Background

External counterpulsation (ECP) increases perfusion to a variety of organs and may be helpful for acute stroke.

Methods

We conducted a single-blinded, prospective, randomized controlled feasibility and safety trial of ECP for acute middle cerebral artery (MCA) ischemic stroke. Twenty-three patients presenting within 48 hours of symptom onset were randomized into one of two groups. One group was treated with ECP for 1 hour at a pressure of up to 300 mmHg ("full pressure"). During the procedure, we also determined the highest possible pressure that would augment MCA mean flow velocity (MFV) by 15%. The other group was treated with ECP at 75 mmHg ("sham pressure"). Transcranial Doppler MCA flow velocities and National Institutes of Health Stroke Scale (NIHSS) scores of both groups were checked before, during, and after ECP. Outcomes were assessed at 30 days after randomization.

Results

Although the procedures were feasible to implement, there was a frequent inability to augment MFV by 15% despite maximal pressures in full-pressure patients. In sham-pressure patients, however, MFV frequently increased as shown by increases in peak systolic velocity and end diastolic velocity. In both groups, starting ECP was often associated with contemporaneous improvements in NIHSS stroke scores. There were no between-group differences in NIHSS, modified Rankin Scale Scores, and Barthel Indices, and no device or treatment-related serious adverse events, deaths, intracerebral hemorrhages, or episodes of acute neuro-worsening.

Conclusions

ECP was safe and feasible to use in patients with acute ischemic stroke. It was associated with unexpected effects on flow velocity, and contemporaneous improvements in NIHSS score regardless of pressure used, with a possibility that even very low ECP pressures had an effect. Further study is warranted.

Background

Parkinson's disease (PD) is a chronic, disabling neurodegenerative disorder.

Objective

To predict a future diagnosis of PD using questionnaires and simple non-invasive clinical tests.

Methods

Participants in the prospective Kuakini Honolulu-Asia Aging Study (HAAS) were evaluated biannually between 1995-2017 by PD experts using standard diagnostic criteria. Autopsies were sought on all deaths. We input simple clinical and risk factor variables into an ensemble-tree based machine learning algorithm and derived models to predict the probability of developing PD. We also investigated relationships of predictive models and neuropathologic features such as nigral neuron density.

Results

The study sample included 292 subjects, 25 of whom developed PD within 3 years and 41 by 5 years. 116 (46%) of 251 subjects not diagnosed with PD underwent autopsy. Light Gradient Boosting Machine modeling of 12 predictors correctly classified a high proportion of individuals who developed PD within 3 years (area under the curve (AUC) 0.82, 95%CI 0.76-0.89) or 5 years (AUC 0.77, 95%CI 0.71-0.84). A large proportion of controls who were misclassified as PD had Lewy pathology at autopsy, including 79%of those who died within 3 years. PD probability estimates correlated inversely with nigral neuron density and were strongest in autopsies conducted within 3 years of index date (r = -0.57, p < 0.01).

Conclusion

Machine learning can identify persons likely to develop PD during the prodromal period using questionnaires and simple non-invasive tests. Correlation with neuropathology suggests that true model accuracy may be considerably higher than estimates based solely on clinical diagnosis.