Predicting Optimal Basal Insulin Infusion Patterns in Kids
Predicting Optimal Basal Insulin Infusion Patterns in Kids
The German/Austrian DPV-Wiss database for quality control and scientific surveys in pediatric diabetology served as the data source. Data collection in DPV-Wiss is in compliance with the hospital data-protection agencies in all participating centers. Only anonymous data are transmitted for centralized analysis at the Institute of Epidemiology and Medical Biometry, University of Ulm, Ulm, Germany.
We first retrieved all patients on CSII <20 years of age as documented in DPV-Wiss (November 2009) excluding all 1,248 individuals from our first study (dataset 1) resulting in 6,063 CSII patients (dataset 2). Only the most recent BR individually adjusted during the course of diabetes was considered. BR data of patients using normal insulin instead of rapid-acting insulin analogs were corrected by 1 h. Mean ± SD age of patients in dataset 2 was 10.6 ± 4.3 years (12.6 ± 3.7 years in dataset 1). Age at onset of diabetes in dataset 2 was 6.6 ± 3.8 years (7.3 ± 3.7 years in dataset 1). Duration of diabetes was 4.0 ± 3.4 years in dataset 2 (5.2 ± 3.4 years in dataset 1). Dataset 2 contained 48% boys (43% boys in dataset 1). Secondly, we performed unsupervised hierarchical average linkage clustering of BR data as previously described to sort the 6,063 dataset 2 children according to BR patterns.
Subsequently, we used logistic regression analysis to identify the prediction factors for clustering of individual patients in the distinct BR patterns. Because of the results from our previous study, we only considered age, duration of diabetes, and sex. We performed this calculation in both the new 6,063 patients (dataset 2) and the previous 1,248 patients (dataset 1). In order to be able to assess the probabilities of a patient for clustering in a distinct BR group, we then calculated the maximum probability estimates with the corresponding SEs, Wald χ, and P values for the parameters intercept, age, duration of diabetes, and sex—again, for both datasets 1 and 2.
To display the correlation of probabilities for clustering in a distinct BR cluster to age of the patient, duration of diabetes, and male or female sex, we created typical "test patients" and introduced their data into the following equations containing the respective dataset-specific and BR cluster–specific maximum probability estimates of either of the two datasets:
We used the following characteristics for the test patients: age 4, 8, 12, and 16 years; duration of diabetes 1, 2, 4, 8, and 12 years (where applicable); and assignment of either male or female sex.
Research Design and Methods
The German/Austrian DPV-Wiss database for quality control and scientific surveys in pediatric diabetology served as the data source. Data collection in DPV-Wiss is in compliance with the hospital data-protection agencies in all participating centers. Only anonymous data are transmitted for centralized analysis at the Institute of Epidemiology and Medical Biometry, University of Ulm, Ulm, Germany.
We first retrieved all patients on CSII <20 years of age as documented in DPV-Wiss (November 2009) excluding all 1,248 individuals from our first study (dataset 1) resulting in 6,063 CSII patients (dataset 2). Only the most recent BR individually adjusted during the course of diabetes was considered. BR data of patients using normal insulin instead of rapid-acting insulin analogs were corrected by 1 h. Mean ± SD age of patients in dataset 2 was 10.6 ± 4.3 years (12.6 ± 3.7 years in dataset 1). Age at onset of diabetes in dataset 2 was 6.6 ± 3.8 years (7.3 ± 3.7 years in dataset 1). Duration of diabetes was 4.0 ± 3.4 years in dataset 2 (5.2 ± 3.4 years in dataset 1). Dataset 2 contained 48% boys (43% boys in dataset 1). Secondly, we performed unsupervised hierarchical average linkage clustering of BR data as previously described to sort the 6,063 dataset 2 children according to BR patterns.
Subsequently, we used logistic regression analysis to identify the prediction factors for clustering of individual patients in the distinct BR patterns. Because of the results from our previous study, we only considered age, duration of diabetes, and sex. We performed this calculation in both the new 6,063 patients (dataset 2) and the previous 1,248 patients (dataset 1). In order to be able to assess the probabilities of a patient for clustering in a distinct BR group, we then calculated the maximum probability estimates with the corresponding SEs, Wald χ, and P values for the parameters intercept, age, duration of diabetes, and sex—again, for both datasets 1 and 2.
To display the correlation of probabilities for clustering in a distinct BR cluster to age of the patient, duration of diabetes, and male or female sex, we created typical "test patients" and introduced their data into the following equations containing the respective dataset-specific and BR cluster–specific maximum probability estimates of either of the two datasets:
We used the following characteristics for the test patients: age 4, 8, 12, and 16 years; duration of diabetes 1, 2, 4, 8, and 12 years (where applicable); and assignment of either male or female sex.
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