Identifying Sequential Complication and Mortality Patterns in Diabetes Mellitus: Comparisons of Machine Learning Methodologies

  1. Jiandong Zhou
  2. Sharen Lee
  3. Wing Tak Wong
  4. Tong Liu
  5. Leonardo Roever
  6. Kamalan Jeevaratnam
  7. William KK Wu
  8. Ian Chi Kei Wong
  9. Gary Tse
  10. Qingpeng Zhang

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Abstract

Background

Diabetes mellitus-related complications adversely affect the quality of life. Better risk-stratified care through mining of sequential complication patterns is needed to enable early detection and prevention.

Methods

Univariable and multivariate logistic regression was used to identify significant variables that can predict mortality. A sequence analysis method termed Prefixspan was applied to identify the most common couple, triple, quadruple, quintuple and sextuple sequential complication patterns in the directed comorbidity pathology network. A knowledge enhanced CPT+ (KCPT+) sequence prediction model is developed to predict the next possible outcome along the progression trajectories of diabetes-related complications.

Findings

A total of 14,144 diabetic patients (51% males) were included. Acute myocardial infarction (AMI) without known ischaemic heart disease (IHD) (odds ratio [OR]: 2.8, 95% CI: [2.3, 3.4]), peripheral vascular disease (OR: 2.3, 95% CI: [1.9, 2.8]), dementia (OR: 2.1, 95% CI: [1.8, 2.4]), and IHD with AMI (OR: 2.4, 95% CI: [2.1, 2.6]) are the most important multivariate predictors of mortality. KCPT+ shows high accuracy in predicting mortality (F1 score 0.90, ACU 0.88), osteoporosis (F1 score 0.86, AUC 0.82), ophthalmological complications (F1 score 0.82, AUC 0.82), IHD with AMI (F1 score 0.81, AUC 0.85) and neurological complications (F1 score 0.81, AUC 0.83) with a particular prior complication sequence.

Interpretation

Sequence analysis identifies the most common pattern characteristics of disease-related complications efficiently. The proposed sequence prediction model is accurate and enables clinicians to diagnose the next complication earlier, provide better risk-stratified care, and devise efficient treatment strategies for diabetes mellitus patients.

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    SciScore for 10.1101/2020.12.21.20248646: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analyses were performed using RStudio software (Version: 1.1.456) and Python (Version: 3.6).
    RStudio
    suggested: (RStudio, RRID:SCR_000432)
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations: Several limitations of this study should be noted. Firstly, as this was an administrative database study, -coding and coding error is a possibility. Secondly, given the retrospective nature of this study, missing data may lead to information bias.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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  2. Version published to 10.1101/2020.12.21.20248646v2 on medRxiv
  3. Version published to 10.1101/2020.12.21.20248646v1 on medRxiv