Decision support: Stroke risk in atrial fibrillation

Posted by Pieter Kubben in Neurosurgery, Technology | Tagged , , , | Leave a comment
18February2012

In their Chest 2010 article, Lip et al described a new risk stratification schema for assessing stroke risk in atrial fibrillation. It is called the CHA2DS2-VASc score, which is an improved version of the classic CHADS2-score. The new score consists of these items:

Risk factors in the CHA2DS2-VASc score

Besides a scoring systems and corresponding risk rates, the authors also propose a flowchart that offers decision support on whether to prescribe oral anticoagulants:

CHA2DS2-VASc based decision support on OAC prescription (click for full size version)

Fortunately, the good thing of NeuroMind 2 is that it offers both functions for medical calculations and clinical decision support. So here are some screenshots:

Select risk factors (click for full size version)

And based on your input, you will get all information that is available. This means the CHA2DS2-VASc score, corresponding risk rates and an advise based on the flowchart whether to prescribe oral anticoagulants or not:

In return you get the score, the risk rates, and a treatment advise (click for full size version)

Like Einstein said: we should try to make things as simple as possible (but not any simpler)!

More screenshots are available from the gallery below:

Decision support: Stroke risk after TIA

Posted by Pieter Kubben in Neurosurgery, Technology | Tagged , , , , | Leave a comment
9February2012

According to this summary on Stroke.org, “the ABCD2 score is a risk assessment tool designed to improve the prediction of short-term stroke risk after a transient ischemic attack (TIA). The score is optimized to predict the risk of stroke within 2 days after a TIA”. More information is available here.

Here is how it looks in NeuroMind 2:

Risk parameters for the ABCD2 score

Just swipe to the bottom to read the last line and see the “Proceed” button:

Input based on the individual patient

The combinations of blood pressure values and clinical features (unilateral weakness, speech impairment) are processed according to the original reference, but presented separately in the user interface to facilitate reading and make it less confusing.

Results include 2-day stroke risk and management advise

More images are available from the gallery below…

Decision support: Spinal instability neoplastic score

Posted by Pieter Kubben in Neurosurgery, Technology | Tagged , , , , | Leave a comment
7February2012

“The Spine Instability Neoplastic Score is a comprehensive classification system with content validity that can guide clinicians in identifying when patients with neoplastic disease of the spine may benefit from surgical consultation. It can also aid surgeons in assessing the key components of spinal instability due to neoplasia and may become a prognostic tool for surgical decision-making when put in context with other key elements such as neurologic symptoms, extent of disease, prognosis, patient health factors, oncologic subtype, and radiosensitivity of the tumor.” (Fisher et al, Spine 2010).

Looks like an excellent opportunity for decision support, as this 6-tier score is a little too complex to remember by heart, in my opinion. Nevertheless, it is very useful, so it should have a place in clinical practice for neurosurgeons and orthopedic surgeons.

Here is how it looks in NeuroMind 2 on iPhone:

The 6 predictors from the SINS

All values are multiple choice items, so on iPhone they are selected using a co-called “picker” interface item (which is the official term for that rolling thing…):

Selecting a predictor value

As you can see, the text in the picker is a little too long for the small iPhone screen. In that case, the full text is displayed below the picker to facilitate reading. On the larger iPad screen, it will be completely visible within the picker itself.

Based on the user input, an advise for the individual patient is returned:

SINS score and treatment suggestion

Later I will also implement the SINS score in the classical Score menu (which is not visible in the development interface shown above). So you can choose whether to use the new decision support features of NeuroMind 2, or just browse the score and perform the calculations yourself (as you are doing with NeuroMind 1.x at the moment).

More images from the SINS decision support module are available in the gallery below…

Decision support: mortality after traumatic brain injury

Posted by Pieter Kubben in Neurosurgery, Technology | Tagged , , , | Leave a comment
6February2012

Maas et al described in an article in Neurosurgery (2005) a new scoring system to predict 6 month mortality after traumatic brain injury, derived from the Marshall classification. The new scoring system is called the “Rotterdam CT score”, after the location where the research was performed.

Here is how it looks in NeuroMind 2:

Select predictors

After selecting the appropriate values for the individual patient, tap “Proceed” to get a prediction of the 6-month mortality:

6-month mortality prediction

More images are available from the gallery below…

NeuroMind 2 menu update

Posted by Pieter Kubben in Technology | Tagged , | Leave a comment
5February2012

Some of you may already have noticed it in my previous post, but I rearranged the menu in the Decision Support section for NeuroMind 2:

Updated menu for decision support (click for full size version)

Previously I had only one category besides “Brain Trauma Foundation” which was called “Individual articles”. By now, it started to become too cluttered, a mix of different topics. So I created new categories that make more sense.

It is not final yet, but at least it makes more sense now. I intend to add some more content to “Oncology” and there will be more Trauma and Vascular as well. Stay tuned!

Decision support: Canadian C-Spine Rule

Posted by Pieter Kubben in Neurosurgery, Technology | Tagged , , | Leave a comment
4February2012

The Canadian C-Spine (cervical-spine) Rule (CCR) and the National Emergency X-Radiography Utilization Study (NEXUS) Low-Risk Criteria (NLC) are decision rules to guide the use of cervical-spine radiography in patients with trauma. According to this study in the New Engl J Med the CCR is superior to the NLC with respect to sensitivity and specificity for cervical-spine injury for alert patients with trauma who are in stable condition, and its use would result in reduced rates of radiography.

Let’s make an app of that, shall we? This is how it looks on iPad in NeuroMind 2:

First selection criteria (click for full size version)

If necessary, additional questions will be asked:

Additional questions (click for full size version)

Tap the info-button in the right upper corner for more detailed information and definitions of used terms. The info-button is only available if more information is present.

More info is available (click for full size version)

And of course, an advise is presented as soon as the requested input has been provided:

Advise based on user input (click for full size version)

More images are available from the gallery below…

 

Decision support: TLICS score for Thoracolumbar Injuries

Posted by Pieter Kubben in Neurosurgery, Technology | Tagged , , | 1 Comment
30January2012

In 2005 Vaccaro et al published the TLICS score, which is “a new (proposed) classification system for thoracolumbar spine injuries, including injury severity assessment, designed to assist in clinical management. ”

Actually it is the thoracolumbar version of the SLIC score of which a (recently updated) separate app is available.

Now the TLICS score is available in NeuroMind 2 as an interactive decision support format as well:

Select value for each parameter

And after selecting the appropriate input for the individual patient, the results are presented in combination with a treatment advise:

TLICS score and treatment suggestion

More images are available in the gallery below…

Decision support: Intracerebral hemorrhage (ICH) mortality

Posted by Pieter Kubben in Neurosurgery, Technology | Tagged , , | Leave a comment
29January2012

The ICH-score has been described by Hemphill et al (2001) as “a simple clinical grading scale that allows risk stratification on presentation with intracerebral hemorrhage (ICH).”

It is a 5-tier score that consists of the Glasgow Coma Scale, ICH volume, intraventricular hemorrhage, infratentorial origin of ICH, and age. The total score ranges from 0 – 6 and predicts 30-day mortality after ICH.

So, here is how it looks in NeuroMind 2:

Select appropriate parameters

As you see, the third line is a little too long in this font. This will be corrected later.

Estimated 30-day mortality and ICH score

More images are available in the gallery below…

Decision support: low grade glioma survival prognosis

Posted by Pieter Kubben in Neurosurgery, Technology | Tagged , , | Leave a comment
29January2012

“In adult patients with low grade glioma (LGG), older age, astrocytoma histology, presence of neurologic deficits before surgery, largest tumor diameter, and tumor crossing the midline were important prognostic factors for survival. These factors can be used to identify low-risk and high-risk patients.” – Pignatti et al, 2002

Estimating life expectancy can be useful to optimize treatment for the individual patient. However, manual calculation of scores can be cumbersome. Apps can simplify the math, and help the physician to focus on the communication and treatment.

Here is my implementation of low grade glioma survival estimation for NeuroMind 2.

Input of risk factors for individual patient

Click “Proceed” for the actual survival estimation:

Survival estimation for individual patient

More images are available from the gallery below…

Update: I changed the description “risk factors” in the menu to “parameters”, as “age < 40yr” and “diameter < 6cm” are actually beneficial factors. I just did not want to use “age >= 40 yr” as I do not like the “>=’ combination.

Decision support: AVM grading and treatment

Posted by Pieter Kubben in Neurosurgery, Technology | Tagged , , | Leave a comment
28January2012

A well known classification system for the grading of arteriovenous malformations (AVMs) is the Spetzler-Martin classification. Recently an improved version has been introduced, the Spetzler-Ponce classification, that offers a simplified 3-tier grading system with more robust data on optimal treatment and risk for postoperative deficit.

Here is my implementation for NeuroMind 2:

Select appropriate values for risk factors

Press “Proceed” to go to the results:

Detailed feedback on grade, treatment and complication risk

More images are available from the gallery below…