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Procedure-based severity index for inpatients: development and validation using administrative database.

Yamana H, Matsui H, Fushimi K, Yasunaga H - BMC Health Serv Res (2015)

Bottom Line: Therefore, we aimed to develop and validate a severity index calculable from procedure records.In the validation cohort, c-statistic of mortality-predicting model was 0.767 (95 % confidence interval: 0.764-0.770).The ω-statistic representing contribution of the index relative to other variables was 1.09 (95 % confidence interval: 1.03-1.17).

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan. yamana-tky@umin.ac.jp.

ABSTRACT

Background: Risk adjustment is important in studies using administrative databases. Although utilization of diagnostic and therapeutic procedures can represent patient severity, the usability of procedure records in risk adjustment is not well-documented. Therefore, we aimed to develop and validate a severity index calculable from procedure records.

Methods: Using the Japanese nationwide Diagnosis Procedure Combination database of acute-care hospitals, we identified patients discharged between 1 April 2012 and 31 March 2013 with an admission-precipitating diagnosis of acute myocardial infarction, congestive heart failure, acute cerebrovascular disease, gastrointestinal hemorrhage, pneumonia, or septicemia. Subjects were randomly assigned to the derivation cohort or the validation cohort. In the derivation cohort, we used multivariable logistic regression analysis to identify procedures performed on admission day which were significantly associated with in-hospital death, and a point corresponding to regression coefficient was assigned to each procedure. An index was then calculated in the validation cohort as sum of points for performed procedures, and performance of mortality-predicting model using the index and other patient characteristics was evaluated.

Results: Of the 539 385 hospitalizations included, 270 054 and 269 331 were assigned to the derivation and validation cohorts, respectively. Nineteen significant procedures were identified from the derivation cohort with points ranging from -3 to 23, producing a severity index with possible range of -13 to 69. In the validation cohort, c-statistic of mortality-predicting model was 0.767 (95 % confidence interval: 0.764-0.770). The ω-statistic representing contribution of the index relative to other variables was 1.09 (95 % confidence interval: 1.03-1.17).

Conclusions: Procedure-based severity index predicted mortality well, suggesting that procedure records in administrative database are useful for risk adjustment.

No MeSH data available.


Related in: MedlinePlus

Calibration plots of models predicting in-hospital death in the validation cohort for six admissionprecipitating diagnoses (a, acute myocardial infarction; b, congestive heart failure; c, acute cerebrovascular disease; d, gastrointestinal hemorrhage; e, pneumonia; f, septicemia)
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Fig3: Calibration plots of models predicting in-hospital death in the validation cohort for six admissionprecipitating diagnoses (a, acute myocardial infarction; b, congestive heart failure; c, acute cerebrovascular disease; d, gastrointestinal hemorrhage; e, pneumonia; f, septicemia)

Mentions: The mortality rates and c-statistics of the subgroups of patients are presented in Table 4. The model was well-calibrated for each diagnosis, as shown in Fig. 3. The model was also well-calibrated for other subgroups (data not shown).Table 4


Procedure-based severity index for inpatients: development and validation using administrative database.

Yamana H, Matsui H, Fushimi K, Yasunaga H - BMC Health Serv Res (2015)

Calibration plots of models predicting in-hospital death in the validation cohort for six admissionprecipitating diagnoses (a, acute myocardial infarction; b, congestive heart failure; c, acute cerebrovascular disease; d, gastrointestinal hemorrhage; e, pneumonia; f, septicemia)
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4495704&req=5

Fig3: Calibration plots of models predicting in-hospital death in the validation cohort for six admissionprecipitating diagnoses (a, acute myocardial infarction; b, congestive heart failure; c, acute cerebrovascular disease; d, gastrointestinal hemorrhage; e, pneumonia; f, septicemia)
Mentions: The mortality rates and c-statistics of the subgroups of patients are presented in Table 4. The model was well-calibrated for each diagnosis, as shown in Fig. 3. The model was also well-calibrated for other subgroups (data not shown).Table 4

Bottom Line: Therefore, we aimed to develop and validate a severity index calculable from procedure records.In the validation cohort, c-statistic of mortality-predicting model was 0.767 (95 % confidence interval: 0.764-0.770).The ω-statistic representing contribution of the index relative to other variables was 1.09 (95 % confidence interval: 1.03-1.17).

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan. yamana-tky@umin.ac.jp.

ABSTRACT

Background: Risk adjustment is important in studies using administrative databases. Although utilization of diagnostic and therapeutic procedures can represent patient severity, the usability of procedure records in risk adjustment is not well-documented. Therefore, we aimed to develop and validate a severity index calculable from procedure records.

Methods: Using the Japanese nationwide Diagnosis Procedure Combination database of acute-care hospitals, we identified patients discharged between 1 April 2012 and 31 March 2013 with an admission-precipitating diagnosis of acute myocardial infarction, congestive heart failure, acute cerebrovascular disease, gastrointestinal hemorrhage, pneumonia, or septicemia. Subjects were randomly assigned to the derivation cohort or the validation cohort. In the derivation cohort, we used multivariable logistic regression analysis to identify procedures performed on admission day which were significantly associated with in-hospital death, and a point corresponding to regression coefficient was assigned to each procedure. An index was then calculated in the validation cohort as sum of points for performed procedures, and performance of mortality-predicting model using the index and other patient characteristics was evaluated.

Results: Of the 539 385 hospitalizations included, 270 054 and 269 331 were assigned to the derivation and validation cohorts, respectively. Nineteen significant procedures were identified from the derivation cohort with points ranging from -3 to 23, producing a severity index with possible range of -13 to 69. In the validation cohort, c-statistic of mortality-predicting model was 0.767 (95 % confidence interval: 0.764-0.770). The ω-statistic representing contribution of the index relative to other variables was 1.09 (95 % confidence interval: 1.03-1.17).

Conclusions: Procedure-based severity index predicted mortality well, suggesting that procedure records in administrative database are useful for risk adjustment.

No MeSH data available.


Related in: MedlinePlus