scholarly journals Current multivariate risk scores in patients undergoing non-cardiac surgery

2017 ◽  
Vol 87 (2) ◽  
Author(s):  
Gian Francesco Mureddu
Keyword(s):  
Risk Index ◽  
External Validation ◽  
Noncardiac Surgery ◽  
Risk Scores ◽  
Cardiac Complications ◽  
Vascular Events ◽  
Improvement Program ◽  
Discharge Data ◽  
C Statistic ◽  
History Of

<p class="p1">Several indexes to predict perioperative cardiovascular risk have been proposed overtime. The most widely used is the Revised Cardiac Risk Index (RCRI) developed by Lee since 1999. It predicts major cardiac outcomes from five independent clinical determinants: history of ischemic heart disease, history of cardiovascular disease, heart failure, insulin-dependent diabetes mellitus, and chronic renal failure (<em>i.e.</em> serum creatinine &gt;2 mg/dl). In external validation studies, the RCRI showed high negative predictive value in all groups of age, indicating that it may be used to identify people at low risk for perioperative adverse cardiovascular events in noncardiac surgery. However its accuracy is suboptimal in many clinical settings. More recently the National Surgical Quality Improvement Program database) (NSQIP) hasdeveloped a new index to predict perioperative myocardial infarction (MI) or cardiac arrest (MICA) from a cohort of 211,410 patients (the Gupta index) and afterwards a universal surgical risk estimation tool has been developed, using standardized clinical data from 393 ACSNSQIP hospitals in US (a cohort based on 1,414,006 patients), showing a good performance for mortality (C-statistic = 0.944) and morbidity (C-statistic =0.816) as compared with procedure-specific models. Other risk scores include the Vascular events In noncardiac Surgery patIents cOhort evaluatioN (VISION), which has evaluated cardiac complications in 15,065 patients, the Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM) and the large Preoperative Score to Predict Postoperative Mortality (POSPOM) that was built up from data collected in the National Hospital Discharge Data Base (NHDBB) including a cohort of 7.059.447 patients. In Italy a new risk index (the Orion score) builkt up from a cohort of 9000 patients generated four classes of major cardiovascular adverse events perioperative risk ranging from 1 (0.6%); 2 (2.4%); 3 (7.4%) and 4 (23.1%). The AUROC curves showed higher accuracy as compared to the RCRI score both in the derivation than in the validation cohort (AUROC= 0.872 ± 0.028 <em>vs</em> 0.807 ± 0.037). Thus, many risk indices are available nowadays. Despite the latest European guidelines recommended them for risk stratification (class I, level of evidence B), their use in clinical practice is still scarce.</p>

scholarly journals Simple Postoperative AKI Risk (SPARK) Classification before Noncardiac Surgery: A Prediction Index Development Study with External Validation

2018 ◽  
Vol 30 (1) ◽  
pp. 170-181 ◽  
Author(s):  
Sehoon Park ◽  
Hyunjeong Cho ◽  
Seokwoo Park ◽  
Soojin Lee ◽  
Kwangsoo Kim ◽  
...  
Keyword(s):  
Risk Index ◽  
External Validation ◽  
Emergency Operation ◽  
Noncardiac Surgery ◽  
Risk Scores ◽  
Calibration Plot ◽  
Discrimination Power ◽  
C Statistic ◽  
Tertiary Hospitals ◽  
Postoperative Aki

BackgroundResearchers have suggested models to predict the risk of postoperative AKI (PO-AKI), but an externally validated risk index that can be practically implemented before patients undergo noncardiac surgery is needed.MethodsWe performed a retrospective observational study of patients without preexisting renal failure who underwent a noncardiac operation (≥1 hour) at two tertiary hospitals in Korea. We fitted a proportional odds model for an ordinal outcome consisting of three categories: critical AKI (defined as Kidney Disease Improving Global Outcomes AKI stage ≥2, post-AKI death, or dialysis within 90 days after surgery), low-stage AKI (defined as PO-AKI events not fulfilling the definition of critical AKI), and no PO-AKI.ResultsThe study included 51,041 patients in a discovery cohort and 39,764 patients in a validation cohort. The Simple Postoperative AKI Risk (SPARK) index included a summation of the integer scores of the following variables: age, sex, expected surgery duration, emergency operation, diabetes mellitus, use of renin-angiotensin-aldosterone inhibitors, baseline eGFR, dipstick albuminuria hypoalbuminemia, anemia, and hyponatremia. The model calibration plot showed tolerable distribution of observed and predicted probabilities in both cohorts. The discrimination power of the SPARK index was acceptable in both the discovery (c-statistic 0.80) and validation (c-statistic 0.72) cohorts. When four SPARK classes were defined on the basis of the sum of the risk scores, the SPARK index and classes fairly reflected the risks of PO-AKI and critical AKI.ConclusionsClinicians may consider implementing the SPARK index and classifications to stratify patients’ PO-AKI risks before performing noncardiac surgery.

scholarly journals Development and Validation of a Novel Dementia Risk Score in the UK Biobank Cohort

2021 ◽  
Author(s):  
Melis Anatürk ◽  
Raihaan Patel ◽  
Georgios Georgiopoulos ◽  
Danielle Newby ◽  
Anya Topiwala ◽  
...  
Keyword(s):  
At Risk ◽  
Cardiovascular Risk ◽  
Risk Score ◽  
Disease Risk ◽  
Risk Index ◽  
Risk Scores ◽  
Uk Biobank ◽  
Dementia Risk ◽  
History Of ◽  
The Uk

INTRODUCTION: Current prognostic models of dementia have had limited success in consistently identifying at-risk individuals. We aimed to develop and validate a novel dementia risk score (DRS) using the UK Biobank cohort.METHODS: After randomly dividing the sample into a training (n=166,487, 80%) and test set (n=41,621, 20%), logistic LASSO regression and standard logistic regression were used to develop the UKB-DRS.RESULTS: The score consisted of age, sex, education, apolipoprotein E4 genotype, a history of diabetes, stroke, and depression, and a family history of dementia. The UKB-DRS had good-to-strong discrimination accuracy in the UKB hold-out sample (AUC [95%CI]=0.79 [0.77, 0.82]) and in an external dataset (Whitehall II cohort, AUC [95%CI]=0.83 [0.79,0.87]). The UKB-DRS also significantly outperformed four published risk scores (i.e., Australian National University Alzheimer’s Disease Risk Index (ANU-ADRI), Cardiovascular Risk Factors, Aging, and Dementia score (CAIDE), Dementia Risk Score (DRS), and the Framingham Cardiovascular Risk Score (FRS) across both test sets.CONCLUSION: The UKB-DRS represents a novel easy-to-use tool that could be used for routine care or targeted selection of at-risk individuals into clinical trials.

scholarly journals Improving Identification of Patients at Low Risk for Major Cardiac Events After Noncardiac Surgery Using Intraoperative Data

2020 ◽  
Vol 15 (10) ◽  
pp. 581-587
Author(s):  
Amol S Navathe ◽  
Victor J Lei ◽  
Lee A Fleisher ◽  
ThaiBinh Luong ◽  
Xinwei Chen ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Characteristic Curve ◽  
Risk Index ◽  
Model Performance ◽  
The United States ◽  
Noncardiac Surgery ◽  
Low Risk ◽  
Major Adverse Cardiovascular Events ◽  
Cardiac Complications ◽  
Cardiac Events

BACKGROUND/OBJECTIVE: Risk-stratification tools for cardiac complications after noncardiac surgery based on preoperative risk factors are used to inform postoperative management. However, there is limited evidence on whether risk stratification can be improved by incorporating data collected intraoperatively, particularly for low-risk patients. METHODS: We conducted a retrospective cohort study of adults who underwent noncardiac surgery between 2014 and 2018 at four hospitals in the United States. Logistic regression with elastic net selection was used to classify in-hospital major adverse cardiovascular events (MACE) using preoperative and intraoperative data (“perioperative model”). We compared model performance to standard risk stratification tools and professional society guidelines that do not use intraoperative data. RESULTS: Of 72,909 patients, 558 (0.77%) experienced MACE. Those with MACE were older and less likely to be female. The perioperative model demonstrated an area under the receiver operating characteristic curve (AUC) of 0.88 (95% CI, 0.85-0.92). This was higher than the Lee Revised Cardiac Risk Index (RCRI) AUC of 0.79 (95% CI, 0.74-0.84; P < .001 for AUC comparison). There were more MACE complications in the top decile (n = 1,465) of the perioperative model’s predicted risk compared with that of the RCRI model (n = 58 vs 43). Additionally, the perioperative model identified 2,341 of 7,597 (31%) patients as low risk who did not experience MACE but were recommended to receive postoperative biomarker testing by a risk factor–based guideline algorithm. CONCLUSIONS: Addition of intraoperative data to preoperative data improved prediction of cardiovascular complication outcomes after noncardiac surgery and could potentially help reduce unnecessary postoperative testing.

scholarly journals Development and Validation of a Prediction Model for Stroke, Cardiac, and Mortality Risk After Non‐Cardiac Surgery

2021 ◽  
Vol 10 (4) ◽  
Author(s):  
Sang H. Woo ◽  
Gregary D. Marhefka ◽  
Scott W. Cowan ◽  
Lily Ackermann
Keyword(s):  
Myocardial Infarction ◽  
Cardiac Arrest ◽  
Validation Cohort ◽  
Cardiac Complications ◽  
Improvement Program ◽  
Training Cohort ◽  
Major Cardiovascular Events ◽  
American College Of Surgeons ◽  
History Of ◽  
Artery Disease

Background Commonly used cardiovascular risk calculators do not provide risk estimation of stroke, a major postoperative complication with high morbidity and mortality. We developed and validated an accurate cardiovascular risk prediction tool for stroke, major cardiac complications (myocardial infarction or cardiac arrest), and mortality after non‐cardiac surgery. Methods and Results This retrospective cohort study included 1 165 750 surgical patients over a 4‐year period (2007–2010) from the American College of Surgeons National Surgical Quality Improvement Program Database. A predictive model was developed with the following preoperative conditions: age, history of coronary artery disease, history of stroke, emergency surgery, preoperative serum sodium (≤130 mEq/L, >146 mEq/L), creatinine >1.8 mg/dL, hematocrit ≤27%, American Society of Anesthesiologists physical status class, and type of surgery. The model was trained using American College of Surgeons National Surgical Quality Improvement Program data from 2007 to 2009 (n=809 880) and tested using data from 2010 (n=355 870). Risk models were developed using multivariate logistic regression. The outcomes were postoperative 30‐day stroke, major cardiovascular events (myocardial infarction, cardiac arrest, or stroke), and 30‐day mortality. Major cardiac complications occurred in 0.66% (n=5332) of patients (myocardial infarction, 0.28%; cardiac arrest, 0.41%), postoperative stroke in 0.25% (n=2005); 30‐day mortality was 1.66% (n=13 484). The risk prediction model had high predictive accuracy with area under the receiver operating characteristic curve for stroke (training cohort=0.869, validation cohort=0.876), major cardiovascular events (training cohort=0.871, validation cohort=0.868), and 30‐day mortality (training cohort=0.922, validation cohort=0.925). Surgery types, history of stroke, and coronary artery disease are significant risk factors for stroke and major cardiac complications. Conclusions Postoperative stroke, major cardiac complications, and 30‐day mortality can be predicted with high accuracy using this web‐based predictive model.

scholarly journals Derivation and External Validation of a Risk Index for Predicting Acute Kidney Injury Requiring Kidney Replacement Therapy After Noncardiac Surgery

2021 ◽  
Vol 4 (8) ◽  
pp. e2121901
Author(s):  
Todd A. Wilson ◽  
Lawrence de Koning ◽  
Robert R. Quinn ◽  
Kelly B. Zarnke ◽  
Eric McArthur ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Replacement Therapy ◽  
Risk Index ◽  
External Validation ◽  
Kidney Injury ◽  
Noncardiac Surgery ◽  
Kidney Replacement Therapy

Medical Evaluation of the Surgical Patient (SAGHE)

2014 ◽  
Author(s):  
Marie Gerhard-Herman ◽  
Jonathan Gates
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Stress Test ◽  
Risk Index ◽  
Fluid Management ◽  
Postoperative Mortality ◽  
Noncardiac Surgery ◽  
Surgical Patient ◽  
Improvement Program ◽  
Medical Evaluation

Medical evaluation prior to surgery includes risk assessment and the institution of therapies to decrease perioperative morbidity and mortality to improve patient outcomes. The most effective medical consultation for surgical patients begins with an assessment of the individual patient and knowledge of the planned surgery and anesthesia followed by clear communication of a concise and specific recommended plan of perioperative care to the surgical team. This chapter describes anesthetic, cardiac, pulmonary, hepatic, nutritional, and endocrine risk assessment. Perioperative thrombotic management and postoperative care and complications, including fluid management; pulmonary, cardiac, renal complications; and delirium are discussed. Tables outline the American Society of Anesthesiologists class and perioperative mortality risk, a comparison of the Revised Cardiac Risk Index and National Surgery Quality Improvement Program, Duke Activity Status Index, high-risk stress test findings, markers for increased perioperative risk in pulmonary hypertension, aortic stenosis and nonemergent noncardiac surgery, risk factors for pulmonary complications in noncardiac surgery, the Model for End-Stage Liver Disease score to predict postoperative mortality, venous thromboembolism risk factors and options for pharmacologic prophylactic regimens, perioperative management of warfarin, and Brigham and Women’s Hospital guidelines for postoperative blood product replacement. Figures include a care algorithm for noncardiac surgery, an illustration of types of myocardial infarction, and an algorithm for the treatment of postoperative delirium. This review contains 3 highly rendered figures, 12 tables, and 68 references.

External Validation of the Recalibrated Thoracic Revised Cardiac Risk Index for Predicting the Risk of Major Cardiac Complications After Lung Resection

2011 ◽  
Vol 92 (2) ◽  
pp. 445-448 ◽  
Author(s):  
Alessandro Brunelli ◽  
Stephen D. Cassivi ◽  
Juan Fibla ◽  
Lisa A. Halgren ◽  
Dennis A. Wigle ◽  
...  
Keyword(s):  
Risk Index ◽  
Lung Resection ◽  
External Validation ◽  
Cardiac Risk ◽  
Cardiac Complications ◽  
Cardiac Risk Index

scholarly journals Postoperative Myocardial Injury in Patients Classified as Low Risk Preoperatively Is Associated With a Particularly Increased Risk of Long‐Term Mortality After Noncardiac Surgery

2021 ◽  
Author(s):  
Sunil K. Vasireddi ◽  
Erica Pivato ◽  
Enrique Soltero‐Mariscal ◽  
Raghuram Chava ◽  
Laurence O. James ◽  
...  
Keyword(s):  
Risk Index ◽  
Cardiac Risk ◽  
Noncardiac Surgery ◽  
Low Risk ◽  
Preoperative Risk ◽  
Improvement Program ◽  
Troponin Elevation ◽  
American College Of Surgeons ◽  
All Cause Mortality

Background Prior studies have shown an association between myocardial injury after noncardiac surgery (MINS) and all‐cause mortality in patients following noncardiac surgery. However, the association between preoperative risk assessments, Revised Cardiac Risk Index and American College of Surgeons National Surgical Quality Improvement Program, and postoperative troponin elevations and long‐term mortality is unknown. Methods and Results A retrospective chart review identified 548 patients who had a troponin I level drawn within 14 days of noncardiac surgery that required an overnight hospital stay. Patients aged 40 to 80 years with at least 2 cardiovascular risk factors were included, while those with trauma, pulmonary embolism, and neurosurgery were excluded. Kaplan–Meier survival and odds ratio (OR) with sensitivity/specificity analysis were performed to assess the association between preoperative risk and postoperative troponin elevation and all‐cause mortality at 1 year. Overall, 69%/31% were classified as low‐risk/high‐risk per the Revised Cardiac Risk Index and 66%/34% per American College of Surgeons National Surgical Quality Improvement Program. Comparing the low‐risk versus high‐risk groups, preoperative risk assessment was not associated with either postoperative troponin elevation or 1‐year mortality. MINS portended a 1‐year mortality of OR, 3.9 (95% CI, 2.44–6.33) in the total population. Patients classified as low risk preoperatively with MINS had the highest risk of 1‐year mortality (OR, 9.6; 95% CI, 4.27–24.38), with a low prevalence of statin use. Conclusions Current preoperative risk stratification tools do not prognosticate the risk of postoperative troponin elevation and all‐cause mortality at 1 year. Interestingly, patients classified as low risk preoperatively with MINS had a markedly higher 1‐year mortality risk compared with the general population, and most of them are not taking a statin. Our results suggest that evaluating preoperatively low‐risk patients for MINS presents an opportunity for prognostication, risk reclassification, and initiating therapies such as statins to mitigate long‐term risk.

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