Dataset Information

Simplified Acute Physiology Score II as Predictor of Mortality in Intensive Care Units: A Decision Curve Analysis.

ABSTRACT:

Background

End-of-life decision-making in Intensive care Units (ICUs) is difficult. The main problems encountered are the lack of a reliable prediction score for death and the fact that the opinion of patients is rarely taken into consideration. The Decision Curve Analysis (DCA) is a recent method developed to evaluate the prediction models and which takes into account the wishes of patients (or surrogates) to expose themselves to the risk of obtaining a false result. Our objective was to evaluate the clinical usefulness, with DCA, of the Simplified Acute Physiology Score II (SAPS II) to predict ICU mortality.

Methods

We conducted a retrospective cohort study from January 2011 to September 2015, in a medical-surgical 23-bed ICU at University Hospital. Performances of the SAPS II, a modified SAPS II (without AGE), and age to predict ICU mortality, were measured by a Receiver Operating Characteristic (ROC) analysis and DCA.

Results

Among the 4.370 patients admitted, 23.3% died in the ICU. Mean (standard deviation) age was 56.8 (16.7) years, and median (first-third quartile) SAPS II was 48 (34-65). Areas under ROC curves were 0.828 (0.813-0.843) for SAPS II, 0.814 (0.798-0.829) for modified SAPS II and of 0.627 (0.608-0.646) for age. DCA showed a net benefit whatever the probability threshold, especially under 0.5.

Conclusion

DCA shows the benefits of the SAPS II to predict ICU mortality, especially when the probability threshold is low. Complementary studies are needed to define the exact role that the SAPS II can play in end-of-life decision-making in ICUs.

SUBMITTER: Allyn J

PROVIDER: S-EPMC5065161 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Simplified Acute Physiology Score II as Predictor of Mortality in Intensive Care Units: A Decision Curve Analysis.

Allyn Jérôme J Ferdynus Cyril C Bohrer Michel M Dalban Cécile C Valance Dorothée D Allou Nicolas N

PloS one 20161014 10

<h4>Background</h4>End-of-life decision-making in Intensive care Units (ICUs) is difficult. The main problems encountered are the lack of a reliable prediction score for death and the fact that the opinion of patients is rarely taken into consideration. The Decision Curve Analysis (DCA) is a recent method developed to evaluate the prediction models and which takes into account the wishes of patients (or surrogates) to expose themselves to the risk of obtaining a false result. Our objective was t ...[more]

PMID: 27741304

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Project description:Objectives: In critical care it is crucial to appropriately assess the risk of mortality for each patient. This is especially relevant in pediatrics, with its need for accurate and repeatable scoring. Aim of this study was to evaluate an age-adapted version of the expanded Simplified Acute Physiology Score II; (p-SAPS II), a repeatable, newly-designed scoring system compared to established scores (Pediatric Sequential Organ Failure Assessment Score/pSOFA, Pediatric Logistic Organ Dysfunction Score-2/PELOD-2 and Pediatric Index of Mortality 3/PIM3). Design: This retrospective cohort pilot study included data collected from patients admitted to the Pediatric Intensive Care Unit (PICU) at the Medical University of Vienna between July 2017 through December 2018. Patients: 231 admissions were included, comprising neonates (gestational age of ≥ 37 weeks) and patients up to 18 years of age with a PICU stay longer than 48 h. Main Outcomes: Mortality risk prediction and discrimination between survivors and non-survivors were the main outcomes of this study. The primary statistical methods for evaluating the performance of each score were the area under the receiver operating characteristic curve (AUROC) and goodness-of-fit test. Results: Highest AUROC curve was calculated for p-SAPS II (AUC = 0.86; 95% CI: 0.77-0.96; p < 0.001). This was significantly higher than the AUROCs of PELOD-2/pSOFA but not of PIM3. However, in a logistic regression model including p-SAPS II and PIM3 as covariates, p-SAPS II had a significant effect on the accuracy of prediction (p = 0.003). Nevertheless, according to the goodness-of-fit test for p-SAPS II and PIM3, p-SAPS II overestimated the number of deaths, whereas PIM3 showed acceptable estimations. Repeatability testing showed increasing AUROC values for p-SAPS II throughout the clinical stay (0.96 at day 28) but still no significant difference to PIM 3. The prediction accuracy, although improved over the days and even exceeded PIM 3. Conclusions: The newly-created p-SAPS II performed better than the established PIM3 in terms of discriminating between survivors and non-survivors. Furthermore, p-SAPS II can be assessed repeatably throughout a patient's PICU stay what improves mortality prediction. However, there is still a need to optimize calibration of the score to accurately predict mortality sooner throughout the clinical stay.

Project description:OBJECTIVES:Coronavirus disease 2019 has emerged as a major global health threat with a great number of deaths in China. We aimed to assess the association between Acute Physiology and Chronic Health Evaluation II score and hospital mortality in patients with coronavirus disease 2019, and to compare the predictive ability of Acute Physiology and Chronic Health Evaluation II score, with Sequential Organ Failure Assessment score and Confusion, Urea, Respiratory rate, Blood pressure, Age 65 (CURB65) score. DESIGN:Retrospective observational cohort. SETTING:Tongji Hospital in Wuhan, China. SUBJECTS:Confirmed patients with coronavirus disease 2019 hospitalized in the ICU of Tongji hospital from January 10, 2020, to February 10, 2020. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:Of 178 potentially eligible patients with symptoms of coronavirus disease 2019, 23 patients (12.92%) were diagnosed as suspected cases, and one patient (0.56%) suffered from cardiac arrest immediately after admission. Ultimately, 154 patients were enrolled in the analysis and 52 patients (33.77%) died. Mean Acute Physiology and Chronic Health Evaluation II score (23.23?±?6.05) was much higher in deaths compared with the mean Acute Physiology and Chronic Health Evaluation II score of 10.87?±?4.40 in survivors (p < 0.001). Acute Physiology and Chronic Health Evaluation II score was independently associated with hospital mortality (adjusted hazard ratio, 1.07; 95% CI, 1.01-1.13). In predicting hospital mortality, Acute Physiology and Chronic Health Evaluation II score demonstrated better discriminative ability (area under the curve, 0.966; 95% CI, 0.942-0.990) than Sequential Organ Failure Assessment score (area under the curve, 0.867; 95% CI, 0.808-0.926) and CURB65 score (area under the curve, 0.844; 95% CI, 0.784-0.905). Based on the cut-off value of 17, Acute Physiology and Chronic Health Evaluation II score could predict the death of patients with coronavirus disease 2019 with a sensitivity of 96.15% and a specificity of 86.27%. Kaplan-Meier analysis showed that the survivor probability of patients with coronavirus disease 2019 with Acute Physiology and Chronic Health Evaluation II score less than 17 was notably higher than that of patients with Acute Physiology and Chronic Health Evaluation II score greater than or equal to 17 (p < 0.001). CONCLUSIONS:Acute Physiology and Chronic Health Evaluation II score was an effective clinical tool to predict hospital mortality in patients with coronavirus disease 2019 compared with Sequential Organ Failure Assessment score and CURB65 score. Acute Physiology and Chronic Health Evaluation II score greater than or equal to 17 serves as an early warning indicator of death and may provide guidance to make further clinical decisions.

Project description:IntroductionSepsis is a severe medical condition that can be life-threatening. If sepsis progresses to septic shock, the mortality rate increases to around 40%, much higher than the 10% mortality observed in sepsis. Diabetes increases infection and sepsis risk, making management complex. Various scores of screening tools, such as Modified Early Warning Score (MEWS), Simplified Acute Physiology Score (SAPS II), Sequential Organ Failure Assessment Score (SOFA), and Acute Physiology and Chronic Health Evaluation (APACHE II), are used to predict the severity or mortality rate of disease. Our study aimed to compare the effectiveness and optimal cutoff points of these scores. We focused on the early prediction of septic shock in patients with diabetes in the Emergency Department (ED).MethodsWe conducted a retrospective cohort study to collect data on patients with diabetes. We collected prediction factors and MEWS, SOFA, SAPS II and APACHE II scores to predict septic shock in these patients. We determined the optimal cutoff points for each score. Subsequently, we compared the identified scores with the gold standard for diagnosing septic shock by applying the Sepsis-3 criteria.ResultsSystolic blood pressure (SBP), peripheral oxygen saturation (SpO2), Glasgow Coma Scale (GCS), pH, and lactate concentrations were significant predictors of septic shock (p < 0.001). The SOFA score performed well in predicting septic shock in patients with diabetes. The area under the receiver operating characteristics (ROC) curve for the SOFA score was 0.866 for detection within 48 h and 0.840 for detection after 2 h of admission to the ED, with the optimal cutoff score of ≥ 6.ConclusionSBP, SpO2, GCS, pH, and lactate concentrations are crucial for the early prediction of septic shock in patients with diabetes. The SOFA score is a superior predictor for the onset of septic shock in patients with diabetes compared with MEWS, SAPS II, and APACHE II scores. Specifically, a cutoff of ≥ 6 in the SOFA score demonstrates high accuracy in predicting shock within 48 h post-ED visit and as early as 2 h after ED admission.

Project description:PurposeSeverity scores including the Simplified Acute Physiology Score (SAPS) II and the Sequential Organ Failure Assessment (SOFA) score are used in intensive care units (ICUs) to assess disease severity, predict mortality and in research. We aimed to assess the predictive performance of SAPS II and the initial SOFA score for in-hospital and 90-day mortality in a contemporary international cohort.MethodsThis was a post-hoc study of the Stress Ulcer Prophylaxis in the Intensive Care Unit (SUP-ICU) inception cohort study, which included acutely ill adults from ICUs across 11 countries (n = 1034). We compared the discrimination of SAPS II and initial SOFA scores, compared the discrimination of SAPS II in our cohort with the original cohort, assessed the calibration of SAPS II customised to our cohort, and compared the discrimination for 90-day mortality vs. in-hospital mortality for both scores. Discrimination was evaluated using areas under the receiver operating characteristics curves (AUROC). Calibration was evaluated using Hosmer-Lemeshow's goodness-of-fit Ĉ-statistic.ResultsAUROC for in-hospital mortality was 0.80 (95% confidence interval (CI) 0.77-0.83) for SAPS II and 0.73 (95% CI 0.69-0.76) for initial SOFA score (P<0.001 for the comparison). Calibration of the customised SAPS II for predicting in-hospital mortality was adequate (P = 0.60). Discrimination of SAPS II was reduced compared with the original SAPS II validation sample (AUROC 0.80 vs. 0.86; P = 0.001). AUROC for 90-day mortality was 0.79 (95% CI 0.76-0.82; P = 0.74 for comparison with in-hospital mortality) for SAPS II and 0.71 (95% CI 0.68-0.75; P = 0.66 for comparison with in-hospital mortality) for the initial SOFA score.ConclusionsThe predictive performance of SAPS II was similar for in-hospital and 90-day mortality and superior to that of the initial SOFA score, but SAPS II's performance has decreased over time. Use of a contemporary severity score with improved predictive performance may be of value.

Project description:Pediatric Index of Mortality 3 is a validated tool including 11 variables for the assessment of mortality risk in PICU patients. With the recent advances in explainable machine learning algorithms, we aimed to assess feasibility of application of these machine learning models to simplify the Pediatric Index of Mortality 3 scoring system in order to decrease time and labor required for data collection and entry for Pediatric Index of Mortality 3.DesignSingle-center, retrospective cohort study. Data from the Virtual Pediatric Systems for patients admitted to Cleveland Clinic Children`s PICU between January 2008 and December 2019 was obtained. Light Gradient Boosting Machine Regressor (a gradient boosting decision tree algorithm) was used for building the machine learning models. Variable importance was analyzed by SHapley Additive exPlanations. All of the 11 Pediatric Index of Mortality 3 variables were used as input variables in the machine learning models to predict Pediatric Index of Mortality 3 risk of mortality as the outcome variable. Mean absolute error, root mean squared error, and R-squared were calculated for each of the 11 machine learning models as model performance parameters.SettingQuaternary children's hospital.PatientsPICU patients.InterventionsNone.Measurements and main resultsFive-thousand sixty-eight patients were analyzed. The machine learning models were able to maintain similar predictive error until the number of input variables decreased to four. The machine learning model with five input variables (mechanical ventilation in the first hour of PICU admission, very-high-risk diagnosis, surgical recovery from a noncardiac procedure, low-risk diagnosis, and base excess) produced lowest mean root mean squared error of 1.49 (95% CI, 1.05-1.93) and highest R-squared of 0.73 (95% CI, 0.6-0.86) with mean absolute error of 0.43 (95% CI, 0.35-0.5) among all the 11 machine learning models.ConclusionsExplainable machine learning methods were feasible in simplifying the Pediatric Index of Mortality 3 scoring system with similar risk of mortality predictions compared to the original Pediatric Index of Mortality 3 model tested in a single-center dataset.

Dataset Information

Simplified Acute Physiology Score II as Predictor of Mortality in Intensive Care Units: A Decision Curve Analysis.

Background

Methods

Results

Conclusion

Publications

Simplified Acute Physiology Score II as Predictor of Mortality in Intensive Care Units: A Decision Curve Analysis.

Similar Datasets

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