Association of preoperative glucose concentration with mortality in patients undergoing craniotomy for brain tumor

*Yu Zhang Departments of Neurosurgery and
Affiliated Hospital of Chengdu University, Chengdu, Sichuan;

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Huiwen Tan Endocrinology, West China Hospital, Sichuan University, Chengdu, Sichuan;

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Lu Jia Department of Neurosurgery, Shanxi Provincial People’s Hospital, Taiyuan, Shanxi;

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Jialing He Departments of Neurosurgery and
Department of Neurosurgery, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong;

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Pengfei Hao Department of Neurosurgery, Shanxi Provincial People’s Hospital, Taiyuan, Shanxi;

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Tiangui Li Department of Neurosurgery, Longquan Hospital, Chengdu, Sichuan, China;

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Yangchun Xiao Affiliated Hospital of Chengdu University, Chengdu, Sichuan;

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Liyuan Peng Affiliated Hospital of Chengdu University, Chengdu, Sichuan;

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Yuning Feng Affiliated Hospital of Chengdu University, Chengdu, Sichuan;

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Xin Cheng Departments of Neurosurgery and

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Haidong Deng Affiliated Hospital of Chengdu University, Chengdu, Sichuan;

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Peng Wang Affiliated Hospital of Chengdu University, Chengdu, Sichuan;

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Weelic Chong Department of Medical Oncology, Thomas Jefferson University, Philadelphia; and

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Yang Hai Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania

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Lvlin Chen Affiliated Hospital of Chengdu University, Chengdu, Sichuan;

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Chao You Departments of Neurosurgery and

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Fang Fang Departments of Neurosurgery and

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OBJECTIVE

Hyperglycemia is associated with worse outcomes in ambulatory settings and specialized hospital settings, but there are sparse data on the importance of preoperative blood glucose measurement before brain tumor craniotomy. The authors sought to investigate the association between preoperative glucose level and 30-day mortality rate in patients undergoing brain tumor resection.

METHODS

This retrospective cohort study included patients undergoing craniotomy for brain tumors at West China Hospital, Sichuan University, from January 2011 to March 2021. Surgical mortality rates were evaluated in patients who had normal glycemia (< 5.6 mmol/L) as well as mild (5.6–6.9 mmol/L), moderate (7.0–11.0 mmol/L), and severe hyperglycemia (> 11.0 mmol/L).

RESULTS

The study included 12,281 patients who underwent tumor resection via craniotomy. The overall 30-day mortality rate was 2.0% (242/12,281), whereas the rates for normal glycemia and mild, moderate, and severe hyperglycemia were 1.5%, 2.5%, 3.8%, and 6.5%, respectively. Compared with normal glycemia, the odds of mortality at 30 days were higher in patients with mild hyperglycemia (adjusted odds ratio [OR] 1.44, 95% confidence interval [CI] 1.05–2.00), moderate hyperglycemia (OR 2.04, 95% CI 1.41–2.96), and severe hyperglycemia (OR 3.76, 95% CI 1.96–7.20; p < 0.001 for trend). When blood glucose was analyzed as a continuous variable, for each 1 mmol/L increase in blood glucose, the adjusted OR of 30-day mortality was 1.13 (95% CI 1.08–1.19). The addition of a preoperative glucose level significantly improved the area under the curve and categorical net reclassification index for prediction of mortality.

CONCLUSIONS

In patients undergoing craniotomy for brain tumors, even mild hyperglycemia was associated with an increased mortality rate, at a glucose level that was much lower than the commonly applied level.

ABBREVIATIONS

ASA = American Society of Anesthesiologists; AUC = area under the receiver operating characteristic curve; CI = confidence interval; HR = hazard ratio; IDI = integrated discrimination improvement; NRI = net reclassification index; OR = odds ratio; SBP = systolic blood pressure.

OBJECTIVE

Hyperglycemia is associated with worse outcomes in ambulatory settings and specialized hospital settings, but there are sparse data on the importance of preoperative blood glucose measurement before brain tumor craniotomy. The authors sought to investigate the association between preoperative glucose level and 30-day mortality rate in patients undergoing brain tumor resection.

METHODS

This retrospective cohort study included patients undergoing craniotomy for brain tumors at West China Hospital, Sichuan University, from January 2011 to March 2021. Surgical mortality rates were evaluated in patients who had normal glycemia (< 5.6 mmol/L) as well as mild (5.6–6.9 mmol/L), moderate (7.0–11.0 mmol/L), and severe hyperglycemia (> 11.0 mmol/L).

RESULTS

The study included 12,281 patients who underwent tumor resection via craniotomy. The overall 30-day mortality rate was 2.0% (242/12,281), whereas the rates for normal glycemia and mild, moderate, and severe hyperglycemia were 1.5%, 2.5%, 3.8%, and 6.5%, respectively. Compared with normal glycemia, the odds of mortality at 30 days were higher in patients with mild hyperglycemia (adjusted odds ratio [OR] 1.44, 95% confidence interval [CI] 1.05–2.00), moderate hyperglycemia (OR 2.04, 95% CI 1.41–2.96), and severe hyperglycemia (OR 3.76, 95% CI 1.96–7.20; p < 0.001 for trend). When blood glucose was analyzed as a continuous variable, for each 1 mmol/L increase in blood glucose, the adjusted OR of 30-day mortality was 1.13 (95% CI 1.08–1.19). The addition of a preoperative glucose level significantly improved the area under the curve and categorical net reclassification index for prediction of mortality.

CONCLUSIONS

In patients undergoing craniotomy for brain tumors, even mild hyperglycemia was associated with an increased mortality rate, at a glucose level that was much lower than the commonly applied level.

In Brief

The authors investigated the association between preoperative glucose level and 30-day mortality in patients undergoing brain tumor resection. Even mild hyperglycemia was associated with increased mortality risk in patients undergoing craniotomy for brain tumors. The optimal preoperative glucose concentration level may be much lower than the commonly applied level of 10 mmol/L.

A growing body of evidence in the diabetes literature demonstrates the prognostic value of managing preoperative hyperglycemia in ambulatory and specialized hospital settings.1 A meta-analysis of brain tumors found that hyperglycemia was associated with worsened neurological outcomes and mortality.2 Persistent outpatient hyperglycemia was associated with shorter survival in patients after glioma surgery.3,4

Although there appears to be an association between poor outcomes and sustained hyperglycemia in patients with brain tumors,2 the relation between preoperative glucose level and mortality in patients undergoing craniotomy for brain tumors has not been systematically studied.5,6 In the absence of strong clinical evidence or guidelines, no consensus has been reached on the importance of glucose management in the general population before undergoing surgery.714 For example, the European Society of Anesthesiology guidelines from 2018 do not suggest routine preoperative assessment of glucose for nondiabetic patients scheduled for elective noncardiac surgery.11 The threshold of preoperative glucose levels that are predictive of risk is unknown. Using a large retrospective cohort approach, the present study aimed to precisely quantify the association between preoperative glucose levels and mortality in patients undergoing craniotomy for brain tumors.

Methods

Study Design and Data Source

This is a retrospective cohort study of patients undergoing craniotomy for brain tumors. This study evaluated the consecutive electronic health records of West China Hospital, Sichuan University, from January 2011 to March 2021. The study was performed in accordance with the Declaration of Helsinki. The ethics committee of West China Hospital approved this study and waived informed consent.

Patient Selection

The study included patients undergoing brain tumor resection. We excluded the following patients: 1) those who underwent urgent or emergency surgery; 2) patients undergoing repeat craniotomy or burr hole procedures; 3) patients whose preoperative fasting blood glucose level was not collected within 72 hours before craniotomy; and 4) patients whose personal identification number was not found in the electronic health record or whose death record was not found in the Household Registration Administration System.

Preoperative Glucose Level

Preoperative glucose was systematically collected as the fasting venous blood glucose level measured within 72 hours before craniotomy. At our hospital, fasting venous blood glucose is a routine test given before patients undergo brain tumor craniotomy. According to the 2021 guidelines of the American Diabetes Association, prediabetes is defined as 5.6–6.9 mmol/L, while diabetes is considered > 7 and 11 mmol/L in the fasting plasma glucose and oral glucose tolerance tests, respectively.13,15 Thus, we defined normoglycemia and mild, moderate, and severe hyperglycemia as blood glucose levels of < 5.6, 5.6–6.9, 7.0–11.0, and > 11.0 mmol/L, respectively. Moreover, to assess the linear associations of preoperative glucose with outcomes, we also categorized glucose in 1.0-mmol/L increments from 4.0 to 10.0 mmol/L and chose 5.0 mmol/L as a reference value. The optimal cutoff of preoperative glucose was defined by the Youden index, which maximizes the sum of sensitivity and specificity.16

Covariate Assessment

Patients with diabetes were defined as either those with a documented history of diabetes that appeared on the electronic health record or those who had a documented use of hypoglycemic medication. The diagnosis of tumor was identified by a review of medical charts and detection of ICD-10 codes, i.e., benign (D32–D35), malignant (C70, C71, C75.1–C75.3), and unclear (D42–44, R90). The diagnosis of chronic liver disease was also defined by ICD-10 codes (B18, K70–74). Alcohol abuse was defined as currently having one or more drinks per day on a regular basis.

Outcome

The primary outcome was 30-day mortality. Secondary outcomes included mortality at 90 days, 180 days, 1 year, and the longest follow-up. Date of death was ascertained from the Household Registration Administration System, also known as The Chinese Hukou System. In China, the law mandates that if a citizen dies, the head of household, relatives, dependents, or neighbors shall report the death registration to the household registration authority and cancel the household registration within 1 month. Recently, this system has been updated by the Seventh National Census. The National Bureau of Statistics reported that the missing registration rate of the seventh national census was 0.05%;17 thus, this system has accurate death records.18 Therefore, the rate of loss to follow-up of this study was negligible.19 For all participants, the median follow-up was 3.8 years, the longest follow-up period was 10.6 years, and the censoring date was August 15, 2021.

Statistical Analysis

R software (version R 4.0.3, R Foundation for Statistical Computing) was used for statistical analyses. Baseline cohort characteristics were presented as values and percentages or means (standard deviations). All tests of significance were two-sided, and p values < 0.05 were considered statistically significant. We used k-nearest neighbor imputation to impute missing variables.

Logistic regression was performed to obtain odds ratios (ORs) and 95% confidence intervals (CIs). We used logistic regression analysis with adjustments for age, sex, smoking, alcohol abuse, hypertension, diabetes mellitus, chronic liver disease, coronary artery disease, body temperature, systolic blood pressure (SBP), type of brain tumor, American Society of Anesthesiologists (ASA) class, and preoperative steroid use. Variables with p values < 0.10 were entered into the multivariable logistic regression model. Multi-collinearity was assessed by computing variance inflation factors for all variables, and variance inflation factor values > 10 were considered problematic. Kaplan-Meier survival curves were used to estimate survival time and adjusted hazard ratios (HRs) by fitting a Cox proportional hazards survival model.

We used the E-value to measure the robustness of the association between preoperative glucose and 30-day mortality for unmeasured or unadjusted confounding.20 E-values were computed with an online E-value calculator (https://www.evalue-calculator.com/evalue/).21 Although preoperative steroid use was adjusted in the multivariable logistic regression model, we conducted a sensitivity analysis to additionally adjust the dose of preoperative steroid use.

We also constructed the reference prediction models using variables that were significant in multivariable logistic regression analysis with and without preoperative glucose levels. The original data set was randomly split into training (70%) and validation (30%) sets. Discrimination and calibration of the models were evaluated according to current recommendations.22,23 The discrimination included the area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI),24 and categorized net reclassification index (NRI)25 with prespecified cutoffs of 1.5% and 4.5%. The DeLong test was used to estimate the significance between differences in the AUC. Calibration of the models was performed by plotting the predicted probabilities against the observed outcomes and by the Hosmer-Lemeshow test.

Results

Patient Characteristics

During the study period, 23,613 patients had a diagnosis of brain tumor, of whom 16,774 patients underwent craniotomy. Excluded were patients without a preoperative fasting blood glucose level (n = 1767) and patients without a personal identification number or death record (n = 2726). This retrospective study included 12,281 patients (eFig. 1). The overall 30-day mortality rate was 2.0% (242/12,281). The mortality rates for normal glycemia and mild, moderate, and severe hyperglycemia were 1.5%, 2.5%, 3.8%, and 6.5%, respectively. Baseline preoperative glucose levels are presented in Table 1. Among them, preoperative hyperglycemia, defined as > 5.6 mmol/L, was observed in 3420 patients (27.8%), and normal glycemia was observed in 8861 patients (72.2%). Participants with higher blood glucose levels were more likely to be older and male; present with hypertension, diabetes, and coronary artery disease; and have a higher ASA class. Two parameters contained missing data: body temperature (n = 106, 0.9%) and SBP (n = 236, 2.1%).

TABLE 1.

Baseline characteristics of the patients according to preoperative glucose level

CharacteristicNormoglycemiaHyperglycemiap Value
MildModerateSevere
No. of patients886121831069168
Demographics
 Mean age (SD), yrs45.22 (16.89)48.91 (16.57)53.21 (14.47)53.62 (12.63)<0.001
 Female, n (%)4931 (55.6)1123 (51.4)572 (53.5)85 (50.6)0.003
 Smoking, n (%)1502 (17.0)448 (20.5)220 (20.6)35 (20.8)<0.001
 Alcohol abuse, n (%)1168 (13.2)361 (16.5)172 (16.1)27 (16.1)<0.001
Medical history, n (%)
 Hypertension1070 (12.1)413 (18.9)278 (26.0)50 (29.8)<0.001
 Diabetes 252 (2.8)226 (10.4)298 (27.9)93 (55.4)<0.001
 Chronic liver disease 403 (4.5)119 (5.5)62 (5.8)11 (6.5)0.09
 Coronary artery disease65 (0.7)27 (1.2)21 (2.0)5 (3.0)<0.001
Mean body temp (SD), °C36.55 (0.40)36.57 (0.32)36.59 (0.36)36.60 (0.43)0.006
Mean SBP (SD)122.99 (16.58)125.95 (17.20)130.11 (18.42)129.16 (18.28)<0.001
Preop steroids
 Patient use, n (%)1491 (16.8)662 (30.3)346 (32.4)69 (41.1)<0.001
 Mean dose (SD), mg*61.14 (28.28)66.59 (29.44)71.15 (27.27)74.32 (28.88)<0.001
 Mean duration (SD), days4.12 (1.09)4.03 (1.11)4.06 (1.07)4.07 (1.08)0.41
ASA class, n (%)<0.001
 I–III8247 (93.1)1940 (88.9)893 (83.5)141 (83.9)
 IV–V614 (6.9)243 (11.1)173 (16.2)27 (16.1)
Type of brain tumor, n (%)<0.001
 Benign5619 (63.4)1185 (54.3)605 (56.6)100 (59.5)
 Malignant2695 (30.4)804 (36.8)381 (35.6)52 (31.0)
 Unclear547 (6.2)194 (8.9)83 (7.8)16 (9.5)

temp = temperature.

Normoglycemia = < 5.6 mmol/L; hyperglycemia = mild (5.6–7.0 mmol/L), moderate (7.0–11.0 mmol/L), or severe (> 11.0 mmol/L). Number of missing values: body temperature, n = 106 (0.9%); SBP, n = 236 (2.1%).

Methylprednisolone or equivalent.

Glucose and 30-Day Mortality

Figure 1 shows restricted cubic spline analysis of the preoperative glucose level with 30-day mortality and demonstrates a dose-response, nonlinear association between blood glucose and 30-day mortality. Compared with blood glucose levels < 5 mmol/L (reference), patients with higher blood glucose levels had higher odds of death. The observed rates of mortality increased according to higher baseline blood glucose level.

FIG. 1.
FIG. 1.

Restricted cubic spline graph of preoperative glucose level with 30-day mortality in patients undergoing craniotomy for brain tumors. The fitted curve is shown for adjusted OR (left y-axis), and the points with error bars are shown for observed mortality (right y-axis).

The associations between preoperative glucose levels and 30-day mortality are presented in Table 2. When blood glucose was analyzed as a continuous variable, for each 1 mmol/L increase in blood glucose, the adjusted OR of 30-day mortality was 1.13 (95% CI 1.08–1.19), adjusted for age, sex, ASA class, coronary artery disease, body temperature, steroid use before craniotomy, and type of brain tumor (eTable 1). The receiver operating characteristic curves for glucose showed an AUC of 0.60 (eFig. 2) and indicated that 5.135 mmol/L was the optimal cutoff level for mortality at 30 days. Based on the cutoff level, the adjusted OR was 1.69 (95% CI 1.30–2.21) for the association between hyperglycemia and mortality. The E-value obtained for this association was 2.77, with a lower limit of 1.92.

TABLE 2.

Unadjusted and adjusted associations between preoperative glucose level and 30-day mortality

CategoryGlucose (mmol/L)No. of Events/Total (%)Unadjusted ORp ValueAdjusted ORp Value
Continuous variableper 1.0NA1.15 (1.10–1.21)<0.0011.13 (1.08–1.19)<0.001
Dichotomous variable*≥5.135NA2.01 (1.55–2.61)<0.0011.69 (1.30–2.21)<0.001
Clinical threshold<5.6135/8861 (1.5)1 (Ref)<0.0011 (Ref)<0.001
5.6–7.055/2183 (2.5)1.67 (1.22–2.29)1.44 (1.05–2.00)
7.1–11.041/1069 (3.8)2.58 (1.81–3.68)2.04 (1.41–2.96)
>11.011/168 (6.5)4.53 (2.40–8.54)3.76 (1.96–7.20)
Value threshold<4.07/381 (1.8)1.38 (0.63–3.01)<0.0011.28 (0.58–2.79)<0.001
4.0–5.077/5749 (1.3)1 (Ref)1 (Ref)
5.0–5.980/3747 (2.1)1.61 (1.17–2.20)1.50 (1.09–2.07)
6.0–6.926/1167 (2.2)1.68 (1.07–2.63)1.36 (0.86–2.15)
7.0–7.917/588 (2.9)2.19 (1.29–3.73)1.74 (1.01–3.00)
8.0–8.98/264 (3)2.30 (1.10–4.82)1.68 (0.79–3.59)
9.0–9.97/131 (5.3)4.16 (1.88–9.20)3.21 (1.43–7.20)
≥10.020/254 (7.9)6.30 (3.78–10.47)5.13 (3.03–8.68)

NA = not applicable; Ref = reference.

The cutoff value was based on Youden index.

The p value for linear trend.

We conducted a sensitivity analysis of additionally adjusting the dose of preoperative steroid use. The results did not change, i.e., the adjusted OR of 30-day mortality was 1.13 (95% CI 1.08–1.19) when blood glucose was analyzed as a continuous variable for each 1 mmol/L increase in blood glucose.

Even a small increase in glucose was associated with higher mortality risk at 30 days. Compared with patients with normoglycemia (blood glucose < 5.6 mmol/L), the odds of mortality at 30 days increased in patients with mild hyperglycemia (adjusted OR 1.44, 95% CI 1.05–2.00), moderate hyperglycemia (OR 2.04, 95% CI 1.41–2.96), and severe hyperglycemia (OR 3.76, 95% CI 1.96–7.20; p < 0.001 for linear trend). When blood glucose levels were evaluated in 1.0 mmol/L increments from 4.0 to 10.0 mmol/L (using 5.0 mmol/L as a reference value; Table 2), higher blood glucose was still associated with increased surgical mortality risk. Mortality rates were increased at 90-day time points and later (Table 3).

TABLE 3.

Associations between preoperative glucose level and mortality at various time points

OutcomeGlucose (mmol/L)No. of Events/Total (%)Unadjusted ORp Value for TrendAdjusted ORp Value for Trend
Mortality at 90 days<5.6272/8861 (3.1)1 (Ref)<0.0011 (Ref)<0.001
5.6–7.0128/2183 (5.9)1.97 (1.59–2.44)1.65 (1.32–2.06)
7.8–11.068/1069 (6.4)2.15 (1.63–2.82)1.65 (1.24–2.20)
>11.016/168 (9.5)3.32 (1.96–5.64)2.72 (1.57–4.69)
Mortality at 180 days<5.6486/8861 (5.5)1 (Ref)<0.0011 (Ref)<0.001
5.6–7.0216/2183 (9.9)1.89 (1.60–2.24)1.56 (1.30–1.86)
7.8–11.0120/1069 (11.2)2.18 (1.76–2.69)1.68 (1.34–2.10)
>11.026/168 (15.5)3.16 (2.06–4.84)2.72 (1.72–4.29)
Mortality at 1 yr<5.6805/8861 (9.1)1 (Ref)<0.0011 (Ref)<0.001
5.6–7.0349/2183 (16)1.90 (1.66–2.18)1.56 (1.34–1.80)
7.8–11.0187/1069 (17.5)2.12 (1.78–2.52)1.62 (1.34–1.97)
>11.034/168 (20.2)2.54 (1.73–3.73)2.22 (1.45–3.40)
Mortality at longest follow-up<5.61691/8861 (19.1)1 (Ref)<0.0011 (Ref)<0.001
5.6–7.0660/2183 (30.2)1.84 (1.65–2.04)1.57 (1.38–1.78)
7.8–11.0362/1069 (33.9)2.17 (1.89–2.49)1.80 (1.52–2.13)
>11.062/168 (36.9)2.48 (1.80–3.41)2.52 (1.72–3.71)

We further assessed the interactions of variables on hyperglycemia based on normoglycemia versus hyperglycemia (> 5.6 mmol/L; Fig. 2). This interaction was present with smoking (p = 0.003 for interaction) and alcohol abuse (p = 0.002 for interaction). However, the association between preoperative hyperglycemia and mortality was not affected by diabetes (p = 0.56 for interaction). Patients without diabetes and a glucose concentration > 5.6 mmol/L were more likely to die within 30 days (OR 1.68, 95% CI 1.27–2.24), but this association was not significant in patients with diabetes (OR 2.25, 95% CI 0.65–7.79; eTable 2).

FIG. 2.
FIG. 2.

Subgroup analysis of associations between hyperglycemia and 30-day mortality. NA = not applicable.

Kaplan-Meier survival curves showed an increased risk of long-term mortality as the severity of hyperglycemia increased (Fig. 3). Cox regression models indicated that patients with mild hyperglycemia had an adjusted HR of 1.49 (95% CI 1.36–1.64), those with moderate hyperglycemia had an adjusted HR of 1.70 (95% CI 1.51–1.91), and those with severe hyperglycemia had an adjusted HR of 2.26 (95% CI 1.75–2.92).

FIG. 3.
FIG. 3.

Kaplan-Meier estimates of survival according to different preoperative glucose levels.

In the validation data set, the addition of preoperative glucose to the basic model (age, body temperature, ASA class, and type of brain tumor) led to a statistically significant improvement in the model for predicting mortality at 30 days in the AUC (∆ = 0.024, p = 0.04; Table 4). Preoperative glucose level significantly improved the classification of patients into death and survival on the NRI (eTable 3) and IDI. The calibration plots indicated that both models were well-calibrated, with a good linear correlation between predicted and observed probabilities (eFig. 3).

TABLE 4.

Discrimination, reclassification, and calibration measures of the basic model with and without preoperative glucose in predicting 30-day mortality in the validation set

StatisticMeasurep Value
C-statistic (95% CI)
 Preop glucose0.643 (0.580–0.706)
 Basic model as reference*0.701 (0.643–0.758)
 Basic model w/ preop glucose0.725 (0.668–0.781)
Discrimination
 Change in C-statistic for basic model w/ & w/o preop glucose0.0240.04
 IDI, % (95% CI)0.2 (0.1–0.4)0.04
Reclassification, % (95% CI)
 Categorical NRI9.5 (2.1–16.9)0.01
 Continuous NRI34.8 (12.7–57.0)0.002
Calibration
 Preop glucose0.23
 Basic model0.17
 Basic model w/ preop glucose0.29

Basic model included age, temperature, ASA class, type of brain tumor, and steroid use before craniotomy.

Categorized NRI prespecified cutoffs were 1.5% and 4.5%.

Hosmer-Lemeshow test.

Discussion

This large cohort study of patients undergoing any craniotomy for brain tumor shows that preoperative glucose concentrations are predictive of postoperative mortality in a linear dose-response manner. Moreover, even mild hyperglycemia was associated with a 1.5-fold increased risk of mortality. The optimal cutoff value for mortality at 30 days was 5.135 mmol/L. This study indicates that the optimal preoperative glucose concentration may be lower than the concentration conventionally used in the assessment of mortality risk in current surgical practice.

Various mechanisms may explain the association between preoperative blood sugar level and mortality risk in patients undergoing brain tumor craniotomy. First, hyperglycemia is known to induce neuronal apoptosis26 as well as inflammatory27 and toxic effects.28 In rat models, hyperglycemia increases superoxide production,29 leads to blood-brain barrier dysfunction,30 and exacerbates cerebral edema.31 Second, both hyperglycemia and hyperinsulinemia (resulting from hyperglycemia) promote tumor growth.32,33 And third, hyperglycemia increases other adverse events after surgery, including postoperative infections.34 Thus, it is not surprising that hyperglycemia can precipitate poor outcomes in patients undergoing craniotomy. Significantly, this study demonstrated a robust association between preoperative glucose concentrations and mortality after craniotomy. Similar differences have been noted in other surgical reports.2,3

Previous data showed that hyperglycemia was associated with worsened neurological outcomes and mortality in patients with brain tumors.2 Persistent outpatient hyperglycemia was associated with shorter survival in patients undergoing glioma surgery.3,35 Hyperglycemia was associated with postoperative infection after craniotomy.36 However, the association between preoperative hyperglycemia and mortality in patients undergoing craniotomy for brain tumor remains unclear, and a consensus has not been reached on the risk threshold of preoperative glucose concentration for poor outcomes.

Because of the lack of strong clinical evidence for perioperative glucose control, clinical practice guidelines for patients undergoing surgery have not reached consensus on perioperative control in the general population.713 The European Society of Anesthesiology guidelines do not recommend routine preoperative glucose assessments for patients undergoing elective noncardiac surgery.11 Diabetes Canada recommends maintaining blood glucose levels of 6–10 mmol/L for patients who undergo major surgery.12 Guidelines from the Society of Thoracic Surgeons and the Society for Ambulatory Anesthesia recommend a target intraoperative blood glucose level of < 10 mmol/L.9,10 The 2022 American Diabetes Association guidelines suggest a target glucose range of 7.8–10.0 mmol/L for the majority of critically and noncritically ill patients, and more stringent goals (e.g., 6.1–7.8 mmol/L) for select patients if they can be achieved without significant hypoglycemia (class C recommendation).13 As no definitive optimal preoperative glucose concentration target range is indicated by relevant guidelines, perioperative blood glucose management is typically guided by physician experience. A survey suggested that perceptions and practices related to glycemic management in patients undergoing brain tumor resection are variable.14 Our findings provide evidence that the optimal preoperative glucose concentration level may be much lower than that of current guideline recommendations and clinical practice.

Findings from the current study raise the question of whether strict control of hyperglycemia would reduce deaths in patients undergoing craniotomy for brain tumors. To answer this question, a further randomized controlled trial is needed to assess the effect of strict glucose control compared with standard care in patients undergoing craniotomy. Current evidence is unclear regarding the role of strict glucose control in neurocritical care patients. A meta-analysis found that strict glycemic control in the management of neurocritical care patients significantly increased the risk of hypoglycemia but did not influence mortality.37 A second meta-analysis concluded exactly the opposite, i.e., that tight glycemic control improved neurological outcomes despite increased rates of hypoglycemic events.38

Strengths and Limitations of the Study

This study offers several advantages over previous studies. First, to our knowledge, this is the first study to evaluate the association between preoperative glucose levels and mortality in patients undergoing craniotomy for brain tumors. Second, one of the strengths of the present study includes its large sample size, which allowed us to perform statistical inference on the 30-day mortality outcomes that has a low incidence and yielded great precision in estimates. Third, using household registration data, we confirmed that such mortality was reported accurately.

Our study also has several limitations. First, it is a retrospective observational analysis. While this study allows associations to be detected between exposures and outcomes, it cannot infer causality. Second, glycated hemoglobin was not routinely measured in our hospital, which could result in some patients with diabetes being miscategorized as nondiabetic. It is possible that serum HbA1c would be a better indicator of postoperative outcomes than preoperative glucose concentrations. Third, despite the large patient volume, the outcomes from this single-center study may not be generalizable to all medical health care centers.

Conclusions

Preoperative glucose concentration was associated with mortality in a linear dose-response manner in patients undergoing craniotomy for brain tumors. The optimal preoperative glucose concentration level may be much lower than the commonly applied level of 10 mmol/L. Enhanced glucose management may be pursued to improve the risks of craniotomy. Further randomized controlled trials are needed to assess the effect of tight glucose control in patients before they undergo brain tumor craniotomy.

Acknowledgments

We thank L. Dade Lunsford, MD, University of Pittsburgh Medical Center, for his support in preparing the final draft of this paper. This work is supported by National Key R&D Program of China (grant no. 2018YFA0108604), the 1-3-5 project for disciplines of excellence–Clinical Research Incubation Project, West China Hospital, Sichuan University (grant no. 21HXFH046), the innovation team project of the Affiliated Hospital of Clinical Medicine College of Chengdu University (grant no. CDFYCX202203), the project of the Sichuan Science and Technology Bureau (grant no. 22ZDYF0798), and the Clinical Incubation Program of West China Hospital, SCU (grant no. 2018HXFU008).

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: Fang, Zhang, Tan, Jia. Acquisition of data: all authors. Analysis and interpretation of data: Fang, Zhang, Tan, Jia. Drafting the article: Zhang, Tan, Jia. Critically revising the article: Zhang, Tan, Jia. Reviewed submitted version of manuscript: all authors. Statistical analysis: Fang Zhang, Tan, Jia, He. Administrative/technical/material support: Zhang, Tan, Jia. Study supervision: Fang, Zhang, Tan, Jia.

Supplemental Information

Online-Only Content

Supplemental material is available with the online version of the article.

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    Lazar HL, McDonnell M, Chipkin SR, et al. The Society of Thoracic Surgeons practice guideline series: blood glucose management during adult cardiac surgery. Ann Thorac Surg. 2009;87(2):663669.

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    De Hert S, Staender S, Fritsch G, et al. Pre-operative evaluation of adults undergoing elective noncardiac surgery: updated guideline from the European Society of Anaesthesiology. Eur J Anaesthesiol. 2018;35(6):407465.

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    Ivers NM, Jiang M, Alloo J, et al. Diabetes Canada 2018 clinical practice guidelines. Can Fam Physician. 2019;65(1):1424.

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    Gruenbaum SE, Guay CS, Gruenbaum BF, et al. Perioperative glycemia management in patients undergoing craniotomy for brain tumor resection: a global survey of neuroanesthesiologists’ perceptions and practices. World Neurosurg. 2021;155:e548e563.

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    Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350:g7594.

    • PubMed
    • Search Google Scholar
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    Alba AC, Agoritsas T, Walsh M, et al. Discrimination and calibration of clinical prediction models: users’ guides to the medical literature. JAMA. 2017;318(14):13771384.

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    • Search Google Scholar
    • Export Citation
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    Kerr KF, McClelland RL, Brown ER, Lumley T. Evaluating the incremental value of new biomarkers with integrated discrimination improvement. Am J Epidemiol. 2011;174(3):364374.

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    • Search Google Scholar
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    Thomas LE, O’Brien EC, Piccini JP, D’Agostino RB, Pencina MJ. Application of net reclassification index to non-nested and point-based risk prediction models: a review. Eur Heart J. 2019;40(23):18801887.

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    Chiu CD, Chen TY, Chin LT, et al. Investigation of the effect of hyperglycemia on intracerebral hemorrhage by proteomic approaches. Proteomics. 2012;12(1):113123.

    • PubMed
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  • 27

    Asakawa H, Miyagawa J, Hanafusa T, Kuwajima M, Matsuzawa Y. High glucose and hyperosmolarity increase secretion of interleukin-1 beta in cultured human aortic endothelial cells. J Diabetes Complications. 1997;11(3):176179.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 28

    Schlenk F, Vajkoczy P, Sarrafzadeh A. Inpatient hyperglycemia following aneurysmal subarachnoid hemorrhage: relation to cerebral metabolism and outcome. Neurocrit Care. 2009;11(1):5663.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 29

    Won SJ, Tang XN, Suh SW, Yenari MA, Swanson RA. Hyperglycemia promotes tissue plasminogen activator-induced hemorrhage by increasing superoxide production. Ann Neurol. 2011;70(4):583590.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 30

    Prasad S, Sajja RK, Kaisar MA, Cucullo L. Hyperglycemia exacerbates antiretroviral drug combination induced blood-brain barrier endothelial toxicity. Neurotoxicology. 2016;56:16.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 31

    Song EC, Chu K, Jeong SW, et al. Hyperglycemia exacerbates brain edema and perihematomal cell death after intracerebral hemorrhage. Stroke. 2003;34(9):22152220.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 32

    Rhodes CG, Wise RJ, Gibbs JM, et al. In vivo disturbance of the oxidative metabolism of glucose in human cerebral gliomas. Ann Neurol. 1983;14(6):614626.

  • 33

    Tran TT, Naigamwalla D, Oprescu AI, et al. Hyperinsulinemia, but not other factors associated with insulin resistance, acutely enhances colorectal epithelial proliferation in vivo. Endocrinology. 2006;147(4):18301837.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 34

    Golden SH, Peart-Vigilance C, Kao WH, Brancati FL. Perioperative glycemic control and the risk of infectious complications in a cohort of adults with diabetes. Diabetes Care. 1999;22(9):14081414.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35

    Derr RL, Ye X, Islas MU, Desideri S, Saudek CD, Grossman SA. Association between hyperglycemia and survival in patients with newly diagnosed glioblastoma. J Clin Oncol. 2009;27(7):10821086.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36

    Gruenbaum SE, Toscani L, Fomberstein KM, et al. Severe intraoperative hyperglycemia is independently associated with postoperative composite infection after craniotomy: an observational study. Anesth Analg. 2017;125(2):556561.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37

    Kramer AH, Roberts DJ, Zygun DA. Optimal glycemic control in neurocritical care patients: a systematic review and meta-analysis. Crit Care. 2012;16(5):R203.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38

    Ooi YC, Dagi TF, Maltenfort M, et al. Tight glycemic control reduces infection and improves neurological outcome in critically ill neurosurgical and neurological patients. Neurosurgery. 2012;71(3):692702.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Collapse
  • Expand

Illustrations from Esen Aydin et al. (pp 1443–1456). © Gökhan Canaz, published with permission.

  • FIG. 1.

    Restricted cubic spline graph of preoperative glucose level with 30-day mortality in patients undergoing craniotomy for brain tumors. The fitted curve is shown for adjusted OR (left y-axis), and the points with error bars are shown for observed mortality (right y-axis).

  • FIG. 2.

    Subgroup analysis of associations between hyperglycemia and 30-day mortality. NA = not applicable.

  • FIG. 3.

    Kaplan-Meier estimates of survival according to different preoperative glucose levels.

  • 1

    Petersen MC, Vatner DF, Shulman GI. Regulation of hepatic glucose metabolism in health and disease. Nat Rev Endocrinol. 2017;13(10):572587.

  • 2

    Liu H, Liu Z, Jiang B, et al. Prognostic significance of hyperglycemia in patients with brain tumors: a meta-analysis. Mol Neurobiol. 2016;53(3):16541660.

  • 3

    Chaichana KL, McGirt MJ, Woodworth GF, et al. Persistent outpatient hyperglycemia is independently associated with survival, recurrence and malignant degeneration following surgery for hemispheric low grade gliomas. Neurol Res. 2010;32(4):442448.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4

    McGirt MJ, Chaichana KL, Gathinji M, et al. Persistent outpatient hyperglycemia is independently associated with decreased survival after primary resection of malignant brain astrocytomas. Neurosurgery. 2008;63(2):286291.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5

    Hardy SJ, Nowacki AS, Bertin M, Weil RJ. Absence of an association between glucose levels and surgical site infections in patients undergoing craniotomies for brain tumors. J Neurosurg. 2010;113(2):161166.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6

    Chiang HY, Kamath AS, Pottinger JM, et al. Risk factors and outcomes associated with surgical site infections after craniotomy or craniectomy. J Neurosurg. 2014;120(2):509521.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7

    Jacobi J, Bircher N, Krinsley J, et al. Guidelines for the use of an insulin infusion for the management of hyperglycemia in critically ill patients. Crit Care Med. 2012;40(12):32513276.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8

    Dhatariya K, Levy N, Kilvert A, et al. NHS Diabetes guideline for the perioperative management of the adult patient with diabetes. Diabet Med. 2012;29(4):420433.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9

    Joshi GP, Chung F, Vann MA, et al. Society for Ambulatory Anesthesia consensus statement on perioperative blood glucose management in diabetic patients undergoing ambulatory surgery. Anesth Analg. 2010;111(6):13781387.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10

    Lazar HL, McDonnell M, Chipkin SR, et al. The Society of Thoracic Surgeons practice guideline series: blood glucose management during adult cardiac surgery. Ann Thorac Surg. 2009;87(2):663669.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11

    De Hert S, Staender S, Fritsch G, et al. Pre-operative evaluation of adults undergoing elective noncardiac surgery: updated guideline from the European Society of Anaesthesiology. Eur J Anaesthesiol. 2018;35(6):407465.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12

    Ivers NM, Jiang M, Alloo J, et al. Diabetes Canada 2018 clinical practice guidelines. Can Fam Physician. 2019;65(1):1424.

  • 13

    American Diabetes Association Professional Practice Committee. 16. Diabetes care in the hospital: Standards of Medical Care in Diabetes—2022. Diabetes Care. 2022;45(suppl 1):S244S253.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14

    Gruenbaum SE, Guay CS, Gruenbaum BF, et al. Perioperative glycemia management in patients undergoing craniotomy for brain tumor resection: a global survey of neuroanesthesiologists’ perceptions and practices. World Neurosurg. 2021;155:e548e563.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15

    American Diabetes Association Professional Practice Committee. 2. Classification and diagnosis of diabetes: Standards of Medical Care in Diabetes—2022. Diabetes Care. 2022;45(1 suppl):S17S38.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16

    Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3(1):3235.

  • 17

    The National Bureau of Statistics. Bulletin of the Seventh National Census. (No. 1). Site in Chinese. Accessed September 14, 2022. http://www.stats.gov.cn/tjsj/tjgb/rkpcgb/qgrkpcgb/202106/t20210628_1818820.html

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18

    Chan KW. The Chinese hukou system at 50. Eurasian Geogr Econ. 2009;50(2):197221.

  • 19

    Sun J, Guo X, Lu Z, et al. The gap between cause-of-death statistics and Household Registration reports in Shandong, China during 2011-2013: evaluation and adjustment for underreporting in the mortality data for 262 subcounty level populations. PLoS One. 2018;13(6):e0199133.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20

    VanderWeele TJ, Ding P. Sensitivity analysis in observational research: introducing the E-value. Ann Intern Med. 2017;167(4):268274.

  • 21

    Mathur MB, Ding P, Riddell CA, VanderWeele TJ. Web site and R package for computing E-values. Epidemiology. 2018;29(5):e45e47.

  • 22

    Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350:g7594.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23

    Alba AC, Agoritsas T, Walsh M, et al. Discrimination and calibration of clinical prediction models: users’ guides to the medical literature. JAMA. 2017;318(14):13771384.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24

    Kerr KF, McClelland RL, Brown ER, Lumley T. Evaluating the incremental value of new biomarkers with integrated discrimination improvement. Am J Epidemiol. 2011;174(3):364374.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25

    Thomas LE, O’Brien EC, Piccini JP, D’Agostino RB, Pencina MJ. Application of net reclassification index to non-nested and point-based risk prediction models: a review. Eur Heart J. 2019;40(23):18801887.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26

    Chiu CD, Chen TY, Chin LT, et al. Investigation of the effect of hyperglycemia on intracerebral hemorrhage by proteomic approaches. Proteomics. 2012;12(1):113123.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 27

    Asakawa H, Miyagawa J, Hanafusa T, Kuwajima M, Matsuzawa Y. High glucose and hyperosmolarity increase secretion of interleukin-1 beta in cultured human aortic endothelial cells. J Diabetes Complications. 1997;11(3):176179.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 28

    Schlenk F, Vajkoczy P, Sarrafzadeh A. Inpatient hyperglycemia following aneurysmal subarachnoid hemorrhage: relation to cerebral metabolism and outcome. Neurocrit Care. 2009;11(1):5663.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 29

    Won SJ, Tang XN, Suh SW, Yenari MA, Swanson RA. Hyperglycemia promotes tissue plasminogen activator-induced hemorrhage by increasing superoxide production. Ann Neurol. 2011;70(4):583590.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 30

    Prasad S, Sajja RK, Kaisar MA, Cucullo L. Hyperglycemia exacerbates antiretroviral drug combination induced blood-brain barrier endothelial toxicity. Neurotoxicology. 2016;56:16.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 31

    Song EC, Chu K, Jeong SW, et al. Hyperglycemia exacerbates brain edema and perihematomal cell death after intracerebral hemorrhage. Stroke. 2003;34(9):22152220.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 32

    Rhodes CG, Wise RJ, Gibbs JM, et al. In vivo disturbance of the oxidative metabolism of glucose in human cerebral gliomas. Ann Neurol. 1983;14(6):614626.

  • 33

    Tran TT, Naigamwalla D, Oprescu AI, et al. Hyperinsulinemia, but not other factors associated with insulin resistance, acutely enhances colorectal epithelial proliferation in vivo. Endocrinology. 2006;147(4):18301837.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 34

    Golden SH, Peart-Vigilance C, Kao WH, Brancati FL. Perioperative glycemic control and the risk of infectious complications in a cohort of adults with diabetes. Diabetes Care. 1999;22(9):14081414.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35

    Derr RL, Ye X, Islas MU, Desideri S, Saudek CD, Grossman SA. Association between hyperglycemia and survival in patients with newly diagnosed glioblastoma. J Clin Oncol. 2009;27(7):10821086.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36

    Gruenbaum SE, Toscani L, Fomberstein KM, et al. Severe intraoperative hyperglycemia is independently associated with postoperative composite infection after craniotomy: an observational study. Anesth Analg. 2017;125(2):556561.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37

    Kramer AH, Roberts DJ, Zygun DA. Optimal glycemic control in neurocritical care patients: a systematic review and meta-analysis. Crit Care. 2012;16(5):R203.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38

    Ooi YC, Dagi TF, Maltenfort M, et al. Tight glycemic control reduces infection and improves neurological outcome in critically ill neurosurgical and neurological patients. Neurosurgery. 2012;71(3):692702.

    • PubMed
    • Search Google Scholar
    • Export Citation

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