CASE PRESENTATION: A 42-year-old Chinese man presented with polytrauma (severe head injury, lung contusions, and right femur fracture). Emergency craniotomy and debridement of right thigh wound were performed on presentation. Intraoperative hypotension secondary to bleeding was complicated by transient need for vasopressors and acute liver enzyme elevation indicating shock liver. Beginning on postoperative day 5, he developed an acute platelet count fall (from 559 to 250 × 109/L over 3 days) associated with left iliofemoral deep vein thrombosis that evolved to bilateral lower limb ischemic necrosis; ultimately, the extent of limb ischemic injury was greater in the left (requiring below-knee amputation) versus the right (transmetatarsal amputation). As the presence of deep vein thrombosis is a key feature known to localize microthrombosis and hence ischemic injury in venous limb gangrene, the concurrence of unilateral lower limb deep vein thrombosis in a typical clinical setting of symmetrical peripheral gangrene (hypotension, proximate shock liver, platelet count fall consistent with disseminated intravascular coagulation) helps to explain asymmetric limb injury - manifesting as a greater degree of ischemic necrosis and extent of amputation in the limb affected by deep vein thrombosis - in a patient whose clinical picture otherwise resembled symmetrical peripheral gangrene.
CONCLUSIONS: Concurrence of unilateral lower limb deep vein thrombosis in a typical clinical setting of symmetrical peripheral gangrene is a potential explanation for greater extent of acral ischemic injury in the limb affected by deep vein thrombosis.
OBJECTIVE: Apply machine learning for the prediction and identification of factors associated with short and long-term mortality in Asian STEMI patients and compare with a conventional risk score.
METHODS: The National Cardiovascular Disease Database for Malaysia registry, of a multi-ethnic, heterogeneous Asian population was used for in-hospital (6299 patients), 30-days (3130 patients), and 1-year (2939 patients) model development. 50 variables were considered. Mortality prediction was analysed using feature selection methods with machine learning algorithms and compared to Thrombolysis in Myocardial Infarction (TIMI) score. Invasive management of varying degrees was selected as important variables that improved mortality prediction.
RESULTS: Model performance using a complete and reduced variable produced an area under the receiver operating characteristic curve (AUC) from 0.73 to 0.90. The best machine learning model for in-hospital, 30 days, and 1-year outperformed TIMI risk score (AUC = 0.88, 95% CI: 0.846-0.910; vs AUC = 0.81, 95% CI:0.772-0.845, AUC = 0.90, 95% CI: 0.870-0.935; vs AUC = 0.80, 95% CI: 0.746-0.838, AUC = 0.84, 95% CI: 0.798-0.872; vs AUC = 0.76, 95% CI: 0.715-0.802, p < 0.0001 for all). TIMI score underestimates patients' risk of mortality. 90% of non-survival patients are classified as high risk (>50%) by machine learning algorithm compared to 10-30% non-survival patients by TIMI. Common predictors identified for short- and long-term mortality were age, heart rate, Killip class, fasting blood glucose, prior primary PCI or pharmaco-invasive therapy and diuretics. The final algorithm was converted into an online tool with a database for continuous data archiving for algorithm validation.
CONCLUSIONS: In a multi-ethnic population, patients with STEMI were better classified using the machine learning method compared to TIMI scoring. Machine learning allows for the identification of distinct factors in individual Asian populations for better mortality prediction. Ongoing continuous testing and validation will allow for better risk stratification and potentially alter management and outcomes in the future.