Medtronic, Inc.

Honeywell G.m.b.h.

Other

Medtronic Vascular, Inc.

William Frederick Kaemmerer

Walter H Olson

Michael D Kaytor

Eric J Panken

Mark L Brown

Siddharth Dani

Eric Burright

Ashutosh Chaturvedi

Timothy J Denison

Jennifer Heisel

Deepak Ramesh Thakker

Scott R Stanslaski

Gabriela C Molnar

Duane L Bourget

Shahram Malekkhosravi

Maciej T Lazarewicz

David Kent Stiles

James R Allard

Brandon Zingsheim

Allison T Connolly

Muthiah Manoharan

Andrei P Guzaev

Brian Dale Nelson

Jeffrey P Bodner

John G Keimel

Matthew H Adams

Timothy Denison

Erica M TenBroek

Douglas Ulen Gwost

Pei Ge

Sonny Yamasaki

Paul Van Bilsen

Dinah W Y Sah

Gregory Robert Stewart

Don Marshall Gash

Allison T Connelly

Brandon M Zingsheim

Eric N Burright

Janelle L Blum

In one example, a method includes selecting, by one or more processors and based on a representation of sensed electrical signals for a particular patient and a plurality of representations of sensed electrical signals for a plurality of other patients, a combination of electrodes of a plurality of combinations of one or more implantable electrodes for delivery of electrical stimulation to the particular patient.

Electrode selection for electrical stimulation therapy

In some examples, a processor of a system evaluates a therapy program based on a score determined based on a volume of tissue expected to be activated ('VTA') by therapy delivery according to the therapy program. The score may be determined using an efficacy map comprising a plurality of voxels that are each assigned a value. In some examples, the efficacy map is selected from a plurality of stored efficacy maps based on a patient condition, one or more patient symptoms, or both the patient condition and one or more patient symptoms. In addition, in some examples, voxels of the efficacy map are assigned respective values that are associated with a clinical rating scale.

Therapy program selection for electrical stimulation therapy based on a volume of tissue activation

A medical device may receive sensor data from sensing sources, and determine confidence levels for sensor data received from each of the plurality of sensing sources. Each of the confidence levels of the sensor data from each of the sensing sources is a measure of accuracy of the sensor data received from respective sensing sources. The medical device may also determine one or more therapy parameter values based on the determined confidence levels, and cause delivery of therapy based on the determined one or more therapy parameter values.

Closed-loop stimulation therapy in event of loss of sensor data

In some examples, a processor of a system evaluates a therapy program based on a score determined based on a volume of tissue expected to be activated ('VTA') by therapy delivery according to the therapy program. The score may be determined using a three-dimensional (3D) grid comprising a plurality of voxels that are each assigned a value. The processor may register the VTA with the 3D grid and determine the score for the therapy program based on the values assigned to voxels with which the VTA overlaps. One or more therapy programs for electrical stimulation therapy (e.g., deep brain stimulation) may be selected based on the scores determined based on the 3D grid.

A therapeutic window for at least one electrode of a medical system may be determined based on a volume of tissue expected to be activated ('VTA') by electrical stimulation delivered by the at least one electrode. In some examples, a processor determines the therapeutic window for a particular electrode by at least determining an efficacy threshold based on the VTA expected to result from the delivery of electrical stimulation according to a set of electrical stimulation parameter values including the stimulation parameter at the efficacy threshold, and determining an adverse-effects threshold based on the VTA expected to result from the delivery of electrical stimulation according to a set of electrical stimulation parameter values including the stimulation parameter at the adverse-effects threshold.

Therapeutic window determination

The present invention is directed to compositions methods and kits for regulation of gene therapies, including, without limitation, reversible gene therapies and allele-specific therapies.

Compositions and methods for making therapies delivered by viral vectors reversible for safety and allele-specificity

Methods and kits are provided for determining which single nucleotide polymorphism ('SNP') variant of an allele of a heterozygous patient is on the same allele as a disease-causing mutation that is at a remote region of the gene's mRNA comprising a) an allele specific reverse transcription reaction using an allele specific primer which recognizes one SNP variant, wherein further the 3' end of the primer is positioned at the SNP nucleotide position, and b) analysis of the resulting cDNA product from the reverse transcription reaction at the region of the mutation to determine the presence or absence of the mutation on this allele specific cDNA product, wherein the allele specific primer is shorter than about 20 nucleotides.

Methods and kits for linking polymorphic sequences to expanded repeat mutations

A system and method for determining, planning, and/or administering a therapy of a selected material from a catheter to a selected specific region of an anatomy is disclosed. The catheter can be used to deliver a drug to the patient to achieve a volume of efficacy (VOE) within a selected target, region, or volume. The drug delivered can be used for treating at least a symptom in the patient.

Volume of efficacy model capture

Growing community of inventors

Edina, MN, United States of America

William Frederick Kaemmerer