Dataset Information

Development and validation of a risk-adjustment tool in acute asthma.

ABSTRACT:

Objective

To develop and prospectively validate a risk-adjustment tool in acute asthma.

Data sources

Data were obtained from two large studies on acute asthma, the Multicenter Airway Research Collaboration (MARC) and the National Emergency Department Safety Study (NEDSS) cohorts. Both studies involved >60 emergency departments (EDs) and were performed during 1996-2001 and 2003-2006, respectively. Both included patients aged 18-54 years presenting to the ED with acute asthma.

Study design

Retrospective cohort studies.

Data collection

Clinical information was obtained from medical record review. The risk index was derived in the MARC cohort and then was prospectively validated in the NEDSS cohort.

Principle findings

There were 3,515 patients in the derivation cohort and 3,986 in the validation cohort. The risk index included nine variables (age, sex, current smoker, ever admitted for asthma, ever intubated for asthma, duration of symptoms, respiratory rate, peak expiratory flow, and number of beta-agonist treatments) and showed satisfactory discrimination (area under the receiver operating characteristic curve, 0.75) and calibration ( p=.30 for Hosmer-Lemeshow test) when applied to the validation cohort.

Conclusions

We developed and validated a novel risk-adjustment tool in acute asthma. This tool can be used for health care provider profiling to identify outliers for quality improvement purposes.

SUBMITTER: Tsai CL

PROVIDER: S-EPMC2754555 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature

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Publications

Development and validation of a risk-adjustment tool in acute asthma.

Tsai Chu-Lin CL Clark Sunday S Sullivan Ashley F AF Camargo Carlos A CA

Health services research 20090713 5 Pt 1

<h4>Objective</h4>To develop and prospectively validate a risk-adjustment tool in acute asthma.<h4>Data sources</h4>Data were obtained from two large studies on acute asthma, the Multicenter Airway Research Collaboration (MARC) and the National Emergency Department Safety Study (NEDSS) cohorts. Both studies involved >60 emergency departments (EDs) and were performed during 1996-2001 and 2003-2006, respectively. Both included patients aged 18-54 years presenting to the ED with acute asthma.<h4>St ...[more]

PMID: 19619246

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Project description:ImportanceCurrent risk assessment tools for domestic violence against family members were developed with small and selected samples, have low accuracy with few external validations, and do not report key performance measures.ObjectiveTo develop new tools to assess risk of reoffending among individuals who have perpetrated domestic violence.Design, setting, and participantsThis prognostic study investigated a national cohort of all individuals arrested for domestic violence between 1998 and 2013 in Sweden using information from multiple national registers, including National Crime Register, National Patient Register, Longitudinal Integrated Database for Health Insurance and Labour Market Studies Register, and Multi-Generation Register. Data were analyzed from August 2022 to June 2023.ExposureArrest for domestic violence.Main outcomes and measuresPrediction models were developed for 3 reoffending outcomes after arrest for domestic violence: conviction of a new violent crime (including domestic violence), conviction of any new crime, and rearrest for domestic violence at 1 year, 3 years, and 5 years. The prediction models were created using sociodemographic factors, criminological factors, and mental health status-related factors, linking data from multiple population-based longitudinal registers. Cox proportional hazard multivariable regression was used to develop prediction models and validate them in external samples. Key performance measures, including discrimination at prespecified cutoffs and calibration statistics, were investigated.ResultsThe cohort included 27 456 individuals (mean [SD] age, 39.4 [11.6] years; 24 804 men [90.3%]) arrested for domestic violence, of whom 4222 (15.4%) reoffended and were convicted for a new violent crime during a mean (SD) follow-up of 26.5 (27.0) months, 9010 (32.8%) reoffended and were convicted for a new crime (mean [SD] follow-up, 22.4 [25.1] months), and 2080 (7.6%) were rearrested for domestic violence (mean [SD] follow-up, 25.7 [30.6] months). Prediction models were developed with sociodemographic, criminological, and mental health factors and showed good measures of discrimination and calibration for violent reoffending and any reoffending. The area under the receiver operating characteristic curve (AUC) for risk of violent reoffending was 0.75 (95% CI, 0.74-0.76) at 1 year, 0.76 (95% CI, 0.75-0.77) at 3 years, and 0.76 (95% CI, 0.75-0.77) 5 years. The AUC for risk of any reoffending was 0.76 (95% CI, 0.75-0.77) at 1 year and at 3 years and 0.76 (95% CI, 0.75-0.76) at 5 years. The model for domestic violence reoffending showed modest discrimination (C index, 0.63; 95% CI, 0.61-0.65) and good calibration. The validation models showed discrimination and calibration performance similar to those of derivation models for all 3 reoffending outcomes. The prediction models have been translated into 3 simple online risk calculators that are freely available to use.Conclusions and relevanceThis prognostic study developed scalable, evidence-based prediction tools that could support decision-making in criminal justice systems, particularly at the arrest stage when identifying those at higher risk of reoffending and screening out individuals at low risk of reoffending. Furthermore, these tools can enhance treatment allocation by enabling criminal justice services to focus on modifiable risk factors identified in the tools for individuals at high risk of reoffending.

Dataset Information

Development and validation of a risk-adjustment tool in acute asthma.

Objective

Data sources

Study design

Data collection

Principle findings

Conclusions

Publications

Development and validation of a risk-adjustment tool in acute asthma.

Similar Datasets

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