LD TECHNOLOGY

TM-FLOW/CHRONIC CARE SYSTEM (CCS) CLOUD PLATFORM

VASCULAR FUNCTION AND AUTONOMIC NERVOUS SYSTEM DATA MANAGEMENT

The TM-Flow system is a  Medical Device Data System (MDDS).

The New version 7 of the TM-Flow system named Flow7 integrates the CCS Cloud data management platform. It enables the formation of effective teamwork by helping align primary and specialty care physicians to deliver better patient care.

The TM Flow system comprises:

Part 1: Three Devices

The TBL-ABI is:

The TBL-ABI is:

The Sweat C is:

The Sweat C is:

The Oxi_W is

The Oxi W is:

Part 2: A dedicated software

Software features

CCS Cloud data management

Why TM-FLOW/ CCS Cloud Platform?

The 3 medical devices (TBL ABI, OXI W, SweatC) are used by physicians to assess patients who have suffered from complications of chronic diseases.

The devices allow for the early detection of chronic disease complications.

If there is no early diagnostic then there is no timely treatment, and time is of the essence when considering treatment for chronic disease complications.

Peer reviews and clinical studies demonstrate that chronic diseases, as well as their treatments, affect both vascular (endothelial function and Lower extremity artery) and autonomic nervous system (ANS) function (sudomotor and cardiac autonomic function).

How are the data managed with CCS Cloud?

After each device is used and the measurements are recorded, the software then sends the data to the CCS cloud. The online and secure cloud software allows the invited qualified physicians (neurologist, cardiologist, or vascular specialist), and also billing experts, to provide an interpretation and guidance regarding the device test results related to vascular or ANS functions and billing.

What is the Vision of the TM-Flow/CCS Cloud Platform.

Collaborating as a team to improve patient care.

Effective teamwork between the performing physician and qualified interpreting physician (e.g., specialists) is a must in today’s healthcare environment due to increasing patient comorbidities and the complexity of specialization care required.

The CCS cloud data management platform enables the formation of effective teamwork by helping align primary and specialty care physicians to deliver better patient care.

CCS ASSESMENTS​

Chronic Care Management - LD Technology

BLOOD PRESSURE AND ARTERIAL STIFFNESS ANALYSIS

Monitoring and treatment.
Management of hypertension

Chronic care management- LD Technology

PHOTOPLETHYSMOGRAPHY

Mathematical Analysis of the pulse Ox wave

Chronic care management- LD Technology

ANKLE BRAKIAL INDEX (ABI)

Peripheral Artery Disease (PAD). Blood flow blockage or calcification

Chronic care management- LD Technology

HEART RATE VARIABILITY (HRV)

Cardiac Autonomic Dysfunction Assessment

Chronic care management- LD Technology

CARDIAC AUTOMATIC REFLEX TESTs (CARTs)

Cardiac Autonomic Neuropathy assessment

Chronic care management- LD Technology

SUDOMOTOR FUNCTION TESTS

Skin Microcirculation and small fiber Assessments

TM-FLOW/CCS CLOUD ASSESMENTS

Chronic Care Management - LD Technology

MAIN SYMPTOMS OF AUTONOMIC NEUROPATHY AND VASCULAR DYSFUNCTION ​​

MAIN SYMPTOMS OF AUTONOMIC NEUROPATHY AND VASCULAR DYSFUNCTION ​​

USA POPULATION THAT SHOULD BE TESTED BY LD TECHNOLOGY PRODUCTS

50+

Population over 50 years old
with cardiovascular risk factors
(Hypertensive, Overweight, Smoker, Diabetic)

Chronic care management- LD Technology

70+

Population over 70 years old.

USA POPULATION THAT SHOULD BE TESTED BY LD TECHNOLOGY PRODUCTS

USA POPULATION THAT SHOULD BE TESTED BY LD TECHNOLOGY PRODUCTS

50+

Population over 50 years old
with cardiovascular risk factors
(Hypertensive, Overweight, Smoker, Diabetic)

Chronic care management- LD Technology

70+

Population over 70 years old.

MAIN MARKERS ​​

VASCULAR FUNCTION ASSESSMENT

VASCULAR FUNCTION ASSESSMENT

AUTONOMIC NERVOUS SYSTEM ASSESSMENT

AUTONOMIC NERVOUS SYSTEM ASSESSMENT

CLINICAL VALIDATION

See results in page ​​Clinical Studies 

Early Detection of Disease Complications - LD Technology

Oxi_W clinical studies:

The ROC curves showed that the most relevant cutoff to the whole study group was a PTG-TP > 406.2. This cut-off had a sensitivity = 95.7%, specificity = 84,4% and the area under the ROC curve (AUC) = 0.929 for identifying insulin resistance. All AUC ROC curve analysis were significant (p < 0.0001).

the PTG CVD score had a sensitivity of 82.5% and specificity of 96.8%, at a cutoff of 2, when used to detect CAD (P=0.0001; area under the receiver operating characteristic curve =0.967). The PTG spectral analysis markers were well-correlated to other autonomic nervous system and endothelial function markers. CAD diabetic patients (n=27) had a lower PTGi value compared with the CAD non-diabetic patients (n=38): and patients that underwent CABG (n=18) had a higher PTGi value compared with the CAD without CABG surgery patients (n=47).

Comparisons between the healthy subjects and type 2 diabetes mellitus patients

The PTGi had a sensitivity of 92% and specificity of 80% (cut-off score > 35.5) with the area under the curve = 0.92 (SE = 0.04; 95% CI = 0.84, 1.0) and an asymptotic significance < 0.001. The PTGVLFi had a sensitivity of 92% and specificity of 87% (cut-off score > 25.5) with the area under the curve = 0.91 (SE = 0.05; 95% CI = 0.81, 1.0) and an asymptotic significance < 0.001.

Stress Index marker correlated with CRP (ρ = −0.38, p < 0.0001 and PTG VLFi correlated with fibrinogen (ρ = 0.43, p < 0.0001).

was also significantly different between groups, with vitamin D insufficient individuals having lower TP values compared to vitamin D sufficient participants (P = 0.045)

Sweat C Clinical study:

inversely correlated with the severity of symptoms on the peripheral neuropathy scale (ρ = −0.56, p < 0.0001).

had a sensitivity of 88% and a specificity of 68% (Area Under the Curve = 0.81, p < 0.0001) to detect retinopathy. The NO Sweat Peak response marker inversely correlated with BUN (ρ = −0.41, p < 0.0001), homocysteine (ρ = −0.44, p < 0.0001), fibrinogen (ρ = −0.41, p < 0.0001), the Cardiac Autonomic Neuropathy score (ρ = −0.68, p < 0.0001), and the heart rate variability Total Power (ρ = −0.57, p < 0.0001)

TBL-ABI Clinical study

The overall ABI gave the same specificity and sensitivity values of 77.8%, with a cutoff ≤ 0.9 (P = 0.024 and AUC = 0.747) for detecting vascular color Doppler ultrasound biphasic and monophasic waveforms versus triphasic waveforms.

The overall TBI gave a specificity of 55.6% and sensitivity of 100%, with a cutoff ≤ 0.55. (P = 0.001 and AUC = 0.824) for detecting vascular color Doppler ultrasound biphasic and monophasic waveforms versus triphasic waveforms.

The overall PTG index marker gave a specificity of 83.3% and a sensitivity of 100%, with a cutoff ≤ 26 (P = 0.001 and AUC = 0.917) for detecting vascular color Doppler ultrasound biphasic and monophasic waveforms versus triphasic waveforms

The overall sum the ABI and TBI (SBI) values gave a specificity of 88.9% and a sensitivity of 100% with a cutoff ≤ 1.36 (P = 0.001 and AUC = 0.960) for detecting vascular color Doppler ultrasound biphasic and monophasic waveforms versus triphasic waveforms

HARDWARE COMPONENTS

This video animation shows you the full components of the SweatC, OXI_W and TBL-ABI systems.
It will help the user to properly setup the hardware and how to charge the wireless devices.
PATIENT SETUP​​​​​​​​
TM FLOW - Chronic care management- LD Technology
Plethysmography-Chronic care management- LD Technology
Ankle Brachial Index- Chronic care management- LD Technology
SUDOMOTOR Sudomotor-SweatC-Chronic care management- LD Technology

BROCHURE