scholarly journals In-hospital mortality risk assessment in elective and non-elective cardiac surgery: a comparison between EuroSCORE II and age, creatinine, ejection fraction score

2014 ◽  
Vol 46 (1) ◽  
pp. 44-48 ◽  
Author(s):  
F. Barili ◽  
D. Pacini ◽  
F. Rosato ◽  
M. Roberto ◽  
A. Battisti ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Cardiac Surgery ◽  
Ejection Fraction ◽  
Hospital Mortality ◽  
Mortality Risk ◽  
Elective Cardiac Surgery

Bleeding risk-assessment in elective cardiac surgery - a reply

Anaesthesia ◽  
2016 ◽  
Vol 71 (9) ◽  
pp. 1121-1122 ◽  
Author(s):  
M. Petricevic ◽  
B. Biocina ◽  
D. Dirkmann ◽  
K. Goerlinger
Keyword(s):  
Risk Assessment ◽  
Cardiac Surgery ◽  
Bleeding Risk ◽  
Elective Cardiac Surgery

scholarly journals Rule-Based Models for Risk Estimation and Analysis of In-hospital Mortality in Emergency and Critical Care

2021 ◽  
Vol 8 ◽  
Author(s):  
Oliver Haas ◽  
Andreas Maier ◽  
Eva Rothgang
Keyword(s):  
Risk Assessment ◽  
Critical Care ◽  
Hospital Mortality ◽  
Prediction Model ◽  
Mortality Risk ◽  
Association Rule ◽  
Minimal Model ◽  
Association Rule Mining ◽  
Full Model ◽  
Rule Mining

We propose a novel method that uses associative classification and odds ratios to predict in-hospital mortality in emergency and critical care. Manual mortality risk scores have previously been used to assess the care needed for each patient and their need for palliative measures. Automated approaches allow providers to get a quick and objective estimation based on electronic health records. We use association rule mining to find relevant patterns in the dataset. The odds ratio is used instead of classical association rule mining metrics as a quality measure to analyze association instead of frequency. The resulting measures are used to estimate the in-hospital mortality risk. We compare two prediction models: one minimal model with socio-demographic factors that are available at the time of admission and can be provided by the patients themselves, namely gender, ethnicity, type of insurance, language, and marital status, and a full model that additionally includes clinical information like diagnoses, medication, and procedures. The method was tested and validated on MIMIC-IV, a publicly available clinical dataset. The minimal prediction model achieved an area under the receiver operating characteristic curve value of 0.69, while the full prediction model achieved a value of 0.98. The models serve different purposes. The minimal model can be used as a first risk assessment based on patient-reported information. The full model expands on this and provides an updated risk assessment each time a new variable occurs in the clinical case. In addition, the rules in the models allow us to analyze the dataset based on data-backed rules. We provide several examples of interesting rules, including rules that hint at errors in the underlying data, rules that correspond to existing epidemiological research, and rules that were previously unknown and can serve as starting points for future studies.

scholarly journals Bleeding risk assessment in patients undergoing elective cardiac surgery using ROTEM®platelet and Multiplate®impedance aggregometry

Anaesthesia ◽  
2016 ◽  
Vol 71 (6) ◽  
pp. 636-647 ◽  
Author(s):  
M. Petricevic ◽  
S. Konosic ◽  
B. Biocina ◽  
D. Dirkmann ◽  
A. White ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Cardiac Surgery ◽  
Bleeding Risk ◽  
Elective Cardiac Surgery ◽  
Impedance Aggregometry

Abstract 133: Outcomes Associated With Perioperative Amiodarone in Cardiac Surgery Patients Receiving Metoprolol

2017 ◽  
Vol 10 (suppl_3) ◽  
Author(s):  
Auras R Atreya ◽  
Aruna Priya ◽  
Mihaela S Stefan ◽  
Quinn R Pack ◽  
Tara Lagu ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Cardiac Surgery ◽  
Length Of Stay ◽  
Hospital Mortality ◽  
Treated Group ◽  
Matched Cohort ◽  
Elective Cardiac Surgery ◽  
Randomized Controlled Studies ◽  
Post Operative Atrial Fibrillation ◽  
Risk Patients

Background: Post-operative atrial fibrillation (POAF) after cardiac surgery occurs frequently and the guidelines make a Class I recommendation for peri-operative betablocker use and a Class IIa recommendation for amiodarone use in high risk patients to reduce length of stay and mortality. Our aim was to study the association between perioperative amiodarone use and clinical outcomes in patients already receiving metoprolol, in a real-world cohort. Methods: Using the PREMIER, Inc. data warehouse, we identified patients ≥18 years without atrial fibrillation at baseline, who underwent elective cardiac surgery during years 2013-2014. We included patients with conditions replicating prior randomized controlled studies. We then excluded all patients not receiving metoprolol. Perioperative amiodarone use was defined as administered on the day of surgery or prior to surgery within the same hospitalization. After propensity matching, we compared outcomes for patients receiving perioperative amiodarone + metoprolol vs. those who received only metoprolol. The primary outcome was POAF and secondary outcomes were in-hospital mortality, length of stay and 1 month readmission among survivors. Results: Among 4351 patients who underwent cardiac surgery and received metoprolol at 212 hospitals, 997 (23%) were treated with perioperative amiodarone. We matched 928 (94%) of perioperative amiodarone treated group based on the propensity score. Table 1 shows baseline characteristics and outcomes of interest in the propensity matched cohort. Median age was 66 years and 74% were male. The propensity matched cohort was well balanced on type of surgery and comorbidities and some imbalances remained in demographic variables. After adjusting for unbalanced factors in the matched cohort, perioperative amiodarone+ metoprolol was associated with reduction in POAF (ARR 5.1%; RR 0.81, 95% CI 0.69-0.95). There were no differences in in-hospital mortality, length of stay, 1 month readmission or cost of hospitalization. Conclusions: In this large cohort of propensity matched patients undergoing elective cardiac surgery, perioperative amiodarone use was associated with a modestly significant reduction in POAF rates, but there were no significant relationships with mortality, length of stay, 1 month readmission or costs.

scholarly journals A Novel Risk Stratification System for Predicting In-Hospital Mortality Following Coronary Artery Bypass Grafting Surgery with Impaired Left Ventricular Ejection Fraction

2020 ◽  
Vol 23 (5) ◽  
pp. E621-E626
Author(s):  
Hongyuan Lin ◽  
Jianfeng Hou ◽  
Hanwei Tang ◽  
Kai Chen ◽  
Shaoxian Guo ◽  
...  
Keyword(s):  
Cardiac Surgery ◽  
Coronary Artery ◽  
Coronary Artery Bypass Grafting ◽  
Ejection Fraction ◽  
Hospital Mortality ◽  
Coronary Artery Bypass ◽  
Artery Bypass ◽  
Bypass Grafting ◽  
Artery Bypass Grafting ◽  
Cabg Patients

Background: Coronary artery disease (CAD) is the most common cause of heart failure (HF), and impaired ejection fraction (EF<50%) is a crucial precursor to HF. Coronary artery bypass grafting (CABG) is an effective surgical solution to CAD-related HF. In light of the high risk of cardiac surgery, appropriate scores for groups of patients are of great importance. We aimed to establish a novel score to predict in-hospital mortality for impaired EF patients undergoing CABG. Methods: Clinical information of 1,976 consecutive CABG patients with EF<50% was collected from January 2012 to December 2017. A novel system was developed using the logistic regression model to predict in-hospital mortality among patients with EF<50% who were to undergo CABG. The scoring system was named PGLANCE, which is short for seven identified risk factors, including previous cardiac surgery, gender, load of surgery, aortic surgery, NYHA stage, creatinine, and EF. AUC statistic was used to test discrimination of the model, and the calibration of this model was assessed by the Hosmer-lemeshow (HL) statistic. We also evaluated the applicability of PGLANCE to predict in-hospital mortality by comparing the 95% CI of expected mortality to the observed one. Results were compared with the European Risk System in Cardiac Operations (EuroSCORE), EuroSCORE II, and Sino System for Coronary Operative Risk Evaluation (SinoSCORE). Results: By comparing with EuroSCORE, EuroSCORE II and SinoSCORE, PGLANCE was well calibrated (HL P = 0.311) and demonstrated powerful discrimination (AUC=0.846) in prediction of in-hospital mortality among impaired EF CABG patients. Furthermore, the 95% CI of mortality estimated by PGLANCE was closest to the observed value. Conclusion: PGLANCE is better with predicting in-hospital mortality than EuroSCORE, EuroSCORE II, and SinoSCORE for Chinese impaired EF CABG patients.

Genetic Fuzzy System for Mortality Risk Assessment in Cardiac Surgery

2014 ◽  
Vol 38 (12) ◽  
Author(s):  
Hoda Javadikasgari ◽  
Alireza Alizadeh Ghavidel ◽  
Maziar Gholampour
Keyword(s):  
Risk Assessment ◽  
Cardiac Surgery ◽  
Mortality Risk ◽  
Fuzzy System ◽  
Genetic Fuzzy System
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