Abstract
Aim
To temporally and externally validate our previously developed prediction model, which used data from University Hospitals Birmingham to identify inpatients with diabetes at high risk of adverse outcome (mortality or excessive length of stay), in order to demonstrate its applicability to other hospital populations within the UK.
Methods
Temporal validation was performed using data from University Hospitals Birmingham and external validation was performed using data from both the Heart of England NHS Foundation Trust and Ipswich Hospital. All adult inpatients with diabetes were included. Variables included in the model were age, gender, ethnicity, admission type, intensive therapy unit admission, insulin therapy, albumin, sodium, potassium, haemoglobin, C‐reactive protein, estimated GFR and neutrophil count. Adverse outcome was defined as excessive length of stay or death.
Results
Model discrimination in the temporal and external validation datasets was good. In temporal validation using data from University Hospitals Birmingham, the area under the curve was 0.797 (95% CI 0.785–0.810), sensitivity was 70% (95% CI 67–72) and specificity was 75% (95% CI 74–76). In external validation using data from Heart of England NHS Foundation Trust, the area under the curve was 0.758 (95% CI 0.747–0.768), sensitivity was 73% (95% CI 71‐74) and specificity was 66% (95% CI 65–67). In external validation using data from Ipswich, the area under the curve was 0.736 (95% CI 0.711–0.761), sensitivity was 63% (95% CI 59–68) and specificity was 69% (95% CI 67–72). These results were similar to those for the internally validated model derived from University Hospitals Birmingham.
Conclusions
The prediction model to identify patients with diabetes at high risk of developing an adverse event while in hospital performed well in temporal and external validation. The externally validated prediction model is a novel tool that can be used to improve care pathways for inpatients with diabetes. Further research to assess clinical utility is needed.
To temporally and externally validate our previously developed prediction model, which used data from University Hospitals Birmingham to identify inpatients with diabetes at high risk of adverse outcome (mortality or excessive length of stay), in order to demonstrate its applicability to other hospital populations within the UK.
Methods
Temporal validation was performed using data from University Hospitals Birmingham and external validation was performed using data from both the Heart of England NHS Foundation Trust and Ipswich Hospital. All adult inpatients with diabetes were included. Variables included in the model were age, gender, ethnicity, admission type, intensive therapy unit admission, insulin therapy, albumin, sodium, potassium, haemoglobin, C‐reactive protein, estimated GFR and neutrophil count. Adverse outcome was defined as excessive length of stay or death.
Results
Model discrimination in the temporal and external validation datasets was good. In temporal validation using data from University Hospitals Birmingham, the area under the curve was 0.797 (95% CI 0.785–0.810), sensitivity was 70% (95% CI 67–72) and specificity was 75% (95% CI 74–76). In external validation using data from Heart of England NHS Foundation Trust, the area under the curve was 0.758 (95% CI 0.747–0.768), sensitivity was 73% (95% CI 71‐74) and specificity was 66% (95% CI 65–67). In external validation using data from Ipswich, the area under the curve was 0.736 (95% CI 0.711–0.761), sensitivity was 63% (95% CI 59–68) and specificity was 69% (95% CI 67–72). These results were similar to those for the internally validated model derived from University Hospitals Birmingham.
Conclusions
The prediction model to identify patients with diabetes at high risk of developing an adverse event while in hospital performed well in temporal and external validation. The externally validated prediction model is a novel tool that can be used to improve care pathways for inpatients with diabetes. Further research to assess clinical utility is needed.
| Original language | English |
|---|---|
| Journal | Diabetic Medicine |
| Early online date | 24 Mar 2018 |
| DOIs | |
| Publication status | Published - 24 Mar 2018 |
Keywords
- Diabetes
- Inpatient
- Secondary care
- Prediction model
- Adverse outcomes
- Statistical model
- Diabetes Complications
- Health care delivery
Access to Document
- Adderley_et_al_Temporal_External_Validation_Diabetic_Medicine
This is the peer reviewed version of the following article: Temporal and external validation of a prediction model for adverse outcomes among inpatients with diabetes. N. J. Adderley S. Mallett T. Marshall S. Ghosh G. Rayman S. Bellary J. Coleman F. Akiboye K. A. Toulis K. Nirantharakumar which has been published in final form at https://doi.org/10.1111/dme.13612, This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.