An artificial intelligence approach to early predict non-ST-elevation myocardial infarction patients with chest pain

BACKGROUND AND AIMS

Hospital admission rate for the patients with chest pain has already been increased worldwide but no existing risk score has been designed to stratify non-ST-elevation myocardial infarction (NSTEMI) from non-cardiogenic chest pain. Clinical diagnosis of chest pain in the emergency department is always highly subjective and variable. We, therefore, aimed to develop an artificial intelligence approach to predict stable NSTEMI that would give valuable insight to reduce misdiagnosis in the real clinical setting.

METHODS

A standard protocol was developed to collect data from chest pain patients who had visited the emergency department between December 2016 and February 2017. All the chest pain patients with aged <20 years were primarily included in this study. However, STEMI, previous history of ACS, and out-of-hospital cardiac arrest were excluded from our study. An artificial neural network (ANN) model was then developed to predict NSTEMI patients. The accuracy, sensitivity, specificity, and receiver operating characteristic curve was used to measure the performance of this model.

RESULTS

A total of 268 chest pain patients were included in this study; of those, 47 (17.5%) was stable NSTEMI, and 221 (82.5%) was unstable angina patients. Serval risk factors such as cardiac risk factor, systolic blood pressure, hemoglobin, corrected QT interval (QTc), PR interval, glutamic-oxaloacetic transaminase, glutamic pyruvic transaminase and troponin were independently associated with stable NSTEMI. The area under the receiver operating characteristic (AUROC) and accuracy of ANN were 98.4, and 92.86. Additionally, the sensitivity, specificity, positive predictive value, and negative predictive value of the ANN model was 90.91, 93.33, 76.92, and 97.67 respectively.

CONCLUSION

Our prediction model showed a higher accuracy to predict NSTEMI patients. This model has a potential application in disease detection, monitoring, and prognosis of chest pain at risk of AMI.

Prediction of fatty liver disease using machine learning algorithms

BACKGROUND AND OBJECTIVE

Fatty liver disease (FLD) is a common clinical complication; it is associated with high morbidity and mortality. However, an early prediction of FLD patients provides an opportunity to make an appropriate strategy for prevention, early diagnosis and treatment. We aimed to develop a machine learning model to predict FLD that could assist physicians in classifying high-risk patients and make a novel diagnosis, prevent and manage FLD.

METHODS

We included all patients who had an initial fatty liver screening at the New Taipei City Hospital between 1st and 31st December 2009. Classification models such as random forest (RF), Naïve Bayes (NB), artificial neural networks (ANN), and logistic regression (LR) were developed to predict FLD. The area under the receiver operating characteristic curve (ROC) was used to evaluate performances among the four models.

RESULTS

A total of 577 patients were included in this study; of those 377 patients had fatty liver. The area under the receiver operating characteristic (AUROC) of RF, NB, ANN, and LR with 10 fold-cross validation was 0.925, 0.888, 0.895, and 0.854 respectively. Additionally, The accuracy of RF, NB, ANN, and LR 87.48, 82.65, 81.85, and 76.96%.

CONCLUSION

In this study, we developed and compared the four classification models to predict fatty liver disease accurately. However, the random forest model showed higher performance than other classification models. Implementation of a random forest model in the clinical setting could help physicians to stratify fatty liver patients for primary prevention, surveillance, early treatment, and management.

Nationwide epidemiology and prognosis of dialysis-requiring acute kidney injury (NEP-AKI-D) study: Design and methods

AIM

Acute kidney injury (AKI) carries an increasing incidence rate worldwide and increases the risk of developing end-stage renal disease (ESRD) as well as the medical expenses during the post-AKI course. The Taiwan Consortium for Acute Kidney Injury and Renal Diseases (CAKs) has thus launched a nationwide epidemiology and prognosis of dialysis-requiring acute kidney injury (NEP-AKI-D) study, which prospectively enrols critically ill patients with AKI. Through thoroughly evaluating the risk and prognostic factors of AKI, we hope to lower the incidence of AKI and ESRD from the perspective of AKI-ESRD interaction.

METHODS

The CAKs includes 30 hospitals which distribute widely through the four geographical regions (north, middle, south, and east) of Taiwan, and have a 1:1 ratio of medical centres to regional hospitals in each region. The NEP-AKI-D study enrols intensive care unit-based AKI patients who receive dialysis in the four seasonal sampled months (October 2014, along with January, April, and July 2015) in the included hospitals. The collected data include demographic information, pertaining laboratory results, dialysis settings and patient outcomes. The data are uploaded in a centre website and will be audited by on-site principal investigators, computer logic gates, and the CAKs staffs. The outcomes of interest are in-hospital mortality, dialysis-dependency and readmission rate within 90 days after discharge.

CONCLUSION

The NEP-AKI-D study enrols a large number of representative AKI patients throughout Taiwan. The results of the current study are expected to provide more insight into the risk and prognostic factors of AKI and further attenuated further chronic kidney disease transition.

A novel tool for visualizing chronic kidney disease associated polymorbidity: a 13-year cohort study in Taiwan

OBJECTIVE

The aim of this study is to analyze and visualize the polymorbidity associated with chronic kidney disease (CKD). The study shows diseases associated with CKD before and after CKD diagnosis in a time-evolutionary type visualization.

MATERIALS AND METHODS

Our sample data came from a population of one million individuals randomly selected from the Taiwan National Health Insurance Database, 1998 to 2011. From this group, those patients diagnosed with CKD were included in the analysis. We selected 11 of the most common diseases associated with CKD before its diagnosis and followed them until their death or up to 2011. We used a Sankey-style diagram, which quantifies and visualizes the transition between pre- and post-CKD states with various lines and widths. The line represents groups and the width of a line represents the number of patients transferred from one state to another.

RESULTS

The patients were grouped according to their states: that is, diagnoses, hemodialysis/transplantation procedures, and events such as death. A Sankey diagram with basic zooming and planning functions was developed that temporally and qualitatively depicts they had amid change of comorbidities occurred in pre- and post-CKD states.

DISCUSSION

This represents a novel visualization approach for temporal patterns of polymorbidities associated with any complex disease and its outcomes. The Sankey diagram is a promising method for visualizing complex diseases and exploring the effect of comorbidities on outcomes in a time-evolution style.

CONCLUSIONS

This type of visualization may help clinicians foresee possible outcomes of complex diseases by considering comorbidities that the patients have developed.

Are both psychological and physical dimensions in health-related quality of life associated with mortality in hemodialysis patients: a 7-year Taiwan cohort study

BACKGROUND

Psychological depression and physical disability are closely correlated in hemodialysis patients. A retrospective cohort study was conducted to examine the independent association of physical and psychological functioning with mortality in a hemodialysis cohort in Taiwan.

METHODS

A total of 888 stable hemodialysis patients were included. Patients completed two questionnaires: the 36-item Short Form Health Survey Questionnaire (SF-36, Taiwan Standard Version 1.0) and the Beck Depression Inventory (BDI, Chinese Version). Mortality outcomes were recorded for a seven-year follow-up period.

RESULTS

There were 303 deaths recorded. BDI scores were inversely related to all health-related quality of life (HRQoL) domains (p < 0.001). In the Cox-proportional hazard model, only poor physical dimension of HRQoL was independently associated with higher mortality.

CONCLUSION

Poor physical dimension in HRQoL is a strong predictor of mortality among hemodialysis patients in Taiwan. Psychological depression is closely correlated with poor HRQoL but does not predict mortality.

Topical antifungal treatment cures exit-site fungal infection

Exit-site fungal infection, although rarely reported, may be a critical complication in patients on peritoneal dialysis. There is no optimal treatment of exit-site fungal infection. We report four cases of exit-site infection with Candida parapsilosis. Four-week treatment with topical sulconazole cream was administered. Fungal infection was cured in all patients but followed by exit-site bacterial infection in two patients. The topical application of sulconazole cream is recommended for exit-site fungal infection.