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Using clinical variables and drug prescription data to control for confounding in outcome comparisons between hospitals.

Colais P, Di Martino M, Fusco D, Davoli M, Aylin P, Perucci CA - BMC Health Serv Res (2014)

Bottom Line: Doubts remain as to whether the addition of clinical and drug consumption information would improve the prediction of health outcomes and reduce confounding in inter-hospital comparisons.Risk-adjusted outcome rates were derived at the hospital level.The results show that the available clinical variables and drug prescription information were important complements to the hospital discharge data for characterising the acute severity of the patients.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology, Regional Health Service, Lazio Region, Via Santa Costanza 53, Rome, 00198, Italy. p.colais@deplazio.it.

ABSTRACT

Background: Hospital discharge records are an essential source of information when comparing health outcomes among hospitals; however, they contain limited information on acute clinical conditions. Doubts remain as to whether the addition of clinical and drug consumption information would improve the prediction of health outcomes and reduce confounding in inter-hospital comparisons. The objective of the study is to compare the performance of two multivariate risk adjustment models, with and without clinical data and drug prescription information, in terms of their capability to a) predict short-term outcome rates and b) compare hospitals' risk-adjusted outcome rates using two risk-adjustment procedures.

Methods: Observational, retrospective study based on hospital data collected at the regional level.Two cohorts of patients discharged in 2010 from hospitals located in the Lazio Region, Italy: acute myocardial infarction (AMI) and hip fracture (HF). Multivariate logistic regression models were implemented to predict 30-day mortality (AMI) or 48-hour surgery (HF), adjusting for demographic characteristics and comorbidities plus clinical data and drug prescription information. Risk-adjusted outcome rates were derived at the hospital level.

Results: The addition of clinical data and drug prescription information improved the capability of the models to predict the study outcomes for the two conditions investigated. The discriminatory power of the AMI model increases when the clinical data and drug prescription information are included (c-statistic increases from 0.761 to 0.797); for the HF model the increase was more slight (c-statistic increases from 0.555 to 0.574). Some differences were observed between the hospital-adjusted proportion estimated using the two different models. However, the estimated hospital outcome rates were weakly affected by the introduction of clinical data and drug prescription information.

Conclusions: The results show that the available clinical variables and drug prescription information were important complements to the hospital discharge data for characterising the acute severity of the patients. However, when these variables were used for adjustment purposes their contribution was negligible. This conclusion might not apply at other locations, in other time periods and for other health conditions if there is heterogeneity in the clinical conditions between hospitals.

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Related in: MedlinePlus

Adjusted proportion of interventions performed within 48 hours of HF admission, by hospital (No. 6348).
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Fig2: Adjusted proportion of interventions performed within 48 hours of HF admission, by hospital (No. 6348).

Mentions: In Figure 2, the hospital-adjusted proportion of patients who received an intervention within 48 hours after HF admission estimated using the “hospital discharge data” model and the hospital-adjusted proportion estimated using the “hospital discharge data + clinical variables + drug prescriptions” model are plotted in a funnel plot.Figure 2


Using clinical variables and drug prescription data to control for confounding in outcome comparisons between hospitals.

Colais P, Di Martino M, Fusco D, Davoli M, Aylin P, Perucci CA - BMC Health Serv Res (2014)

Adjusted proportion of interventions performed within 48 hours of HF admission, by hospital (No. 6348).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4209232&req=5

Fig2: Adjusted proportion of interventions performed within 48 hours of HF admission, by hospital (No. 6348).
Mentions: In Figure 2, the hospital-adjusted proportion of patients who received an intervention within 48 hours after HF admission estimated using the “hospital discharge data” model and the hospital-adjusted proportion estimated using the “hospital discharge data + clinical variables + drug prescriptions” model are plotted in a funnel plot.Figure 2

Bottom Line: Doubts remain as to whether the addition of clinical and drug consumption information would improve the prediction of health outcomes and reduce confounding in inter-hospital comparisons.Risk-adjusted outcome rates were derived at the hospital level.The results show that the available clinical variables and drug prescription information were important complements to the hospital discharge data for characterising the acute severity of the patients.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology, Regional Health Service, Lazio Region, Via Santa Costanza 53, Rome, 00198, Italy. p.colais@deplazio.it.

ABSTRACT

Background: Hospital discharge records are an essential source of information when comparing health outcomes among hospitals; however, they contain limited information on acute clinical conditions. Doubts remain as to whether the addition of clinical and drug consumption information would improve the prediction of health outcomes and reduce confounding in inter-hospital comparisons. The objective of the study is to compare the performance of two multivariate risk adjustment models, with and without clinical data and drug prescription information, in terms of their capability to a) predict short-term outcome rates and b) compare hospitals' risk-adjusted outcome rates using two risk-adjustment procedures.

Methods: Observational, retrospective study based on hospital data collected at the regional level.Two cohorts of patients discharged in 2010 from hospitals located in the Lazio Region, Italy: acute myocardial infarction (AMI) and hip fracture (HF). Multivariate logistic regression models were implemented to predict 30-day mortality (AMI) or 48-hour surgery (HF), adjusting for demographic characteristics and comorbidities plus clinical data and drug prescription information. Risk-adjusted outcome rates were derived at the hospital level.

Results: The addition of clinical data and drug prescription information improved the capability of the models to predict the study outcomes for the two conditions investigated. The discriminatory power of the AMI model increases when the clinical data and drug prescription information are included (c-statistic increases from 0.761 to 0.797); for the HF model the increase was more slight (c-statistic increases from 0.555 to 0.574). Some differences were observed between the hospital-adjusted proportion estimated using the two different models. However, the estimated hospital outcome rates were weakly affected by the introduction of clinical data and drug prescription information.

Conclusions: The results show that the available clinical variables and drug prescription information were important complements to the hospital discharge data for characterising the acute severity of the patients. However, when these variables were used for adjustment purposes their contribution was negligible. This conclusion might not apply at other locations, in other time periods and for other health conditions if there is heterogeneity in the clinical conditions between hospitals.

Show MeSH
Related in: MedlinePlus