Risk Calculator For Prediction Research Articles

Obesity and cardiovascular risk in an oncology day ward population.

The burden of obesity and risk of cardiovascular (CV) disease amongst an oncology population receiving active treatment is ill-defined. We performed a retrospective analysis assessing the incidence of obesity and cardiovascular (CV) risk factors in this group (grp) of patients as well as the predicted 10-year risk of a CV event. Data from all patients (pts) receiving intravenous chemotherapy in an Irish oncology satellite unit over an 18-month period was extracted from chemotherapy prescriptions and electronic patient records. To calculate patients' 10-year risk of developing CV disease, we used QRISK, a predictive risk calculator. The prevalence of obesity (BMI > 30) amongst the total population was 19% (n = 21), with 26% (n = 28) overweight (BMI, 25-< 30). Information on CV risk factors was available in 93 pts. with the following rates being observed: hypertension 34%, dyslipidaemia 19%, current smoker 18% and diabetes 11%. The average 10-year risk of a CV event (stroke/MI) in this cohort was 19.2% (± 16.6), with a relative risk of 1.4 compared to their age-matched controls without CV risk factors. We observed similar or lower rates of obesity and CV risk factors in this cohort compared to the general adult Irish population. The average predicted risk of developing CV disease in this grp was moderate to high. This can have significant future implications with regard to cancer survivorship, disease recurrence and suitability for further oncological treatments.

Lipids and Lipoproteins in Risk Prediction.

Ischemic heart disease remains the leading cause of death worldwide. Low-density lipoprotein cholesterol (LDL-C) has proved to have a causal relationship with atherosclerotic cardiovascular disease. Lowering LDL-C improves outcomes, although some patients continue to have residual risk of cardiovascular disease. Cardiovascular risk prediction calculators are routinely used in to identify patients most at risk. Research into other lipoprotein factors has suggested that they may have advantages over LDL-C and improve the ability to identify those most at risk. Although some technology is not widely available, there is potential for better risk prediction in specific groups.

PD15-04 ACCURACY OF ACS NSQIP® UNIVERSAL SURGICAL RISK CALCULATOR IN PREDICTING COMPLICATIONS FOLLOWING ROBOT-ASSISTED RADICAL CYSTECTOMY AT A NATIONAL COMPREHENSIVE CANCER CENTER

You have accessJournal of UrologyBladder Cancer: Invasive II1 Apr 2018PD15-04 ACCURACY OF ACS NSQIP® UNIVERSAL SURGICAL RISK CALCULATOR IN PREDICTING COMPLICATIONS FOLLOWING ROBOT-ASSISTED RADICAL CYSTECTOMY AT A NATIONAL COMPREHENSIVE CANCER CENTER Tomoaki Terakawa, Ahmed Aly Hussein, Youssef Ahmed, Paul May, Austin Miller, Zhe Jing, Luciano A. Nunez Bragayrac, Jared Cohen, Hijab Khan, Adam Cole, Omer Rana, Michelle Whittum, Renuka Kannappan, Christian Prechtl, Terrell Johnson, Qiang Li, and Khurshid Guru Tomoaki TerakawaTomoaki Terakawa More articles by this author , Ahmed Aly HusseinAhmed Aly Hussein More articles by this author , Youssef AhmedYoussef Ahmed More articles by this author , Paul MayPaul May More articles by this author , Austin MillerAustin Miller More articles by this author , Zhe JingZhe Jing More articles by this author , Luciano A. Nunez BragayracLuciano A. Nunez Bragayrac More articles by this author , Jared CohenJared Cohen More articles by this author , Hijab KhanHijab Khan More articles by this author , Adam ColeAdam Cole More articles by this author , Omer RanaOmer Rana More articles by this author , Michelle WhittumMichelle Whittum More articles by this author , Renuka KannappanRenuka Kannappan More articles by this author , Christian PrechtlChristian Prechtl More articles by this author , Terrell JohnsonTerrell Johnson More articles by this author , Qiang LiQiang Li More articles by this author , and Khurshid GuruKhurshid Guru More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.803AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP®) surgical risk calculator has been used to predict 30-day outcomes for quality of surgical care. However, there is paucity of literature about the validation ACS NSQIP® risk calculator for prediction of outcomes after robot-assisted radical cystectomy (RARC). We sought to evaluate the accuracy of the ACS NSQIP® surgical risk calculator in the patients who underwent RARC at our institution. METHODS We retrospectively reviewed our prospectively maintained database for patients who underwent RARC between 2005 and 2017. Accuracy of the ACS NSQIP® surgical risk calculator was assessed, by comparing the rate of actual complication events after surgery with the Receiver Operating Characteristics (ROC) curve analysis by calculating the fractional area under the curve (AUC) and the Brier score (BS). We utilized the code number 51595 and 51596 in ACS NSQIP® calculator for the patients reconstructed with ileal conduit and neobladder, respectively. RESULTS A total of 464 consecutive patients were included in this study. Median age of the cohort was 67 years (range 58 to 75) and median BMI was 27.9 kg/m2. Of these patients, 99 (22%) had diabetes, 302 (66%) had hypertension requiring medication and 241 (52%) were classified ASA class III or higher. The actual observed rate of any complication and serious complication (Clavien score ≥3) in patients after RARC were 48% and 11%, while the mean predicted rate calculated by the ACS NSQIP® were 29% and 25%, respectively. The actual mean length of hospital stay (10.6±7.8 days) was longer compared to the predicted length (8.5±1.6 days). Calculated by the ACS NSQIP®, the AUC values were low and the Brier scores were high in all the domains for any complication (AUC:0.50 and BS:0.29), serious complication (AUC:0.53 and BS:0.12), urinary tract infection (AUC:0.61 and BS:0.14), renal insufficiency (AUC:0.68 and BS:0.07), return to operation room (AUC:0.58 and BS:0.07) and readmission (AUC:0.55 and BS:0.11, respectively). CONCLUSIONS The ACS NSQIP® calculator demonstrated low accuracy in predicting postoperative outcomes following RARC. These findings highlight the need for development of procedure and technique-specific RARC calculators. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e311 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Tomoaki Terakawa More articles by this author Ahmed Aly Hussein More articles by this author Youssef Ahmed More articles by this author Paul May More articles by this author Austin Miller More articles by this author Zhe Jing More articles by this author Luciano A. Nunez Bragayrac More articles by this author Jared Cohen More articles by this author Hijab Khan More articles by this author Adam Cole More articles by this author Omer Rana More articles by this author Michelle Whittum More articles by this author Renuka Kannappan More articles by this author Christian Prechtl More articles by this author Terrell Johnson More articles by this author Qiang Li More articles by this author Khurshid Guru More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Development of the Combined Assessment of Risk Encountered in Surgery (CARES) surgical risk calculator for prediction of postsurgical mortality and need for intensive care unit admission risk: a single-center retrospective study

IntroductionAccurate surgical risk prediction is paramount in clinical shared decision making. Existing risk calculators have limited value in local practice due to lack of validation, complexities and inclusion of non-routine...

Letter to the Editor. Use of the ACS NSQIP Surgical Risk Calculator for prediction of adverse outcomes after neurosurgery.

Letter to the Editor. Use of the ACS NSQIP Surgical Risk Calculator for prediction of adverse outcomes after neurosurgery.

Prevalence of High-Risk Nodules in Screened Individuals Selected According to Standard Guideline Criteria vs a Lung Cancer Risk Prediction Calculator in the Alberta Lung Cancer Screening Program

Prevalence of High-Risk Nodules in Screened Individuals Selected According to Standard Guideline Criteria vs a Lung Cancer Risk Prediction Calculator in the Alberta Lung Cancer Screening Program

Limited Utility of Pulmonary Nodule Risk Calculators for Managing Large Nodules

The optimal management of large pulmonary nodules, at higher risk for lung cancer, has not been determined, and it remains unclear as to which patients should undergo follow-up imaging vs invasive tissue diagnosis via biopsy or surgical resection. Through search of radiology reports, 86 nodules from our institution were identified using the inclusion criterion of solid nodules measuring greater than 8mm. We evaluated these nodules with a number of risk prediction calculators, including the Brock University model, and compared these against the proven diagnosis. Of 86 nodules, 59 (69%) nodules were malignant. The most accurate predictive model, the Brock University calculator, underestimated the risk for this group at 33%. At its optimal threshold, this model had a positive predictive value of 81% and negative predictive value of 53%. Notwithstanding the low negative predictive value, the positive predictive value was no better than patients clinically selected for biopsy (86% of biopsies were malignant). Existing nodule risk prediction calculators are of limited usage in guiding the management of large pulmonary nodules. At present, the accuracy of these models in this setting is inferior to expert clinical judgment, and future work is needed to develop management algorithms for higher-risk nodules.

A calculator for mortality following emergency general surgery based on the American College of Surgeons National Surgical Quality Improvement Program database.

The complex nature of current morbidity and mortality predictor models do not lend themselves to clinical application at the bedside of patients undergoing emergency general surgery (EGS). Our aim was to develop a simplified risk calculator for prediction of early postoperative mortality after EGS. EGS cases other than appendectomy and cholecystectomy were identified within the American College of Surgeons National Surgery Quality Improvement Program database from 2005 to 2014. Seventy-five percent of the cases were selected at random for model development, whereas 25% of the cases were used for model testing. Stepwise logistic regression was performed for creation of a 30-day mortality risk calculator. Model accuracy and reproducibility was investigated using the concordance index (c statistic) and Pearson correlations. A total of 79,835 patients met inclusion criteria. Overall, 30-day mortality was 12.6%. A simplified risk model formula was derived from five readily available preoperative variables as follows: 0.034*age + 0.8*nonindependent status + 0.88*sepsis + 1.1 (if bun ≥ 29) or 0.57 (if bun ≥18 and < 29) + 1.16 (if albumin < 2.7), or 0.61 (if albumin ≥ 2.7 and < 3.4). The risk of 30-day mortality was stratified into deciles. The risk of 30-day mortality ranged from 2% for patients in the lowest risk level to 31% for patients in the highest risk level. The c statistic was 0.83 in both the derivation and testing samples. Five readily available preoperative variables can be used to predict the 30-day mortality risk for patients undergoing EGS. Further studies are needed to validate this risk calculator and to determine its bedside applicability. Prognostic/epidemiological study, level III.

Endothelial function assessed by peripheral arterial tonometry is not related to cardiovascular risk score in healthy subjects

&lt;p&gt;&lt;span style="font-size: medium;"&gt;&lt;strong&gt;Background: &lt;/strong&gt;The majority of first cases of myocardial infarction stem from those classified as intermediate- or low-risk in CV risk prediction calculators. Risk prediction models need to be refined in their capacity to discriminate high-risk from low-risk. Peripheral arterial tonometry (PAT) is a non-invasive method to assess peripheral endothelial function that could potentially improve primary prevention of CVD. We investigated the relation of PAT results to CV risk factors and risk score in a healthy cohort.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span style="font-size: medium;"&gt;&lt;strong&gt;Methods: &lt;/strong&gt;PAT measurements were performed on 102 individuals, with no previous history of CAD, attending a Risk Preventive Clinic.&lt;strong&gt; &lt;/strong&gt;Traditional CV risk factors were evaluated and integrated into a CV risk score calculator. Outcome was expressed as absolute and relative 10-year risk of developing CVD. &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;span style="font-size: medium;"&gt;Results: &lt;/span&gt;&lt;/strong&gt;&lt;span style="font-size: medium;"&gt;PAT results of reactive hyperemia index (RHI) and augmentation index (AI) had no statistically significant relation to risk score while baseline pulse amplitude was positively correlated with both absolute (&lt;em&gt;p &lt;/em&gt;= 0.02) and relative (&lt;em&gt;p &lt;/em&gt;= 0.02) 10-year risk of developing CVD. Among CV risk factors only heart rate was significantly associated with RHI (r = - 0.24, R&lt;/span&gt;&lt;sup&gt;&lt;span style="font-size: small;"&gt;2&lt;/span&gt;&lt;/sup&gt;&lt;span style="font-size: medium;"&gt; = 0.06, &lt;em&gt;p&lt;/em&gt; = 0.01). AI and baseline pulse amplitude both correlated with several risk factors.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span style="font-size: medium;"&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Endothelial dysfunction, as measured by PAT, was not associated with an increased 10-year risk of developing CVD. The relation between endothelial dysfunction and established CV risk factors was limited. Further clinical end point studies are needed to fully comprehend the diagnostic and predictive value of PAT measurements in healthy subjects.&lt;/span&gt;&lt;/p&gt;

'Should we reconsider the role of age in treatment allocation for primary prevention of cardiovascular disease?' No, but we can improve risk communication metrics.

The opinion piece published in this edition of the European Heart Journal by Leening and colleagues1 represents one of the two main opposing views on the role of age in cardiovascular disease (CVD) risk prediction scores and for determining risk-reducing treatment thresholds. Leening et al . raise concerns about the dominance of age in CVD risk prediction scores and propose the use of two new risk metrics that do not rely on age. In contrast Wald and colleagues have advocated that for simplicity, age, rather than multivariable predicted risk, should be the sole criterion used to determine eligibility for treatment.2 In our opinion both positions are problematic. We agree with Wald et al . that age is the single best predictor of a patient's short-term (e.g. 5- or 10-year) CVD risk and is also the single best predictor of the short-term benefits of CVD risk reduction.2 However, we disagree that the simplicity of an age-only treatment threshold would outweigh the incremental improvement in predictive power and the greater acceptability (to patients and clinicians) of risk prediction scores that include the standard modifiable CVD risk factors alongside age. We also disagree with Leening and colleagues that the dominant role of age in CVD risk prediction calculators is a problem because, as stated above, age is best single predictor of future CVD risk and treatment benefit. We acknowledge the concerns that they and many others have regarding the apparent dominance of age in CVD risk prediction, but we disagree with their assumptions about the role of age in CVD risk prediction and about how predicted CVD risk should be used to inform management decisions. …

Predictors of mortality in Parkinson's disease and the development of a prognostic model

BackgroundMost previous prognostic studies in Parkinson's disease have used unrepresentative cohorts with selection biases resulting in major heterogeneity in estimates of mortality. Prognosis is best studied by long-term follow-up of community-based incident cohorts. Prognostic models can predict a patient's risk and improve clinical trial design, but none has been published for use in Parkinson's disease. We aimed to describe mortality in this disorder, identify independent prognostic factors, and develop a valid prognostic model. MethodsAll incident cases of Parkinson's disease in Aberdeen, UK (Nov 1, 2002, to April 30, 2004, and April 1, 2006, to March 31, 2009) identified with multiple, community-based ascertainment strategies were followed prospectively with death notification from the National Health Service central register. Mortality rates and age-adjusted and sex-adjusted standardised mortality ratios (SMRs) were calculated with indirect standardisation to regional mortality data. Weibull regression was used to create a prognostic model with clinical predictors measured at diagnosis and validated internally. Findings198 incident cases of Parkinson's disease were identified. 91 of these 198 patients died after follow-up of up to 12 years (mean 6·1, SD 2·4). No losses to follow-up occurred. The mortality rate was 8·1 deaths per 100 person-years (95% CI 6·6–9·9) and was increased by 50% over those without Parkinson's disease (SMR 1·51, 95% CI 1·22–1·87). Independent baseline prognostic factors for increasing mortality were age (hazard ratio for 10 year increase 1·91, 1·43–2·54), male sex (1·84, 1·17–2·91), more bradykinesia (5 point increase 1·40, 1·14–1·72), more axial relative to limb features (1 unit increase in axial to limb ratio 1·81, 1·22–2·68), and comorbidity (1 unit increase in Charlson score 1·33, 1·14–1·56). A prognostic model incorporating these prognostic factors had good discrimination (Harrel's C-statistic=0·84) and calibration. An electronic predictive risk calculator was created. InterpretationThis estimate of mortality is greater than many previous estimates, probably because we used a more representative cohort with a higher proportion of frail and elderly people than in most previous studies. Identification of prognostic factors and their combination in a prognostic model will allow individualised risk prediction, and facilitate stratified medicine and improved clinical trial design. Further work will include external validation in an international cohort. FundingChief Scientist Office of the Scottish Government, Parkinson's UK.

A Clinical Tool for the Prediction of Venous Thromboembolism in Pediatric Trauma Patients.

Although rare, the incidence of venous thromboembolism (VTE) in pediatric trauma patients is increasing, and the consequences of VTE in children are significant. Studies have demonstrated increasing VTE risk in older pediatric trauma patients and improved VTE rates with institutional interventions. While national evidence-based guidelines for VTE screening and prevention are in place for adults, none exist for pediatric patients, to our knowledge. To develop a risk prediction calculator for VTE in children admitted to the hospital after traumatic injury to assist efforts in developing screening and prophylaxis guidelines for this population. Retrospective review of 536,423 pediatric patients 0 to 17 years old using the National Trauma Data Bank from January 1, 2007, to December 31, 2012. Five mixed-effects logistic regression models of varying complexity were fit on a training data set. Model validity was determined by comparison of the area under the receiver operating characteristic curve (AUROC) for the training and validation data sets from the original model fit. A clinical tool to predict the risk of VTE based on individual patient clinical characteristics was developed from the optimal model. Diagnosis of VTE during hospital admission. Venous thromboembolism was diagnosed in 1141 of 536,423 children (overall rate, 0.2%). The AUROCs in the training data set were high (range, 0.873-0.946) for each model, with minimal AUROC attenuation in the validation data set. A prediction tool was developed from a model that achieved a balance of high performance (AUROCs, 0.945 and 0.932 in the training and validation data sets, respectively; P = .048) and parsimony. Points are assigned to each variable considered (Glasgow Coma Scale score, age, sex, intensive care unit admission, intubation, transfusion of blood products, central venous catheter placement, presence of pelvic or lower extremity fractures, and major surgery), and the points total is converted to a VTE risk score. The predicted risk of VTE ranged from 0.0% to 14.4%. We developed a simple clinical tool to predict the risk of developing VTE in pediatric trauma patients. It is based on a model created using a large national database and was internally validated. The clinical tool requires external validation but provides an initial step toward the development of the specific VTE protocols for pediatric trauma patients.

Measures of user experience in a streptococcal pharyngitis and pneumonia clinical decision support tools.

To understand clinician adoption of CDS tools as this may provide important insights for the implementation and dissemination of future CDS tools. Clinicians (n=168) at a large academic center were randomized into intervention and control arms to assess the impact of strep and pneumonia CDS tools. Intervention arm data were analyzed to examine provider adoption and clinical workflow. Electronic health record data were collected on trigger location, the use of each component and whether an antibiotic, other medication or test was ordered. Frequencies were tabulated and regression analyses were used to determine the association of tool component use and physician orders. The CDS tool was triggered 586 times over the study period. Diagnosis was the most frequent workflow trigger of the CDS tool (57%) as compared to chief complaint (30%) and diagnosis/antibiotic combinations (13%). Conversely, chief complaint was associated with the highest rate (83%) of triggers leading to an initiation of the CDS tool (opening the risk prediction calculator). Similar patterns were noted for initiation of the CDS bundled ordered set and completion of the entire CDS tool pathway. Completion of risk prediction and bundled order set components were associated with lower rates of antibiotic prescribing (OR 0.5; CI 0.2-1.2 and OR 0.5; CI 0.3-0.9, respectively). Different CDS trigger points in the clinician user workflow lead to substantial variation in downstream use of the CDS tool components. These variations were important as they were associated with significant differences in antibiotic ordering. These results highlight the importance of workflow integration and flexibility for CDS success.

Risk prediction models

Prognostic research focuses on the prediction of the future course of a given disease in probability terms. Prognostication is performed by clinical decision makers by using risk prediction models that allow us to estimate the probability that a specific event occurs in a given patient over a predefined time period conditional on prognostic factors (predictors). Before application in clinical practice, risk prediction models should be properly validated by assessing their discrimination and calibration, or explained variation. Reclassification analyses allow us to evaluate the gain in risk prediction by using a new model compared with an established one. We discuss the concepts of developing and validating risk prediction models by means of two examples, the Framingham risk calculator for prediction of coronary heart disease (CHD), and the recently published Renal Risk Score to predict progression of chronic kidney disease (CKD).

Derivation of a nomogram to estimate probability of revisit in at-risk older adults discharged from the emergency department

Estimation of the risk of revisit to the emergency department (ED) soon after discharge in the older population may assist discharge planning and targeting of post discharge intervention in high risk patients. In this study we sought to derive a risk prediction calculator for this purpose. In a prospective observational study in two tertiary ED, we conducted a comprehensive assessment of people aged 65 and over, and followed them for a minimum of 28days post discharge. Cox proportional hazard models relating any unplanned ED revisit in the follow up period to observed risk factors were used to compute a probability nomogram. From 1,439 patients, 189 (13.1%) had at least one unplanned revisit within 28days. Revisit probability was weighted towards chronic and difficult to modify risk factors such as depression, malignancy and cognitive impairment. We conclude that the risk of revisit post discharge is calculable using a probability nomogram. However, revisit is largely related to immutable factors reflecting chronic illness burden, and does not necessarily reflect poor ED care during the initial index presentation.

Development and validation of a risk calculator for prediction of mortality after infrainguinal bypass surgery

For peripheral arterial disease, infrainguinal bypass grafting (BPG) carries a higher perioperative risk compared with peripheral endovascular procedures. The choice between the open and endovascular therapies is to an extent dependent on the expected periprocedural risk associated with each. Tools for estimating the periprocedural risk in patients undergoing BPG have not been reported in the literature. The objective of this study was to develop and validate a calculator to estimate the risk of perioperative mortality ≤30 days of elective BPG. We identified 9556 patients (63.9% men) who underwent elective BPG from the 2007 to 2009 National Surgical Quality Improvement Program data sets. Multivariable logistic regression analysis was performed to identify risk factors associated with 30-day perioperative mortality. Bootstrapping was used for internal validation. The risk factors were subsequently used to develop a risk calculator. Patients had a median age of 68 years. The 30-day mortality rate was 1.8% (n = 170). Multivariable logistic regression analysis identified seven preoperative predictors of 30-day mortality: increasing age, systemic inflammatory response syndrome, chronic corticosteroid use, chronic obstructive pulmonary disease, dependent functional status, dialysis dependence, and lower extremity rest pain. Bootstrapping was used for internal validation. The model demonstrated excellent discrimination (C statistic, 0.81; bias-corrected C statistic, 0.81) and calibration. The validated risk model was used to develop an interactive risk calculator using the logistic regression equation. The validated risk calculator has excellent predictive ability for 30-day mortality in a patient after an elective BPG. It is anticipated to aid in surgical decision making, informed patient consent, preoperative optimization, and consequently, risk reduction.

Letter by Lee and Goldman Regarding Article, “Development and Validation of a Risk Calculator for Prediction of Cardiac Risk After Surgery”

To the Editor: We read with interest the article by Gupta et al1 on the development of a risk calculator for cardiac risk after noncardiac surgery based on data from the National Surgical Quality Improvement Program database, and write to dispute the conclusion that the performance of the resulting cardiac risk calculator “surpasses that of the Revised Cardiac Risk Index” (RCRI).2 The major issue is that Gupta et al are using a different outcome from that used by our research group in development of the RCRI. Their outcome includes cardiac arrests within 30 days after surgery and intraoperative events, as well. The likelihood that their outcomes were dominated by postoperative …

Response to Letter Regarding Article, “Development and Validation of a Risk Calculator for Prediction of Cardiac Risk After Surgery”

We thank Drs Lee and Goldman for their comments on our publication.1The doctors state that the ratio of postoperative cardiac arrest to myocardial infarction (MI) is 2.5:1 in our study in comparison with a ratio of 0.3:1 in their study.2 Based on this finding, they contend that cardiac arrests in our study should more appropriately be attributed to noncardiac causes, and thus the Revised Cardiac Risk Index (RCRI) more accurately estimates the risk of cardiac complications after surgery. It should be remembered that, among the patients in the dataset used to develop the RCRI (n=2893) in 1999, 56 (2%) patients developed a major cardiac complication. There were only 9 (0.3%) with cardiac arrest, 2 (0.1%) with complete heart block, 28 (1%) with …

Development and Validation of a Risk Calculator for Prediction of Cardiac Risk After Surgery

Development and Validation of a Risk Calculator for Prediction of Cardiac Risk After Surgery

Development and Validation of a Risk Calculator for Prediction of Cardiac Risk After Surgery

Development and Validation of a Risk Calculator for Prediction of Cardiac Risk After Surgery