P2761Assessment of perioperative mortality risk in patients with infective endocarditis undergoing cardiac surgery: performance of the EuroSCORE II, PALSUSE, STS risk score for IE and modified AEPEI score

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
C Brizido ◽  
S Madeira ◽  
P Oliveira ◽  
C Silva ◽  
F F Gama ◽  
...  
Keyword(s):  
Cardiac Surgery ◽  
Infective Endocarditis ◽  
Risk Score ◽  
Heart Surgery ◽  
Characteristic Curve ◽  
Population Heterogeneity ◽  
Risk Scores ◽  
Perioperative Mortality ◽  
Active Infective Endocarditis ◽  
Lemeshow Test

Abstract Introduction and aim Infective endocarditis (IE) is a complex and heterogeneous disease which might lead to cardiac surgery. For such cases, several perioperative risk predictive tools have emerged. We aimed to validate the recently developed PALSUSE, STS risk score for IE and modified AEPEI score and to compare their performances with the established EuroSCORE II. Methods We retrospectively accessed 128 patients from a single center registry who underwent heart surgery for active infective endocarditis between January 2007 and November 2014. Discrimination and calibration of models were assessed by receiver operating characteristic curve analysis and Hosmer-Lemeshow test. Results Perioperative mortality was 16.4% (n=21). The median EuroSCORE II, PALSUSE, STS risk score for IE and modified AEPEI score were 6.6% [IQR 3.5–18.2], 5 [IQR 3–7], 25 [IQR 16–32] and 1 [IQR 0–1.8], respectively. Discriminative power was numerically higher for EuroSCORE II (AUC of 0.83, 95% CI, 0.75–0.91) followed by STS risk score for IE (AUC of 0.75, 95% CI 0.64–0.86), PALSUSE (AUC of 0.74, 95% CI 0.64–0.86) and modified AEPEI (AUC of 0.68, 95% CI 0.57–0.788) – figure 1. The Hosmer-Lemeshow test showed good calibration for EuroSCORE II (p=0.08) and STS risk score for IE (p=0.03) but not for PALSUSE (p=0.65), modified AEPEI (p=0.12). Figure 1 Conclusion All scores adequately stratified peri-operative risk in active infective endocarditis, however EuroSCORE II in the overall comparison performed better in this population. Heterogeneity of performance of risk scores in different cohorts of infective endocarditis highlights the complexity of this disease.

scholarly journals Risk score to stratify miscarriage risk levels in preconception women

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Hui Choo ◽  
Chee Wai Ku ◽  
Yin Bun Cheung ◽  
Keith M. Godfrey ◽  
Yap-Seng Chong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Score ◽  
Smoking Status ◽  
Characteristic Curve ◽  
Lifestyle Changes ◽  
Risk Scores ◽  
Risk Level ◽  
Multivariable Logistic Regression Model ◽  
Spontaneous Miscarriage ◽  
Risk Levels

AbstractSpontaneous miscarriage is one of the most common complications of pregnancy. Even though some risk factors are well documented, there is a paucity of risk scoring tools during preconception. In the S-PRESTO cohort study, Asian women attempting to conceive, aged 18-45 years, were recruited. Multivariable logistic regression model coefficients were used to determine risk estimates for age, ethnicity, history of pregnancy loss, body mass index, smoking status, alcohol intake and dietary supplement intake; from these we derived a risk score ranging from 0 to 17. Miscarriage before 16 weeks of gestation, determined clinically or via ultrasound. Among 465 included women, 59 had miscarriages and 406 had pregnancy ≥ 16 weeks of gestation. Higher rates of miscarriage were observed at higher risk scores (5.3% at score ≤ 3, 17.0% at score 4–6, 40.0% at score 7–8 and 46.2% at score ≥ 9). Women with scores ≤ 3 were defined as low-risk level (< 10% miscarriage); scores 4–6 as intermediate-risk level (10% to < 40% miscarriage); scores ≥ 7 as high-risk level (≥ 40% miscarriage). The risk score yielded an area under the receiver-operating-characteristic curve of 0.74 (95% confidence interval 0.67, 0.81; p < 0.001). This novel scoring tool allows women to self-evaluate their miscarriage risk level, which facilitates lifestyle changes to optimize modifiable risk factors in the preconception period and reduces risk of spontaneous miscarriage.

Comparison of contemporary risk scores for predicting outcomes after surgery for active infective endocarditis

2014 ◽  
Vol 30 (2) ◽  
pp. 227-234 ◽  
Author(s):  
Tom Kai Ming Wang ◽  
Timothy Oh ◽  
Jamie Voss ◽  
Greg Gamble ◽  
Nicholas Kang ◽  
...  
Keyword(s):  
Infective Endocarditis ◽  
Risk Scores ◽  
Active Infective Endocarditis

Intraoperative Hemofiltration in Adults: Prevention of Hypercirculatory Syndrome?

2005 ◽  
Vol 13 (1) ◽  
pp. 17-19 ◽  
Author(s):  
Theodor Tirilomis ◽  
Martin Friedrich ◽  
Horia Sîrbu ◽  
Ivan Aleksic ◽  
Thomas Busch
Keyword(s):  
Cardiac Output ◽  
Cardiac Surgery ◽  
Cohort Study ◽  
Heart Surgery ◽  
Statistical Significance ◽  
Open Heart Surgery ◽  
Group Versus ◽  
Coronary Bypass ◽  
Open Heart ◽  
Perioperative Mortality

Hypercirculatory syndrome (HCS) after cardiac surgery may be a sequela of extracorporeal circulation due to hemodilution and release of inflammatory mediators. The aim of this study was to investigate the influence of intraoperative hemofiltration (HF) on the incidence of HCS. A prospective cohort study of 80 patients scheduled for elective coronary bypass was performed. The patients were randomized to two groups: in the conventional (CONV) group 40 patients were treated conventionally and in the HF group 40 patients underwent intraoperative HF. There was no perioperative mortality. The incidence of HCS was comparable in both groups (32% in CONV group versus 40% in HF group; n.s.). Mean cardiac output was higher and systemic vascular resistance lower in CONV group patients than in HF group patients, however these differences did not reach statistical significance. According to this data intraoperative HF does not prevent postoperative HCS induced by cardiopulmonary bypass. Further studies are required to identify the etiology of HCS, and to prevent it occurring after open-heart surgery.

scholarly journals Comparing non-invasive diabetes risk scores for detecting patients in clinical practice: a cross-sectional validation study

2021 ◽  
Vol 4 ◽  
pp. 70
Author(s):  
Sinéad Flynn ◽  
Seán Millar ◽  
Claire Buckley ◽  
Kate Junker ◽  
Catherine Phillips ◽  
...  
Keyword(s):  
Risk Score ◽  
Characteristic Curve ◽  
Diabetes Risk ◽  
Detection Methods ◽  
Risk Scores ◽  
Continuous Variables ◽  
Irish Population ◽  
Cross Sectional ◽  
Non Invasive ◽  
Diabetes Risk Score

Background: Type 2 diabetes (T2DM) is a significant cause of morbidity and mortality, thus early identification is of paramount importance. A high proportion of T2DM cases are undiagnosed highlighting the importance of effective detection methods such as non-invasive diabetes risk scores (DRSs). Thus far, no DRS has been validated in an Irish population. Therefore, the aim of this study was to compare the ability of nine DRSs to detect T2DM cases in an Irish population. Methods: This was a cross-sectional study of 1,990 men and women aged 46–73 years. Data on DRS components were collected from questionnaires and clinical examinations. T2DM was determined according to a fasting plasma glucose level ≥7.0 mmol/l or a glycated haemoglobin A1c level ≥6.5% (≥48 mmol/mol). Receiver operating characteristic curve analysis assessed the ability of DRSs and their components to discriminate T2DM cases. Results: Among the examined scores, area under the curve (AUC) values ranged from 0.71–0.78, with the Cambridge Diabetes Risk Score (AUC=0.78, 95% CI: 0.75–0.82), Leicester Diabetes Risk Score (AUC=0.78, 95% CI: 0.75–0.82), Rotterdam Predictive Model 2 (AUC=0.78, 95% CI: 0.74–0.82) and the U.S. Diabetes Risk Score (AUC=0.78, 95% CI: 0.74–0.81) demonstrating the largest AUC values as continuous variables and at optimal cut-offs. Regarding individual DRS components, anthropometric measures displayed the largest AUC values. Conclusions: The best performing DRSs were broadly similar in terms of their components; all incorporated variables for age, sex, BMI, hypertension and family diabetes history. The Cambridge Diabetes Risk Score, had the largest AUC value at an optimal cut-off, can be easily accessed online for use in a clinical setting and may be the most appropriate and cost-effective method for case-finding in an Irish population.

scholarly journals MetS Risk Score: A Clear Scoring Model to Predict a 3-Year Risk for Metabolic Syndrome

2018 ◽  
Vol 50 (09) ◽  
pp. 683-689 ◽  
Author(s):  
Tian-Tian Zou ◽  
Yu-Jie Zhou ◽  
Xiao-Dong Zhou ◽  
Wen-Yue Liu ◽  
Sven Van Poucke ◽  
...  
Keyword(s):  
Risk Factors ◽  
Metabolic Syndrome ◽  
Risk Score ◽  
Validation Cohort ◽  
Medical Center ◽  
Characteristic Curve ◽  
Model Development ◽  
Predictive Performance ◽  
Risk Scores ◽  
Individual Risk

AbstractAlthough several risk factors for metabolic syndrome (MetS) have been reported, there are few clinical scores that predict its incidence. Therefore, we created and validated a risk score for prediction of 3-year risk for MetS. Three-year follow-up data of 4395 initially MetS-free subjects, enrolled for an annual physical examination from Wenzhou Medical Center were analyzed. Subjects at enrollment were randomly divided into the training and the validation cohort. Univariate and multivariate logistic regression models were employed for model development. The selected variables were assigned an integer or half-integer risk score proportional to the estimated coefficient from the logistic model. Risk scores were tested in a validation cohort. The predictive performance of the model was tested by computing the area under the receiver operating characteristic curve (AUROC). Four independent predictors were chosen to construct the MetS risk score, including BMI (HR=1.906, 95% CI: 1.040–1.155), FPG (HR=1.507, 95% CI: 1.305–1.741), DBP (HR=1.061, 95% CI: 1.002–1.031), HDL-C (HR=0.539, 95% CI: 0.303–0.959). The model was created as –1.5 to 4 points, which demonstrated a considerable discrimination both in the training cohort (AUROC=0.674) and validation cohort (AUROC=0.690). Comparison of the observed with the estimated incidence of MetS revealed satisfactory precision. We developed and validated the MetS risk score with 4 risk factors to predict 3-year risk of MetS, useful for assessing the individual risk for MetS in medical practice.

scholarly journals Perioperative poor grip strength recovery is associated with 30-day complication rate after cardiac surgery discharge in middle-aged and older adults - a prospective observational study

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Liyuan Fu ◽  
Yuanyuan Zhang ◽  
Bohan Shao ◽  
Xiangjing Liu ◽  
Bo Yuan ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cardiac Surgery ◽  
Postoperative Complications ◽  
Grip Strength ◽  
Heart Surgery ◽  
Characteristic Curve ◽  
Postoperative Recovery ◽  
Cutoff Point ◽  
Middle Aged ◽  
Related Factors

Abstract Background Although perioperative care during heart surgery has improved considerably, the rate of postoperative complications has remained stable. It has not been concluded how to better apply grip strength to clinical, postoperative complications. So our study aimed at researching the best way for using grip value for predicting early postoperative complications. Methods A total of 212 patients with mean age 63.8 ± 6.3 who underwent cardiac surgery participated in our study. We analyzed the ROC curve of grip strength, grip/weight and grip recovery with complications, found the best cutoff point. Logistic regression confirmed the association between grip strength grouping and complications. Results We found that 36 patients had 30-day complications. EuroSCORE were 2.15 ± 1.52 and 2.42 ± 1.58 between normal and complication groups, respectively. The area under the receiver-operating characteristic curve (AUC) of grip recovery take the most area (0.837, p < 0.001), and the cutoff point was 83.92%. In logistic regression, lower grip recovery has higher risk impact on 30-day complications for 25.68 times than normal group, after adjusted surgery-related factors. After regrouped characteristic information by grip recovery cutoff point, we found that percentage of the estimated 6 min walk distance (41.5 vs 48.3, p = 0.028) and hospitalization time (7.2 vs 6.1, p = 0.042) had worse trends in lower recovery group. Conclusions Poor grip recovery may be related to higher risk of postoperative complications within 30 days after discharge in middle-aged and older people independent of surgical risk. The results of this study provide a reference for the development of rehabilitation programs in the early postoperative recovery, and may also be a prognostic indicator for postoperative high-risk groups. Trial registration Our research was registered on Research Registry website, the registry number was ChiCTR1800018465. Date: 2018/9/20. Status: Successful.

scholarly journals A New Surgical Site Infection Risk Score: Infection Risk Index in Cardiac Surgery

2019 ◽  
Vol 8 (4) ◽  
pp. 480 ◽  
Author(s):  
Juan Bustamante-Munguira ◽  
Francisco Herrera-Gómez ◽  
Miguel Ruiz-Álvarez ◽  
Ana Hernández-Aceituno ◽  
Angels Figuerola-Tejerina
Keyword(s):  
Cardiac Surgery ◽  
Surgical Site Infection ◽  
Risk Score ◽  
Artery Bypass ◽  
Characteristic Curve ◽  
Risk Index ◽  
Bypass Graft ◽  
Test Sample ◽  
Infection Risk ◽  
Site Infection

Various scoring systems attempt to predict the risk of surgical site infection (SSI) after cardiac surgery, but their discrimination is limited. Our aim was to analyze all SSI risk factors in both coronary artery bypass graft (CABG) and valve replacement patients in order to create a new SSI risk score for such individuals. A priori prospective collected data on patients that underwent cardiac surgery (n = 2020) were analyzed following recommendations from the Reporting of studies Conducted using Observational Routinely collected health Data (RECORD) group. Study participants were divided into two periods: the training sample for defining the new tool (2010–2014, n = 1298), and the test sample for its validation (2015–2017, n = 722). In logistic regression, two preoperative variables were significantly associated with SSI (odds ratio (OR) and 95% confidence interval (CI)): diabetes, 3.3/2–5.7; and obesity, 4.5/2.2–9.3. The new score was constructed using a summation system for punctuation using integer numbers, that is, by assigning one point to the presence of either diabetes or obesity. The tool performed better in terms of assessing SSI risk in the test sample (area under the Receiver-Operating Characteristic curve (aROC) and 95% CI, 0.67/055–0.76) compared to the National Nosocomial Infections Surveillance (NNIS) risk index (0.61/0.50–0.71) and the Australian Clinical Risk Index (ACRI) (0.61/0.50–0.72). A new two-variable score to preoperative SSI risk stratification of cardiac surgery patients, named Infection Risk Index in Cardiac surgery (IRIC), which outperforms other classical scores, is now available to surgeons. Personalization of treatment for cardiac surgery patients is needed.

scholarly journals Development and internal validation of a prediction model for hospital-acquired acute kidney injury

2019 ◽  
Author(s):  
Catalina Martin-Cleary ◽  
Luis Miguel Molinero-Casares ◽  
Alberto Ortiz ◽  
Jose Miguel Arce-Obieta
Keyword(s):  
Acute Kidney Injury ◽  
Risk Score ◽  
Bayesian Model Averaging ◽  
Characteristic Curve ◽  
Tertiary Care ◽  
Kidney Injury ◽  
Risk Scores ◽  
Internal Validation ◽  
Clinical Records ◽  
Hospital Acquired

Abstract Background Predictive models and clinical risk scores for hospital-acquired acute kidney injury (AKI) are mainly focused on critical and surgical patients. We have used the electronic clinical records from a tertiary care general hospital to develop a risk score for new-onset AKI in general inpatients that can be estimated automatically from clinical records. Methods A total of 47 466 patients met inclusion criteria within a 2-year period. Of these, 2385 (5.0%) developed hospital-acquired AKI. Step-wise regression modelling and Bayesian model averaging were used to develop the Madrid Acute Kidney Injury Prediction Score (MAKIPS), which contains 23 variables, all obtainable automatically from electronic clinical records at admission. Bootstrap resampling was employed for internal validation. To optimize calibration, a penalized logistic regression model was estimated by the least absolute shrinkage and selection operator (lasso) method of coefficient shrinkage after estimation. Results The area under the curve of the receiver operating characteristic curve of the MAKIPS score to predict hospital-acquired AKI at admission was 0.811. Among individual variables, the highest odds ratios, all >2.5, for hospital-acquired AKI were conferred by abdominal, cardiovascular or urological surgery followed by congestive heart failure. An online tool (http://www.bioestadistica.net/MAKIPS.aspx) will facilitate validation in other hospital environments. Conclusions MAKIPS is a new risk score to predict the risk of hospital-acquired AKI, based on variables present at admission in the electronic clinical records. This may help to identify patients who require specific monitoring because of a high risk of AKI.

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