Incorporating intraoperative blood pressure time-series variables to assist in prediction of acute kidney injury after type a acute aortic dissection repair: an interpretable machine learning model

BACKGROUND

Acute kidney injury (AKI) is a common and serious complication after the repair of Type A acute aortic dissection (TA-AAD). However, previous models have failed to account for the impact of blood pressure fluctuations on predictive performance. This study aims to develop machine learning (ML) models combined with intraoperative medicine and blood pressure time-series data to improve the accuracy of early prediction for postoperative AKI risk.

METHODS

Indicators reflecting the duration and depth of hypotension were obtained by analyzing continuous mean arterial pressure (MAP) monitored intraoperatively with multiple thresholds (<65, 60, 55, 50) set in the study. The predictive features were selected by logistic regression and the least absolute shrinkage and selection operator (LASSO), and 4 ML models were built based on the above features. The performance of the models was evaluated by area under receiver operating characteristic curve (AUROC), calibration curve and decision curve analysis (DCA). Shapley additive interpretation (SHAP) was used to explain the prediction models.

RESULTS

Among the indicators reflecting intraoperative hypotension, 65 mmHg showed a statistically superior difference to other thresholds in patients with or without AKI (p < .001). Among 4 models, the extreme gradient boosting (XGBoost) model demonstrated the highest AUROC: 0.800 (95% 0.683-0.917) and sensitivity: 0.717 in the testing set and was verified the best-performing model. The SHAP summary plot indicated that intraoperative urine output, cumulative time of mean arterial pressure lower than 65 mmHg outside cardiopulmonary bypass (OUT_CPB_MAP_65 time), autologous blood transfusion, and smoking were the top 4 features that contributed to the prediction model.

CONCLUSION

With the introduction of intraoperative blood pressure time-series variables, we have developed an interpretable XGBoost model that successfully achieve high accuracy in predicting the risk of AKI after TA-AAD repair, which might aid in the perioperative management of high-risk patients, particularly for intraoperative hemodynamic regulation.

Assessing hypertension and diabetes knowledge, attitudes and practices among residents in Akatsi South District, Ghana using the KAP questionnaire

Objective

Low awareness of hypertension and diabetes is a public health concern in Ghana. Assessing the general population's behaviour via knowledge, attitude, and practice (KAP) will be invaluable in these diseases, where prevention and control need a lifelong commitment to a healthy lifestyle. Hence, our goal was to assess the behaviour of Akatsi South residents towards the diseases to assist health providers in implementing tailored intervention programs.

Methods

This was a population-based cross-sectional study with 150 adults (18-70 years) from November to December 2021. A semi-structured questionnaire with face-to-face interviews was used to obtain data. All variables in the model had descriptive statistics. The Chi-square (χ²) test was used to examine correlations between variables, and a value of p < 0.05 was considered statistically significant. The factors associated with checking blood sugar levels and blood pressure were determined using binary logistic regression.

Results

The respondents' mean age and BMI were 32.40 years (± 12.07) and 24.98 kg/m² (± 2.36), respectively. Only 46.67% of the respondents frequently monitor their blood pressure and 17.33% their blood glucose (at least once a year). Less than half of those surveyed had a good knowledge of hypertension (42.7%) and diabetes (32.0%), whereas nearly 3/4 had poor attitudes regarding both conditions. A binary logistic regression analysis revealed that having a good attitude toward hypertension (exp B = 2.479, p = 0.036) and diabetes (exp B = 4.547, p = 0.009) were the participants' strongest predictor of blood pressure and sugar level checks. However, being overweight (exp B = 0.046, p = 0.002,) or obese (exp B = 0.144, p = 0.034) negatively influenced the frequency with which our respondents checked their blood glucose levels.

Conclusion

In the study, we found that the population generally has poor knowledge, which affects their behaviour (attitudes and practices) towards the diseases. To enable healthcare practitioners to reduce disease-associated mortality and morbidity in the future, frequent public health education and promotion about the conditions is critical to closing the knowledge gap.

Jatrorrhizine: a review of its pharmacological effects

OBJECTIVES

Jatrorrhizine is an isoquinoline alkaloid found in medicinal plants. It is the main bioactive compound of the Chinese herbs, Coptis chinensis, Rhizoma coptidis, and Phellodendron chinense Schneid, plants that are predominantly used in traditional Chinese medicine (TCM) for the treatment of metabolic disorders, gastritis, stomachache among a host of others. This manuscript aims to provide a comprehensive review of the pharmacological effects of jatrorrhizine, proffer suggestions on research areas that need redress and potentially serve as a reference for future studies.

KEY FINDINGS

Published scientific literature was therefore retrieved from all credible sources including Pubmed, Elsevier, Research Gate, Web of Science, Google Scholar, Science Direct, Europe PMC and Wiley Online library using key words such as 'jatrorrhizine', 'botanical sources', 'pharmacology', 'toxicology', 'pharmacokinetics' or their combinations. A cursory examination of relevant scientific literature using the aforementioned key words produced more than 400 publications.

CONCLUSIONS

Using an inclusion/exclusion criteria the subject matter of this review was adequately addressed. It is our hope that this review will provide a good platform for further research on fully harnessing the potential of this bioactive compound.