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Performance of in-hospital mortality prediction models for acute hospitalization: hospital standardized mortality ratio in Japan.

Miyata H, Hashimoto H, Horiguchi H, Matsuda S, Motomura N, Takamoto S - BMC Health Serv Res (2008)

Bottom Line: C-index values were 0.869 for the model that excluded length of stay and 0.841 for the model that included length of stay.Risk models developed in this study included a set of variables easily accessible from administrative data, and still successfully exhibited a high degree of prediction accuracy.These models can be used to estimate in-hospital mortality rates of various diagnoses and procedures.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Healthcare Quality Assessment, Graduate School of Medicine, University of Tokyo, Japan. h-m@umin.ac.jp

ABSTRACT

Objective: In-hospital mortality is an important performance measure for quality improvement, although it requires proper risk adjustment. We set out to develop in-hospital mortality prediction models for acute hospitalization using a nation-wide electronic administrative record system in Japan.

Methods: Administrative records of 224,207 patients (patients discharged from 82 hospitals in Japan between July 1, 2002 and October 31, 2002) were randomly split into preliminary (179,156 records) and test (45,051 records) groups. Study variables included Major Diagnostic Category, age, gender, ambulance use, admission status, length of hospital stay, comorbidity, and in-hospital mortality. ICD-10 codes were converted to calculate comorbidity scores based on Quan's methodology. Multivariate logistic regression analysis was then performed using in-hospital mortality as a dependent variable. C-indexes were calculated across risk groups in order to evaluate model performances.

Results: In-hospital mortality rates were 2.68% and 2.76% for the preliminary and test datasets, respectively. C-index values were 0.869 for the model that excluded length of stay and 0.841 for the model that included length of stay.

Conclusion: Risk models developed in this study included a set of variables easily accessible from administrative data, and still successfully exhibited a high degree of prediction accuracy. These models can be used to estimate in-hospital mortality rates of various diagnoses and procedures.

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Related in: MedlinePlus

Model1 hospital mortality prediction model calibration (n = 45051). * Figure 1 shows the result of the goodness of fit test regarding the model 1 based on test dataset (n = 45051).
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Figure 1: Model1 hospital mortality prediction model calibration (n = 45051). * Figure 1 shows the result of the goodness of fit test regarding the model 1 based on test dataset (n = 45051).

Mentions: Table 5 shows the c-indexes for models 1 and 2, and those using a partial set of predictors. C-index values were fairly high in both models (0.841 and 0.869 for models 1 and 2, respectively). A partial model which only included patient characteristics had a c-index of 0.727, and the addition of MDC increased the c-index to 0.786. Further including the comorbidity index resulted in only a marginal increase to 0.841. The model that included more information on comorbidities showed a higher c-index. Figures 1 and 2 demonstrate the goodness of fit regarding the models (i.e., how well the predicted mortality rates match the observed mortality rates among patient subgroups of risk). Close agreement between the predicted and observed mortality rates with our models was seen across various patient risk subgroups analyzed.


Performance of in-hospital mortality prediction models for acute hospitalization: hospital standardized mortality ratio in Japan.

Miyata H, Hashimoto H, Horiguchi H, Matsuda S, Motomura N, Takamoto S - BMC Health Serv Res (2008)

Model1 hospital mortality prediction model calibration (n = 45051). * Figure 1 shows the result of the goodness of fit test regarding the model 1 based on test dataset (n = 45051).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2606685&req=5

Figure 1: Model1 hospital mortality prediction model calibration (n = 45051). * Figure 1 shows the result of the goodness of fit test regarding the model 1 based on test dataset (n = 45051).
Mentions: Table 5 shows the c-indexes for models 1 and 2, and those using a partial set of predictors. C-index values were fairly high in both models (0.841 and 0.869 for models 1 and 2, respectively). A partial model which only included patient characteristics had a c-index of 0.727, and the addition of MDC increased the c-index to 0.786. Further including the comorbidity index resulted in only a marginal increase to 0.841. The model that included more information on comorbidities showed a higher c-index. Figures 1 and 2 demonstrate the goodness of fit regarding the models (i.e., how well the predicted mortality rates match the observed mortality rates among patient subgroups of risk). Close agreement between the predicted and observed mortality rates with our models was seen across various patient risk subgroups analyzed.

Bottom Line: C-index values were 0.869 for the model that excluded length of stay and 0.841 for the model that included length of stay.Risk models developed in this study included a set of variables easily accessible from administrative data, and still successfully exhibited a high degree of prediction accuracy.These models can be used to estimate in-hospital mortality rates of various diagnoses and procedures.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Healthcare Quality Assessment, Graduate School of Medicine, University of Tokyo, Japan. h-m@umin.ac.jp

ABSTRACT

Objective: In-hospital mortality is an important performance measure for quality improvement, although it requires proper risk adjustment. We set out to develop in-hospital mortality prediction models for acute hospitalization using a nation-wide electronic administrative record system in Japan.

Methods: Administrative records of 224,207 patients (patients discharged from 82 hospitals in Japan between July 1, 2002 and October 31, 2002) were randomly split into preliminary (179,156 records) and test (45,051 records) groups. Study variables included Major Diagnostic Category, age, gender, ambulance use, admission status, length of hospital stay, comorbidity, and in-hospital mortality. ICD-10 codes were converted to calculate comorbidity scores based on Quan's methodology. Multivariate logistic regression analysis was then performed using in-hospital mortality as a dependent variable. C-indexes were calculated across risk groups in order to evaluate model performances.

Results: In-hospital mortality rates were 2.68% and 2.76% for the preliminary and test datasets, respectively. C-index values were 0.869 for the model that excluded length of stay and 0.841 for the model that included length of stay.

Conclusion: Risk models developed in this study included a set of variables easily accessible from administrative data, and still successfully exhibited a high degree of prediction accuracy. These models can be used to estimate in-hospital mortality rates of various diagnoses and procedures.

Show MeSH
Related in: MedlinePlus