Temporal and external validation of a prediction model for adverse outcomes among inpatients with diabetes. Issue 6 (24th March 2018)

• Record Type: Journal Article
• Authors: Adderley, N. J.; Mallett, S.; Marshall, T.; Ghosh, S.; Rayman, G.; Bellary, S.; Coleman, J.; Akiboye, F.; Toulis, K. A.; Nirantharakumar, K.
• Is Part Of: Diabetic medicine. Volume 35:Issue 6(2018)
• Page Start: 798
• Page End: 806
• Publication Date: 2018-03-24
• Subjects: Diabetes -- Periodicals; 616.462
• DOI: 10.1111/dme.13612
• Languages: English
• ISSNs: 0742-3071

Abstract

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. What's new?: National audits have highlighted suboptimal care for inpatients with diabetes. Evidence suggests targeted review of hospitalized patients with diabetes by a specialist team, using electronic triggers, could improve clinical outcomes. To date, no externally validated tool to identify inpatients with diabetes at risk of adverse outcomes has been published. In the present study we temporally and externally validated a prediction model to identify inpatients with diabetes who are at high risk of developing adverse outcomes. Model performance was found to be optimal and sufficient for further evaluation in clinical practice, where it may be used to prevent harm, improve clinical outcomes, and prioritize care for inpatients with diabetes.