Dataset Information

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

ABSTRACT:

Introduction

Candidemia 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.

Methods

To 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.

Results

The RPART identified 6 variables as the best discriminators: age < 65 years, temperature 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.

Conclusions

A simple equal-weight risk score differentiated patients' risk for candidemia in a graded fashion upon hospital presentation.

SUBMITTER: Shorr AF

PROVIDER: S-EPMC2784380 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Shorr Andrew F AF Tabak Ying P YP Johannes Richard S RS Sun Xiaowu X Spalding James J Kollef Marin H MH

Critical care (London, England) 20090929 5

<h4>Introduction</h4>Candidemia 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.<h4>Methods</h4>To 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) ...[more]

PMID: 19788756

Similar Datasets

Project description:Background Bleeding risk stratification in acute coronary syndrome is of highest clinical interest but current risk scores have limitations. We sought to develop and validate a new in-hospital bleeding risk score for patients with acute myocardial infarction. Methods and Results From the nationwide SWEDEHEART (Swedish Web-System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies) register, 97,597 patients with acute myocardial infarction enrolled from 2009 until 2014 were selected. A full model with 23 predictor variables and 8 interaction terms was fitted using logistic regression. The full model was approximated by a model with 5 predictors and 1 interaction term. Calibration, discrimination, and clinical utility was evaluated and compared with the ACTION (Acute Coronary Treatment and Intervention Outcomes Network) and CRUSADE (Can Rapid Risk Stratification of Unstable Angina Patients Suppress Ad verse Outcomes With Early Implementation of the ACC /AHA Guidelines) scores. Internal and temporal validity was assessed. In-hospital major bleeding, defined as fatal, intracranial, or requiring surgery or blood transfusion, occurred in 1356 patients (1.4%). The 5 predictors in the approximate model that constituted the SWEDEHEART score were hemoglobin, age, sex, creatinine, and C-reactive protein. The ACTION and CRUSADE scores were poorly calibrated in the derivation cohort and therefore were recalibrated. The SWEDEHEART score showed higher discriminative ability than both recalibrated scores, overall ( C-index 0.80 versus 0.73/0.72) and in all predefined subgroups. Decision curve analysis demonstrated consistently positive and higher net benefit for the SWEDEHEART score compared with both recalibrated scores across all clinically relevant decision thresholds. The original ACTION and CRUSADE scores showed negative net benefit. Conclusions The 5-item SWEDEHEART score discriminates in-hospital major bleeding in patients with acute myocardial infarction and has superior model performance compared with the recalibrated ACTION and CRUSADE scores.

Project description:BackgroundOlder people are increasing users of health care globally. We aimed to establish whether older people with characteristics of frailty and who are at risk of adverse health-care outcomes could be identified using routinely collected data.MethodsA three-step approach was used to develop and validate a Hospital Frailty Risk Score from International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnostic codes. First, we carried out a cluster analysis to identify a group of older people (≥75 years) admitted to hospital who had high resource use and diagnoses associated with frailty. Second, we created a Hospital Frailty Risk Score based on ICD-10 codes that characterised this group. Third, in separate cohorts, we tested how well the score predicted adverse outcomes and whether it identified similar groups as other frailty tools.FindingsIn the development cohort (n=22 139), older people with frailty diagnoses formed a distinct group and had higher non-elective hospital use (33·6 bed-days over 2 years compared with 23·0 bed-days for the group with the next highest number of bed-days). In the national validation cohort (n=1 013 590), compared with the 429 762 (42·4%) patients with the lowest risk scores, the 202 718 (20·0%) patients with the highest Hospital Frailty Risk Scores had increased odds of 30-day mortality (odds ratio 1·71, 95% CI 1·68-1·75), long hospital stay (6·03, 5·92-6·10), and 30-day readmission (1·48, 1·46-1·50). The c statistics (ie, model discrimination) between individuals for these three outcomes were 0·60, 0·68, and 0·56, respectively. The Hospital Frailty Risk Score showed fair overlap with dichotomised Fried and Rockwood scales (kappa scores 0·22, 95% CI 0·15-0·30 and 0·30, 0·22-0·38, respectively) and moderate agreement with the Rockwood Frailty Index (Pearson's correlation coefficient 0·41, 95% CI 0·38-0·47).InterpretationThe Hospital Frailty Risk Score provides hospitals and health systems with a low-cost, systematic way to screen for frailty and identify a group of patients who are at greater risk of adverse outcomes and for whom a frailty-attuned approach might be useful.FundingNational Institute for Health Research.

Dataset Information

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

Introduction

Methods

Results

Conclusions

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets