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ObjectivesSynthetic datasets are artificially manufactured based on real health systems data but do not contain real patient information. We sought to validate the use of synthetic data in stroke and cancer research by conducting a comparison study of cancer patients with ischemic stroke to non-cancer patients with ischemic stroke.Designretrospective cohort study.SettingWe used synthetic data generated by MDClone and compared it to its original source data (i.e. real patient data from the Ottawa Hospital Data Warehouse).Outcome measuresWe compared key differences in demographics, treatment characteristics, length of stay, and costs between cancer patients with ischemic stroke and non-cancer patients with ischemic stroke. We used a binary, multivariable logistic regression model to identify risk factors for recurrent stroke in the cancer population.ResultsUsing synthetic data, we found cancer patients with ischemic stroke had a lower prevalence of hypertension (52.0% in the cancer cohort vs 57.7% in the non-cancer cohort, pConclusionWe demonstrated the utility of synthetic data in stroke and cancer research and provided key differences between cancer and non-cancer patients with ischemic stroke. Synthetic data is a powerful tool that can allow researchers to easily explore hypothesis generation, enable data sharing without privacy breaches, and ensure broad access to big data in a rapid, safe, and reliable fashion.</div

Baseline demographics, treatment characteristics, and outcomes for cancer patients diagnosed with ischemic stroke within a 2-year period compared to patients with ischemic stroke and no history of malignancy.

Baseline demographics, treatment characteristics, and outcomes for cancer patients diagnosed with ischemic stroke within a 2-year period compared to patients with ischemic stroke and no history of malignancy.</p

Final logistic regression model for the association between covariates and recurrent stroke in the cancer and stroke cohort using synthetic and real patient datasets.

Only covariates meeting requirement for inclusion with likelihood ratio test (p<0.10) are included. Measures of association are reported as adjusted Odds Ratios (aOR) with 95% confidence intervals (95%CI).</p

A comparison of the baseline characteristics and outcomes between the synthetic dataset generated from MDClone and real patient dataset for a cohort of stroke patients with a diagnosis of cancer at the Ottawa Hospital.

A comparison of the baseline characteristics and outcomes between the synthetic dataset generated from MDClone and real patient dataset for a cohort of stroke patients with a diagnosis of cancer at the Ottawa Hospital.</p

Synthetic deidentified study dataset–non-cancer patients.

Synthetic deidentified study dataset–non-cancer patients.</p

Synthetic deidentified study dataset–cancer patients.

Synthetic deidentified study dataset–cancer patients.</p

Fig 1 -

Histograms of the distribution of age at cancer diagnosis in the synthetic dataset (A) and the original dataset (B).</p

Dynamic CT angiographic image (Axial, sagittal, 3-D reformat) demonstrates the development of a spot sign over the arterial, mid-cycle and venous phases in a subject with large right parasagittal frontal hematoma.

<p>It is notable how the spot temporally evolved in its size, shape, volume and density and then eventually faded at the end of venous phase.</p

The dCTA image acquisition protocol: The timing line (x-axis in seconds) explains the acquisition of whole brain volumes at sequential time points starting at 7 sec from the time of contrast injection.

<p>This volume uses 300(10 to 35 sec), whole brain volumes are acquired every 2 sec. In the venous phase (40 to 60 sec) volumes are acquired at 5 sec interval.</p

Dynamic Characterization of the CT Angiographic ‘Spot Sign’

<div><p>Background and purpose</p><p>Standard (static) CT angiography is used to identify the intracerebral hemorrhage (ICH) spot sign. We used dynamic CT-angiography to describe spot sign characteristics and measurement parameters over 60-seconds of image acquisition.</p><p>Methods</p><p>We prospectively identified consecutive patients presenting with acute ICH within 4.5 hours of symptom onset, and collected whole brain dynamic CT-angiography (dCTA). Spot parameters (earliest appearance, duration, maximum Hounsfield unit (HU), time to maximum HU, time to spot diagnostic definition, spot volume and hematoma volumes) were measured using volumetric analysis software.</p><p>Result</p><p>We enrolled 34 patients: three were excluded due to secondary causes of ICH. Of the remaining 31 patients there were 18 females (58%) with median age 70 (range 47–86) and baseline hematoma volume 33 ml (range 0.7–103 ml). Positive dCTA spot sign was present in 13 patients (42%) visualized as an expanding 3-dimensional structure temporally evolving its morphology over the scan period. Median time to spot appearance was 21 s (range 15–35 seconds). This method allowed tracking of spots evolution until the end of venous phase (active extravasation) with median duration of 39 s (range 25–45 seconds). The average density and time to maximum density was 204HU and 30.8 s (range 23–31 s) respectively. Median time to spot diagnosis was 20.8 s using either 100 or 120HU definitions.</p><p>Conclusion</p><p>Dynamic CTA allows a 3-dimensional assessment of spot sign formation during acute ICH, and captured higher spot sign prevalence than previously reported. This is the first study to describe and quantify spot sign characteristics using dCTA; these can be used in ongoing and upcoming ICH studies.</p></div