Temporal and external validation of a prediction model for adverse outcomes among inpatients with diabetes

Research output: Contribution to journalArticlepeer-review

Authors

  • Sandip Ghosh
  • Gerry Rayman
  • Srikanth Bellary
  • Funke Akiboye
  • Konstantinos Toulis

Colleges, School and Institutes

External organisations

  • Ipswich Hospital NHS Trust
  • Queen Elizabeth Hospital Birmingham

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.

Details

Original languageEnglish
JournalDiabetic Medicine
Early online date24 Mar 2018
Publication statusPublished - 24 Mar 2018

Keywords

  • Diabetes, Inpatient, Secondary care, Prediction model, Adverse outcomes, Statistical model, Diabetes Complications, Health care delivery

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