Risk stratification for in-hospital mortality in spontaneous intracerebral haemorrhage: A Classification and Regression Tree Analysis

Takahashi, Osamu; Cook, E. Francis; Nakamura, Takehiro; Saito, Jun; Ikawa, Fusao; Fukui, Tsuguya

doi:10.1093/qjmed/hcl107

Cited by 54 publications

(47 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the hematoma size has been proposed as an independent predictor of mortality and used in ICH score for ICH in some researches [4], [8], [14]–[15], it is not considered as an independent predictor for risk stratification and is discarded from ICH score calculation by other studies due to the concern of potential imprecision of hematoma size estimated by the ABC/2 formula [11], [16]–[17]. The ABC/2 formula has been found to overestimate the hematoma size in some studies [7], [18] but to underestimate the hematoma size in other studies [19].…”

Section: Discussionmentioning

confidence: 99%

Hematoma Shape, Hematoma Size, Glasgow Coma Scale Score and ICH Score: Which Predicts the 30-Day Mortality Better for Intracerebral Hematoma?

et al. 2014

View full text Add to dashboard Cite

PurposeTo investigate the performance of hematoma shape, hematoma size, Glasgow coma scale (GCS) score, and intracerebral hematoma (ICH) score in predicting the 30-day mortality for ICH patients. To examine the influence of the estimation error of hematoma size on the prediction of 30-day mortality.Materials and MethodsThis retrospective study, approved by a local institutional review board with written informed consent waived, recruited 106 patients diagnosed as ICH by non-enhanced computed tomography study. The hemorrhagic shape, hematoma size measured by computer-assisted volumetric analysis (CAVA) and estimated by ABC/2 formula, ICH score and GCS score was examined. The predicting performance of 30-day mortality of the aforementioned variables was evaluated. Statistical analysis was performed using Kolmogorov-Smirnov tests, paired t test, nonparametric test, linear regression analysis, and binary logistic regression. The receiver operating characteristics curves were plotted and areas under curve (AUC) were calculated for 30-day mortality. A P value less than 0.05 was considered as statistically significant.ResultsThe overall 30-day mortality rate was 15.1% of ICH patients. The hematoma shape, hematoma size, ICH score, and GCS score all significantly predict the 30-day mortality for ICH patients, with an AUC of 0.692 (P = 0.0018), 0.715 (P = 0.0008) (by ABC/2) to 0.738 (P = 0.0002) (by CAVA), 0.877 (P<0.0001) (by ABC/2) to 0.882 (P<0.0001) (by CAVA), and 0.912 (P<0.0001), respectively.ConclusionOur study shows that hematoma shape, hematoma size, ICH scores and GCS score all significantly predict the 30-day mortality in an increasing order of AUC. The effect of overestimation of hematoma size by ABC/2 formula in predicting the 30-day mortality could be remedied by using ICH score.

show abstract

Section: Discussionmentioning

confidence: 99%

Hematoma Shape, Hematoma Size, Glasgow Coma Scale Score and ICH Score: Which Predicts the 30-Day Mortality Better for Intracerebral Hematoma?

et al. 2014

View full text Add to dashboard Cite

show abstract

“…CART is a non-parametric, empiric statistical method21 that has been increasingly utilised for clinical applications across a number of disease groups22 – 24 but not as yet for clinical prediction in AP. Patients are classified into two groups at each stage of analysis based on classification variables.…”

Section: Methodsmentioning

confidence: 99%

The early prediction of mortality in acute pancreatitis: a large population-based study

Johannes

Sun

et al. 2008

Gut

706

516

View full text Add to dashboard Cite

Background: Identification of patients at risk for mortality early in the course of acute pancreatitis (AP) is an important step in improving outcome. Methods: Using Classification and Regression Tree (CART) analysis, a clinical scoring system was developed for prediction of in-hospital mortality in AP. The scoring system was derived on data collected from 17 992 cases of AP from 212 hospitals in [2000][2001]. The new scoring system was validated on data collected from 18 256 AP cases from 177 hospitals in [2004][2005]. The accuracy of the scoring system for prediction of mortality was measured by the area under the receiver operating characteristic curve (AUC). The performance of the new scoring system was further validated by comparing its predictive accuracy with that of Acute Physiology and Chronic Health Examination (APACHE) II. Results: CART analysis identified five variables for prediction of in-hospital mortality. One point is assigned for the presence of each of the following during the first 24 h: blood urea nitrogen (BUN) .25 mg/dl; impaired mental status; systemic inflammatory response syndrome (SIRS); age .60 years; or the presence of a pleural effusion (BISAP). Mortality ranged from .20% in the highest risk group to ,1% in the lowest risk group. In the validation cohort, the BISAP AUC was 0.82 (95% CI 0.79 to 0.84) versus APACHE II AUC of 0.83 (95% CI 0.80 to 0.85). Conclusions: A new mortality-based prognostic scoring system for use in AP has been derived and validated. The BISAP is a simple and accurate method for the early identification of patients at increased risk for in-hospital mortality.Acute pancreatitis (AP) is a disease with a substantial burden on the US healthcare system. Recent data indicate a rise in the absolute number as well as the rate of emergency room visits, hospital admissions and direct healthcare costs for AP in the USA (210 000 admissions in 2002; 4.6 of every 1000 hospitalisations from 1988 to 2003, annual direct costs in excess of US$2 billion). 1-3With an overall mortality rate of 2-5%, 3 4 a reliable method of risk stratification for AP is of significant clinical importance.Current methods of risk stratification in AP have important limitations. The Ranson 5 and modified Glasgow score 6 contain data not routinely collected at the time of hospitalisation. In addition, both require 48 h to complete, missing a potentially valuable early therapeutic window.5 7 The most commonly utilised prediction scoring system for clinical research studies in AP is the Acute Physiology and Chronic Health Examination (APACHE) II.8 9 However, the APACHE II was originally developed as an intensive care instrument and requires the collection of a large number of parameters, some of which may not be relevant to prognosis in AP. The purpose of this study was to develop a simple and accurate clinical scoring system for stratifying patents according to their risk of inhospital mortality. To develop a clinical tool useful early in the disease course, we examined data collected within th...

show abstract

“…Also referred to as classification and regression tree analysis [24], RPART has been used to derive prediction rules for acute chest pain [25], heart failure [26], and other conditions [27,28]. RPART first identifies the variable with the highest discrimination for the outcome of interest (node) and then repeats the process to partition subsequent nodes.…”

Section: Methodsmentioning

confidence: 99%

Candidemia on presentation to the hospital: development and validation of a risk score

Shorr

Tabak²,

Johannes

et al. 2009

Crit Care

View full text Add to dashboard Cite

IntroductionCandidemia results in substantial morbidity and mortality, especially if initial antifungal therapy is delayed or is inappropriate; however, candidemia is difficult to diagnose because of its nonspecific presentation.MethodsTo develop a risk score for identifying hospitalized patients with candidemia, we performed a retrospective analysis of a large database of 176 acute-care hospitals in the United States. We studied 64,019 patients with bloodstream infection (BSI) on presentation from 2000 through 2005 (derivation cohort) and 24,685 from 2006 to 2007 (validation cohort). We used recursive partitioning (RPART) to identify the best discriminators for Candida as the cause of BSI. We compared three sets of models (equal-weight, unequal-weight, vs full model with additional variables from logistic regression model) for sensitivity analysis.ResultsThe RPART identified 6 variables as the best discriminators: age < 65 years, temperature ≤ 98°F or severe altered mental status, cachexia, previous hospitalization within 30 days, admitted from other healthcare facility, and need for mechanical ventilation. The prevalence for patients presented with 0 through 6 risk factors in the derivation cohort was 28.7%, 38.8%, 21.8%, 8.3%, 2.1%, 0.3%, and < 0.1% respectively. The corresponding candidemia rates were 0.4% (69/18,355), 0.8% (196/24,811), 1.6% (229/13,984), 3.2% (168/5,330), 4.2% (58/1,371), 9.6% (15/157), and 27.3% (3/11) respectively (P < 0.0001). Findings were similar in the validation cohort (P < 0.0001). The equal-weight risk score model, which signed 1 point to each risk factor, yielded good discrimination in both cohorts with areas under the receiver operating curve (AUROCs) of 0.70 versus 0.71 (derivation versus validation). AUROC values were similar for the unequal-weight model, which signed different weight to each risk factor based on multivariable logistic regression coefficient, (AUROCs, 0.70-0.72). Both equal-weight and unequal-weight models were well calibrated (all Hosmer-Lemshow P > 0.10, indicating predicted and observed candidemia rates did not differ significant across the 7 risk stratus). The full model with 16 risk factors had slightly higher AUROCs (0.74 versus 0.73 for derivation versus validation); however, 7 variables were no longer significant in the recalibrated model for the validation cohort, indicating that the additional items did not materially enhance the model.ConclusionsA simple equal-weight risk score differentiated patients' risk for candidemia in a graded fashion upon hospital presentation.

show abstract

Risk stratification for in-hospital mortality in spontaneous intracerebral haemorrhage: A Classification and Regression Tree Analysis

Abstract: ICH patients can easily be stratified for mortality risk, based on three predictors available on admission. This simple decision tree model provides clinicians with a reliable and practical tool.

Cited by 54 publications

References 34 publications

Hematoma Shape, Hematoma Size, Glasgow Coma Scale Score and ICH Score: Which Predicts the 30-Day Mortality Better for Intracerebral Hematoma?

Hematoma Shape, Hematoma Size, Glasgow Coma Scale Score and ICH Score: Which Predicts the 30-Day Mortality Better for Intracerebral Hematoma?

The early prediction of mortality in acute pancreatitis: a large population-based study

Candidemia on presentation to the hospital: development and validation of a risk score

Contact Info

Product

Resources

About