Treatment queries are under the Query tab in Cleo’s Explore application; see Figure 1. Creating a Treatment query is a two step process. First the contents of the treatment table are selected. Then, the treatment table is filtered.

Figure 1
Queries Tab in Cleo’s Explore Application

Queries tab in Colabrativ’s Clinical Entry and Operations (Cleo) Explore application before the creation of any treatment tables, or any queries have been run.

Selecting Treatments Table Content

Treatments in the treatments tables are grouped by patient, and chronologically ordered by the start date of each treatment. There are a number of options that control the contents of a treatments table; each option is reviewed below.

Treatment Table Options

Option	Description
Treatments Included	Single Treatment adds a single treatment to each row in the treatments table, and is the default for the Treatments Included option. The Treatment and Post-treatment option adds both a treatment and the next treatment (post-treatment) to each row of the treatments table. If the treatment is the last treatment and if a Deceased or Last Contact or Lost to Follow Up event is after the last treatment, then the Deceased or Last Contact or Lost to Follow Up event is added as the post-treatment. The Pre-treatment to Post-treatment option adds three treatments to each row in the treatment table. As with the Treatment and Post-treatment option above, Deceased or Last Contact or Lost to Follow Up events can be the final “post-treatment” for a patient. The All Treatment option puts all of a patient’s treatments in a single row. As with the Treatment and Post-treatment and Pre-treatment to Post-treatment options above, Deceased, Last Contact or Lost to Follow Up events can be the final “post-treatment” for a patient.
There are five therapy types that can be included in a treatments table.
Include Chemotherapies	If checked, chemoetherapy events will be included in the treatments table.
Include Complementary Therapies	If checked, complementary therapy events will be included in the treatments table, such as treatments with Curcumin.
Hormone Therapies	If checked, hormone therapy events will be included in the treatments table, such as treatments with Tamoxifen.
Include Immunotherapies	If checked, immunotherapy events will be included in the treatments table.
Include Vaccines	If checked, vaccine events will be included in the treatments table, such as MUC1 Dendritic Cell Vaccine treatments.
The next four Include options add additional information to the treatment table, including the patient’s immunohistochemistry (IHC) and genetic results.
Include Agent 0 1 Columns	If checked, agent columns will be added to the treatments table. Each treatment agent in a treatment will have a 1 in the column labeled with that agent’s name. This option was designed to facilitate cluster analysis.
Include IHC Results	If checked, the H-score for each IHC test run on the patient’s specimen collected just prior to the treatment. e.g. pretreatment specimen, will be added to a column that uses the IHC test code as the column header.
Include Full IHC Results	If checked, the intensity, percent positive cells and H-score for each IHC test run on the patient’s pretreatment specimen will be added to the columns that use the IHC test code as prefix for the column header.
Include Genetic Results	If checked, the results for each genetic test, such as polymerase chain reaction (PCR), fluorescent in situ hybridization (FISH) or next generation sequencing run on the patient’s pretreatment specimen will be added to a column that uses the test code as the column header.
There are a number of situations in which one might not want to include the next treatment in a table. The next five options allow one to merge a number of difference treatment types into the treatment added to the treatments table.
Merge Neoadjuvant Treatments	If checked, neoadjuvant treatments will be merged with the following treatment. The start date of the neoadjuvent treatment will be used as the start for the merged treatment, if it is before the start of the treatment. A Neo flag will be added to the Flag column when a neoadjuvent merge is performed.
Merge HIPEC Treatments	If checked, Heated Intraperitoneal Chemoperfusion (HIPEC) treatments will be merged with the following treatment. The start date of the HIPEC treatment will be used as the start for the merged treatment, if it is before the start of the treatment. A HIPEC flag will be added to the Flag column when a HIPEC merge is performed.
Merge Maintenance Treatments	If checked, maintenance treatments will be merged with the previous treatment. The end date of the maintenance treatment will be used as the end date for the merged treatment, if it is after the end date of the treatment. A Main flag is be added to the Flag column when a maintenance merge is performed.
Merge Continuation Treatments	If checked, continuation treatments will be merged with the previous treatment. The end date of the continuation treatment will be used as the end date for the merged treatment, if it is after the end date of the treatment. A Cont flag is be added to the Flag column when a continuation merge is performed.
Simultaneous Treatments	If checked, treatments that overlap in time, but are of different treatment event types, e.g. a chemotherapy and a hormone therapy, will be treated as simultaneous. The agents in the simultaneous treatment will be added to an inserted Simultaneous Agents column.

Treatments Table Creation

Once treatments table options have been selected, the table can be created without applying any filtering to it by clicking on the “Create” button; see Figure 2. This is the only action that can be taken for the “All Treatment” Treatments Option. The treatments table creation options are disabled after a treatments table has been created, but the options used to create the treatments table are still displayed.

Prior to the creating the treatments table it is a good idea to supply a title in the “Query Title” box. The title may also be added after creating the table by filling in the “Query Title” box and clicking the “Update Title” button.

Once the treatments table has been created, it can be downloaded as a tab separated values (TSV) table by clicking on the green link. For the All Treatments table in Figure 2, the link is PatientTherapies_2013-11-29_09-45-05.tsv.

Figure 2
Queries Tab in Cleo’s After Creating an All Treatments Table

The Queries tab in Colabrativ’s Clinical Entry and Operations (Cleo) application after creating an All Treatments table. The treatments table creation options have been disabled, but still display the options that were selected when creating the treatments table, PatientTherapies_2013-11-29_09-45-05.tsv.

Filtering the Treatments Table

Filters are applied to a treatments table by selecting a filter from the “Select Filter” pull-down menu. There are a number of filters that can be applied to a treatments table. Each filter is shown below.

Treatment Table Filters

Once an agent name has been entered in the Chemotherapy Agents box, a new box will be created for an additional agent name. If the wild-card (*) is added as an agent then the filter will not remove treatment rows that match the agents supplied, but table row may have additional agents. Without a wildcard, the number of agents must match the number of agents supplied in the filter.

After setting up the desired filters for treatments table click on the “Run Query” button. The filtering will be applied to a preexisting treatments table, or a new treatments table will be created from the treatments table options which have been selected. The starting treatments table can be downloaded using the green link supplied for it. See Figure 3, in which the start treatment table link is Treatments_2013-11-28_11-08-16.tsv. The number of patients, specimens and treatments are provided for before and after the filtering. The filtered table can also be downloaded using the green link supplied for it. See again Figure 3, in which the query results table link is Query_2013-11-28_11-08-16.tsv.

Figure 3
Queries Tab in Cleo’s After Filtering Treatments Table

The Queries Builder after using a specimen type filter of O and P on a treatments table. The treatments tables created before and after filtering are Treatments_2013-11-28_11-08-16.tsv, and Query_2013-11-28_11-08-16.tsv, respectively.

Multiple filters can be run at the same time by simply selecting another filter from the “Select Filter” pull-down menu. After a Query has been run, additional filtering can be performed. For example, one might want to first filter on Cancer Codes and then Patient Histologies initially, and then to use the resulting filtered treatments table as the starting table for several treatment agents filterings.

Select Query

Just below the Query Builder banner is the “Select Query” pull-down menu (see Figure 3). All of the queries that have been run are displayed in this pull-down menu with their time-stamps plus their queries titles. To clear the query, an empty query can be selected from the “Select Query” pull-down menu. When a query is selected, the Query Builder panel will be cleared and replaced with the selected query’s information.

Cleo is the result of a close collaboration between Clearity and Colabrativ. The project was under the direction of Dr. Deborah Zajchowski, The Clearity Foundation’s Scientific Director. The goal of the project was to move Clearity’s operations from Microsoft’s Access to an enterprise system. The project has two phases. The first phase was to migrate patient, physician, specimen data and test results from Access to Cleo. As of today, this has been accomplished. In the second phase we will be migrating patient medical histories from Access to Cleo.

Analysis of a patient’s test results is performed by a third party application on a second server. The Clearity staff use Cleo to initialize analysis requests and archive data that was sent for analysis. Cleo sends the patient information to the analysis server in a de-identified form (removal of information from the data that could be used to identify an individual). The analysis is then compiled by the Clearity staff into a report that is sent to the patient and their physician. The report highlights clinical treatment options based on the patient’s tumor molecular profile that is generated from the test results.

If you are interested in learning more about Cleo, please contact us at Colabrativ, Inc.

Patient Information Entry

Our initial focus is on entry of patient information and test results by the foundation staff, with the initial goal of automating the process as much as possible. The forms in the application are organized as tabs; see Figure 1. The tabs are organized to match the work flow of entering and analyzing patient information and test results.

Figure 1
People Tab in the Application

People tab in The Clearity Foundation application. All of the personal information displayed in this figure is fictitious and does not represent real individuals.

Figure 2
Specimens Tab in The Clearity Foundation Application

Specimens tab in The Clearity Foundation application. All of the personal information displayed in this figure is fictitious and does not represent real individuals or their specimens.

Figure 3
Results Tab in The Clearity Foundation Application

Results tab in The Clearity Foundation application. At the top, the Add (upload) section and one of the “Test-Lab” test set have been opened. In the open test set one of the IHC results is being edited. All of the personal information displayed in this figure is fictitious and does not represent real individuals or their results.

Our goal is to enhance the Clearity database ability to track the patient outcomes in a retrospective analysis.  The current database was developed by Michael L. Petka.  It works well for the profiling and reporting needs of the organization.  However, Mike is currently occupied with other aspects of database enhancement. 

In order to ensure the privacy of the patient, we needed a test database that did not contain any actual patient information. In this blog I will describe how we created a test Patients table in Excel.

The Patients table was created by Michael L. Petka, and is made up of the following columns:

Patient Identification Key

The patient identification key (PatientID) is a sequential numbering of the patient rows.

Patient Name

We were fortunate to find a table of 10,000 random names at The ColdFusion Open Source Software Blog. In addition to the columns we needed, this database has complete name strings, both first name first, and last name first. The first name, last name and middle initial columns were cut and pasted into our test Patients table in Excel.

Patient Sex

The sex of the random names was determined in Excel by comparing the first name of a random name with a list of girls’ names from RandomNames.com. If the name matched, F (female) was assigned, otherwise M (male) was assigned, using the function below.

=IF(EXACT(A5,LOOKUP(A5,I$2:I$895)),"F","M")

Street Address

We found 261 of the most popular neighborhood street names at Living Places. We generated random house numbers and random street names, then concatenated them together in Excel.

=INT(11000*RAND())

=INDIRECT("StreetNames!B"&RANDBETWEEN(2,262))

=CONCATENATE(A5," ", B5)

City, State and ZIP code

A table containing city, state and ZIP codes of 80,810 places was downloaded from A Free Zip Code Database as the Excel file free-zipcode-database.xlsx. This file has more than just city, state and ZIP codes. It also includes type (STANDARD, PO BOX ONLY, MILITARY, etc.), county name, latitude, longitude, population, land area and water area. In Excel, the table was sorted on type, for we only wanted to use types STANDARD and PO BOX ONLY. After removing everything else, the list was reduced from 80,180 to 73,756 entries. We then generated 10,000 random ZIP keys to match up with our 10,000 random names in Excel using RANDBETWEEN(2,73757). All of this was stored in the City worksheet in Excel. In the Patients table (worksheet), the city, state and ZIP codes were added using arandom ZIP code key. The city name was capitalized using the PROPER function; see below.

=PROPER(INDIRECT("City!D"&City!M5))

=INDIRECT("City!E"&City!M5))

=INDIRECT("City!A"&City!M5))

Social Security Number

A random social security number (SSN) was generated for each fictitious patient by concatenating three RANDBETWEEN operations together; see below.

=CONCATENATE(RANDBETWEEN(100,999),"-",RANDBETWEEN(10,99),"-",MID((RANDBETWEEN(10000,19999)),2,4))

Date of Birth

We wanted a normal distribution of birthdays, so we randomly seeded the NORMINV function with random value between 0 and 1. We used a mean of 21,000 (June 29, 1957) and a standard deviation of 5,000 days (13.7 years) in the NORMINV function to get an acceptable distribution; see below.

=NORMINV(RAND(),21000,5000)

Telephone Number

Random telephone numbers were generated in the same manner as the social security numbers, by concatenation of three RANDBETWEEN operations; see below.

=CONCATENATE(INT(RANDBETWEEN(100,999)),"-",INT(RANDBETWEEN(100,999)),"-",MID((RANDBETWEEN(10000,19999)),2,4))

Email Address

Fictitious patient email addresses were constructed from each fictitious patient’s first name, last name and the domain name “@example.com”; see below.

=CONCATENATE(C5,".",B5,"@example.com")

Stability of the Excel Random Cells

We found that the randomly created cells in Excel were not stable. If such a cell were edited, previous random values would be regenerated, effecting all the cell that depended on that random number. Therefore, the final step was to write out the Patients worksheet as a comma-separated values (CSV) file, and then to read the CSV back into Excel. In this way we were able to create a stable test Patients table in Excel.