scholarly journals Validation of the EuroSCORE II in a Greek Cardiac Surgical Population: A Prospective Study

2017 ◽  
Vol 11 (1) ◽  
pp. 94-101 ◽  
Author(s):  
G. Stavridis ◽  
D. Panaretos ◽  
O. Kadda ◽  
D. B. Panagiotakos
Keyword(s):  
Cardiac Surgery ◽  
Hospital Mortality ◽  
Roc Curve ◽  
Goodness Of Fit ◽  
Operating Characteristic ◽  
Goodness Of Fit Test ◽  
Actual Mortality ◽  
Single Center Study ◽  
A Prospective Study

Objective: The objective of this study was to examine the validity of EuroSCORE II in the Greek population. Methods: A prospective single-center study was performed during November 1, 2013 and November 5, 2016; 621 patients undergoing cardiac surgery were enrolled. The EuroSCORE II values and the actual mortality of the patients were recorded in a special database. Calibration of the model was evaluated with the Hosmer-Lemeshow goodness-of-fit test, and discrimination with the areas under the receiver operating characteristic (ROC) curve. Results: The observed in-hospital mortality rate was 3% (i.e. 18/621 patients). The median EuroSCORE II value was 1.3% (1st quartile: 0.86%, 3rd quartile: 2.46%), which indicates a low in-hospital mortality. Area under the ROC curve for EuroSCORE II was 0.85 (95% CI: 0.75-0.94), suggesting very good correct classification of the patients. Conclusion: The findings of the present work suggest that EuroSCORE II is a very good predictor of in-hospital mortality after cardiac surgery, in our population and, therefore can safely be used for quality assurance and risk assessment.

Validation of the CASUS score in Tunisian patients: a quality management tool in intensive care units

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
T Besbes ◽  
S Mleyhi ◽  
J Sahli ◽  
M Messai ◽  
J Ziadi ◽  
...  
Keyword(s):  
Cardiac Surgery ◽  
Roc Curve ◽  
Goodness Of Fit ◽  
Cardiovascular Surgery ◽  
Management Tool ◽  
Medical Decision ◽  
Tunisian Population ◽  
Correct Classification ◽  
Goodness Of Fit Test ◽  
A Value

Abstract Background Early prediction of patients at highest risk of a poor outcome after cardiovascular surgery, including death can aid medical decision making, and adapt health care management in order to improve prognosis. In this context, we conducted this study to validate the CASUS severity score after cardiac surgery in the Tunisian population. Methods This is a retrospective cohort study conducted among patients who underwent cardiac surgery under extracorporeal circulation during the year 2018 at the Cardiovascular Surgery Department of La Rabta University Hospital in Tunisia. Data were collected from the patients hospitalization records. The discrimination of the score was assessed using the ROC curve and the calibration using the Hosmer-Lemeshow goodness of fit test and then by constructing the calibration curve. Overall correct classification was also obtained. Results In our study, the observed mortality rate was 10.52% among the 95 included patients. The discriminating power of the CASUS score was estimated by the area under the ROC curve (AUC), this scoring system had a good discrimination with AUC greater than 0.9 from postoperative Day 0 to Day 5.From postoperative day 0 to day 5, the Hosmer-Lemeshow's test gave a value of chi square test statistic ranging from 1.474 to 8.42 and a value of level of significance ranging from 0.39 to 0.99 indicating a good calibration. The overall correct classification rate from postoperative day 0 to day 5 ranged from 84.4% to 92.4%. Conclusions Despite the differences in the profile of the risk factors between the Tunisian population and the population constituting the database used to develop the CASUS score, we can say that this risk model presents acceptable performances in our population, attested by adequate discrimination and calibration. Prospective and especially multicentre studies on larger samples are needed before definitively conclude on the performance of this model in our country. Key messages The casus score seems to be valid to predict mortality among patients undergoing cardiac surgery. Multicenter study on larger sample is needed to derive and validate models able to predict in-hospitals mortality.

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 ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Risk Evaluation ◽  
Goodness Of Fit ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Cardiac Anesthesia ◽  
Goodness Of Fit Test ◽  
Significant Difference ◽  
Receiver Operating

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.

scholarly journals Pharmacogenetics–Based Preliminary Algorithm to Predict the Incidence of Infection in Patients Receiving Cytotoxic Chemotherapy for Hematological Malignancies: A Discovery Cohort

2021 ◽  
Vol 12 ◽  
Author(s):  
Matias F. Martinez ◽  
Enzo Alveal ◽  
Tomas G. Soto ◽  
Eva I. Bustamante ◽  
Fernanda Ávila ◽  
...  
Keyword(s):  
Inflammatory Response ◽  
Roc Curve ◽  
Goodness Of Fit ◽  
Health Resources ◽  
Cytotoxic Chemotherapy ◽  
Goodness Of Fit Test ◽  
Lemeshow Test ◽  
Life Threatening ◽  
Limited Health ◽  
Good Diagnostic Accuracy

Introduction: Infections in hematological cancer patients are common and usually life-threatening; avoiding them could decrease morbidity, mortality, and cost. Genes associated with antineoplastics’ pharmacokinetics or with the immune/inflammatory response could explain variability in infection occurrence.Objective: To build a pharmacogenetic-based algorithm to predict the incidence of infections in patients undergoing cytotoxic chemotherapy.Methods: Prospective cohort study in adult patients receiving cytotoxic chemotherapy to treat leukemia, lymphoma, or myeloma in two hospitals in Santiago, Chile. We constructed the predictive model using logistic regression. We assessed thirteen genetic polymorphisms (including nine pharmacokinetic—related genes and four inflammatory response-related genes) and sociodemographic/clinical variables to be incorporated into the model. The model’s calibration and discrimination were used to compare models; they were assessed by the Hosmer-Lemeshow goodness-of-fit test and area under the ROC curve, respectively, in association with Pseudo-R2.Results: We analyzed 203 chemotherapy cycles in 50 patients (47.8 ± 16.1 years; 56% women), including 13 (26%) with acute lymphoblastic and 12 (24%) with myeloblastic leukemia.Pharmacokinetics-related polymorphisms incorporated into the model were CYP3A4 rs2242480C>T and OAT4 rs11231809T>A. Immune/inflammatory response-related polymorphisms were TLR2 rs4696480T>A and IL-6 rs1800796C>G. Clinical/demographic variables incorporated into the model were chemotherapy type and cycle, diagnosis, days in neutropenia, age, and sex. The Pseudo-R2 was 0.56, the p-value of the Hosmer-Lemeshow test was 0.98, showing good goodness-of-fit, and the area under the ROC curve was 0.93, showing good diagnostic accuracy.Conclusions: Genetics can help to predict infections in patients undergoing chemotherapy. This algorithm should be validated and could be used to save lives, decrease economic costs, and optimize limited health resources.

scholarly journals Application of concomitant disease scoring in acute cerebral infarction

2019 ◽  
Author(s):  
Xiao-Jing Zhao ◽  
Qun-Xi Li ◽  
Ying Liu ◽  
Li-Sha Chang ◽  
Rui-Ying Chen ◽  
...  
Keyword(s):  
Cerebral Infarction ◽  
Predictive Value ◽  
Goodness Of Fit ◽  
Operating Characteristic ◽  
Acute Cerebral Infarction ◽  
Concomitant Disease ◽  
Disease Score ◽  
Goodness Of Fit Test ◽  
Ideal System ◽  
Disease Scoring

Abstract Background: This study aims to explore the predictive value of concomitant disease scoring for the prognosis of patients with acute cerebral infarction (ACI). Methods: A total of 399 patients with ACI, who met the inclusion criteria, were enrolled into the present study. The concomitant disease score was assessed within 24 hours after admission, and the risk degree of death was analyzed. Then, the goodness of fit test and validity analysis were carried out, the best survival/death cut-off value was determined, and its predictive value for the prognosis of ACI patients was assessed. Results: The area under the receiver operating characteristic (ROC) curve for the concomitant disease score was 0.700, the distinctiveness was relatively good, and the prediction cut-off value was 10 points. Furthermore, the mortality rate of patients with a higher score was significantly higher, when compared to patients with a lower score. Conclusion: This concomitant disease score has good predictive value for the prognosis of ACI patients, and is an ideal system for evaluating the condition of cerebral infarction. The survival/death cut-off value was 10 points.

scholarly journals Predictive model for in-hospital mortality following cardiogenic shock

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
F Garcia-Rodeja Arias ◽  
M Perez Dominguez ◽  
J Martinon Martinez ◽  
J M Garcia Acuna ◽  
C Abou Joch Casas ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Cardiogenic Shock ◽  
Predictive Model ◽  
Risk Score ◽  
Roc Curve ◽  
Goodness Of Fit ◽  
Simplified Model ◽  
P Value ◽  
Final Model ◽  
Risk Of Death

Abstract Introduction and objectives Cardiogenic shock is a condition caused by reduced cardiac output and hypotension, resulting in end-organ damage and multiorgan failure. Although prognosis has been improved in recent years, this state is still associated with high morbidity and mortality. The aim of our study was to perform a predictive model for in-hospital mortality that allows stratifying the risk of death in patients with cardiogenic shock. Methods This is a retrospective analysis from a prospective registry, that included 135 patients from one Spanish Universitary Hospital between 2011 and 2020. Multivariate analysis was performed among those variables with significant association with short-term outcome of univariate analysis with a p-value <0.2. Those variables which had a p-value >0.1 in the multivariable analysis were excluded of the final model. Our method was assessed using the area under the ROC-curve (AUC). Goodness of fit was tested using Hosmer-Lemeshow statistic test. Finally, we performed a risk score using the pondered weight of the coefficients of a simplified model created after categorizing the continuous quantitative variables included in the final model, giving a maximum of 16 points and creating three categories of risk. Results The in-hospital mortality rate was 41.5%, the average of age was 74.2 years, 35.6% were females and acute coronary syndrome (ACS) was the main cause of shock (60.7%). Mitral regurgitation (moderate-severe), age, ACS etiology, NT-proBNP, blood hemoglobin and lactate at admission were included in the final model. Risk-adjustment model had good accuracy in predicting in-hospital mortality (AUC 0.85; 95% CI 0,78–0,90) and the goodness of fit test was p-value>0.10. According to the risk score made with the simplified model, these patients were stratified into three categories: low (scores 0–6), intermediate (scores 7–10), and high (scores 11–16) risk with observed mortality of 12.9%, 49.1% and 87.5% respectively (p<0,001). Conclusions Our predictive model using six variables, shows good discernment for in-hospital mortality and the risk score has identified three groups with significant differences in prognosis. This model could help in guiding treatments and clinical decision-making, so it needs external validation and to be compared with other models already published. FUNDunding Acknowledgement Type of funding sources: None. ROC curve Risk Score

scholarly journals Intra-abdominal infections: the role of different classifications on the selection of the best antibiotic treatment.

2019 ◽  
Author(s):  
João Silva Nunes ◽  
Teresa Maria Costa Cardoso
Keyword(s):  
Hospital Mortality ◽  
Antibiotic Therapy ◽  
Antibiotic Treatment ◽  
Goodness Of Fit ◽  
Individual Characteristics ◽  
Empiric Antibiotic Therapy ◽  
Operating Characteristics ◽  
Goodness Of Fit Test ◽  
Abdominal Infections ◽  
Selection Of

Abstract Background: Intra-abdominal infections (IAIs) represent a serious cause of morbimortality. A full classification, including all facets of IAIs, does not exist. Two classifications are used to subdivide IAIs: uncomplicated or complicated, considering infection extent; and community-acquired, healthcare-associated or hospital-acquired, regarding the place of acquisition. Inadequate antibiotic therapy is associated with treatment failure and increased mortality. This study was designed to determine accuracy of different classifications of IAIs to identify infections by pathogens sensitive to current treatment guidelines helping the selection of the best antibiotic therapy. Methods: A retrospective cohort study including all adult patients discharged from hospital with a diagnosis of IAI between 1st of January and 31st of October 2016. All variables potentially associated with pre-defined outcomes: infection by a pathogen sensitive to non-pseudomonal cephalosporin or ciprofloxacin plus metronidazole (ATB 1, primary outcome), sensitive to piperacillin-tazobactam (ATB 2) and hospital mortality (secondary outcomes) were studied through logistic regression. Accuracy of the models was assessed by area under receiver operating characteristics (AUROC) curve and calibration was tested using the Hosmer-Lemeshow goodness-of-fit test. Results: Of 1804 patients screened 154 met inclusion criteria. Sensitivity to ATB 1 was independently associated with male gender (adjusted OR=2.612) and previous invasive procedures in the last year (adjusted OR=0.424) (AUROC curve=0,65). Sensitivity to ATB 2 was independently associated with liver disease (adjusted OR=3.580) and post-operative infections (adjusted OR=2.944) (AUROC curve=0.604). Hospital mortality was independently associated with age≥70 (adjusted OR=4.677), solid tumour (adjusted OR=3.127) and sensitivity to non-pseudomonal cephalosporin or ciprofloxacin plus metronidazole (adjusted OR=0.368). The accuracy of pre-existing classifications to identify infection by a pathogen sensitive to ATB 1 was 0.59 considering place of acquisition, 0.61 infection extent and 0.57 local of infection, for ATB 2 it was 0.66, 0.50 and 0.57, respectively. Conclusion: None of existing classifications had a good discriminating power to identify IAIs caused by pathogens sensitive to current antibiotic treatment recommendations. A new classification, including patients’ individual characteristics like those included in the current model, might have a higher potential to distinguish IAIs by resistant pathogens allowing a better choice of empiric antibiotic therapy. Keywords: intra-abdominal infections; classification; antibiotic therapy; hospital mortality.

scholarly journals Comparison of Pediatric Risk of Mortality III, Pediatric Index of Mortality 2, and Pediatric Index of Mortality 3 in Predicting Mortality in a Pediatric Intensive Care Unit

2018 ◽  
Vol 07 (04) ◽  
pp. 201-206 ◽  
Author(s):  
Priyamvada Tyagi ◽  
Mukesh Agrawal ◽  
Milind Tullu
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Cardiopulmonary Resuscitation ◽  
Roc Curve ◽  
Goodness Of Fit ◽  
Pediatric Intensive Care ◽  
Goodness Of Fit Test ◽  
Risk Of Mortality ◽  
Pediatric Index Of Mortality ◽  
Good Calibration

Aims To compare and validate the Pediatric Risk of Mortality (PRISM) III, Pediatric Index of Mortality (PIM) 2, and PIM 3 scores in a tertiary care pediatric intensive care unit (PICU) (Indian setting). Materials and Methods All consecutively admitted patients in the PICU of a public hospital (excluding those with unstable vital signs or cardiopulmonary resuscitation within 2 hours of admission, cardiopulmonary resuscitation before admission, and discharge or death in less than 24 hours after admission) were included. PRISM III, PIM 2, and PIM 3 scores were calculated. Mortality discrimination for the three scores was calculated using the receiver operating characteristic (ROC) curve, and calibration was performed using the Hosmer–Lemeshow goodness-of-fit test. Results A total of 350 patients were included (male:female = 1.3:1) over the study duration of 18 months (median age: 12 months [interquartile range: 4–60 months]). Nearly half were infants (47.4%). Patients with central nervous system disease were the highest (22.8%) followed by cardiovascular system (20.6%). Mortality rate was 39.4% (138 deaths). The area under the ROC curve for the PRISM III score was 0.667, and goodness-of-fit test showed no significant difference between the observed and expected mortalities in any of these categories (p > 0.5), showing good calibration. Areas under the ROC curve for the PIM 2 and PIM 3 scores were 0.728 and 0.726, respectively. For both the scores, the goodness-of-fit test showed good calibration. Conclusions Although all the three scores demonstrate good calibration, the PIM 2 and PIM 3 scores have an advantage regarding the better discrimination ability, ease of data collection, simplicity of computation, and inherent capacity of not being affected by treatment in PICU.

The Braden Scale for Predicting the Outcome and Prognosis of Pressure Injuries in Older Inpatients: A Multicenter, Retrospective Cohort Study

2021 ◽  
Vol 33 (5) ◽  
pp. 127-135
Author(s):  
Yi-Ping Song ◽  
Man-Li Zha ◽  
Hong-Wu Shen ◽  
Yang Li ◽  
Lin Du ◽  
...  
Keyword(s):  
Respiratory Diseases ◽  
Goodness Of Fit ◽  
Operating Characteristic ◽  
Healing Rate ◽  
Area Under The Curve ◽  
Diagnostic Value ◽  
Goodness Of Fit Test ◽  
Braden Scale ◽  
Older Inpatients ◽  
Digestive System Diseases

Introduction. The Braden scale is used to assess the risk of patients with pressure injuries (PIs), but there are limitations to the prediction of PI healing. There is a lack of tools for evaluating PI healing and outcome in clinical practice. Objective. The purpose of this study was to examine the ability of the Braden scale to predict the outcome and prognosis of PIs in older patients. Materials and Methods. Outcome indicator was the wound healing rate of patients with PIs at discharge. The receiver operating characteristic (ROC) and Hosmer-Lemeshow goodness-of-fit test were used to evaluate the discrimination and calibration. Results. Completed data were available for 309 patients, 181 of whom (58.6%) were male. The Braden scale had poor discrimination to predict the outcome and prognosis of PIs with an area under the curve (AUC) of 0.63 (95% CI, 0.56–0.70; P = .01). Subgroup analyses showed the Braden scale had low diagnostic value for patients aged over 90 years (AUCROC = 0.56; 95% CI, 0.17–0.96; P = .738), patients with respiratory diseases (AUCROC = 0.51; 95% CI, 0.37–0.65; P = .908), and digestive system diseases (AUCROC = 0.59; 95% CI, 0.42–0.75; P = .342). The level of calibration ability by Hosmer-Lemeshow goodness-of-fit test was acceptable, defined as P >.200 (χ2 = 6.59; P = .473). In patients aged more than 90 years (χ2 = 4.88; P = .431) and female patients (χ2 = 7.03; P = .425), the Braden scale was also fitting. It was not suitable for patients with respiratory diseases (χ2 = 11.35; P = .078). Conclusions. The Braden scale had low discrimination for predicting the outcome and prognosis of PIs in older inpatients. The development of a new tool is needed to predict healing in patients with preexisting PIs.

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