Screening the General Population for Non-Alcoholic Fatty Liver Disease: Model Development and Validation.

BACKGROUND: The prevalence of non-alcoholic fatty liver disease (NAFLD) is increasing worldwide. Screening the general population for this may help to select appropriate diagnostic and preventive measures before disease progression. AIMS: We aimed to develop a screening method to identify patients with NAFLD in the general population. METHODS: We analyzed cross-sectional data from a large Japanese study of NAFLD. Principal component analysis was used to analyze the data. Candidate predictors were patients' demographic, clinical, and laboratory characteristics. The resulting model was externally validated using three data sets from different populations. RESULTS: Of 15,464 (54.5% men) included patients, 2,741 (17.7%) had NAFLD as determined by ultrasonography. An index was calculated as the arithmetic mean of the scaled body mass index and serum triglyceride levels for both men and women. The area under the receiver operating characteristic curve, sensitivity, specificity, and false positive rate were 0.875, 0.824, 0.770, and 17.6%, respectively. The mean index values were significantly different between the patients with and without non-alcoholic fatty liver disease (p <0.001). The odds ratio of the index cutoff was 15.6 (95% confidence interval [CI]:14.05, 17.39). The model yielded areas under the curve of 0.828, 0.851, and 0.836 for a Chinese (N = 2,319), an Iranian (N = 2,160), and a Brazilian (N = 45,029) data set, respectively. CONCLUSIONS: The proposed composite index demonstrated high performance and generalizability, suggesting its potential use as a screening tool for NAFLD in the general population.

An Index for Evaluating Exercise Capacity Improvement After Cardiac Rehabilitation in Patients After Myocardial Infarction.

OBJECTIVE: We investigated relationships among predictors of improvement in exercise capacity after cardiac rehabilitation programs in patients after acute myocardial infarction. METHODS: We carried out a secondary analysis of data from 41 patients with a left ventricular ejection fraction ≥ 40% who underwent cardiac rehabilitation after the first myocardial infarction. Participants were assessed using a cardiopulmonary exercise test and stress echocardiography. A cluster analysis was performed, and the principal components were analyzed. RESULTS: Two distinct clusters with significantly different ( P = .005) proportions of response to treatment (peak VO 2 ≥ 1 mL/kg/min) were identified among patients. The first principal component explained 28.6% of the variance. We proposed an index composed of the top 5 variables from the first component to represent the improvement in exercise capacity. The index was the average of scaled O 2 uptake and CO 2 output at peak exercise, minute ventilation at peak, load achieved at peak exercise, and exercise time. The optimal cutoff for the improvement index was 0.12, which outperformed the peak VO 2 ≥ 1 mL/kg/min criterion in recognizing the clusters, with a C-statistic of 91.7% and 72.3%, respectively. CONCLUSION: The assessment of change in exercise capacity after cardiac rehabilitation could be improved using the composite index.

Prognostic Clinical Phenotypes of Patients with Acute Decompensated Heart Failure.

INTRODUCTION: Acute decompensated heart failure (AHF) is a clinical syndrome with a poor prognosis. AIM: This study was conducted to identify clusters of inpatients with acute decompensated heart failure that shared similarities in their clinical features. METHODS: We analyzed data from a cohort of patients with acute decompensated heart failure hospitalized between February 2013 and January 2017 in a Department of Cardiology. Patients were clustered using factorial analysis of mixed data. The clusters (phenotypes) were then compared using log-rank tests and profiled using a logistic model. In total, 458 patients (255; 55.7% male) with a mean (SD) age of 72.7 (11.1) years were included in the analytic dataset. The demographic, clinical, and laboratory features were included in the cluster analysis. RESULTS: The two clusters were significantly different in terms of time to mortality and re-hospitalization (all P < 0.001). Cluster profiling yielded an accurate discriminating model (AUC = 0.934). Typically, high-risk patients were elderly females with a lower estimated glomerular filtration rate and hemoglobin on admission compared to the low-risk phenotype. Moreover, the high-risk phenotype had a higher likelihood of diabetes type 2, transient ischemic attack/cerebrovascular accident, previous heart failure or ischemic heart disease, and a higher serum potassium concentration on admission. Patients with the high-risk phenotype were of higher New York Heart Association functional classes and more positive in their medication history. CONCLUSIONS: There are two phenotypes among patients with decompensated heart failure, high-risk and low-risk for mortality and re-hospitalization. They can be distinguished by easy-to-measure patients' characteristics.

The importance of predictors for in-hospital COVID-19 mortality changes over one month.

BACKGROUND: Risk stratification enables care providers to make the proper clinical decision for the management of patients with COVID-19 infection. We aimed to explore changes in the importance of predictors for inpatient mortality of COVID-19 over one month. METHODS: This research was a secondary analysis of data from in-hospital patients with COVID-19 infection. Individuals were admitted to four hospitals, New York, USA. Based on the length of hospital stay, 4370 patients were categorized into three mutually exclusive interval groups, day 1, day 2-7, and day 8-28. We measured changes in the importance of twelve confirmed predictors for mortality over one month, using principal component analysis. RESULTS: On the first day of admission, there was a higher risk for organ dysfunction, particularly in elderly patients. On day 1, serum aspartate aminotransferase and sodium were also associated with an increased risk of mortality, while normal troponin opposes in-hospital death. With time, the importance of high aspartate aminotransferase and sodium concentrations decreases, while the variable quality of high troponin levels increases. Our study suggested the importance of maintaining normal blood pressure early in the management of patients. High serum concentrations of creatinine and C-reactive protein remain poor prognostic factors throughout the 28 days. The association of age with mortality increases with the length of hospital stay. CONCLUSION: The importance of some patients' characteristics changes with the length of hospital stay. This should be considered in developing and deploying predictive models and the management of patients with COVID-19 infection.

Development and validation of a clinical model for predicting the severity of carpal tunnel syndrome.

OBJECTIVES: Pain, discomfort, and cost may result in incomplete or inconclusive electrodiagnostic studies to assess the severity of carpal tunnel syndrome. We aimed to develop a clinical instrument for stratifying patients based on easy-to-measure variables to assess carpal tunnel syndrome severity. METHODS: We performed a secondary analysis of data from patients diagnosed with a diagnosis of carpal tunnel syndrome using a factor analysis of mixed data. In total, 1037 patients (405; 39.1% male) with a mean (SD) age of 58.0 (10.8) years were included. For each patient, demographic information, physical examination findings, ultrasonographic findings, and the severity of the syndrome based on electrodiagnostic studies were recorded. RESULTS: We devised a composite index incorporating a pain numeric rating scale (NRS) rated from 0 (no pain at all) to 10 (the worst pain ever possible), presence of thenar muscle weakness or atrophy (TW), cross-sectional area (CSA) of the median nerve (mm2), and occurrence of nocturnal pain (NP). The composite index was calculated as [scale(NRS)+scale(CSA)+NP+TW]/4, where both NP and TW are binary features (0 or 1). The overall accuracy and area under the curve of the index for stratifying the syndrome severity were 0.85 and 0.71, respectively (Cohen's Kappa = 0.51, McNemar's test P = 0.249). The composite index increased pretest probability by 1.6, 1.8, and 3.3 times with positive likelihood ratios of 3.3, 2.5, and 13.5, and false-positive rates of 26.6, 17.6, and 4.8% for mild, moderate, and severe syndrome, respectively. The index thresholds for mild, moderate, and severe carpal tunnel syndrome were <0.8, ≥0.8 to <1.1, and ≥1.1, respectively. CONCLUSION: Using a composite index, patients with carpal tunnel syndrome can be categorized for the severity of the syndrome before carrying out electrodiagnostic studies.

Appraising features and outlook of women and men discharged after an acute coronary syndrome: evidence from the 23,700-patient PRAISE International Registry.

BACKGROUND: Acute coronary syndromes (ACS) are a common cause of morbidity and mortality. Several studies have focused on ACS at admission, but limited evidence is available on sex-based comparison of patients discharged after ACS. We appraised the outlook of women and men discharged after ACS. METHODS: Details on women enrolled in the PRAISE registry, an international cohort study spanning 23,700 patients included between 2003 and 2019, were systematically collected. We focused on patient and procedural features, medications at discharge, and 1-year outcomes. The primary endpoint was the composite of death, myocardial infarction, or major bleeding after discharge. RESULTS: A total of 17,804 (76.5%) men and 5466 (23.5%) women were included. Several baseline differences were found, including risk factors and prior revascularization (all P<0.05). Men underwent more frequently radial access, and at discharge they received more commonly dual antiplatelet therapy and guideline-directed medical therapy (P<0.001). At 1-year follow-up, risks of death, reinfarction, major bleeding, and non-fatal major bleeding, jointly or individually, were all significantly higher in women (all P≤0.01). All such differences however did not hold true at multivariable analysis, with the exception of major bleeding, which appeared surprisingly less common in females at fully adjusted analysis (P=0.017). CONCLUSIONS: Women, albeit only apparently, had worse outcomes 1 year after discharge for ACS, but adjusted analysis suggested instead that they faced a lower risk of major bleeding after discharge. These findings support the call for more aggressive management of women after ACS.

The association of stroke risk factors with the future thickness of carotid atherosclerotic plaques.

OBJECTIVES: An advancing atherosclerotic plaque is a risk factor for stroke. We conducted this study to assess the relationship between risk factors of stroke with changing in the thickness of carotid plaques thickness evident on sonography. METHODS: We carried out a secondary analysis of data from a study on carotid bifurcation plaques. Data were collected in the sonography laboratories of two university hospitals. In total, 564 (240; 42.6% men) patients with atherosclerotic plaques in the carotid bifurcation and internal carotid artery with stenosis ≥ 30% evident on duplex sonography were included. We developed machine learning models using an extreme gradient boosting algorithm with Shapley additive explanation method to find important risk factors and their interactions. The outcome was a change in the carotid plaque thickness after 36 months, and the predictors were initial plaque thickness and the risk factors of stroke. RESULTS: Two regression models were developed for left and right carotid arteries. The R-squared values were 0.964 for the left, and 0.993 for the right model. Overall, the three top features were BMI, age, and initial plaque thickness for both left and right plaques. However, the risk factors of stroke showed stronger interaction in predicting plaque thickening of the left carotid more than the right carotid artery. DISCUSSION: The effect of each predictor on plaque thickness is complicated by interactions with other risk factors, particularly for the left carotid artery. The side of carotid artery involvement should be considered for stroke prevention.

Unsupervised Machine Learning with Cluster Analysis in Patients Discharged after an Acute Coronary Syndrome: Insights from a 23,270-Patient Study.

Characterization and management of patients admitted for acute coronary syndromes (ACS) remain challenging, and it is unclear whether currently available clinical and procedural features can suffice to inform adequate decision making. We aimed to explore the presence of specific subsets among patients with ACS. The details on patients discharged after ACS were obtained by querying an extensive multicenter registry and detailing patient features, as well as management details. The clinical outcomes included fatal and nonfatal cardiovascular events at 1-year follow-up. After missing data imputation, 2 unsupervised machine learning approaches (k-means and Clustering Large Applications [CLARA]) were used to generate separate clusters with different features. Bivariate- and multivariable-adjusted analyses were performed to compare the different clusters for clinical outcomes. A total of 23,270 patients were included, with 12,930 cases (56%) of ST-elevation myocardial infarction (STEMI). K-means clustering identified 2 main clusters: a first 1 including 21,998 patients (95%) and a second 1 including 1,282 subjects (5%), with equal distribution for STEMI. CLARA generated 2 main clusters: a first 1 including 11,268 patients (48%) and a second 1 with 12,002 subjects (52%). Notably, the STEMI distribution was significantly different in the CLARA-generated clusters. The clinical outcomes were significantly different across clusters, irrespective of the originating algorithm, including death reinfarction and major bleeding, as well as their composite. In conclusion, unsupervised machine learning can be leveraged to explore the patterns in ACS, potentially highlighting specific patient subsets to improve risk stratification and management.

Neuropsychological features in children and adults with congenital heart disease: an exploratory data analysis.

We aimed to recognize clinically meaningful patterns among patients with congenital heart disease to support clinical decision-making and better classification in practice. This research was a secondary analysis of data from the Congenital Heart Disease Genetic Network Study conducted from December 2010 to November 2014 in the United States. The analytic dataset included 6002 patients ≥1 year of age with non-syndromic congenital heart disease. For each patient, features included demographic, clinical, maternal and paternal characteristics. We clustered patients to identify subgroups that shared similarities in their clinical features. The performance of the clustering algorithm was evaluated with a random forest. Next, we used the apriori algorithm to generate clinical rules from patients' characteristics. The clustering algorithm identified two discernible groups of patients. The two classes of patients were different in maternal diabetes and in neuropsychological indicators [Accuracy (95% CI) = 97.1% (96.2, 97.8), area under the ROC curve = 96.8%]. Our rule extraction suggested the presence of clinical pictures with high lift values among patients with maternal diabetes or with seizure, depression, attention-deficit hyperactivity disorder, anxiety, developmental delay, learning disability and speech problem. Beyond the age of 1 year, maternal diabetes and neuropsychological characteristics identify two clusters of patients with congenital heart disease. These characteristics have the potential of being incorporated into the current systems for the classification of congenital heart disease.

The long-term prognostic value provided by Coronary CT angiography.

BACKGROUND: Risk-stratification of patients has a major role in the prevention and treatment of cardiovascular disease. The aim was to find the most informative predictors of cardiovascular events in patients undergoing Coronary CT Angiography. METHODS: We carried out a secondary analysis of a large registry dataset. The included population comprises adults aged 18 or older who underwent Coronary CT Angiography of 64-detector rows or greater. We clustered patients based on their characteristics and compared the risk for poor clinical outcomes between the two clusters. RESULTS: There were two clusters of patients having different risks for all-cause death, myocardial infarction, and late revascularization [hazard ratios (95%CI) = 2.28 (2.02, 2.57), 1.63 (1.40, 1.89), and 2.46 (2.1, 2.88), all P < 0.001]. The severity of stenosis in the left main coronary artery adjusted for age and sex was the most significant predictor of the high-risk cluster [adjusted odds ratio (95%CI) = 3.35 (2.98, 3.77), P < 0.001]. The severity of stenosis in the first obtuse marginal branch of the left circumflex, distal left circumflex, distal left anterior descending, posterior descending, the first diagonal branch of the left anterior descending, and proximal right coronary artery were important as well (all adjusted odds ratios ≥ 2.52). Cluster profiling showed a higher performance for CT Angiography features (sensitivity = 97.4%, specificity = 85.7%, C-statistic = 98.7%) than calcium, Framingham, and Duke scores in identifying high-risk patients (C-statistic = 82.1, 77.0, and 88.2%, respectively). CONCLUSION: Coronary CT Angiography can accurately risk-stratify patients concerning poor clinical outcomes.