Abstract
Background/motivation:
Patients are frequently multimorbid at presentation in secondary care. Comorbidities can interact and the presence of particular comorbidities can be symptomatic of a common underlying cause. The complexity of observed multimorbidity combinations means that there is a pressing need to better understand how presenting and preexisting comorbid diseases relate to patient outcomes.
Methods:
We performed a sex-stratified analysis of whole-cohort UK Biobank hospital inpatient data and assembled disease sequence trajectories of ICD10 blocks to identify statistically significant disease combinations and orderings. Age-relative 1-year post-trajectory mortality and hospitalisation rates were calculated for each trajectory using Accelerated Failure Time (AFT) models with a 1:3 case-control ratio.
Results:
We identified 1784 and 1762 significant disease sequence trajectories for women and men, respectively. We used mortality and hospitalisation outcomes to develop triage rules that identify the highest risk multimorbid patients for females and males presenting with cardiometabolic, respiratory or digestive diseases, cancers, infections, renal failure, mental disorders, and more based on prior diagnostic trajectories.
Conclusions:
We provide a useful resource for triaging multimorbid patients based on prior histories of disease and for facilitating further research into drug discovery/repurposing and biomarker identification which may aid in preventing these high risk disease combinations from developing.
Patients are frequently multimorbid at presentation in secondary care. Comorbidities can interact and the presence of particular comorbidities can be symptomatic of a common underlying cause. The complexity of observed multimorbidity combinations means that there is a pressing need to better understand how presenting and preexisting comorbid diseases relate to patient outcomes.
Methods:
We performed a sex-stratified analysis of whole-cohort UK Biobank hospital inpatient data and assembled disease sequence trajectories of ICD10 blocks to identify statistically significant disease combinations and orderings. Age-relative 1-year post-trajectory mortality and hospitalisation rates were calculated for each trajectory using Accelerated Failure Time (AFT) models with a 1:3 case-control ratio.
Results:
We identified 1784 and 1762 significant disease sequence trajectories for women and men, respectively. We used mortality and hospitalisation outcomes to develop triage rules that identify the highest risk multimorbid patients for females and males presenting with cardiometabolic, respiratory or digestive diseases, cancers, infections, renal failure, mental disorders, and more based on prior diagnostic trajectories.
Conclusions:
We provide a useful resource for triaging multimorbid patients based on prior histories of disease and for facilitating further research into drug discovery/repurposing and biomarker identification which may aid in preventing these high risk disease combinations from developing.
| Original language | English |
|---|---|
| Publication status | Published online - 2023 |
| Event | The 31st Annual Intelligent Systems For Molecular Biology and the 22nd Annual European Conference on Computational Biology - Centre de Congrès de Lyon, Lyon, France Duration: 23 Jul 2023 → 27 Jul 2023 https://www.iscb.org/ismbeccb2023 |
Conference
| Conference | The 31st Annual Intelligent Systems For Molecular Biology and the 22nd Annual European Conference on Computational Biology |
|---|---|
| Abbreviated title | ISMB/ECCB 2023 |
| Country/Territory | France |
| City | Lyon |
| Period | 23/07/23 → 27/07/23 |
| Internet address |
Keywords
- Multimorbidity
- Comorbidity
- Biomarker Discovery
- UK Biobank
- Bioinformatics
