Dataset Information

Identifying admitted patients at risk of dying: a prospective observational validation of four biochemical scoring systems.

ABSTRACT:

Objectives

Risk assessment is an important part of emergency patient care. Risk assessment tools based on biochemical data have the advantage that calculation can be automated and results can be easily provided. However, to be used clinically, existing tools have to be validated by independent researchers. This study involved an independent external validation of four risk stratification systems predicting death that rely primarily on biochemical variables.

Design

Prospective observational study.

Setting

The medical admission unit at a regional teaching hospital in Denmark.

Participants

Of 5894 adult (age 15 or above) acutely admitted medical patients, 205 (3.5%) died during admission and 46 died (0.8%) within one calendar day.

Interventions

None.

Main outcome measures

The main outcome measure was the ability to identify patients at an increased risk of dying (discriminatory power) as area under the receiver-operating characteristic curve (AUROC) and the accuracy of the predicted probability (calibration) using the Hosmer-Lemeshow goodness-of-fit test. The endpoint was all-cause mortality, defined in accordance with the original manuscripts.

Results

Using the original coefficients, all four systems were excellent at identifying patients at increased risk (discriminatory power, AUROC ?0.80). The accuracy was poor (we could assess calibration for two systems, which failed). After recalculation of the coefficients, two systems had improved discriminatory power and two remained unchanged. Calibration failed for one system in the validation cohort.

Conclusions

Four biochemical risk stratification systems can risk-stratify the acutely admitted medical patients for mortality with excellent discriminatory power. We could improve the models for use in our setting by recalculating the risk coefficient for the chosen variables.

SUBMITTER: Brabrand M

PROVIDER: S-EPMC3693413 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identifying admitted patients at risk of dying: a prospective observational validation of four biochemical scoring systems.

Brabrand Mikkel M Knudsen Torben T Hallas Jesper J

BMJ open 20130620 6

<h4>Objectives</h4>Risk assessment is an important part of emergency patient care. Risk assessment tools based on biochemical data have the advantage that calculation can be automated and results can be easily provided. However, to be used clinically, existing tools have to be validated by independent researchers. This study involved an independent external validation of four risk stratification systems predicting death that rely primarily on biochemical variables.<h4>Design</h4>Prospective obse ...[more]

PMID: 23794564

Similar Datasets

Project description:BackgroundMultiple scoring systems have been developed for both the intensive care unit (ICU) and the emergency department (ED) to risk stratify patients and predict mortality. However, it remains unclear whether the additional data needed to compute ICU scores improves mortality prediction for critically ill patients compared to the simpler ED scores.MethodsWe studied a prospective observational cohort of 227 critically ill patients admitted to the ICU directly from the ED at an academic, tertiary care medical center. We compared Acute Physiology and Chronic Health Evaluation (APACHE) II, APACHE III, Simplified Acute Physiology Score (SAPS) II, Modified Early Warning Score (MEWS), Rapid Emergency Medicine Score (REMS), Prince of Wales Emergency Department Score (PEDS), and a pre-hospital critical illness prediction score developed by Seymour et al. (JAMA 2010, 304(7):747-754). The primary endpoint was 60-day mortality. We compared the receiver operating characteristic (ROC) curves of the different scores and their calibration using the Hosmer-Lemeshow goodness-of-fit test and visual assessment.ResultsThe ICU scores outperformed the ED scores with higher area under the curve (AUC) values (p = 0.01). There were no differences in discrimination among the ED-based scoring systems (AUC 0.698 to 0.742; p = 0.45) or among the ICU-based scoring systems (AUC 0.779 to 0.799; p = 0.60). With the exception of the Seymour score, the ED-based scoring systems did not discriminate as well as the best-performing ICU-based scoring system, APACHE III (p = 0.005 to 0.01 for comparison of ED scores to APACHE III). The Seymour score had a superior AUC to other ED scores and, despite a lower AUC than all the ICU scores, was not significantly different than APACHE III (p = 0.09). When data from the first 24 h in the ICU was used to calculate the ED scores, the AUC for the ED scores improved numerically, but this improvement was not statistically significant. All scores had acceptable calibration.ConclusionsIn contrast to prior studies of patients based in the emergency department, ICU scores outperformed ED scores in critically ill patients admitted from the emergency department. This difference in performance seemed to be primarily due to the complexity of the scores rather than the time window from which the data was derived.

Project description:BackgroundCutaneous squamous cell carcinoma (cSCC) is the second most common cancer worldwide with relatively low metastatic potential (2-5%). Developments in therapeutic options have highlighted the need to better identify high-risk patients who could benefit from closer surveillance, adjuvant therapies and baseline/follow-up imaging, while at the same time safely omitting low-risk patients from further follow-up. Controversy remains regarding the predictive performance of current cSCC staging systems and which methodology to adopt.ObjectivesTo validate the performance of four cSCC staging systems [American Joint Committee on Cancer 8th edition (AJCC8), Brigham and Women's Hospital (BWH), Tübingen and Salamanca T3 refinement] in predicting metastasis using a nationwide cohort.MethodsA nested case-control study using data from the National Disease Registration Service, England, 2013-2015 was conducted. Metastatic cSCC cases were identified using an algorithm to identify all potential cases for manual review. These were 1 : 1 matched on follow-up time to nonmetastatic controls randomly selected from 2013. Staging systems were analysed for distinctiveness, homogeneity, monotonicity, specificity, positive predictive value (PPV), negative predictive value (NPV) and c-index.ResultsWe included 887 metastatic cSCC cases and 887 nonmetastatic cSCC controls. The BWH system showed the highest specificity [92.8%, 95% confidence interval (CI) 90.8-94.3%, PPV (13.2%, 95% CI 10.6-16.2) and c-index (0.84, 95% CI 0.82-0.86). The AJCC8 showed superior NPV (99.2%, 95% CI 99.2-99.3), homogeneity and monotonicity compared with the BWH and Tübingen diameter and thickness classifications (P < 0.001). Salamanca refinement did not show any improvement in AJCC8 T3 cSCC staging.ConclusionsWe validated four cSCC staging systems using the largest nationwide dataset of metastatic cSCC so far. Although the BWH system showed the highest overall discriminative ability, PPV was low for all staging systems, which shows the need for further improvement and refining of current cSCC staging systems.

Project description:BackgroundTraumatic brain injury (TBI) is a major contributor to morbidity and mortality. Computerized tomography (CT) scanning of the brain is essential for diagnostic screening of intracranial injuries in need of neurosurgical intervention, but may also provide information concerning patient prognosis and enable baseline risk stratification in clinical trials. Novel CT scoring systems have been developed to improve current prognostic models, including the Stockholm and Helsinki CT scores, but so far have not been extensively validated. The primary aim of this study was to evaluate the Stockholm and Helsinki CT scores for predicting functional outcome, in comparison with the Rotterdam CT score and Marshall CT classification. The secondary aims were to assess which individual components of the CT scores best predict outcome and what additional prognostic value the CT scoring systems contribute to a clinical prognostic model.Methods and findingsTBI patients requiring neuro-intensive care and not included in the initial creation of the Stockholm and Helsinki CT scoring systems were retrospectively included from prospectively collected data at the Karolinska University Hospital (n = 720 from 1 January 2005 to 31 December 2014) and Helsinki University Hospital (n = 395 from 1 January 2013 to 31 December 2014), totaling 1,115 patients. The Marshall CT classification and the Rotterdam, Stockholm, and Helsinki CT scores were assessed using the admission CT scans. Known outcome predictors at admission were acquired (age, pupil responsiveness, admission Glasgow Coma Scale, glucose level, and hemoglobin level) and used in univariate, and multivariable, regression models to predict long-term functional outcome (dichotomizations of the Glasgow Outcome Scale [GOS]). In total, 478 patients (43%) had an unfavorable outcome (GOS 1-3). In the combined cohort, overall prognostic performance was more accurate for the Stockholm CT score (Nagelkerke's pseudo-R2 range 0.24-0.28) and the Helsinki CT score (0.18-0.22) than for the Rotterdam CT score (0.13-0.15) and Marshall CT classification (0.03-0.05). Moreover, the Stockholm and Helsinki CT scores added the most independent prognostic value in the presence of other known clinical outcome predictors in TBI (6% and 4%, respectively). The aggregate traumatic subarachnoid hemorrhage (tSAH) component of the Stockholm CT score was the strongest predictor of unfavorable outcome. The main limitations were the retrospective nature of the study, missing patient information, and the varying follow-up time between the centers.ConclusionsThe Stockholm and Helsinki CT scores provide more information on the damage sustained, and give a more accurate outcome prediction, than earlier classification systems. The strong independent predictive value of tSAH may reflect an underrated component of TBI pathophysiology. A change to these newer CT scoring systems may be warranted.

Dataset Information

Identifying admitted patients at risk of dying: a prospective observational validation of four biochemical scoring systems.

Objectives

Design

Setting

Participants

Interventions

Main outcome measures

Results

Conclusions

Publications

Identifying admitted patients at risk of dying: a prospective observational validation of four biochemical scoring systems.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets