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A New Predictive Model for In-Hospital Major Adverse Cardiac and Cerebrovascular Events in Chinese Patients After Major Noncardiac Surgery

Open AccessPublished:October 29, 2022DOI:https://doi.org/10.1016/j.amjcard.2022.09.028
      Prediction tools focused on cardiovascular and cerebrovascular events after noncardiac surgery are lacking, particularly for Chinese patients. We developed and validated what we believe is a new predictive tool for postoperative major cardiovascular and cerebrovascular events (MACCEs) in Chinese patients in this study. Overall, 401 variables derived from 598 patients who received noncardiac surgery at our center were retrospectively analyzed to develop and validate the new predictive model for MACCEs during hospitalization. The 7 strongest predictors for MACCEs in the development cohort were chronic heart failure, age, atrial fibrillation, general anesthesia, history of coronary heart disease, high-risk procedures, and lymphocyte count. The area under the receiver operating characteristic curve was 0.698 (95% confidence interval 0.616 to 0.780) for the new predictive tool with the validation cohort. Receiver operating characteristic curve analysis showed the new predictive tool had better performance than the Revised Cardiac Risk Index and the American College of Surgeons National Surgical Quality Improvement Program scores. This new predictive tool is effective for the prediction of postoperative MACCEs in patients who undergo noncardiac surgery.
      With the aging population and continuous advances in medical technology, patients are increasingly who underwent noncardiac surgery.
      • Devereaux PJ
      • Sessler DI.
      Cardiac complications in patients undergoing major noncardiac surgery.
      Moreover, the total number of patients who undergo major noncardiac surgery and those with cardiac complications is still increasing.
      • Siddiqui NF
      • Coca SG
      • Devereaux PJ
      • Jain AK
      • Li L
      • Luo J
      • Parikh CR
      • Paterson M
      • Philbrook HT
      • Wald R
      • Walsh M
      • Whitlock R
      • Garg AX
      Secular trends in acute dialysis after elective major surgery–1995 to 2009.
      Accordingly, it is important to evaluate the risk of major adverse cardiovascular and cerebrovascular events (MACCEs) for patients scheduled for noncardiac surgeries. Guidelines recommended 3 risk prediction tools to predict the risk of perioperative major adverse cardiovascular events in patients who undergo noncardiac surgery,
      • Davenport DL
      • O'Keeffe SD
      • Minion DJ
      • Sorial EE
      • Endean ED
      • Xenos ES.
      Thirty-day NSQIP database outcomes of open versus endoluminal repair of ruptured abdominal aortic aneurysms.
      • Rabbitts JA
      • Nuttall GA
      • Brown MJ
      • Hanson AC
      • Oliver WC
      • Holmes DR
      • Rihal CS.
      Cardiac risk of noncardiac surgery after percutaneous coronary intervention with drug-eluting stents.
      • Chia KK
      • Park JJ
      • Postle J
      • Cottrill A
      • Ward MR.
      Frequency of late drug-eluting stent thrombosis with non-cardiac surgery.
      including the Revised Cardiac Risk Index (RCRI), American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) Myocardial Infarction and Cardiac Arrest, and ACS NSQIP surgical risk calculator. These predictive tools are focused on adverse cardiovascular events, not cerebrovascular events. The RCRI was developed in 1999, and its predictive efficacy has been questioned.
      • Davis C
      • Tait G
      • Carroll J
      • Wijeysundera DN
      • Beattie WS.
      The Revised Cardiac Risk Index in the new millennium: a single-centre prospective cohort re-evaluation of the original variables in 9,519 consecutive elective surgical patients.
      ,
      • Devereaux PJ
      • Bradley D
      • Chan MT
      • Walsh M
      • Villar JC
      • Polanczyk CA
      • Seligman BG
      • Guyatt GH
      • Alonso-Coello P
      • Berwanger O
      • Heels-Ansdell D
      • Simunovic N
      • Schünemann H
      • Yusuf S.
      VISION Pilot Study Investigators
      An international prospective cohort study evaluating major vascular complications among patients undergoing noncardiac surgery: the VISION Pilot Study.
      NSQIP Myocardial Infarction and Cardiac Arrest only estimates myocardial infarction and cardiac arrest, which limits its application. Research showed that it also underestimates the actual risk.
      • Gupta PK
      • Gupta H
      • Sundaram A
      • Kaushik M
      • Fang X
      • Miller WJ
      • Esterbrooks DJ
      • Hunter CB
      • Pipinos II
      • Johanning JM
      • Lynch TG
      • Forse RA
      • Mohiuddin SM
      • Mooss AN.
      Development and validation of a risk calculator for prediction of cardiac risk after surgery.
      The ACS NSQIP calculator may be too complicated to use in clinical settings.
      • Neuman HB
      • Michelassi F
      • Turner JW
      • Bass BL.
      Surrounded by quality metrics: what do surgeons think of ACS-NSQIP?.
      ,
      • Barazanchi AW
      • Xia W
      • Taneja A
      • MacCormick AD
      • Lightfoot NJ
      • Hill AG.
      Multidisciplinary survey of current and future use of emergency laparotomy risk assessment scores in New Zealand.
      Furthermore, the above predictive tools were mostly developed and validated in patients from Western countries. Therefore, in this study, we developed and validated a new predictive tool for postoperative MACCEs based on Chinese patients who underwent noncardiac surgeries and compared its predictive efficacy with the RCRI and ACS NSQIP scores.

      Methods

      We performed a single-center retrospective study including patients aged ≥18 years who underwent noncardiac surgeries at Beijing Chao Yang Hospital from January 1, 2018 to April 1, 2022. The study was conducted in accordance with the principles of patient research stipulated in the Declaration of Helsinki, and the study protocol was approved by the ethics committee of Beijing Chaoyang Hospital (2021-S-476). All data were anonymous, and the need for informed consent was waived owing to the retrospective study design. Excluded from the study were (1) patients who received low-risk procedures including breast surgery, dental surgery, endoscopic procedures, ophthalmic surgery, gynecological surgery, and plastic surgery,
      • Fleisher LA
      • Fleischmann KE
      • Auerbach AD
      • Barnason SA
      • Beckman JA
      • Bozkurt B
      • Davila-Roman VG
      • Gerhard-Herman MD
      • Holly TA
      • Kane GC
      • Marine JE
      • Nelson MT
      • Spencer CC
      • Thompson A
      • Ting HH
      • Uretsky BF
      • Wijeysundera DN
      2014 ACC/AHA guideline on perioperative cardiovascular evaluation and management of patients undergoing noncardiac surgery: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Developed in collaboration with the American College of Surgeons, American Society of Anesthesiologists, American Society of Echocardiography, American Society of Nuclear Cardiology, Society of Cardiovascular Anesthesiologists, and Society of Vascular Medicine Endorsed by the Society of Hospital Medicine.
      or emergency surgery, transplantation, and trauma; (2) patients with American Society of Anesthesiologists (ASA) classification V or VI; (3) patients receiving palliative surgery for advanced malignant tumors; (4) patients with cardiomyopathy or congenital heart disease; (5) patients with a history of surgery within 4 months before inclusion; or (6) patients with incomplete data for the calculation of RCRI and ACS NSQIP scores.
      The eligible patients were divided into development and validation cohorts based on the time of admission (dates of January 1, 2018 to January 1 2020 for development cohort and dates of January 2, 2020 to April 1, 2022 for validation cohort). Preoperative and intraoperative variables and clinical characteristics were extracted and analyzed from the medical charts. The putative predictors were chosen on the basis of previous studies and the clinical experiences of the investigators.
      The primary outcome measure was MACCEs after surgery during hospitalization. MACCEs were defined as a composite outcome including all-cause death, acute myocardial infarction, heart failure, ventricular fibrillation, complete heart block, cardiac arrest, and ischemic stroke.
      SPSS 23.0 for windows and R version 4.0.2 were used for data analysis. Continuous variables conforming to a normal distribution were presented as mean ± SD, and continuous variables not conforming to a normal distribution were presented as the median and interquartile range. Categorical variables were summarized as absolute values and percentages. To compare the differences between the 2 groups, the t test with normal distribution data, Mann-Whitney U test with tilt data, and chi-square test with classified data were applied separately. We used the adaptive least absolute shrinkage and selection operator to identify independent factors and to create the model for MACCE prediction after noncardiac surgeries. The final model was presented as a nomogram. We applied the receiver operating characteristic (ROC) curve to calculate the sensitivity and specificity of the model. The calibration plot was derived based on regression analysis. The areas under the ROC curves (AUCs) of the new prediction model, RCRI, and ACS NSQIP surgical risk calculator were calculated separately to compare the predictive efficacies of the 3 models in the validation cohort. The differences in the AUCs were also compared using the DeLong test. We used a random forest model to predict missing values. p Values were 2-sided, and p <0.05 was identified as statistically significant.

      Results

      From January 1, 2018 to April 1, 2022, a total of 25,969 surgeries were performed in our hospital, and 14,124 cases were excluded according to the predefined criteria. In the remaining 11,845 cases, MACCEs occurred in 124 cases. In the 124 cases, 18 cases were excluded for the following reasons: 1 patient died unexpectedly because of an unrelated accident; 8 patients were classified as ASA grade 5; and 9 patients received advanced palliative surgery for malignant tumors. Matched by the surgery type, 492 patients with no events after noncardiac surgeries were randomly selected, resulting in a total of 598 patients in this study (Supplementary Figure 1). These patients were divided into development and validation cohorts based on the dates of admission. The baseline characteristics of patients from the 2 cohorts are shown in Table 1.
      Table 1Demographics and baseline characteristics of the patients in the development and validation cohort
      CharacteristicsDevelopment cohortValidation cohortχ2/Z valuep value
      Men186 (54.2)123 (48.2)2.1030.147
      Age (years)65 (21)71 (21)−3.960.000
      HLP120 (35)57 (22.4)11.2010.001
      HBP154 (44.9)146 (57.3)8.9330.003
      Stroke/TIA49 (14.3)44 (17.3)2.4150.299
      Insulin treatment26 (7.6)34 (13.3)5.3630.021
      CHF9 (2.6)13 (5.1)4.7030.095
      AF15 (4.4)32 (12.5)13.5010.000
      CAD64 (18.7)51 (20)0.1690.681
      Chronic respiratory disease24 (7)18 (7.1)0.7450.689
      Disseminated cancer17 (5)12 (4.7)0.0200.888
      Current smoker within 1 year88 (25.7)53 (20.8)1.9260.165
      High-risk type of surgery140 (40.8)110 (43.1)0.3240.569
      General anesthesia246 (71.7)143 (56.1)15.7400.000
      Cr > 2.0 mg/dL10 (2.9)9 (3.5)0.9190.632
      Dialysis3 (0.9)5 (2.0)1.3070.253
      PLT218 (91)195 (92)−3.0100.003
      Neutrophil count4.3 (3)4.12 (3)−0.0550.956
      Lymphocyte count1.47 (1)1.43 (1)−1.5060.132
      N/L%2.89 (3)2.81 (4)−0.7900.429
      ST-T changes on ECG72 (21)80 (31.4)14.5960.001
      VHD11 (3.2)17 (6.7)4.0100.135
      ASA class
      140 (11.7)17 (6.7)4.2320.040
      2238 (69.4)150 (58.8)7.1640.007
      363 (18.4)77 (30.2)11.4130.001
      41 (0.3)11 (4.5)12.7190.000
      MACCEs
      AMI13 (3.8)7 (2.7)0.4940.482
      HF26 (7.6)31 (12.2)3.5530.059
      Death12 (3.5)14 (5.5)1.3950.238
      Stroke8 (2.3)10 (3.9)1.2650.261
      Data presented as mean (standard deviation) for continuous variables or n (%) for counting data. High-risk surgeries included intraperitoneal, intrathoracic or suprainguinal vascular procedures according to the modified RCRI score.
      AF = atrial fibrillation; CAD = history of coronary heart disease; CHF = chronic heart failure; Cr > 2.0mg/dL = preoperative serum creatinine > 2.0 mg/dL; ECG = electrocardiogram; HBP = high blood pressure; HF = heart failure; HLP = hyperlipidemia; MACCEs = major adverse cardiovascular and cerebrovascular events; N/L% = neutrophil to lymphocyte ratio; PLT = platelets; VHD = valvular heart disease.
      The new risk score was developed based on 343 patients (55 with MACCEs) in the development cohort. We evaluated 401 variables, including clinical variables, electrocardiogram ST-T ischemic changes, cardiac ultrasound parameters, and laboratory tests, to construct a new model. Using Lasso logistic regression, we screened 13 predictors from the 401 potential variables. The predictors were history of chronic heart failure (CHF), age, atrial fibrillation (AF), general anesthesia, history of coronary heart disease (CAD), high-risk procedures, lymphocyte count, platelet count, ST-T ischemic change of electrocardiogram, chronic respiratory disease, serum creatinine >2.0 mg/100 ml (creatinine >2.0 mg/100 ml), valvular heart disease, and disseminated cancer. The weights of all predictive variables are shown in Figure 1. Only the 7 strongest predictors (CHF, age, AF, general anesthesia, history of CAD, high-risk procedures, and lymphocyte count) based on the likelihood ratio were selected and used to construct the final model. Integrating the 7 variables, we were able to build a nomogram for predicting in-hospital MACCEs in patients who underwent major noncardiac surgery (Figure 2). The sum of the corresponding scores of each variable in the nomogram is the total score of the patient, and a vertical line is made at the total score. The corresponding prediction probability is the perioperative incidence of MACCE in the patient with noncardiac surgery. The regression equation of the new prediction model is the following: L = age + CHF × 2.872/0.043 + AF × 2.116/0.043 + general anesthesia × 1.809/0.043 + history of CAD × 1.080/0.043 + high-risk procedures × 0.905/0.043 − lymphocyte count × 0.499/0.043. Multivariate logistic analysis of risk factors for perioperative MACCE is shown in Table 2.
      Figure 1
      Figure 1Importance and the nomogram of variables of the prediction model. (A) Images indicating the importance of each variable in the full model as measured with the partial Wald chi-square minus the predictor degrees of freedom; (B) Images indicating the nomogram for each variable of the prediction model. Cr = creatinine; PLT = platelets.
      Figure 2
      Figure 2ROC curves and calibration plots for the new prediction model in the development cohort. (A) ROC curves of the new prediction model with the development cohort; (B) Calibration plots for the new prediction model.
      Table 2Multivariate logistic analysis of risk factors for perioperative MACCE
      VariablesBSEWaldp valueOR95% CI
      Age0.0430.0167.1450.0081.0441.012–1.077
      CHF2.8720.89510.3050.00117.6793.061–102.119
      AF2.1160.66610.0920.0018.3012.249–30.632
      General anesthesia1.0890.4914.9190.0272.9721.135–7.782
      History of CAD1.0800.3977.4200.062.9451.354–6.408
      High-risk procedures0.9050.3905.3880.0202.4731.151–5.312
      Lymphocyte count−0.4990.2892.9870.0440.6070.345–1.069
      Constant−5.7011.44315.0670.0000.003
      The ROC curve for the new risk score is shown in Figure 2. The AUC was 0.804 (95% confidence interval 0.737 to 0.871). The calibration plot for the new prediction model is shown in Figure 2.
      We validated the new risk score in the validation cohort. The ROC curves are shown in Figure 3, and the AUC was 0.698 (95% confidence interval 0.616 to 0.780) for the validation cohort. The calibration plot for the new prediction model is shown in Figure 3, and the Brier score was 0.122.
      Figure 3
      Figure 3Calibration plots for the new prediction model in the validation cohort. (A) ROC curves of the new prediction model with the validation cohort; (B) Calibration plots for the new prediction model with the validation cohort.
      Because the end point of the RCRI does not include ischemic stroke, we removed 10 cases with ischemic stroke as the end point in the validation cohort. The final 245 cases (41 cases with MACCE and 204 cases without MACCE) were subjected to subgroup analysis. Comparisons between the new risk score and RCRI with AUCs are presented in Figure 4, which showed a better predictive efficacy for the new risk score than that of the RCRI.
      Figure 4
      Figure 4ROC curves for the efficacy of the new prediction model, the modified RCRI score, and the ACS NSQIP surgical risk calculator. (A) ROC curves for the efficacy of the new prediction model and the modified RCRI score in the subgroup analysis; (B) ROC curves for the efficacy of the new prediction model and the ACS NSQIP surgical risk calculator in the subgroup analysis.
      The end point of ACS NSQIP calculator also does not include cerebral infarction, so we also removed 10 cases in the validation cohort with cerebral infarction as the end point. In contrast, because the ACS NSQIP calculator does not include the operation type for 32 cases in the validation cohort, the actual number of cases analyzed in the comparison between the new risk score and the ACS NSQIP calculator was 213 (36 cases with MACCE and 177 cases without MACCE). Comparisons between the new risk score and the ACS NSQIP calculator with AUCs are presented in Figure 4, which revealed a better predictive efficacy for the new risk score than that of the ACS NSQIP calculator.

      Discussion

      In this study, we developed and validated what is, to the best of our knowledge, a new risk score for the prediction of MACCEs in patients who underwent major noncardiac surgery. The results showed that the new risk score based on 7 variables (history of CHF, age, AF, general anesthesia, history of CAD, high-risk procedures, and lymphocyte count) was associated with better predictive performance than that of the RCRI and ACS NSQIP scores for MACCEs after noncardiac surgery. The predictive efficacy of the new risk score was further validated in an independent cohort. Taken together, these results suggested that the nomogram showed satisfactory discriminative ability in development and validation cohorts comprising Chinese patients receiving noncardiac surgery.
      The results of the study showed that a history of CHF, age, general anesthesia,
      • Li G
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      Epidemiology of anesthesia-related mortality in the United States, 1999–2005.
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      • Biccard BM
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      Vascular Events In Noncardiac Surgery Patients Cohort Evaluation (VISION) Study Investigators
      Yusuf SAssociation between postoperative troponin levels and 30-day mortality among patients undergoing noncardiac surgery.
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      Rates and patterns of death after surgery in the United States, 1996 and 2006.
      history of CAD, high-risk procedures, and lymphocyte count
      • Seizer P
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      Platelet-monocyte interactions–a dangerous liaison linking thrombosis, inflammation and atherosclerosis.
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      Lymphocyte cell counts in middle age are positively associated with subsequent all-cause and cardiovascular mortality.
      • Provinciali M
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      Reference values for CD4+ and CD8+ T lymphocytes with naïve or memory phenotype and their association with mortality in the elderly.
      were common risk factors related to MACCEs. Notably, the clinical implications of preoperative AF are being recognized.
      • Chugh SS
      • Havmoeller R
      • Narayanan K
      • Singh D
      • Rienstra M
      • Benjamin EJ
      • Gillum RF
      • Kim YH
      • McAnulty JH
      • Jr Zheng ZJ
      • Forouzanfar MH
      • Naghavi M
      • Mensah GA
      • Ezzati M
      • Murray CJ
      Worldwide epidemiology of atrial fibrillation: a Global Burden of Disease 2010 Study.
      Recent studies have shown that preoperative AF is an independent risk factor for MACCEs, and postoperative cardiovascular events such as heart failure and stroke are more common in patients with a preoperative history of AF who undergo noncardiac surgery
      • Urbanek C
      • Palm F
      • Buggle F
      • Wolf J
      • Safer A
      • Becher H
      • Grau AJ.
      Recent surgery or invasive procedures and the risk of stroke.
      • van Diepen S
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      • McAlister FA
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      Mortality and readmission of patients with heart failure, atrial fibrillation, or coronary artery disease undergoing noncardiac surgery: an analysis of 38 047 patients.
      • McAlister FA
      • Jacka M
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      • Devereaux PJ.
      The prediction of postoperative stroke or death in patients with preoperative atrial fibrillation undergoing non-cardiac surgery: a VISION sub-study.
      than in those without a history of AF.
      Several risk factors that were identified in other risk prediction models, including ASA class, diabetes, current smoking status, and body mass index, were also included and analyzed in our study, but were not correlated with complications in our study. The absence of these factors in our risk score should not be considered an indication that these factors are not significantly correlated with postoperative MACCEs in other populations.
      The nomogram had higher accuracy and better clinical practicability than did the RCRI and the ACS NSQIP surgical risk calculator. The RCRI was developed early. Its ability to predict perioperative MACCEs in modern noncardiac surgery is limited. The ACS NSQIP calculator did not show better discrimination ability or clinical practicability than that of the new prediction model, possibly owing to differences in nationality, disease type, disease characteristics, medical technology, and surgical methods. Serious complications in the ACS NSQIP calculator include, but are not limited to, the end point of our study, which may be another reason for the better predictive ability of our risk score.
      The advantages of this new risk score mainly include the simple and practical use. Firstly, it can be immediately implemented in clinical settings using the nomogram. Secondly, surgical details were not part of the new risk score, unlike the ACS NSQIP calculator. Another advantage of the new prediction model is that it is based on data for the Chinese population. China has a large population and a large volume of surgeries, but there is no risk prediction tool based on Chinese patients. It is hoped that the construction of our prediction model can provide an effective prediction tool for Chinese patients and surgeons.
      This study was not a multicenter study, which limited the generalization of its results. External validation is still needed to ensure the generality of the model. Furthermore, the study is subject to the limitations of a retrospective review. Some biomarkers that may have predictive ability could not be systematically monitored during the perioperative period and therefore could not be incorporated into this model. Consequently, further prospective studies are needed. Another limitation of our study was that the influence of preoperative prophylactic measures on the outcome of MACCE was not analyzed, which may also affect the predictive efficacy of the new model.
      In conclusion, we developed, validated, and calibrated a new nomogram to predict MACCEs after major noncardiac surgery using data for Chinese patients. The risk predictors of the nomogram were history of CHF, age, AF, general anesthesia, history of CAD, high-risk procedures, and lymphocyte count. Using a cohort of Chinese patients, our evaluation score showed better discrimination and practicability than those of the RCRI and the ACS NSQIP calculator. Taken together, although further validation is needed, this risk score may become an effective assistant and informed consent tool for clinical decision-making regarding patients with noncardiac surgery.

      Acknowledgment

      The authors thank Jia-Chen Hu, PhD, from GE Healthcare for English language editing and revision of the article.

      Author Contributions

      Xuejiao Wua and Xinchun Yang conceived and designed this study. Xuejiao Wua and Mei Hua collected the data. Xuejiao Wua and Kuibao Lib performed statistical analysis and interpretation of data. Xuejiao Wua wrote the manuscript. Jianjun Zhanga, Kuibao Lib, and Xinchun Yang revised the manuscript. All authors read and approved the final manuscript.

      Disclosures

      The authors have no conflicts of interest to declare.

      Data availability statement

      The data that support the findings of this study are available on request from the corresponding author (Dr. Yang). The data are not publicly available because they contain information that could compromise research participant privacy/consent.

      Appendix. Supplementary materials

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