Predictive Performance and Variability of the Cardiac Anesthesia Risk Evaluation Score

2004 ◽  
Vol 100 (6) ◽  
pp. 1405-1410 ◽  
Author(s):  
Alexandre Ouattara ◽  
Michaëla Niculescu ◽  
Sarra Ghazouani ◽  
Ario Babolian ◽  
Marc Landi ◽  
...  

Background The Cardiac Anesthesia Risk Evaluation (CARE) score, a simple Canadian classification for predicting outcome after cardiac surgery, was evaluated in 556 consecutive patients in Paris, France. The authors compared its performance to those of two multifactorial risk indexes (European System for Cardiac Operative Risk Evaluation [EuroSCORE] and Tu score) and tested its variability between groups of physicians (anesthesiologists, surgeons, and cardiologists). Methods Each patient was simultaneously assessed using the three scores by an attending anesthesiologist in the immediate preoperative period. In a blinded study, the CARE score category was also determined by a cardiologist the day before surgery, by a surgeon in the operating room, and by a second anesthesiologist at arrival in intensive care unit. Calibration and discrimination for predicting outcomes were assessed by goodness-of-fit test and area under the receiver operating characteristic curve, respectively. The level of agreement of the CARE scoring between the three physicians was then assessed. Results The calibration analysis revealed no significant difference between expected and observed outcomes for the three classifications. The areas under the receiver operating characteristic curves for mortality were 0.77 with the CARE score, 0.78 with the EuroSCORE, and 0.73 with the Tu score (not significant). The agreement rate of the CARE scoring between two anesthesiologists, between anesthesiologists and surgeons, and between anesthesiologists and cardiologists were 90%, 83%, and 77%, respectively. Conclusions Despite its simplicity, the CARE score predicts mortality and major morbidity as well the EuroSCORE. In addition, it remains devoid of significant variability when used by groups of physicians of different specialties.

2019 ◽  
Vol 37 (4_suppl) ◽  
pp. 506-506
Author(s):  
Yanlei Ma ◽  
Sheng Zhang ◽  
Xinxiang Li ◽  
Tianye Niu

506 Background: This study evaluates the predictive performance of radiomic features in metastasis of T1 colorectal carcinoma (CRC) to lymph nodes. Methods: A total of 10 200 CRC patients from our clinical cancer center included in this analysis. 225 eligible cases diagnosed with T1 CRC were included and divided into two groups: computed tomography (CT) image group (n = 82) and magnetic resonance image (MRI) group (n = 143) based on the preoperative image data available. A total of 548 radiomic features were extracted from each case and analyzed, and then a panel of radiomic features associated with lymph node metastases (LNM) were selected using Mann-Whitney U test. Combining these selected radiomic features and clinical data, the predictive performance for LNM was calculated using receiver operating characteristic (ROC) curves. Results: The prediction accuracy for LNM of T1 CRC could be improved to 0.88 by area under the receiver operating characteristic curve (AUC) through integration of one radiomic feature and three clinical indicators in CT group. In the group of contrast enhanced T1-weighted MRI (T1w-MRI), combination of two radiomic features and three clinical parameters present an AUC value of 0.85. In the group of T2-weighted MRI (T2w-MRI), combination of four radiomic features and five clinical characteristics identified T1 tumors with LNM with an AUC value of 0.87. Conclusions: The current study present a good predictive performance of combination of radiomic features with clinic characteristic in identifying T1 CRC with LNM, which may provide an important opportunity for us to make clinical treatment decision-making for T1 CRC patients.


Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 497
Author(s):  
Arastoo Nia ◽  
Domenik Popp ◽  
Georg Thalmann ◽  
Fabian Greiner ◽  
Natasa Jeremic ◽  
...  

This study evaluated the use of risk prediction models in estimating short- and mid-term mortality following proximal hip fracture in an elderly Austrian population. Data from 1101 patients who sustained a proximal hip fracture were retrospectively analyzed and applied to four models of interest: Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM), Charlson Comorbidity Index, Portsmouth-POSSUM and the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP®) Risk Score. The performance of these models according to the risk prediction of short- and mid-term mortality was assessed with a receiver operating characteristic curve (ROC). The median age of participants was 83 years, and 69% were women. Six point one percent of patients were deceased by 30 days and 15.2% by 180 days postoperatively. There was no significant difference between the models; the ACS-NSQIP had the largest area under the receiver operating characteristic curve for within 30-day and 180-day mortality. Age, male gender, and hemoglobin (Hb) levels at admission <12.0 g/dL were identified as significant risk factors associated with a shorter time to death at 30 and 180 days postoperative (p < 0.001). Among the four scores, the ACS-NSQIP score could be best-suited clinically and showed the highest discriminative performance, although it was not specifically designed for the hip fracture population.


Author(s):  
Shang-Ying Shiu ◽  
Constantine Gatsonis

Binary test outcomes typically result from dichotomizing a continuous test variable, observable or latent. The effect of the threshold for test positivity on test sensitivity and specificity has been studied extensively in receiver operating characteristic (ROC) analysis. However, considerably less attention has been given to the study of the effect of the positivity threshold on the predictive value of a test. In this paper we present methods for the joint study of the positive (PPV) and negative predictive values (NPV) of diagnostic tests. We define the predictive receiver operating characteristic (PROC) curve that consists of all possible pairs of PPV and NPV as the threshold for test positivity varies. Unlike the simple trade-off between sensitivity and specificity exhibited in the ROC curve, the PROC curve displays what is often a complex interplay between PPV and NPV as the positivity threshold changes. We study the monotonicity and other geometric properties of the PROC curve and propose summary measures for the predictive performance of tests. We also formulate and discuss regression models for the estimation of the effects of covariates.


Sari Pediatri ◽  
2016 ◽  
Vol 10 (4) ◽  
pp. 262
Author(s):  
Linda Marlina ◽  
Dadang Hudaya S ◽  
Herry Garna

Latar belakang. Penilaian derajat kesakitan (severity score of illness) telah dikembangkan sejalan dengan meningkatnyaperhatian terhadap evaluasi dan pemantauan pelayanan kesehatan. Skor yang telah dikembangkanuntuk anak adalah pediatric logistic organ dysfunction, pediatric risk of mortality, dan pediatric index ofmortality.Tujuan. Membandingkan ketepatan pediatric index of mortality-2 dengan skor pediatric logistic organdysfunction dalam memprediksi kematian pasien sakit kritis pada anak.Metode. Rancangan observasi longitudinal dengan subjek penelitian anak yang menderita sakit kritis, dirawatdi Bagian Ilmu Kesehatan Anak RSHS pada bulan Februari-Mei 2008. Dilakukan anamnesis, pemeriksaanfisis, dan laboratorium untuk mendapatkan pediatric index of mortality 2 dan skor pediatric logistic organdysfunction. Analisis statistik dengan menggunakan receiver operating characteristic (ROC) untuk menilaidiskriminasi dan Hosmer-Lemeshow goodness-of-fit untuk menilai kalibrasi.Hasil. Didapatkan 1215 anak berobat ke Bagian Ilmu Kesehatan Anak RS Hasan Sadikin Bandung, 120di antaranya merupakan pasien kritis. Pediatric index of mortality 2 memberikan hasil diskriminasi yanglebih baik (ROC 0,783; 95% CI 0,688–0,878) dibandingkan dengan pediatric logistic organ dysfunction(ROC 0,706; 95% CI 0,592–0,820). Pediatric index of mortality-2 memberikan hasil kalibrasi yang baik(Hosmer-Lemeshow goodness-of-fit test p=0,33; SMR 0,85) dibandingkan pediatric logistic organ dysfunction(p=0,00; SMR 1,37). PIM2 dan skor PELOD mempunyai korelasi positif dihitung dengan menggunakanSpearman’s correlation, r=0,288 (p=0,001).Kesimpulan. Pediatric index of mortality-2 memiliki kemampuan diskriminasi dan kalibrasi lebih baikdibandingkan dengan pediatric logistic organ dysfunction.


2020 ◽  
Vol 8 (7) ◽  
pp. 975
Author(s):  
Chun-Chieh Tseng ◽  
Yi-Chian Lu ◽  
Kai-Chih Chang ◽  
Chien-Che Hung

Rapid monitoring of the microbial content in indoor air is an important issue. In this study, we develop a method for applying a Coriolis sampler coupled with a portable ATP luminometer for characterization of the collection efficiency of bioaerosol samplers and then test this approach in field applications. The biological collection efficiencies of the Coriolis sampler and a BioSampler for collecting four different types of bioaerosols, including Escherichia coli, Staphylococcus aureus, Candida famata and endospores of Bacillus subtilis, were compared in a chamber study. The results showed that the ATP assay may indicate the four microbes’ viability, and that their defined viabilities were positively correlated with their culturability. In addition, the optimal sampling conditions of the Coriolis sampler were a 200 L/min flow rate and a sampling time of 30 min. Under these conditions, there was no significant difference in sampling performance between the BioSampler and Coriolis sampler. In field applications, the best ATP benchmark that corresponded to culturable levels of < 500 CFU/m3 was 287 RLUs (sensitivity: 100%; specificity: 80%) for bacteria and 370 RLUs (sensitivity: 79%; specificity: 82%) for fungi according to receiver operating characteristic curve analysis. Consequently, an ATP criterion is recommended for indicating whether the corresponding airborne culturable concentrations of microbes meet those of published guidelines.


2001 ◽  
Vol 94 (2) ◽  
pp. 194-204 ◽  
Author(s):  
Jean-Yves Dupuis ◽  
Feng Wang ◽  
Howard Nathan ◽  
Miu Lam ◽  
Scott Grimes ◽  
...  

Background The Cardiac Anesthesia Risk Evaluation (CARE) score is a simple risk classification for cardiac surgical patients. It is based on clinical judgment and three clinical variables: comorbid conditions categorized as controlled or uncontrolled, surgical complexity, and urgency of the procedure. This study compared the CARE score with the Parsonnet, Tuman, and Tu multifactorial risk indexes for prediction of mortality and morbidity after cardiac surgery. Methods In this prospective study, 3,548 cardiac surgical patients from one institution were risk stratified by two investigators using the CARE score and the three tested multifactorial risk indexes. All patients were also given a CARE score by their attending cardiac anesthesiologist. The first 2,000 patients served as a reference group to determine discrimination of each classification with receiver operating characteristic curves. The following 1,548 patients were used to evaluate calibration using the Pearson chi-square goodness-of-fit test. Results The areas under the receiver operating characteristic curves for mortality and morbidity were 0.801 and 0.721, respectively, with the CARE score rating by the investigators; 0.786 and 0.710, respectively, with the CARE score rating by the attending anesthesiologists (n = 8); 0.808 and 0.726, respectively, with the Parsonnet index; 0.782 and 0.697, respectively, with the Tuman index; 0.770 and 0.724 with the Tu index, respectively. All risk models had acceptable calibration in predicting mortality and morbidity, except for the Parsonnet classification, which failed calibration for morbidity (P = 0.026). Conclusions The CARE score performs as well as multifactorial risk indexes for outcome prediction in cardiac surgery. Cardiac anesthesiologists can integrate this score in their practice and predict patient outcome with acceptable accuracy.


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