Are risk factors necessary for pretest probability assessment of coronary artery disease? A patient similarity network analysis of the PROMISE trial

BACKGROUND

Pretest probability (PTP) calculators utilize epidemiological-level findings to provide patient-level risk assessment of obstructive coronary artery disease (CAD). However, their limited accuracies question whether dissimilarities in risk factors necessarily result in differences in CAD. Using patient similarity network (PSN) analyses, we wished to assess the accuracy of risk factors and imaging markers to identify ≥50% luminal narrowing on coronary CT angiography (CCTA) in stable chest-pain patients.

METHODS

We created four PSNs representing: patient characteristics, risk factors, non-coronary imaging markers and calcium score. We used spectral clustering to group individuals with similar risk profiles. We compared PSNs to a contemporary PTP score incorporating calcium score and risk factors to identify ≥50% luminal narrowing on CCTA in the CT-arm of the PROMISE trial. We also conducted subanalyses in different age and sex groups.

RESULTS

In 3556 individuals, the calcium score PSN significantly outperformed patient characteristic, risk factor, and non-coronary imaging marker PSNs (AUC: 0.81 vs. 0.57, 0.55, 0.54; respectively, p ​< ​0.001 for all). The calcium score PSN significantly outperformed the contemporary PTP score (AUC: 0.81 vs. 0.78, p ​< ​0.001), and using 0, 1-100 and ​> ​100 cut-offs provided comparable results (AUC: 0.81 vs. 0.81, p ​= ​0.06). Similar results were found in all subanalyses.

CONCLUSION

Calcium score on its own provides better individualized obstructive CAD prediction than contemporary PTP scores incorporating calcium score and risk factors. Risk factors may not be able to improve the diagnostic accuracy of calcium score to predict ≥50% luminal narrowing on CCTA.

Network analytics and machine learning for predicting length of stay in elderly patients with chronic diseases at point of admission

Background

An aging population with a burden of chronic diseases puts increasing pressure on health care systems. Early prediction of the hospital length of stay (LOS) can be useful in optimizing the allocation of medical resources, and improving healthcare quality. However, the data available at the point of admission (PoA) are limited, making it difficult to forecast the LOS accurately.

Methods

In this study, we proposed a novel approach combining network analytics and machine learning to predict the LOS in elderly patients with chronic diseases at the PoA. Two networks, including multimorbidity network (MN) and patient similarity network (PSN), were constructed and novel network features were created. Five machine learning models (eXtreme Gradient Boosting, Gradient Boosting Decision Tree, Random Forest, Linear Support Vector Machine, and Deep Neural Network) with different input feature sets were developed to compare their performance.

Results

The experimental results indicated that the network features can bring significant improvements to the performances of the prediction models, suggesting that the MN and PSN are useful for LOS predictions.

Conclusion

Our predictive framework which integrates network science with data mining can forecast the LOS effectively at the PoA and provide decision support for hospital managers, which highlights the potential value of network-based machine learning in healthcare field.