Perioperative hyperglycemia has been shown to be associated with an increased risk for major adverse events (MAEs) after cardiac surgery.1–3 At the same time, preventing hyperglycemia after cardiac surgery is associated with a decreased frequency of deep sternal wound infection,4 myocardial ischemia, and other adverse events.5 There continues to be a debate about the efficacy and safety of intensive glucose control therapy in the general intensive care unit (ICU) population as data from large clinical trials suggest that intensive glucose control is associated with more frequent episodes of hypoglycemia and mortality.6–8 The Society of Thoracic Surgery (STS) currently recommends maintaining perioperative glucose levels <180 mg/dL in cardiac surgical patients.9
The relationship between perioperative glycemic variability and preoperative long-term glucose control has not been systematically studied. Preoperative HbA1C measures the control of blood glucose levels over the previous to 4 months whereas the admission blood glucose level could reflect an acute stress response. Even though poor long-term glucose control is expected to increase both perioperative10 and long-term11 adverse outcomes, its influence on postoperative glycemic indices such as variability is not clear. In 1 study of 5728 critically ill patients in medical and surgical ICUs, glycemic variability in (mean and SD of blood glucose) was found to be associated with increased ICU and in-hospital mortality.12 Other studies have confirmed that glycemic variability is associated with increased mortality in critically ill patients.13,14 Egi and Bellomo15 in a review proposed reducing glycemic variability in the critically ill patients as a therapeutic target. Unfortunately, glycemic variability is a retrospective diagnosis limiting interventions to prospectively reduce its frequency. Furthermore, whether there is a relationship between glycemic variability and adverse events in patients undergoing cardiac surgery is not known. Thus, identifying patients at risk for glycemic variability might be useful to target therapies to reduce its occurrence. Preoperative HbA1C measurement is recommended as a diagnostic tool for detecting patients at risk for increased glycemic variability.16 An increased preoperative HbA1C level also increases intraoperative insulin resistance.17
In this study, we tested the following hypotheses (a) whether preoperative HbA1C can identify patients at risk for postoperative glycemic variability and (b) whether postoperative glycemic variability is associated with risk for postoperative MAEs after coronary artery bypass grafting (CABG) surgery.
Patients undergoing cardiac surgery from January 2008 to May 2011 at the Beth Israel Deaconess Hospital, Boston, MA, were enrolled in a prospective, observational cohort study with IRB approval. Informed patient consent was waived by the IRB. Patients undergoing CABG with or without valvular surgery were included. Patients having isolated valve surgery, aortic surgery, or other procedures such as atrial fibrillation ablation and pericardial window were excluded from the study. All preoperative medications such as aspirin, statins, and β-blockade were continued until the time of surgery and were restarted in the postoperative period day 1 unless contraindicated by the patient’s condition.
All patients were anesthetized with IV fentanyl and propofol (or etomidate in 10% of patients), and rocuronium was given for skeletal muscle relaxation. Anesthesia was maintained with isoflurane 0.5% to 1.0% in 100% oxygen with supplemental IV fentanyl given as intraoperative analgesic. A standard protocol for perioperative glycemic control has been operational for almost a decade (Appendix 1). Blood glucose levels are measured every hour in the postoperative period. The initial influence of such a protocol on postoperative deep sternal wound infections has been published.18
Standard cardioplegic solution was used throughout the study period (K-60 mEq, Mg 8 mEq, dextrose 2.5 g, Tham 10 mEq, and normal saline 500 mL). Patients were placed on a nonpulsatile cardiopulmonary bypass pump using the arterial and venous cannulae. Alpha stat pH management was used to manage blood gases obtained during the cardiopulmonary bypass run. Mild hypothermia (34°C) was used for CABG, and a temperature of 30°C to 32°C was used for valve surgery.
Blood Glucose and HbA1C Laboratory Methods
Preoperative HbA1C was obtained either during their preoperative anesthesia visit or on the day of surgery per institutional standard of care. HbA1C was determined from venous blood samples using Roche Integra Tina-Quant, version 2.0 (Roche Diagnostics, Indianapolis, IN). The normal range for HbA1C is 4.8% to 5.9%. HbA1C measurements were made within 30 days before surgery. Serum glucose was measured with an enzymatic assay (Roche/Hitachi P-Modules, Roche Diagnostics). The normal range for serum glucose values is 91 to 123 mg/dL. ICU blood glucose was measured by finger-stick point-of-care testing with Lifescan surestep Flexx (Milpitas, CA).
Patient demographic, intraoperative, and postoperative MAE data were obtained from our institutional STS database. The primary outcome of the study was MAE defined as a composite of in-hospital mortality, myocardial infarction (MI), pneumonia, stroke, renal failure, superficial or deep sternal infection requiring operative intervention and mediastinal reexploration for reintervention for bleeding/tamponade, valvular dysfunction, graft dysfunction, or other complications. The STS version 2.61 definitions for MAEs are shown in Appendix 2. If a patient was discharged and sent home, the patient was given a 30-day appointment. Those who missed the 30-day appointment were given a call by the STS database coordinator to note the morbidity and mortality. State STS coordinators also run the Social Security Death Index to capture those who died within 30 days after cardiac surgery, and this information was sent to the individual hospital.
Based on the established target of HbA1C <6.5% for the diagnosis of diabetes mellitus,19 we considered HbA1C as a binary variable (<6.5% and ≥6.5%) in our primary analysis.
Preoperative HbA1C was also evaluated as a continuous variable in the secondary analysis. Postoperative glycemic variability was defined as the coefficient of variation (CV) for glucose values obtained in the cardiac surgery unit in the first 24 hours after CABG surgery. CV is defined as SD divided by mean. It is the reverse of signal to noise ratio and is a dimensionless number. In our patient population, to calculate the CV, all glucose measurements were averaged for each 4-hour period (4, 8, 12, 16, and 24 hours), and then the variability was calculated. This approach minimized the potential bias wherein patients with complications would have a higher frequency of glucose measurements together with a more intensive treatment often based on glucose-containing solution, leading to a higher variance in glucose levels. STS risk scores are expressed as mean and SD. In the final analysis, patients were divided into HbA1C <6.5% and ≥6.5% groups, and postoperative glucose control indices mean glucose, SD, and CV were compared between these 2 groups.
A pre hoc power analysis was not performed since the assumptions for the effect and distribution of the variables in multivariate model (primary analysis) were not well known. Therefore, it was determined that having almost 1500 subjects in the study with the frequency of the outcome being approximately 10%, at least 8 to 10 variables could be introduced into the logistic regression model.
Preoperative HbA1C distribution was tested for normality with Kolmogorov–Smirnov test. Categorical variables were analyzed by Pearson χ2 analysis or Fisher exact test, and continuous variables were compared using Student t test or the Mann-Whitney U test. The primary outcome of MAE was compared using the χ2 test. Similarly, all the components of the primary outcome were compared with the χ2 test as well. Length of stay being nonnormally distributed was compared by the Mann-Whitney U test. Since full follow-up was available on all participants and the primary interest was event occurrence rather than its timing, multivariate logistic regression analysis was used. Variable selection in multivariable modeling was based on clinical importance for MAEs and statistical significance (P < 0.05 in univariate analysis) and performed in a hierarchical fashion set based on the presumed sequence of the clinical assessments. Demographic characteristics such as gender and age were introduced first followed by patient clinical characteristics such as type of procedure, comorbidities (hypertension, chronic obstructive pulmonary disease, congestive heart failure, history of MI, or cerebrovascular accident) and STS score quartiles, laboratory tests, and finally HbA1C level (either <6.5% or ≥6.5%). A parsimonious model was reported. Hosmer–Lemeshow testing was used to test the goodness of fit for the logistic regression models. Logistic regression modeling was done with (a) preoperative variables only and (b) all variables including those from the postoperative period. Quartiles of CV and postoperative blood glucose values were used for the logistic regression model. Analysis for correlation and interaction for the model variables were performed. A sensitivity analysis was performed with tertiles and quintiles for CV and postoperative blood glucose values. The odds of developing MAEs for the individual quartiles of CV and postoperative blood glucose values were obtained from the final logistic regression model. A 2-tailed P value of <0.05 was considered significant. SPSS 18.0 (SPSS Inc., Chicago, IL) was used for statistical analysis.
There were 1461 patients included in this analysis. Preoperative HbA1C distribution is shown in Figure 1. Patient demographics, comorbid conditions, medications, and STS risk scores for all the patients based on whether HbA1C was ≥6.5% (458 patients, 31.3%) or <6.5% are listed in Table 1. Patients with HbA1C ≥6.5% were younger, were more likely to be female, and had a higher incidence of hypertension, cardiovascular disease, and dyslipidemia. Among the 458 patients with HbA1C ≥6.5%, 402 had a clinical diagnosis of diabetes (rate of the diagnosis, 88.5%). In those with HbA1C ≤6.5%, 160 had a diagnosis of diabetes (rate of the diagnosis, 11%).
We observed MAEs in 143 patients (9.8%). MAEs were higher in patients with HbA1C ≥6.5% (12.2% vs 8.7%, P = 0.034). However, median levels of HbA1C did not differ between the patients with and without MAEs: 6.2% (interquartile range [IQR], 5.7%–6.9%) vs 6.0% (interquartile range, 5.7%–6.7%), P = 0.125. The individual complications and their comparison are listed in Table 2. ICU length of stay was longer in the HbA1C ≥6.5% group (median [IQR], 44 [26–72] vs 48 [27–76] hours, P = 0.002). Notably, the rate of deep sternal wound infections was significantly higher in the high HbA1C group (2.2% vs 0.5%, P = 0.008).
The STS risk scores (mean ± SD) in the low versus high HbA1C groups were 0.16 ± 0.13 and 0.18 ± 0.13 (P = 0.03) for mortality and morbidity, 0.003 ± 0.002 and 0.005 ± 0.004 (P < 0.001) for deep sternal wound infection, and 0.015 ± 0.012 and 0.016 ± 0.015 (P = 0.04) for permanent stroke, respectively.
Patients with MAEs had a higher rate of concomitant CABG and valve surgery (44% vs 24%, P < 0.001), higher STS predicted mortality risk (7% vs 2%, P < 0.001), older age (70 vs 68 years, P = 0.004), and a longer cross-clamp time (90 vs 77 minutes, P < 0.001) compared with those with no MAEs based on univariate analysis. The CV was higher in those with MAEs (0.24 ± 0.07) compared with those without MAEs (0.21 ± 0.08, P = 0.001) by univariate analysis. Prior diagnosis of diabetes was seen in 46% of patients with MAEs and in 37% of patients with no MAEs (P = 0.08).
The results of the multivariate logistic regression analysis are shown in Table 3. The final model included adjustment for preoperative factors such as the clinical diagnosis of diabetes, STS risk score, age, gender, concomitant valve surgery, and congestive heart failure. These results showed that HbA1C ≥6.5% was associated with an increased incidence of MAE (odds ratio [OR], 1.6; 95% confidence interval [CI], 1.1–2.3; P = 0.02). Hosmer–Lemeshow lack of goodness-of-fit test for this model was nonsignificant (χ2 = 5.80, P = 0.67).
Glycemic variability in the postoperative period as assessed by the CV was higher in the HbA1C ≥6.5% group compared with the HbA1C ≤6.5% group (0.26 ± 0.09 vs 0.20 ± 0.07, P < 0.001). CV was used as quartiles (≤0.16, 0.17–0.20, 0.21–0.25, ≥0.26) for the multivariable logistic regression model for assessing risk for MAEs. Glycemic variability was associated with risk for MAEs (OR, 1.3; 95% CI, 1.1–1.5; P = 0.01). Other factors in this model were STS score per quartile (OR, 1.6; 95% CI, 1.3–2.0; P < 0.001), CABG and valvular surgery (OR, 1.3; 95% CI, 1.0–1.6; P = 0.045), mean glucose levels averaged over the first 4 hours (OR, 1.2; 95% CI, 1.0–1.4; P = 0.026), and history of MI (OR, 1.8; 95% CI, 1.2–2.6; P = 0.004; Table 3). There were no significant interactions or correlations between the variables (except STS risk score and valvular surgery correlation, ρ = 0.56). Hosmer–Lemeshow lack of goodness-of-fit test for this model was nonsignificant (χ2 = 6.20, P = 0.62).
When glycemic variability was studied using tertiles or quintiles, the multivariate risks associated with risk for MAEs were as follows: OR, 1.4 (95% CI, 1.1–1.8; P = 0.005) and OR, 1.2 (95% CI, 1.0–1.4; P = 0.018), respectively. When postoperative blood glucose was studied using tertiles or quintiles, the multivariate risks associated with risk for MAEs were as follows: OR, 1.2 (95% CI, 0.98–1.5; P = 0.08) and OR, 1.2 (95% CI, 1.0–1.3; P = 0.012), respectively. Logistic regression equations, ORs for the individual quartiles of CV and postoperative blood glucose, correlation, and interactions for the final model variables have been provided in Appendix 3.
HbA1C and Postoperative Glucose Variables
The mean blood glucose values at 4, 24, and 48 hours after surgery are shown in Table 4. Average blood glucose levels in both groups were <180 mg/dL. Glucose levels averaged over the first 4 hours after surgery were higher in the high HbA1C group (135 ± 27 vs 125 ± 25 mg/dL, P < 0.001). Hypoglycemic events (defined as a blood glucose <60 mg/dL per patient per day) were similar between the high and low HbA1C groups (1.20 ± 0.4 vs 1.39 ± 1.1, P = 0.318). Hypoglycemic events when defined as a percentage (number of events/total number of measurements) was higher in the low HbA1C group (6% ± 3% vs 4% ± 2%, P = 0.001). Hyperglycemic events (defined as a blood glucose level >200 mg/dL level per patient per day, 2.79 ± 2.31 vs 1.76 ± 1.68, P < 0.001) or defined as a percentage (number of events/total number of measurements, 9 ±7 vs 7 ± 4, P < 0.001) was higher in the HbA1C ≥6.5% group (Table 4).
We performed a sensitivity analysis in which all the glucose values during the first 24 postoperative hours were used for the calculation of the CV. Similar to the primary analysis, CV adjusted for STS score, type of surgery, history of MI, and glucose levels averaged over the first 4 postoperative hours was associated with the MAE.
The results of this study suggest that postoperative glycemic variability is a predictor of MAEs, especially deep sternal wound infection. Postoperative glycemic variability is increased in patients with poor preoperative glycemic control (HbA1C ≥6.5%). This suggests that improving glycemic variability after surgery, particularly in patients with preoperative HbA1C ≥6.5%, may provide a strategy to reduce MAEs after cardiac surgery.
This work expands on a previous finding that demonstrates the relationship between preoperative blood glucose control and MAEs after cardiac surgery by suggesting the importance of postoperative glycemic variability.10 Halkos et al.10 have previously shown that a preoperative HbA1C >7%, the established standard for long-term glucose control at that time, was associated with MAE. They also derived HbA1C cutoff values for mortality (threshold, 8.6%; OR, 4), renal failure (threshold, 6.7; OR, 2.1), cerebrovascular accident (threshold, 7.6; OR, 2.24), and deep sternal wound infection (threshold, 7.8; OR, 5.3). Our patient population differs from that study in that the Gaussian distribution for HbA1C values was lower with lesser spread as shown in Figure 1. Despite this, there continued to be an association between MAEs and higher preoperative HbA1C values. Furthermore, Halkos et al.11 did not attempt to show the relationship between preoperative HbA1C and postoperative glycemic variability. In our study, glycemic variability was associated with risk for MAEs. Glycemic variability can be due to many factors such as effectiveness of preoperative glycemic control, perioperative stress, and method of blood glucose control used.
Tight blood glucose control in the range of 80 to 110 mg/dL has been questioned.7,8 With moderate control goal of <180 mg/dL, previous work has shown differences in the postoperative outcome in patients undergoing noncardiac surgery between IV insulin infusions and those receiving intermittent insulin therapy.20 The differences in postoperative outcome, even in the moderately controlled patients, with established insulin infusion therapy can perhaps be attributed to either or both their baseline, long-term control and their blood glucose variability as suggested by our study.
Blood glucose variability has been shown by Egi et al.13 to influence MAEs in critically ill patients (both medical and surgical). In this study, the authors concluded that even though both the groups had similar postoperative blood glucose values, blood glucose variability as measured by CV may have played a role in contributing to the adverse postoperative outcomes. This issue was highlighted by Duncan et al.21 in a large retrospective series of cardiac surgery patients. Glycemic variability when measured by a mean absolute glucose change per hour22 or an SD14 was associated with increased mortality in critically ill patients. None of these studies has shown a way to predict those patients who are at increased risk of glycemic variability. Neither have they shown any relationship between long-term glucose control and glycemic variability in the ICU.
There are several plausible explanations of why glycemic variability might lead to MAEs after cardiac surgery. Glycemic variability has been shown to increase cell apoptosis in human umbilical vein endothelial cells,23 cause endothelial injury in diabetics,24 and induce neuropathy.25 Although the exact mechanism is yet to be elucidated, it has been proposed that oscillation of glucose levels leads to increased free radical formation. It is possible that the cells are not able to sufficiently increase their own intracellular antioxidant defenses.24 This results in endothelial dysfunction and oxidative stress during oscillation of glucose levels.
Blood glucose variability as assessed by CV was similar between the high and low HbA1C groups when analyzed in those with a history of diabetes. It is possible that blood glucose variability might play an important role in nondiabetics and their postoperative outcome. Unfortunately, the association between HbA1C and postoperative blood glucose variability may not be modifiable. The possible connection between preoperative HbA1C, postoperative blood glucose control, and postoperative outcomes has not yet been shown. Whether or not blood glucose variability is a modifiable factor is unknown and requires further study. Perhaps patients with higher HbA1C levels might need a different protocol to reduce variability.
In our study, the final model with preoperative risk factors alone showed that STS morbidity and mortality risk index was a significant predictor of MAEs. Future studies should evaluate whether adding preoperative HbA1C instead of a history of diabetes to the existing STS risk prediction model26 or to the European system for cardiac operative risk evaluation27 improves the accuracy and discrimination28–30 of these indices.
Our study has certain limitations. Even though the composite outcome was the primary outcome, deep sternal infection was the major factor influencing the results of the relationship between postoperative glycemic variability and MAEs. Given the small number of patients in our study, we cannot make conclusions on the role of postoperative glycemic variability and other component outcomes of MAEs such as stroke and MI. Postoperative blood glucose measurements were done only during ICU admission or for the first 24 hours, whichever was shorter. The number of measurements and the timing of measurements were not uniform among all patients after the first 24 hours. Unmeasured blood glucose levels during and beyond this period could have influenced the postoperative outcomes. Even though a repeated measures analysis for postoperative blood glucose levels was not performed, graphical representation of mean and SD of blood glucose gives a clear picture of the significant overlap between the 2 groups. Furthermore, the incidence of hypoglycemia can be well captured by a continuous glucose monitoring device. Our hourly measurements could have missed some hypoglycemic episodes. Continuous glucose monitoring is not a standard of care for managing critically ill patients worldwide. We did not analyze the intraoperative blood glucose levels in our study. However, previous work by Gandhi et al.31 and Duncan et al.21 have not shown that intraoperative blood glucose management influences postoperative outcomes when the control and intervention groups have blood glucose levels <200 mg/dL. Although we had established protocols to maintain intraoperative blood glucose levels between 100 and 150 mg/dL, these values were not easily obtained in all patients. Patients were divided to above and below HbA1C groups. There could have been patients with HbA1c >6.5% with no diagnosis of diabetes and <6.5% with diagnosis of diabetes. The influence of diabetes diagnosis could have influenced the results. Finally, we did not have information to distinguish whether patients had type 1 or type 2 diabetes, as STS does not collect these data.
In summary, in patients undergoing CABG surgery, postoperative glycemic variability was found to be associated with MAEs, especially deep sternal wound infection. Postoperative glycemic variability was found to be higher in patients with a preoperative HbA1C ≥6.5%. Currently it is unknown whether blood glucose control variability as measured by CV is a modifiable risk factor.
APPENDIX 2: STS VERSION 2.61 DEFINITIONS FOR POSTOPERATIVE OUTCOMES Cited Here
A history of diabetes, regardless of duration of disease or need for anti-diabetic agents.
Indicate whether a postoperative event occurred during the hospitalization for surgery. This includes the entire postoperative period up to discharge, even if over 30 days.
Myocardial Infarction (0–24 Hours Postop)
Indicate the presence of a peri-operative Myocardial Infarction (MI) (0–24 hours postop) as documented by the following criteria:
- The CK-MB (or CK if MB not available) must be greater than or equal to 5 times the upper limit of normal, with or without new Q waves present in two or more contiguous ECG leads. No symptoms required.
- (>24 hours post-op) Indicate the presence of a perioperative MI (>24 hours post-op) as documented by at least one of the following criteria:
- Evolutionary ST-segment elevations
- Development of new Q-waves in two or more contiguous ECG leads
- New or presumably new LBBB pattern on the ECG
- The CK-MB (or CK if MB not available) must be greater than or equal to 3 times the upper limit of normal.
Operative re-intervention was required for bleeding/tamponade, valvular dysfunction, graft occlusion and or other complications.
Indicate whether the patient, within 30 days postoperatively, had a deep sternal infection involving muscle, bone, and/or mediastinum REQUIRING OPERATIVE INTERVENTION. Must have ALL of the following conditions:
- Wound opened with excision of tissue (I&D) or re-exploration of mediastinum
- Positive culture
- Treatment with antibiotics
Indicate whether the patient had Pneumonia diagnosed by any of the following: Positive cultures of sputum, transtracheal fluid, bronchial washings, and/or clinical findings consistent with the diagnosis of pneumonia (which may include chest x-ray diagnostic of pulmonary infiltrates).
Indicate whether the patient had acute or worsening renal failure resulting in one or more of the following:
- Increase of serum creatinine to >2.0, and 2x most recent preoperative creatinine level.
- A new requirement for dialysis postoperatively.
Indicate whether the patient has a postoperative stroke (i.e., any confirmed neurological deficit of abrupt onset caused by a disturbance in cerebral blood supply) that did not resolve within 24 hours.
A.Logistic regression equations, correlation matrix and Interactions
1) Quartiles of glucose levels and CV for postoperative MAE (Major Adverse Events)
2) Model with preoperative risk factors
We have applied the stepwise forward logistic regression model for pre-operative analysis. Stay criteria p<0.10.
- Step 1: Procedure type: Valvular surgery stays
- Step 2: Comorbidities (HTN, CHF, history of MI, history of CVA, COPD) and STS quartiles. Previous MI and STS quartiles stay.
- Step 3: HBA1C stays
3) Model with perioperative risk factors (including Coefficient of Variation, CV)
We have applied the stepwise forward logistic regression model for postoperative analysis. The following variables were included: age, history of MI, CVA, CHF and COPD; HBA1C levels (above vs. below 6.5%), STS score, valvular surgery, levels of glucose and CV. Final model was re-applied on the total population.
B) Correlation matrix:
We have introduced interactions between the variables included in the final models. Each interaction was added separately to the model. The following tables present p-values for the interactions terms. We have shown the models for the interaction terms with the borderline significance.
1) Pre-operative risk assessment model, p-values for interaction term are presented (a*b)
2) Perioperative risk (includes preoperative and postoperative variables such as Coefficient of Variation) assessment model, p-values for interaction term are presented (a*b)
Name: Balachundhar Subramaniam, MD, MPH.
Contribution: This author helped design the study, conduct the study, analyze the data, and write the manuscript.
Attestation: Balachundhar Subramaniam has seen the original study data, reviewed the analysis of the data, approved the final manuscript, and is the author responsible for archiving the study files.
Name: Adam Lerner, MD.
Contribution: This author helped conduct the study and write the manuscript.
Attestation: Adam Lerner reviewed the analysis of the data and approved the final manuscript
Name: Victor Novack, MD, PhD.
Contribution: This author helped design the study, analyze the data, and write the manuscript.
Attestation: Victor Novack has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.
Name: Kamal Khabbaz, MD.
Contribution: This author helped conduct the study and write the manuscript.
Attestation: Kamal Khabbaz reviewed the analysis of the data and approved the final manuscript.
Name: Maya Paryente-Wiesmann, MD.
Contribution: This author helped analyze the data and write the manuscript.
Attestation: Maya Paryente-Wiesmann has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.
Name: Philip Hess, MD, PhD.
Contribution: This author helped conduct the study and write the manuscript.
Attestation: Philip Hess reviewed the analysis of the data and approved the final manuscript.
Name: Daniel Talmor, MD, MPH.
Contribution: This author helped design the study, conduct the study, analyze the data, and write the manuscript.
Attestation: Daniel Talmor has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.
This manuscript was handled by: Charles W. Hogue, Jr., MD.
The authors thank Michelle A. Doherty, STS coordinator, for database maintenance and Victoria Nielsen and Sapna Govindan for perioperative glycemic control data collection and database merger.
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