Acquired abnormalities of the tricuspid valve--an ultrasonographic study

Precision Health-Enabled Machine Learning to Identify Need for Wraparound Social Services Using Patient- and Population-Level Data Sets: Algorithm Development and Validation

Background

Emerging interest in precision health and the increasing availability of patient- and population-level data sets present considerable potential to enable analytical approaches to identify and mitigate the negative effects of social factors on health. These issues are not satisfactorily addressed in typical medical care encounters, and thus, opportunities to improve health outcomes, reduce costs, and improve coordination of care are not realized. Furthermore, methodological expertise on the use of varied patient- and population-level data sets and machine learning to predict need for supplemental services is limited.

Objective

The objective of this study was to leverage a comprehensive range of clinical, behavioral, social risk, and social determinants of health factors in order to develop decision models capable of identifying patients in need of various wraparound social services.

Methods

We used comprehensive patient- and population-level data sets to build decision models capable of predicting need for behavioral health, dietitian, social work, or other social service referrals within a safety-net health system using area under the receiver operating characteristic curve (AUROC), sensitivity, precision, F1 score, and specificity. We also evaluated the value of population-level social determinants of health data sets in improving machine learning performance of the models.

Results

Decision models for each wraparound service demonstrated performance measures ranging between 59.2%% and 99.3%. These results were statistically superior to the performance measures demonstrated by our previous models which used a limited data set and whose performance measures ranged from 38.2% to 88.3% (behavioural health: F1 score P<.001, AUROC P=.01; social work: F1 score P<.001, AUROC P=.03; dietitian: F1 score P=.001, AUROC P=.001; other: F1 score P=.01, AUROC P=.02); however, inclusion of additional population-level social determinants of health did not contribute to any performance improvements (behavioural health: F1 score P=.08, AUROC P=.09; social work: F1 score P=.16, AUROC P=.09; dietitian: F1 score P=.08, AUROC P=.14; other: F1 score P=.33, AUROC P=.21) in predicting the need for referral in our population of vulnerable patients seeking care at a safety-net provider.

Conclusions

Precision health–enabled decision models that leverage a wide range of patient- and population-level data sets and advanced machine learning methods are capable of predicting need for various wraparound social services with good performance.

Isolated native tricuspid valve endocarditis caused by viridans streptococcus

The present report describes a case of native tricuspid valve endocarditis caused by viridans group streptococcus in a 43-year-old man who had recently undergone dental extraction. The patient had no history of intravenous drug use, heart disease or right heart catheterization. Although there have been scattered reports of unusual organisms, to the authors' knowledge, this is the first case of viridans group streptococcal endocarditis involving only the tricuspid valve after dental manipulation.

MR and CT diagnosis of carcinoid heart disease

This report describes findings present on magnetic resonance (MR) imaging and computed tomography (CT) in a patient with carcinoid heart disease. Major abnormalities included thickening and immobility of the tricuspid valve leaflets and disturbed flow patterns indicative of tricuspid stenosis and regurgitation demonstrated with cine MR. While echocardiography is usually the initial imaging modality in patients with carcinoid heart disease, other modalities such as MR and CT may provide correlative or supplemental information.