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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.
Flow7 Integrates CCS Cloud for Enhanced Teamwork and Better Patient Care
The CCS cloud platform is an innovative solution that aims to transform how chronic diseases are managed, particularly those related to vascular and autonomic nervous system (ANS) functions.
LD Technology devices can be utilized by physicians for technical measurements. These devices seamlessly integrate with the CCS cloud platform, enabling efficient data transfer for interpretation and customization of patient reports by specialist physicians.
CCS cloud platform is a comprehensive solution that facilitates effective teamwork and collaboration among healthcare professionals, ultimately enhancing the quality of referral physicians, and billing experts, fostering a collaborative environment where expertise is shared and leveraged
The data are separated into 3 sections:
1. Interpretation: The invited qualified physicians (neurologist, cardiologist, or vascular specialist), and also billing experts, provide an interpretation and guidance regarding the test results related to vascular or ANS functions and billing.
2. View results and report printing and
3. Editable Patient Questionnaire
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.
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)
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)
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
