicting postoperative outcomes in brain tumor patients with a 5-factor Modified Frailty Index. Neurosurgery. 2020;88(1):147154.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 20

    De la Garza-Ramos R, Kerezoudis P, Tamargo RJ, Brem H, Huang J, Bydon M. Surgical complications following malignant brain tumor surgery: an analysis of 2002–2011 data. Clin Neurol Neurosurg. 2016;140:610.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 21

    Kazim SF, Dicpinigaitis AJ, Bowers CA, et al. Frailty status is a more robust predictor than age of spinal tumor surgery outcomes: a NSQIP analysis of 4,662 patients. Neurospine. 2022;19(1):5362.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 22

    Kweh BTS, Lee HQ, Tan T, et al. Risk stratification of elderly patients undergoing spinal surgery using the Modified Frailty Index. Glob Spine J. Published online March 22, 2021. doi:10.1177/2192568221999650

    • Search Google Scholar
    • Export Citation
  • 23

    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(3):420427.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 24

    Arya S, Varley P, Youk A, et al. Recalibration and external validation of the Risk Analysis Index: a surgical frailty assessment tool. Ann Surg. 2020;272(6):9961005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 25

    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(2):175182.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 26

    Murray CJL, Vos T, Lozano R, et al. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380(9859):21972223.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 27

    Johnston SC, Hauser SL. Neurological disease on the global agenda. Ann Neurol. 2008;64(1):A11A12.

  • 28

    Dong Y, Cao W, Cheng X, et al. Risk factors and stroke characteristic in patients with postoperative strokes. J Stroke Cerebrovasc Dis. 2017;26(7):16351640.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 29

    Campen CJ, Kranick SM, Kasner SE, et al. Cranial irradiation increases risk of stroke in pediatric brain tumor survivors. Stroke. 2012;43(11):30353040.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 30

    Figuracion KCF, Jung W, Martha SR. Ischemic stroke risk among adult brain tumor survivors: evidence to guide practice. J Neurosci Nurs. 2021;53(5):202207.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 31

    Schiff D, Alyahya M. Neurological and medical complications in brain tumor patients. Curr Neurol Neurosci Rep. 2020;20(8):33.

  • 32

    Dardiotis E, Aloizou AM, Markoula S, et al. Cancer-associated stroke: pathophysiology, detection and management (Review). Int J Oncol. 2019;54(3):779796.

    • Search Google Scholar
    • Export Citation
  • 33

    Torres-Perez P, Álvarez-Satta M, Arrazola M, et al. Frailty is associated with mortality in brain tumor patients. Am J Cancer Res. 2021;11(6):32943303.

    • Search Google Scholar
    • Export Citation
  • 34

    Lawton MT, Kim H, McCulloch CE, Mikhak B, Young WL. A supplementary grading scale for selecting patients with brain arteriovenous malformations for surgery. Neurosurgery. 2010;66(4):702713.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Collapse
  • Expand
  • View in gallery
    FIG. 1.

    ROC curve for the ITR-PCVA predictive model (RAI-rev based) with a concordance statistic of 0.64 (95% CI 0.61–0.66).

  • 1

    Vlisides P, Mashour GA. Perioperative stroke. Can J Anaesth. 2016;63(2):193204.

  • 2

    Wong GY, Warner DO, Schroeder DR, et al. Risk of surgery and anesthesia for ischemic stroke. Anesthesiology. 2000;92(2):425432.

  • 3

    Bucerius J, Gummert JF, Borger MA, et al. Stroke after cardiac surgery: a risk factor analysis of 16,184 consecutive adult patients. Ann Thorac Surg. 2003;75(2):472478.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 4

    Bateman BT, Schumacher HC, Wang S, Shaefi S, Berman MF. Perioperative acute ischemic stroke in noncardiac and nonvascular surgery: incidence, risk factors, and outcomes. Anesthesiology. 2009;110(2):231238.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 5

    Mashour GA, Shanks AM, Kheterpal S. Perioperative stroke and associated mortality after noncardiac, nonneurologic surgery. Anesthesiology. 2011;114(6):12891296.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 6

    Leary MC, Varade P. Perioperative stroke. Curr Neurol Neurosci Rep. 2020;20(5):12.

  • 7

    Menon U, Kenner M, Kelley RE. Perioperative stroke. Expert Rev Neurother. 2007;7(8):10031011.

  • 8

    Parikh NS, Burch JE, Kamel H, DeAngelis LM, Navi BB. Recurrent thromboembolic events after ischemic stroke in patients with primary brain tumors. J Stroke Cerebrovasc Dis. 2017;26(10):23962403.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9

    Kamiya-Matsuoka C, Cachia D, Yust-Katz S, et al. Ischemic stroke in patients with gliomas at The University of Texas-M.D. Anderson Cancer Center. J Neurooncol. 2015;125(1):143148.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 10

    Kreisl TN, Toothaker T, Karimi S, DeAngelis LM. Ischemic stroke in patients with primary brain tumors. Neurology. 2008;70(24):23142320.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 11

    Seidel C, Hentschel B, Simon M, et al. A comprehensive analysis of vascular complications in 3,889 glioma patients from the German Glioma Network. J Neurol. 2013;260(3):847855.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 12

    Suri R, Rodriguez-Porcel F, Donohue K, et al. Post-stroke movement disorders: the clinical, neuroanatomic, and demographic portrait of 284 published cases. J Stroke Cerebrovasc Dis. 2018;27(9):23882397.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 13

    Ferro JM, Caeiro L, Figueira ML. Neuropsychiatric sequelae of stroke. Nat Rev Neurol. 2016;12(5):269280.

  • 14

    Doria JW, Forgacs PB. Incidence, implications, and management of seizures following ischemic and hemorrhagic stroke. Curr Neurol Neurosci Rep. 2019;19(7):37.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 15

    Larsen AMG, Cote DJ, Karhade AV, Smith TR. Predictors of stroke and coma after neurosurgery: an ACS-NSQIP analysis. World Neurosurg. 2016;93:299305.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 16

    Wilson JRF, Badhiwala JH, Moghaddamjou A, Yee A, Wilson JR, Fehlings MG. 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(11):E3491.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 17

    Theriault BC, Pazniokas J, Adkoli AS, et al. Frailty predicts worse outcomes after intracranial meningioma surgery irrespective of existing prognostic factors. Neurosurg Focus. 2020;49(4):E16.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 18

    Henry RK, Reeves RA, Wackym PA, Ahmed OH, Hanft SJ, Kwong KM. Frailty as a predictor of postoperative complications following skull base surgery. Laryngoscope. 2021;131(9):19771984.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 19

    Huq S, Khalafallah AM, Jimenez AE, et al. Predicting postoperative outcomes in brain tumor patients with a 5-factor Modified Frailty Index. Neurosurgery. 2020;88(1):147154.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 20

    De la Garza-Ramos R, Kerezoudis P, Tamargo RJ, Brem H, Huang J, Bydon M. Surgical complications following malignant brain tumor surgery: an analysis of 2002–2011 data. Clin Neurol Neurosurg. 2016;140:610.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 21

    Kazim SF, Dicpinigaitis AJ, Bowers CA, et al. Frailty status is a more robust predictor than age of spinal tumor surgery outcomes: a NSQIP analysis of 4,662 patients. Neurospine. 2022;19(1):5362.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 22

    Kweh BTS, Lee HQ, Tan T, et al. Risk stratification of elderly patients undergoing spinal surgery using the Modified Frailty Index. Glob Spine J. Published online March 22, 2021. doi:10.1177/2192568221999650

    • Search Google Scholar
    • Export Citation
  • 23

    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(3):420427.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 24

    Arya S, Varley P, Youk A, et al. Recalibration and external validation of the Risk Analysis Index: a surgical frailty assessment tool. Ann Surg. 2020;272(6):9961005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 25

    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(2):175182.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 26

    Murray CJL, Vos T, Lozano R, et al. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380(9859):21972223.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 27

    Johnston SC, Hauser SL. Neurological disease on the global agenda. Ann Neurol. 2008;64(1):A11A12.

  • 28

    Dong Y, Cao W, Cheng X, et al. Risk factors and stroke characteristic in patients with postoperative strokes. J Stroke Cerebrovasc Dis. 2017;26(7):16351640.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 29

    Campen CJ, Kranick SM, Kasner SE, et al. Cranial irradiation increases risk of stroke in pediatric brain tumor survivors. Stroke. 2012;43(11):30353040.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 30

    Figuracion KCF, Jung W, Martha SR. Ischemic stroke risk among adult brain tumor survivors: evidence to guide practice. J Neurosci Nurs. 2021;53(5):202207.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 31

    Schiff D, Alyahya M. Neurological and medical complications in brain tumor patients. Curr Neurol Neurosci Rep. 2020;20(8):33.

  • 32

    Dardiotis E, Aloizou AM, Markoula S, et al. Cancer-associated stroke: pathophysiology, detection and management (Review). Int J Oncol. 2019;54(3):779796.

    • Search Google Scholar
    • Export Citation
  • 33

    Torres-Perez P, Álvarez-Satta M, Arrazola M, et al. Frailty is associated with mortality in brain tumor patients. Am J Cancer Res. 2021;11(6):32943303.

    • Search Google Scholar
    • Export Citation
  • 34

    Lawton MT, Kim H, McCulloch CE, Mikhak B, Young WL. A supplementary grading scale for selecting patients with brain arteriovenous malformations for surgery. Neurosurgery. 2010;66(4):702713.

    • Crossref
    • Search Google Scholar
    • Export Citation

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