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Published on: September 22, 2020
Risk prediction model for early outcomes of revascularization for chronic limb-threatening ischaemia
T Miyata1, S Mii2, H Kumamaru3
1Office of Medical Education, School of Medicine, International University of Health and Welfare, Chiba, Japan.
Insights
A new risk model accurately predicts 30-day outcomes for chronic limb-threatening ischemia (CLTI) patients undergoing revascularization. Key predictors include abnormal white blood cell count and emergency procedures, aiding treatment decisions.
Area of Science:
- Vascular Surgery
- Medical Informatics
- Predictive Analytics
Background:
- Chronic limb-threatening ischemia (CLTI) poses significant risks, necessitating accurate outcome prediction after revascularization.
- Quantifying risks and benefits is crucial for optimizing treatment strategies in CLTI patients.
- The JAPAN Critical Limb Ischemia Database (JCLIMB) provides valuable data for clinical research.
Purpose of the Study:
- To develop and validate a risk prediction model for 30-day post-revascularization outcomes in CLTI patients.
- To identify key predictors of major amputation/death and major adverse limb events.
- To provide a tool for treatment decision-making and quality assessment in CLTI care.
Main Methods:
- Utilized data from 2906 CLTI patients in the JCLIMB database (2013-2016).
- Developed multivariable risk models using least absolute shrinkage and selection operator (LASSO) logistic regression.
- Validated the model's performance using c-statistic and calibration plots.
Main Results:
- The model predicted major amputation and/or death (5.0%) with a c-statistic of 0.82.
- Predictors for amputation/death included abnormal white blood cell count, emergency procedures, heart failure, fever, and hemodialysis.
- The model predicted major adverse limb events (6.4%) with a c-statistic of 0.79.
- Predictors for adverse limb events included abnormal white blood cell count and fever.
Conclusions:
- The developed risk prediction model demonstrates high accuracy for 30-day outcomes in CLTI patients.
- This model can assist clinicians in selecting appropriate treatment strategies for CLTI.
- The model may serve as a benchmark for evaluating the quality of care at medical facilities treating CLTI.
Background:
Quantifying the risks and benefits of revascularization for chronic limb-threatening ischaemia (CLTI) is important. The aim of this study was to create a risk prediction model for treatment outcomes 30 days after revascularization in patients with CLTI.
Methods:
Consecutive patients with CLTI who had undergone revascularization between 2013 and 2016 were collected from the JAPAN Critical Limb Ischemia Database (JCLIMB). The cohort was divided into a development and a validation cohort. In the development cohort, multivariable risk models were constructed to predict major amputation and/or death and major adverse limb events using least absolute shrinkage and selection operator logistic regression. This developed model was applied to the validation cohort and its performance was evaluated using c-statistic and calibration plots.
Results:
Some 2906 patients were included in the analysis. The major amputation and/or mortality rate within 30 days of arterial reconstruction was 5.0 per cent (144 of 2906), and strong predictors were abnormal white blood cell count, emergency procedure, congestive heart failure, body temperature of 38°C or above, and hemodialysis. Conversely, moderate, low or no risk in the Geriatric Nutritional Risk Index (GNRI) and ambulatory status were associated with improved results. The c-statistic value was 0.82 with high prediction accuracy. The rate of major adverse limb events was 6.4 per cent (185 of 2906), and strong predictors were abnormal white blood cell count and body temperature of 38°C or above. Moderate, low or no risk in the GNRI, and age greater than 84 years were associated with improved results. The c-statistic value was 0.79, with high prediction accuracy.
Conclusion:
This risk prediction model can help in deciding on the treatment strategy in patients with CLTI and serve as an index for evaluating the quality of each medical facility.
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