Application of machine learning in predicting survival outcomes involving real-world data: a scoping review

Prediction model of pressure injury occurrence in diabetic patients during ICU hospitalization--XGBoost machine learning model can be interpreted based on SHAP

BACKGROUND

The occurrence of pressure injury in patients with diabetes during ICU hospitalization can result in severe complications, including infections and non-healing wounds.

AIMS

The aim of this study was to predict the occurrence of pressure injury in ICU patients with diabetes using machine learning models.

STUDY DESIGN

In this study, LASSO regression was used for feature screening, XGBoost was employed for machine learning model construction, ROC curve analysis, calibration curve analysis, clinical decision curve analysis, sensitivity, specificity, accuracy, and F1 score were used for evaluating the model's performance.

RESULTS

Out of the 503 ICU patients with diabetes included in the study, pressure injury developed in 170 cases, resulting in an incidence rate of 33.8 %. The XGBoost model had a higher AUC for predicting pressure injury in patients with diabetes during ICU hospitalization (train: 0.896, 95 %CI: 0.863 to 0.929; test: 0.835, 95 % CI: 0.761-0.908). The importance of SHAP variables in the model from high to low was: 'Days in ICU', 'Mechanical Ventilation', 'Neutrophil Count', 'Consciousness', 'Glucose', and 'Warming Blanket'.

CONCLUSION

The XGBoost machine learning model we constructed has shown high performance in predicting the occurrence of pressure injury in ICU patients with diabetes. Additionally, the SHAP method enables the interpretation of the results provided by the machine learning model.

RELEVANCE TO CLINICAL PRACTICE

Improve the ability to predict the early occurrence of pressure injury in diabetic patients in the ICU. This will enable clinicians to intervene early and reduce the occurrence of complications.

Predicting intraoperative blood loss during cesarean sections based on multi-modal information: a two-center study

PURPOSE

To develop and validate a nomogram model that combines radiomics features, clinical factors, and coagulation function indexes (CFI) to predict intraoperative blood loss (IBL) during cesarean sections, and to explore its application in optimizing perioperative management and reducing maternal morbidity.

METHODS

In this retrospective consecutive series study, a total of 346 patients who underwent magnetic resonance imaging (156 for training and 68 for internal test, center 1; 122 for external test, center 2) were included. IBL+ was defined as more than 1000 mL estimated blood loss during cesarean sections. The prediction models of IBL were developed based on machine-learning algorithms using CFI, radiomics features, and clinical factors. ROC analysis was performed to evaluate the performance for IBL diagnosis.

RESULTS

The support vector machine model incorporating all three modalities achieved an AUC of 0.873 (95% CI 0.769-0.941) and a sensitivity of 1.000 (95% CI 0.846-1.000) in the internal test set, with an AUC of 0.806 (95% CI 0.725-0.872) and a sensitivity of 0.873 (95% CI 0.799-0.922) in the external test set. It was also scored significantly higher than the CFI model (P = 0.035) on the internal test set, and both the CFI (P = 0.002) and radiomics-CFI models (P = 0.007) on the external test set. Additionally, the nomogram constructed based on three modalities achieved an internal testing set AUC of 0.960 (95% CI 0.806-0.999) and an external testing set AUC of 0.869 (95% CI 0.684-0.967) in the pregnant population without a pernicious placenta previa. It is noteworthy that the AUC of the proposed model did not show a statistically significant improvement compared to the Clinical-CFI model in both internal (P = 0.115) and external test sets (P = 0.533).

CONCLUSION

The proposed model demonstrated good performance in predicting intraoperative blood loss (IBL), exhibiting high sensitivity and robust generalizability, with potential applicability to other surgeries such as vaginal delivery and postpartum hysterectomy. However, the performance of the proposed model was not statistically significantly better than that of the Clinical-CFI model.

Deep-learning survival analysis for patients with calcific aortic valve disease undergoing valve replacement

Calcification of the aortic valve (CAVDS) is a major cause of aortic stenosis (AS) leading to loss of valve function which requires the substitution by surgical aortic valve replacement (SAVR) or transcatheter aortic valve intervention (TAVI). These procedures are associated with high post-intervention mortality, then the corresponding risk assessment is relevant from a clinical standpoint. This study compares the traditional Cox Proportional Hazard (CPH) against Machine Learning (ML) based methods, such as Deep Learning Survival (DeepSurv) and Random Survival Forest (RSF), to identify variables able to estimate the risk of death one year after the intervention, in patients undergoing either to SAVR or TAVI. We found that with all three approaches the combination of six variables, named albumin, age, BMI, glucose, hypertension, and clonal hemopoiesis of indeterminate potential (CHIP), allows for predicting mortality with a c-index of approximately 80 % . Importantly, we found that the ML models have a better prediction capability, making them as effective for statistical analysis in medicine as most state-of-the-art approaches, with the additional advantage that they may expose non-linear relationships. This study aims to improve the early identification of patients at higher risk of death, who could then benefit from a more appropriate therapeutic intervention.

Designing hybrid CRISPR-Cas12 and LAMP detection systems for treatment-resistant Plasmodium falciparum with in silico method

Genes associated with drug resistance of first line drugs for Plasmodium falciparum have been identified and characterized of which three genes most commonly associated with drug resistance are P. falciparum chloroquine resistance transporter gene (PfCRT), P. falciparum multidrug drug resistance gene 1 (PfMDR1), and P. falciparum Kelch protein K13 gene (PfKelch13). Polymorphism in these genes could be used as molecular markers for identifying drug resistant strains. Nucleic acid amplification test (NAAT) along with DNA sequencing is a powerful diagnostic tool that could identify these polymorphisms. However, current NAAT and DNA sequencing technologies require specific instruments which might limit its application in rural areas. More recently, a combination of isothermal amplification and CRISPR detection system showed promising results in detecting mutations at a nucleic acid level. Moreover, the Loop-mediated isothermal amplification (LAMP)-CRISPR systems offer robust and straightforward detection, enabling it to be deployed in rural and remote areas. The aim of this study was to develop a novel diagnostic method, based on LAMP of targeted genes, that would enable the identification of drug-resistant P. falciparum strains. The methods were centered on sequence analysis of P. falciparum genome, LAMP primers design, and CRISPR target prediction. Our designed primers are satisfactory for identifying polymorphism associated with drug resistant in PfCRT, PfMDR1, and PfKelch13. Overall, the developed system is promising to be used as a detection method for P. falciparum treatment-resistant strains. However, optimization and further validation the developed CRISPR-LAMP assay are needed to ensure its accuracy, reliability, and feasibility.