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Lin S, Huang X, Zhang Y, Zhang X, Cheng E, Liu J. Intraoperative Variables Enhance the Predictive Performance of Myocardial Injury in Patients with High Cardiovascular Risk After Thoracic Surgery When Added to Baseline Prediction Model. Ther Clin Risk Manag 2023; 19:435-445. [PMID: 37252064 PMCID: PMC10225131 DOI: 10.2147/tcrm.s408135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 05/08/2023] [Indexed: 05/31/2023] Open
Abstract
Purpose Myocardial injury after non-cardiac surgery is closely related to major adverse cardiac and cerebrovascular event and is difficult to identify. This study aims to investigate how to predict the myocardial injury of thoracic surgery and whether intraoperative variables contribute to the prediction of myocardial injury. Methods The prospective study included adult patients with high cardiovascular risk who underwent elective thoracic surgery from May 2022 to October 2022. Multivariate logistic regression was used to establish a model with baseline variables and a model with baseline and intraoperative variables. We compare the predictive performance of two models for postoperative myocardial injury. Results In general, 31.5% (94 of 298) occurred myocardial injury. Age ≥65 years old, obesity, smoking, preoperative hsTnT, and one-lung ventilation time were independent predictors of myocardial injury. Compared with baseline model, the intraoperative variables improved model fit, modestly improved the reclassification (continuous net reclassification improvement 0.409, 95% CI, 0.169 to 0.648, P<0.001, improved integrated discrimination 0.036, 95% CI, 0.011 to 0.062, P<0.01) of myocardial injury cases, and achieved higher net benefit in decision curve analysis. Conclusion The risk stratification and anesthesia management of high-risk patients are essential. The addition of intraoperative variables to the baseline predictive model improved the performance of the overall model of myocardial injury and helped anesthesiologists screen out the patients at the greatest risk for myocardial injury and adjust anesthesia strategies.
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Affiliation(s)
- Shuchi Lin
- Department of Anesthesiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, People’s Republic of China
| | - Xiaofan Huang
- Department of Anesthesiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, People’s Republic of China
| | - Ying Zhang
- Department of Anesthesiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, People’s Republic of China
| | - Xiaohan Zhang
- Department of Anesthesiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, People’s Republic of China
| | - Erhong Cheng
- Department of Anesthesiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, People’s Republic of China
| | - Jindong Liu
- Department of Anesthesiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, People’s Republic of China
- Jiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical University, Xuzhou, Jiangsu, People’s Republic of China
- Jiangsu Province Key Laboratory of Anesthesia and Analgesia Application Technology, Xuzhou Medical University, Xuzhou, Jiangsu, People’s Republic of China
- NMPA Key Laboratory for Research and Evaluation of Narcotic and Psychotropic Drugs, Xuzhou Medical University, Xuzhou, Jiangsu, People’s Republic of China
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Douville NJ, Larach DB, Lewis A, Bastarache L, Pandit A, He J, Heung M, Mathis M, Wanderer JP, Kheterpal S, Surakka I, Kertai MD. Genetic predisposition may not improve prediction of cardiac surgery-associated acute kidney injury. Front Genet 2023; 14:1094908. [PMID: 37124606 PMCID: PMC10133500 DOI: 10.3389/fgene.2023.1094908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 03/21/2023] [Indexed: 05/02/2023] Open
Abstract
Background: The recent integration of genomic data with electronic health records has enabled large scale genomic studies on a variety of perioperative complications, yet genome-wide association studies on acute kidney injury have been limited in size or confounded by composite outcomes. Genome-wide association studies can be leveraged to create a polygenic risk score which can then be integrated with traditional clinical risk factors to better predict postoperative complications, like acute kidney injury. Methods: Using integrated genetic data from two academic biorepositories, we conduct a genome-wide association study on cardiac surgery-associated acute kidney injury. Next, we develop a polygenic risk score and test the predictive utility within regressions controlling for age, gender, principal components, preoperative serum creatinine, and a range of patient, clinical, and procedural risk factors. Finally, we estimate additive variant heritability using genetic mixed models. Results: Among 1,014 qualifying procedures at Vanderbilt University Medical Center and 478 at Michigan Medicine, 348 (34.3%) and 121 (25.3%) developed AKI, respectively. No variants exceeded genome-wide significance (p < 5 × 10-8) threshold, however, six previously unreported variants exceeded the suggestive threshold (p < 1 × 10-6). Notable variants detected include: 1) rs74637005, located in the exonic region of NFU1 and 2) rs17438465, located between EVX1 and HIBADH. We failed to replicate variants from prior unbiased studies of post-surgical acute kidney injury. Polygenic risk was not significantly associated with post-surgical acute kidney injury in any of the models, however, case duration (aOR = 1.002, 95% CI 1.000-1.003, p = 0.013), diabetes mellitus (aOR = 2.025, 95% CI 1.320-3.103, p = 0.001), and valvular disease (aOR = 0.558, 95% CI 0.372-0.835, p = 0.005) were significant in the full model. Conclusion: Polygenic risk score was not significantly associated with cardiac surgery-associated acute kidney injury and acute kidney injury may have a low heritability in this population. These results suggest that susceptibility is only minimally influenced by baseline genetic predisposition and that clinical risk factors, some of which are modifiable, may play a more influential role in predicting this complication. The overall impact of genetics in overall risk for cardiac surgery-associated acute kidney injury may be small compared to clinical risk factors.
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Affiliation(s)
- Nicholas J. Douville
- Department of Anesthesiology, University of Michigan Health System, Ann Arbor, MI, United States
- Center for Computational Medicine and Bioinformatics, University of Michigan Health System, Ann Arbor, MI, United States
- Michigan Integrated Center for Health Analytics and Medical Prediction, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, United States
| | - Daniel B. Larach
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Adam Lewis
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Anita Pandit
- Center for Statistical Genetics and Precision Health Initiative, University of Michigan, Ann Arbor, MI, United States
| | - Jing He
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Michael Heung
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Michael Mathis
- Department of Anesthesiology, University of Michigan Health System, Ann Arbor, MI, United States
- Center for Computational Medicine and Bioinformatics, University of Michigan Health System, Ann Arbor, MI, United States
- Michigan Integrated Center for Health Analytics and Medical Prediction, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, United States
| | - Jonathan P. Wanderer
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Sachin Kheterpal
- Department of Anesthesiology, University of Michigan Health System, Ann Arbor, MI, United States
| | - Ida Surakka
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Miklos D. Kertai
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, United States
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Scarpa JR, Elemento O. Multi-omic molecular profiling and network biology for precision anaesthesiology: a narrative review. Br J Anaesth 2023:S0007-0912(23)00125-3. [PMID: 37055274 DOI: 10.1016/j.bja.2023.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 02/21/2023] [Accepted: 03/04/2023] [Indexed: 04/15/2023] Open
Abstract
Technological advancement, data democratisation, and decreasing costs have led to a revolution in molecular biology in which the entire set of DNA, RNA, proteins, and various other molecules - the 'multi-omic' profile - can be measured in humans. Sequencing 1 million bases of human DNA now costs US$0.01, and emerging technologies soon promise to reduce the cost of sequencing the whole genome to US$100. These trends have made it feasible to sample the multi-omic profile of millions of people, much of which is publicly available for medical research. Can anaesthesiologists use these data to improve patient care? This narrative review brings together a rapidly growing literature in multi-omic profiling across numerous fields that points to the future of precision anaesthesiology. Here, we discuss how DNA, RNA, proteins, and other molecules interact in molecular networks that can be used for preoperative risk stratification, intraoperative optimisation, and postoperative monitoring. This literature provides evidence for four fundamental insights: (1) Clinically similar patients have different molecular profiles and, as a consequence, different outcomes. (2) Vast, publicly available, and rapidly growing molecular datasets have been generated in chronic disease patients and can be repurposed to estimate perioperative risk. (3) Multi-omic networks are altered in the perioperative period and influence postoperative outcomes. (4) Multi-omic networks can serve as empirical, molecular measurements of a successful postoperative course. With this burgeoning universe of molecular data, the anaesthesiologist-of-the-future will tailor their clinical management to an individual's multi-omic profile to optimise postoperative outcomes and long-term health.
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Affiliation(s)
- Joseph R Scarpa
- Department of Anesthesiology, Weill Cornell Medicine, New York, NY, USA.
| | - Olivier Elemento
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, Cornell University, New York, NY, USA
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Larach DB, Lewis A, Bastarache L, Pandit A, He J, Sinha A, Douville NJ, Heung M, Mathis MR, Mosley JD, Wanderer JP, Kheterpal S, Zawistowski M, Brummett CM, Siew ED, Robinson-Cohen C, Kertai MD. Limited clinical utility for GWAS or polygenic risk score for postoperative acute kidney injury in non-cardiac surgery in European-ancestry patients. BMC Nephrol 2022; 23:339. [PMID: 36271344 PMCID: PMC9587619 DOI: 10.1186/s12882-022-02964-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 09/27/2022] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Prior studies support a genetic basis for postoperative acute kidney injury (AKI). We conducted a genome-wide association study (GWAS), assessed the clinical utility of a polygenic risk score (PRS), and estimated the heritable component of AKI in patients who underwent noncardiac surgery. METHODS We performed a retrospective large-scale genome-wide association study followed by a meta-analysis of patients who underwent noncardiac surgery at the Vanderbilt University Medical Center ("Vanderbilt" cohort) or Michigan Medicine, the academic medical center of the University of Michigan ("Michigan" cohort). In the Vanderbilt cohort, the relationship between polygenic risk score for estimated glomerular filtration rate and postoperative AKI was also tested to explore the predictive power of aggregating multiple common genetic variants associated with AKI risk. Similarly, in the Vanderbilt cohort genome-wide complex trait analysis was used to estimate the heritable component of AKI due to common genetic variants. RESULTS The study population included 8248 adults in the Vanderbilt cohort (mean [SD] 58.05 [15.23] years, 50.2% men) and 5998 adults in Michigan cohort (56.24 [14.76] years, 49% men). Incident postoperative AKI events occurred in 959 patients (11.6%) and in 277 patients (4.6%), respectively. No loci met genome-wide significance in the GWAS and meta-analysis. PRS for estimated glomerular filtration rate explained a very small percentage of variance in rates of postoperative AKI and was not significantly associated with AKI (odds ratio 1.050 per 1 SD increase in polygenic risk score [95% CI, 0.971-1.134]). The estimated heritability among common variants for AKI was 4.5% (SE = 4.5%) suggesting low heritability. CONCLUSION The findings of this study indicate that common genetic variation minimally contributes to postoperative AKI after noncardiac surgery, and likely has little clinical utility for identifying high-risk patients.
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Affiliation(s)
- Daniel B Larach
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Adam Lewis
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Anita Pandit
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Jing He
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Anik Sinha
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA
| | - Nicholas J Douville
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA
- Institute of Healthcare Policy & Innovation, University of Michigan, Ann Arbor, MI, USA
| | - Michael Heung
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Michael R Mathis
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Jonathan D Mosley
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jonathan P Wanderer
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sachin Kheterpal
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA
| | | | - Chad M Brummett
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA
| | - Edward D Siew
- Division of Nephrology and Hypertension, Vanderbilt Center for Kidney Disease (VCKD) and Integrated Program for AKI (VIP-AKI), Tennessee Valley Health System, Nashville Veterans Affairs Hospital, Nashville, TN, USA
| | - Cassianne Robinson-Cohen
- Vanderbilt O'Brien Kidney Center, Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Miklos D Kertai
- Division of Adult Cardiothoracic Anesthesiology, Department of Anesthesiology, Vanderbilt University Medical Center, 1211 21st Avenue South, Medical Arts Building, Office 526E, Nashville, TN, 37212, USA.
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Vernooij LM, van Klei WA, Moons KG, Takada T, van Waes J, Damen JA. The comparative and added prognostic value of biomarkers to the Revised Cardiac Risk Index for preoperative prediction of major adverse cardiac events and all-cause mortality in patients who undergo noncardiac surgery. Cochrane Database Syst Rev 2021; 12:CD013139. [PMID: 34931303 PMCID: PMC8689147 DOI: 10.1002/14651858.cd013139.pub2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND The Revised Cardiac Risk Index (RCRI) is a widely acknowledged prognostic model to estimate preoperatively the probability of developing in-hospital major adverse cardiac events (MACE) in patients undergoing noncardiac surgery. However, the RCRI does not always make accurate predictions, so various studies have investigated whether biomarkers added to or compared with the RCRI could improve this. OBJECTIVES Primary: To investigate the added predictive value of biomarkers to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery. Secondary: To investigate the prognostic value of biomarkers compared to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery. Tertiary: To investigate the prognostic value of other prediction models compared to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery. SEARCH METHODS We searched MEDLINE and Embase from 1 January 1999 (the year that the RCRI was published) until 25 June 2020. We also searched ISI Web of Science and SCOPUS for articles referring to the original RCRI development study in that period. SELECTION CRITERIA We included studies among adults who underwent noncardiac surgery, reporting on (external) validation of the RCRI and: - the addition of biomarker(s) to the RCRI; or - the comparison of the predictive accuracy of biomarker(s) to the RCRI; or - the comparison of the predictive accuracy of the RCRI to other models. Besides MACE, all other adverse outcomes were considered for inclusion. DATA COLLECTION AND ANALYSIS We developed a data extraction form based on the CHARMS checklist. Independent pairs of authors screened references, extracted data and assessed risk of bias and concerns regarding applicability according to PROBAST. For biomarkers and prediction models that were added or compared to the RCRI in ≥ 3 different articles, we described study characteristics and findings in further detail. We did not apply GRADE as no guidance is available for prognostic model reviews. MAIN RESULTS We screened 3960 records and included 107 articles. Over all objectives we rated risk of bias as high in ≥ 1 domain in 90% of included studies, particularly in the analysis domain. Statistical pooling or meta-analysis of reported results was impossible due to heterogeneity in various aspects: outcomes used, scale by which the biomarker was added/compared to the RCRI, prediction horizons and studied populations. Added predictive value of biomarkers to the RCRI Fifty-one studies reported on the added value of biomarkers to the RCRI. Sixty-nine different predictors were identified derived from blood (29%), imaging (33%) or other sources (38%). Addition of NT-proBNP, troponin or their combination improved the RCRI for predicting MACE (median delta c-statistics: 0.08, 0.14 and 0.12 for NT-proBNP, troponin and their combination, respectively). The median total net reclassification index (NRI) was 0.16 and 0.74 after addition of troponin and NT-proBNP to the RCRI, respectively. Calibration was not reported. To predict myocardial infarction, the median delta c-statistic when NT-proBNP was added to the RCRI was 0.09, and 0.06 for prediction of all-cause mortality and MACE combined. For BNP and copeptin, data were not sufficient to provide results on their added predictive performance, for any of the outcomes. Comparison of the predictive value of biomarkers to the RCRI Fifty-one studies assessed the predictive performance of biomarkers alone compared to the RCRI. We identified 60 unique predictors derived from blood (38%), imaging (30%) or other sources, such as the American Society of Anesthesiologists (ASA) classification (32%). Predictions were similar between the ASA classification and the RCRI for all studied outcomes. In studies different from those identified in objective 1, the median delta c-statistic was 0.15 and 0.12 in favour of BNP and NT-proBNP alone, respectively, when compared to the RCRI, for the prediction of MACE. For C-reactive protein, the predictive performance was similar to the RCRI. For other biomarkers and outcomes, data were insufficient to provide summary results. One study reported on calibration and none on reclassification. Comparison of the predictive value of other prognostic models to the RCRI Fifty-two articles compared the predictive ability of the RCRI to other prognostic models. Of these, 42% developed a new prediction model, 22% updated the RCRI, or another prediction model, and 37% validated an existing prediction model. None of the other prediction models showed better performance in predicting MACE than the RCRI. To predict myocardial infarction and cardiac arrest, ACS-NSQIP-MICA had a higher median delta c-statistic of 0.11 compared to the RCRI. To predict all-cause mortality, the median delta c-statistic was 0.15 higher in favour of ACS-NSQIP-SRS compared to the RCRI. Predictive performance was not better for CHADS2, CHA2DS2-VASc, R2CHADS2, Goldman index, Detsky index or VSG-CRI compared to the RCRI for any of the outcomes. Calibration and reclassification were reported in only one and three studies, respectively. AUTHORS' CONCLUSIONS Studies included in this review suggest that the predictive performance of the RCRI in predicting MACE is improved when NT-proBNP, troponin or their combination are added. Other studies indicate that BNP and NT-proBNP, when used in isolation, may even have a higher discriminative performance than the RCRI. There was insufficient evidence of a difference between the predictive accuracy of the RCRI and other prediction models in predicting MACE. However, ACS-NSQIP-MICA and ACS-NSQIP-SRS outperformed the RCRI in predicting myocardial infarction and cardiac arrest combined, and all-cause mortality, respectively. Nevertheless, the results cannot be interpreted as conclusive due to high risks of bias in a majority of papers, and pooling was impossible due to heterogeneity in outcomes, prediction horizons, biomarkers and studied populations. Future research on the added prognostic value of biomarkers to existing prediction models should focus on biomarkers with good predictive accuracy in other settings (e.g. diagnosis of myocardial infarction) and identification of biomarkers from omics data. They should be compared to novel biomarkers with so far insufficient evidence compared to established ones, including NT-proBNP or troponins. Adherence to recent guidance for prediction model studies (e.g. TRIPOD; PROBAST) and use of standardised outcome definitions in primary studies is highly recommended to facilitate systematic review and meta-analyses in the future.
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Affiliation(s)
- Lisette M Vernooij
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Department of Anesthesiology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Wilton A van Klei
- Department of Anesthesiology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Anesthesiologist and R. Fraser Elliott Chair in Cardiac Anesthesia, Department of Anesthesia and Pain Management Toronto General Hospital, University Health Network and Professor, Department of Anesthesiology and Pain Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Karel Gm Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Toshihiko Takada
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Judith van Waes
- Department of Anesthesiology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Johanna Aag Damen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
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Serrano AB, Gomez-Rojo M, Ureta E, Nuñez M, Fernández Félix B, Velasco E, Burgos J, Popova E, Urrutia G, Gomez V, Del Rey JM, Sanjuanbenito A, Zamora J, Monteagudo JM, Pestaña D, de la Torre B, Candela-Toha Á. Preoperative clinical model to predict myocardial injury after non-cardiac surgery: a retrospective analysis from the MANAGE cohort in a Spanish hospital. BMJ Open 2021; 11:e045052. [PMID: 34348944 PMCID: PMC8340283 DOI: 10.1136/bmjopen-2020-045052] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVES To determine preoperative factors associated to myocardial injury after non-cardiac surgery (MINS) and to develop a prediction model of MINS. DESIGN Retrospective analysis. SETTING Tertiary hospital in Spain. PARTICIPANTS Patients aged ≥45 years undergoing major non-cardiac surgery and with at least two measures of troponin levels within the first 3 days of the postoperative period. All patients were screened for the MANAGE trial. PRIMARY AND SECONDARY OUTCOME MEASURES We used multivariable logistic regression analysis to study risk factors associated with MINS and created a score predicting the preoperative risk for MINS and a nomogram to facilitate bed-side use. We used Least Absolute Shrinkage and Selection Operator method to choose the factors included in the predictive model with MINS as dependent variable. The predictive ability of the model was evaluated. Discrimination was assessed with the area under the receiver operating characteristic curve (AUC) and calibration was visually assessed using calibration plots representing deciles of predicted probability of MINS against the observed rate in each risk group and the calibration-in-the-large (CITL) and the calibration slope. We created a nomogram to facilitate obtaining risk estimates for patients at pre-anaesthesia evaluation. RESULTS Our cohort included 3633 patients recruited from 9 September 2014 to 17 July 2017. The incidence of MINS was 9%. Preoperative risk factors that increased the risk of MINS were age, American Status Anaesthesiology classification and vascular surgery. The predictive model showed good performance in terms of discrimination (AUC=0.720; 95% CI: 0.69 to 0.75) and calibration slope=1.043 (95% CI: 0.90 to 1.18) and CITL=0.00 (95% CI: -0.12 to 0.12). CONCLUSIONS Our predictive model based on routinely preoperative information is highly affordable and might be a useful tool to identify moderate-high risk patients before surgery. However, external validation is needed before implementation.
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Affiliation(s)
- Ana Belen Serrano
- Department of Anesthesiology and Surgical Critical Care, Ramon y Cajal University Hospital. IRYCIS, Madrid, Spain
| | - Maria Gomez-Rojo
- Department of Anesthesiology and Surgical Critical Care, Ramon y Cajal University Hospital. IRYCIS, Madrid, Spain
| | - Eva Ureta
- Department of Anesthesiology and Surgical Critical Care, Ramon y Cajal University Hospital. IRYCIS, Madrid, Spain
| | - Monica Nuñez
- Department of Anesthesiology and Surgical Critical Care, Ramon y Cajal University Hospital. IRYCIS, Madrid, Spain
| | - Borja Fernández Félix
- Clinical Biostatistics Unit, Ramon y Cajal University Hospital. IRYCIS, Madrid, Spain
| | - Elisa Velasco
- Department of Cardiology, Ramon y Cajal University Hospital. IRYCIS, Madrid, Spain
| | - Javier Burgos
- Department of Urology, Ramon y Cajal University Hospital. IRYCIS, Madrid, Spain
| | - Ekaterine Popova
- Biomedical Research Institute, Iberoamerican Cochrane Center, (IIB Sant Pau), Barcelona, Catalunya, Spain
| | - Gerard Urrutia
- CIBER Epidemiología y Salud Pública (CIBERESP), Biomedical Research Institute Sant Pau (IIB Sant Pau), Universitat Autònoma de Barcelona, Barcelona, Cataluña, Spain
| | - Victoria Gomez
- Department of Urology, Ramon y Cajal University Hospital. IRYCIS, Madrid, Spain
| | - Jose Manuel Del Rey
- Department of Biochemistry, Ramon y Cajal University Hospital. IRYCIS, Madrid, Spain
| | - Alfonso Sanjuanbenito
- Department of General Surgery, Ramon y Cajal University Hospital. IRYCIS, Madrid, Spain
| | - Javier Zamora
- Clinical Biostatistics Unit, Ramon y Cajal University Hospital. IRYCIS, Madrid, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Comunidad de Madrid, Spain
- Institute of metabolism and systems researchs, University of Birmingham, Birmingham, UK
| | | | - David Pestaña
- Department of Anesthesiology and Surgical Critical Care, Ramon y Cajal University Hospital. IRYCIS, Madrid, Spain
- Universidad Alcalá de Henares, Madrid, Spain
| | - Basilio de la Torre
- Department of Traumatology, Ramon y Cajal University Hospital. IRYCIS, Madrid, Spain
| | - Ángel Candela-Toha
- Department of Anesthesiology and Surgical Critical Care, Ramon y Cajal University Hospital. IRYCIS, Madrid, Spain
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Routman J, Boggs SD. Patient monitoring in the nonoperating room anesthesia (NORA) setting: current advances in technology. Curr Opin Anaesthesiol 2021; 34:430-436. [PMID: 34010175 DOI: 10.1097/aco.0000000000001012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
PURPOSE OF REVIEW Nonoperating room anesthesia (NORA) procedures continue to increase in type and complexity as procedural medicine makes technical advances. Patients presenting for NORA procedures are also older and sicker than ever. Commensurate with the requirements of procedural medicine, anesthetic monitoring must meet the American Society of Anesthesiologists standards for basic monitoring. RECENT FINDINGS There have been improvements in the required monitors that are used for intraoperative patient care. Some of these changes have been with new technologies and others have occurred with software refinements. In addition, specialized monitoring devises have also been introduced into NORA locations (depth of hypnosis, respiratory monitoring, point-of care ultrasound). These additions to the monitoring tools available to the anesthesiologist working in the NORA-environment push the boundaries of procedures which may be accomplished in this setting. SUMMARY NORA procedures constitute a growing percentage of total administered anesthetics. There is no difference in the monitoring standard between that of an anesthetic administered in an operating room and a NORA location. Anesthesiologists in the NORA setting must have the same compendium of monitors available as do their colleagues working in the operating suite.
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Affiliation(s)
- Justin Routman
- Department of Anesthesiology and Perioperative Medicine, The University of Alabama at Birmingham, Alabama, USA
| | - Steven Dale Boggs
- Department of Anesthesiology, College of Medicine, The University of Tennessee Health Science Center, Tennessee, USA
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Douville NJ, Kheterpal S, Engoren M, Mathis M, Mashour GA, Hornsby WE, Willer CJ, Douville CB. Genetic mutations associated with susceptibility to perioperative complications in a longitudinal biorepository with integrated genomic and electronic health records. Br J Anaesth 2020; 125:986-994. [PMID: 32891412 DOI: 10.1016/j.bja.2020.08.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 07/06/2020] [Accepted: 08/05/2020] [Indexed: 10/23/2022] Open
Abstract
BACKGROUND Existing genetic information can be leveraged to identify patients with susceptibilities to conditions that might impact their perioperative care, but clinicians generally have limited exposure and are not trained to contextualise this information. We identified patients with genetic susceptibilities to anaesthetic complications using a perioperative biorepository and characterised the concordance with existing diagnoses. METHODS Adult patients undergoing surgery within Michigan Medicine from 2012 to 2017 were consented for genotyping. Genotypes were integrated with the electronic health record (EHR). We retrospectively characterised frequencies of variants associated with butyrylcholinesterase deficiency, factor V Leiden, and malignant hyperthermia, three pharmacogenetic factors with perioperative implications. We calculated the percentage homozygous and heterozygous for each that had been diagnosed previously and searched for EHR findings consistent with a predisposition. RESULTS Analysis of genetic data revealed that 25 out of 40 769 (0.1%) patients were homozygous and 1918 (4.7%) were heterozygous for mutations associated with butyrylcholinesterase deficiency. Of the homozygous individuals, 14 (56%) carried a pre-existing diagnosis. For factor V Leiden, 29 (0.1%) were homozygous and 2153 (5.3%) heterozygous. Of the homozygous individuals, three (10%) were diagnosed by EHR-derived phenotype and six (21%) by clinician review. Malignant hyperthermia was assessed in a subset of patients. We detected two patients with associated mutations. Neither carried clinical diagnoses. CONCLUSIONS We identified patients with genetic susceptibility to perioperative complications using an open source script designed for clinician use. We validated this application in a retrospective analysis for three conditions with well-characterised inheritance, and showed that not all genetic susceptibilities were documented in the EHR.
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Affiliation(s)
- Nicholas J Douville
- Department of Anesthesiology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
| | - Sachin Kheterpal
- Department of Anesthesiology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Milo Engoren
- Department of Anesthesiology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Michael Mathis
- Department of Anesthesiology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - George A Mashour
- Department of Anesthesiology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Whitney E Hornsby
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Cristen J Willer
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA; Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA; Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Christopher B Douville
- Ludwig Center for Cancer Genetics and Therapeutics, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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