Publications

AIMS

Deep learning methods have recently gained success in detecting left ventricular systolic dysfunction (LVSD) from electrocardiogram (ECG) waveforms. Despite their high level of accuracy, they are difficult to interpret and deploy broadly in the clinical setting. In this study, we set out to determine whether simpler models based on standard ECG measurements could detect LVSD with similar accuracy to that of deep learning models.

METHODS AND RESULTS

Using an observational data set of 40 994 matched 12-lead ECGs and transthoracic echocardiograms, we trained a range of models with increasing complexity to detect LVSD based on ECG waveforms and derived measurements. The training data were acquired from the Stanford University Medical Center. External validation data were acquired from the Columbia Medical Center and the UK Biobank. The Stanford data set consisted of 40 994 matched ECGs and echocardiograms, of which 9.72% had LVSD. A random forest model using 555 discrete, automated measurements achieved an area under the receiver operator characteristic curve (AUC) of 0.92 (0.91-0.93), similar to a deep learning waveform model with an AUC of 0.94 (0.93-0.94). A logistic regression model based on five measurements achieved high performance [AUC of 0.86 (0.85-0.87)], close to a deep learning model and better than N-terminal prohormone brain natriuretic peptide (NT-proBNP). Finally, we found that simpler models were more portable across sites, with experiments at two independent, external sites.

CONCLUSION

Our study demonstrates the value of simple electrocardiographic models that perform nearly as well as deep learning models, while being much easier to implement and interpret.

Restrictive federal and state immigration policies create conditions of employment exclusion that may negatively influence the health of immigrants. In particular, these policy effects are reflected in labor market and workplace experiences that determine the types of work and employment opportunities that immigrants are able to access and pursue. This study examines the relationship between both cumulative and individual measures of employment exclusion and self-rated health and psychological distress among Asian and Latino immigrants in California, and whether this relationship is modified by legal status. We used data from the Research on Immigrant Health and State Policy (RIGHTS) study (n = 2010). We used both multivariable logistic regression and linear regression models for our analyses. For cumulative models, was associated with poor health (OR = 1.21, 95% CI: 1.01, 1.46). was also associated with poor self-rated health (OR = 1.45, 95% CI: 1.15, 1.82) and increased psychological distress (β = 0.69, 95% CI: 0.31, 1.07). For individual measures of employment exclusion, settling for a job - a labor market exclusion - and working in a dangerous job and experiencing wage theft - workplace exclusions - were associated with poor health and increased psychological distress. There was no evidence that the association between employment exclusions and health varied by legal status. These findings demonstrate that the combined effect of employment exclusions is detrimental to immigrant health. To improve population health, public health researchers should continue to interrogate the policy conditions at the federal, state, and local level that exclude immigrants from employment opportunities and workplace protections.