阅读说明：本技术 用于识别登记参加临床试验的受试者的方法和系统 (Method and system for identifying subjects enrolled in a clinical trial ) 是由 S·琼斯 于 2019-05-14 设计创作，主要内容包括：本发明的实施例有利于促进临床试验的规划和进行。在实施例中,公开了一种方法,该方法通过识别登记的潜在的受试者和地点来促进临床试验的登记阶段以及临床试验规划。(Embodiments of the present invention are useful in facilitating the planning and performance of clinical trials. In an embodiment, a method is disclosed that facilitates enrollment phases and clinical trial planning for a clinical trial by identifying potential subjects and sites for enrollment.)

1. A method for identifying a subject enrolled in a clinical trial, comprising:

a) identifying a plurality of potential investigators of the clinical trial to create an investigator list;

b) determining a location of each investigator;

c) identifying a plurality of possible subjects for a clinical trial;

d) determining the location of each subject;

e) selecting a specified distance between the subject and the investigator; and

spatial clustering analysis is applied to determine the number of subjects within a specified distance for each investigator.

2. The method of claim 1, wherein the location of each subject comprises a location of a healthcare provider of the subject.

Technical Field

Methods and systems for facilitating the planning and performance of clinical trials are disclosed herein. In an embodiment, a method of facilitating enrollment phases and clinical trial planning for a clinical trial by identifying potential subjects and sites for enrollment is disclosed.

Background

Clinical trials are an important part of the process of introducing new treatments into healthcare systems. Such new treatments may include novel vaccines, compositions (e.g., pharmaceutical compositions), dietary supplements, medical and/or dietary options, and/or medical devices to enter a healthcare system. Clinical trials can be used to generate data on safety, efficacy, patient compliance, ease of use, and other topics relevant to treatment. The size and cost of clinical trials may vary and they may involve a single research center or multiple research centers in a single country or multiple countries.

The organization performing the clinical trial typically seeks to enroll a voluntary subject (e.g., patient) as a investigator of the new treatment or treatment/device/protocol to be tested. Clinical trials may require these subjects to undergo laboratory work at a laboratory or phlebotomy center at the investigator site. The creativity and efficacy of clinical trials is reduced if the distance between the position of the subject and the position of the center of the examination and testing according to the clinical trial protocol creates an obstacle to the subject's ability to provide the necessary samples for laboratory work.

Thus, methods and systems for identifying subjects enrolled in a clinical trial located close enough to a laboratory or phlebotomy center are desirable so as not to hinder the ability of the subject to provide samples for laboratory analysis, and to perform other examinations and consultations in non-emergency situations, where possible, via a telephone conversation with/without a video link. This and other advantages are achieved by the method and system of the present invention.

Disclosure of Invention

In an embodiment, the present invention provides a method for identifying a subject enrolled in a clinical trial, the method comprising:

a) identifying a plurality of potential investigators of the clinical trial to create an investigator list;

b) determining a location of each investigator;

c) identifying a plurality of possible subjects for a clinical trial;

d) determining the location of each subject or their current healthcare provider;

e) selecting a specified distance between the subject and the investigator; and

spatial cluster analysis (spatial cluster analysis) was applied to determine the number of subjects within a specified distance for each investigator.

In an embodiment, a method further comprises: identifying clusters of subjects of a predetermined size outside of a specified distance for the investigator; and additional potential investigators are identified within a specified distance of the cluster.

In an embodiment, identifying the investigator may be based on criteria including, but not limited to: specialization of diseases; performance in past clinical trials; performance on enrolling in past clinical trials; a location; a country; the incidence or prevalence of disease in the area; the prescription in the area is prepared; prescription trends in the area and similar criteria.

In an embodiment, identifying the subject and/or determining the location of the subject may be based on de-identified (identified) information.

In an embodiment, spatial clustering analysis creates clusters with features of interest. The features of interest may include one or more of: the number of possible subjects within a specified distance of the investigator; a number of possible subjects within a specified distance of each other; the incidence or prevalence of disease in the area; the prescription in the area is prepared; prescription trends in the area and similar criteria.

In an embodiment, potential investigators are ranked according to the number of subjects within a specified distance, and potentially selected/rejected based on this number. In an embodiment, the number of possible subjects within the specified distance of the selected investigator comprises a cluster of subject recommendations, also referred to as investigators for the trial. As noted, spatial clustering analysis can create clusters of subjects within a specified distance of each other but outside of the specified distance of the a priori list of identified investigators. In embodiments, additional investigators identified from other information sources that meet/exceed the criteria for the initial list within a desired distance of such a cluster may be added to the investigator list.

In an embodiment, the method further comprises selecting a specified distance and using the specified distance as a criterion for selecting a surveyor without using the initial list of surveyors.

In another embodiment, the invention provides a method for identifying a country and a location in a country of a subject enrolled in a clinical trial, the method comprising:

a) creating a subject profile for the clinical trial;

b) identifying a plurality of subjects based on a subject profile;

c) determining the location of each subject or their healthcare provider;

d) performing a spatial cluster analysis to create at least one cluster;

e) determining the number of subjects in each cluster and their locations;

f) identifying each cluster within a specified distance of the investigator for clinical trials;

g) and determining the optimal cluster number of the clinical trials.

In an embodiment, creating a subject profile may include defining for a test an "ideal" subject profile that meets inclusion/exclusion criteria and conditions that promote 100% compliance with the test in terms of available time (e.g., overnight hospital stay with minimal household commitment to the individual with minimal disruption).

In an embodiment, the optimal number of clusters may include one or more of the following features: the ability to meet the number of subjects required for a clinical trial; the number of investigator sites; the number of countries; ability to meet regulatory agency requirements; clinical trial sponsor requirements. For example, the optimal cluster number may be a number that provides the ability to meet the protocol requirements of a clinical trial with as few countries/investigator sites as possible of the number of subjects, while addressing the regulatory requirements of the clinical trial sponsor.

In embodiments, each possible subject comprises I_cn(k)Where c-1 and n-1 are used within a defined distance having k possibleIdentifying a first investigator location within a first country of energetic subjects (k depends on c and n); c-1 and n-2 identify the second possible investigator location within the first country, and n-1 for C countries and with k (nc) possible subjects.

In an embodiment of the present invention, the spatial clustering analysis may be represented by the following formula:

minimizing the number of countries C and ∑ n_cSo that

Where p is set to a level corresponding to meeting the study timeline and the level of acceptable certainty that the subject actually converted to a clinical trial patient.

In an embodiment, the location information may include a latitude and a longitude. GPS data, zip code, physical address, and/or zip code may be used to determine latitude and longitude.

Possible subjects for clinical trials may include one or more of the following attributes:

diseases or conditions of interest

Genetic characteristics of interest

Age or age range of interest

Sex of interest

History of health concern

Drugs of interest or previous treatment regimens

A behavioral pattern of interest; and/or

Another personal attribute of interest.

Possible investigators for a clinical trial may include one or more of the following attributes:

specific professional knowledge;

specific experience with past clinical trials;

specific research experience;

the level of specificity in medical professional and/or scientific findings in the medical field should be such that individuals are considered key opinion leaders;

association with a research institution and/or medical facility;

the ability to interact with the patient; and/or

Another attribute of interest.

Drawings

FIG. 1 is a schematic diagram of an embodiment of the present invention.

Fig. 2 is a graphical representation of potential subjects enrolled in a clinical trial in the embodiment of the invention discussed in the examples.

Fig. 3 depicts the location of potential subjects enrolled in a clinical trial in the embodiment of the invention discussed in the example.

Fig. 4 depicts the location of potential investigator sites enrolled for participation in a clinical trial in an embodiment of the invention discussed in the examples.

Fig. 5 depicts the location of potential additional investigator sites enrolled for participation in a clinical trial in an embodiment of the present invention discussed in the examples.

FIG. 6 depicts the location of a investigator site from a past clinical trial enrolled in a clinical trial in an embodiment of the invention discussed in the examples.

FIG. 7 is a diagrammatic representation of spatial clustering analysis in an embodiment of the invention discussed in the examples.

FIG. 8 depicts the location of potential sites for clinical trials in embodiments of the invention discussed in the examples.

Detailed Description

In the following description, various possible embodiments will be described. For purposes of explanation, specific configurations and details are set forth in order to provide a thorough understanding of the embodiments. It will be apparent, however, to one skilled in the art that the embodiments may be practiced without these specific details. In addition, well-known features may be omitted or simplified in order not to obscure the described embodiments.

It should also be noted that, as used in this specification, the singular forms "a," "an," and "the" include plural referents unless expressly and unequivocally limited to one referent. The term "and/or" is used generically to refer to at least one or the other. In some cases, the term "and/or" is used interchangeably with the term "or". The term "including" is used herein to mean, and is used interchangeably with, the phrase "including but not limited to". The term "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.

An embodiment of the present invention is schematically depicted in fig. 1. As shown in fig. 1, the proposed clinical trial may include a plurality of parameters. These parameters may include, but are not limited to, the patient population to be studied, the treatment method(s) to be investigated, the endpoint, and how the trial will be conducted (e.g., randomized versus non-randomized). The patient population may include patients who may benefit from the treatment or intervention to be tested. Populations may also be selected so that the results of the test may be generalized to patients in clinical practice. Overall, the more diverse the patient population, the more extensive the spread of the results to a broader patient population. A patient in a clinical trial may also be referred to as a subject, and the terms are used interchangeably herein.

To investigate a patient population with appropriate disease states and levels of diversity, investigators defined criteria to determine whether a patient is eligible for a trial. Inclusion and exclusion criteria may include patient characteristics (e.g., age, genetic profile) as well as disease and treatment specific characteristics, including prior laboratory test results associated with the disease and/or condition. An additional parameter is the number of patients required for the clinical trial. The clinical trial parameters may also include a desired timing for registering patient and/or investigator sites, and a suggested timeline for completing the clinical trial.

As shown in fig. 1, the clinical trial parameters are used to query a laboratory test data database to determine possible subjects and investigators to conduct the clinical trial. The database provides subject data relating to each possible subject, including but not limited to the attributes set forth above and the geographic location of the subject. Similarly, the database provides investigator data related to each possible investigator, including but not limited to the attributes set forth above and the geographical location of the investigator. The geographic location data for each subject and/or each investigator may include global positioning system coordinate data, zip code data for the united states postal service, longitude and/or latitude data, and the like.

As further shown in fig. 1, the subject data and the investigator data undergo spatial clustering analysis, such as the spatial clustering analysis described above. Spatial clustering analysis outputs potential investigators and subjects for clinical trials.

The features and advantages of embodiments of the present invention will be further understood from the following illustrative examples.

Examples of the invention

Embodiments of the present invention are used to select patients and investigators (clinical trial sites) for a hypothetical clinical trial.

A database maintained by the assignee of the present application, health care diagnostics, inc. The database includes: greater than 130 hundred million test results; greater than 500,000 samples per day; over 4000 diagnostic tests; greater than 758,000 healthcare professionals who provided them with results; and over 1.42 million patients.

Hypothetical clinical trial starting parameters included the following:

150 patients with evidence of potentially recurring disease (defined as two positive samples within 1-3 months of each other);

9 month timeline registered in the united states;

draft site list of 120 investigators from 95 different zip codes (locations).

Interrogation of the first 14 months laboratory test database within the database revealed that disease testing had been performed on 9628 patients from across 2848 zip codes, and where 2358 patients (less than 25%) had samples with at least one test positive. Of those testing positive, 262 patients (approximately 3%) had two or more testing positive samples. Of the 262 patients, 156 (approximately 2%) had two samples that tested positive in 1-3 months. These results are shown in fig. 2. The geographical distribution of test positive subjects is shown in fig. 3, where negative tests are red circles, positive tests are yellow circles, relapse positive tests are green circles, and relapse positive tests are black circles within 1-3 months.

In view of this patient and geographic data, in the absence of the present invention, i.e., using the prior art, if an investigator were to be recruited at all 2848 carrier codes that captured all patients tested, a 13-month recruitment window would be required to provide 80% or greater opportunity to randomly allocate 150 patients for clinical trials. If a uniform registration assumption is applied, using only 95 zip code locations will take thirty times the time, i.e., over thirty-two years. Indeed, in the phase II setting, the longest timeline that can be considered is 18 months; assuming that the number of positive tests is proportional to all tests, then the zip code coverage needs to be increased in proportion to the number of tests, assuming that future test patterns may reflect the observed historical pattern. Thus, without the present invention, the desired timeline for enrollment cannot be met, and the logistics of clinical trials would be difficult due to the geographic distribution of trial locations.

To meet the desired clinical trial parameters, embodiments of the present invention are utilized. Initially, a proposed investigator site for the trial is identified. These locations are shown as blue crosses in figure 4 along with the patient data. As depicted, there are several proposed investigator sites (one circled in yellow) away from the subject and several geographic locations (one circled in green) with a large number of potential subjects without investigators.

The zip code is used to determine the coordinates (latitude and longitude) of each test center and investigator. The direct "straight" distance (implemented as geodesist function in SAS) between each test center and each investigator within the same state in the united states was calculated using the Haversine formula:

wherein

Is the Haversene function

Distance between two points

Radius of sphere ()

Andis the longitude and latitude of each radian coordinate

50 miles is used as a general limit (cut-off) for determining potential recommendations using a minimum distance.

The geographic location of a place is shown in fig. 5, where places are represented in yellow and places less than 50 miles are represented in blue.

Investigators from past trials were added to the assessment and the calculation repeated. The results are shown in FIG. 6, where the green asterisks indicate locations less than 50 miles. As shown in fig. 6, the potential of tennessee appears to have not been exploited.

According to the invention, potential new locations are identified by spatial clustering. The approach taken is to check if a new investigator can be identified by calculating the distance matrix between each pair of test centers according to the following and using the spatial clustering model:

ods output clusterhistory＝c_ST；

proc cluster data＝sqlmatz(type＝distance)outtree＝t_st nonorm method＝complete

trim＝10 r＝25；

by phys_std；

copy phys_zipnd numpatd numphyd numpatposd；id phys_zipndd；

run；

the potential usefulness of the cluster is then evaluated with respect to the number of potential patients and the applicability of the investigator. Using the Complete method, clusters are defined such that all locations in the cluster are within a specified distance of each other and the minimum distance between the clusters is greater than the specified distance. Regular cluster shapes are maintained using the pruning option. A schematic representation is shown in fig. 7.

The results of the analysis are shown in fig. 8, where the purple pentagons represent the clusters identified from the cluster analysis of at least 30 patients. It is expected that these results will allow the registration parameters of the clinical trial to be met.

While the present subject matter has been described in detail with respect to specific embodiments thereof, it will be appreciated that those skilled in the art, upon attaining an understanding of the foregoing may readily produce alterations to, variations of, and equivalents to such embodiments. Thus, it should be understood that the present disclosure has been presented for purposes of illustration and not limitation, and does not preclude inclusion of such modifications, variations and/or additions to the present subject matter as would be readily apparent to one of ordinary skill in the art.