Dataset Information

Acute physiology of stroke score.

ABSTRACT:

Background and purpose

The Acute Physiology, Age, Chronic Health Evaluation score for critically ill patients has provided a method of predicting outcome using major physiological variables. We hypothesized that a physiology score for stroke patients (Acute Physiology of Stroke Score [APSS]) when added to a validated clinical prediction model would improve outcome prediction.

Methods

The APSS was developed and validated using multivariable logistic regression. It was added to a previously validated clinical model to assess for increased area under the curve in predicting 3-month outcome.

Results

The bootstrap-validated bias-corrected area under the curve for just the APSS predicting alive/dead at discharge was 0.753. The clinical model area under the curve ranged from 0.77 to 0.88 and the addition of the APSS resulted in areas under the curve of 0.77 to 0.89.

Conclusions

These data suggest that the APSS is related to 3-month clinical outcome in patients with ischemic stroke. However, the APSS adds no clinically relevant additional predictive value when added to our previously validated clinical prediction model.

SUBMITTER: Johnston KC

PROVIDER: S-EPMC3157026 | biostudies-literature | 2011 Aug

REPOSITORIES: biostudies-literature

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Publications

Acute physiology of stroke score.

Johnston Karen C KC Yan Guofen G

Stroke 20110616 8

<h4>Background and purpose</h4>The Acute Physiology, Age, Chronic Health Evaluation score for critically ill patients has provided a method of predicting outcome using major physiological variables. We hypothesized that a physiology score for stroke patients (Acute Physiology of Stroke Score [APSS]) when added to a validated clinical prediction model would improve outcome prediction.<h4>Methods</h4>The APSS was developed and validated using multivariable logistic regression. It was added to a pr ...[more]

PMID: 21680908

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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:BackgroundGastrointestinal bleeding (GIB) is a common and often serious complication after stroke. Although several risk factors for post-stroke GIB have been identified, no reliable or validated scoring system is currently available to predict GIB after acute stroke in routine clinical practice or clinical trials. In the present study, we aimed to develop and validate a risk model (acute ischemic stroke associated gastrointestinal bleeding score, the AIS-GIB score) to predict in-hospital GIB after acute ischemic stroke.MethodsThe AIS-GIB score was developed from data in the China National Stroke Registry (CNSR). Eligible patients in the CNSR were randomly divided into derivation (60%) and internal validation (40%) cohorts. External validation was performed using data from the prospective Chinese Intracranial Atherosclerosis Study (CICAS). Independent predictors of in-hospital GIB were obtained using multivariable logistic regression in the derivation cohort, and ?-coefficients were used to generate point scoring system for the AIS-GIB. The area under the receiver operating characteristic curve (AUROC) and the Hosmer-Lemeshow goodness-of-fit test were used to assess model discrimination and calibration, respectively.ResultsA total of 8,820, 5,882, and 2,938 patients were enrolled in the derivation, internal validation and external validation cohorts. The overall in-hospital GIB after AIS was 2.6%, 2.3%, and 1.5% in the derivation, internal, and external validation cohort, respectively. An 18-point AIS-GIB score was developed from the set of independent predictors of GIB including age, gender, history of hypertension, hepatic cirrhosis, peptic ulcer or previous GIB, pre-stroke dependence, admission National Institutes of Health stroke scale score, Glasgow Coma Scale score and stroke subtype (Oxfordshire). The AIS-GIB score showed good discrimination in the derivation (0.79; 95% CI, 0.764-0.825), internal (0.78; 95% CI, 0.74-0.82) and external (0.76; 95% CI, 0.71-0.82) validation cohorts. The AIS-GIB score was well calibrated in the derivation (P = 0.42), internal (P = 0.45) and external (P = 0.86) validation cohorts.ConclusionThe AIS-GIB score is a valid clinical grading scale to predict in-hospital GIB after AIS. Further studies on the effect of the AIS-GIB score on reducing GIB and improving outcome after AIS are warranted.

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:BackgroundSeverity scoring systems are inherent to ICU practice for multiple purposes. Acute Physiology and Chronic Health Evaluation (APACHE) scoring systems are designed for ICU mortality prediction. This study aims to validate APACHE IV in COVID-19 patients admitted to the ICU.MethodsAll COVID-19 patients admitted to the ICU between March 13, 2020, and October 17, 2020, were retrospectively analyzed. APACHE II and APACHE IV scores as well as SOFA scores were calculated within 24 hours after admission. Discrimination for mortality of all three scoring systems was assessed by receiver operating characteristic curves. Youden index was determined for the scoring system with the best discriminative performance. The Hosmer-Lemeshow goodness-of-fit test was used to assess calibration. All analyses were performed for both the overall population as in a subgroup treated with anti-Xa adjusted dosages of LMWHs.Results116 patients were admitted to our ICU during the study period. 13 were excluded for various reasons, leaving 103 patients in the statistical analysis of the overall population. 57 patients were treated with anti-Xa adjusted prophylactic dosages of LMWH and were supplementary analyzed in a subgroup analysis. APACHE IV had the best discriminative power of the three scoring systems, both in the overall population (APACHE IV ROC AUC 0.67 vs. APACHE II ROC AUC 0.63) as in the subgroup (APACHE IV ROC AUC 0.82 vs. APACHE II ROC AUC 0.7). This model exhibits good calibration. Hosmer-Lemeshow p values for APACHE IV were 0.9234 for the overall population and 0.8017 for the subgroup. Calibration p values of the APACHE II score were 0.1394 and 0.6475 for the overall versus subgroup, respectively.ConclusionsAPACHE IV provided the best discrimination and calibration of the considered scoring systems in critically ill COVID-19 patients, both in the overall group and in the subgroup with anti-Xa adjusted LMWH doses. Only in the subgroup analysis, discriminative abilities of APACHE IV were very good. This trial is registered with NCT04713852.

Dataset Information

Acute physiology of stroke score.

Background and purpose

Methods

Results

Conclusions

Publications

Acute physiology of stroke score.

Similar Datasets

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