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CLASSIF1 Patient Classification:
SAIS1 Trial (Sitagliptin/Glimepiride)
|
Summary: HbA1c (glycated hemoglobin A1c) is the best
discriminating of the
32 SAIS1
clinical trial parameters.
It identifies Sitagliptin and Glimerpiride treated
patients with specificities of 97.4% and 95.3% at
sensitivities of 62.9% and 76.3% for the training set patients
with 100.0% and 88.3% specificities at 75.0% and 63.6%
sensitvities for the validation patients, using the
5/95% percentile thresholds for
CLASSIF1
algorithmic classification.
The less discriminating but parameter fishing percentile thresholds
25/75% and 30/70% identify patients
(tab.2)
with 85.0% and 70.7%% positive predictive
values in the training and 80.0% and 77.8% in the validation
set.
After 26 weeks of therapy,
Glimepiride decreases only the HbA1c parameter while
Sitagliptin as DPP-4 (dipeptidyl peptidase 4) inhibitor is
accompanied by
increases of
(+) CPEPTI (insulin C-peptide index),
(+) HDL-cholesterin (high density lipoprotein-cholesterin) classification mask parameters
in combination with parameters of the oxidative metabolism (+) SOD
(superoxide dismutase) and (+) BAP (biological antioxidant potential),
although when compared to Glimepiride after therapy,
reduced
oxidative stimulation (-) D-ROMS (reactive oxygen metabolites-derived
compounds) at increased proinflammatory parameters (+)
CRP (C-reactive protein) as well as increased
(+) TPRI (peripheral vascular resistance) and
(preexisting)
(+) TNF-alpha (tumor necrosis factor alpha) at decreased
(-) CARDIX (cardiac output index) were observed.
New patients can be
manually
classified without computer as an advantage of the CLASSIF1 algorithm
for everyday medical.practice, in case classification masks and
relevant percentile thresholds are publicly available.
1. Introduction:
Clinical trial data are usually statistically evaluated
(1
2)
or more recently by machine learning algorithms
(3).
Since ultimately individual patients are treated, the utility of
individual patient oriented software like the CLASSIF1 algorithm
(4,
5)
was investigated using the SAIS1 EXCEL data of
(weeks
0
and
26)
2. Sitagliptin after Therapy (26 weeks)
3. Glimepiride after Therapy (26 weeks)
4. Compressed Result Display
The use of confusion matrices
(figs
1/
2/
4)
is suitabe for the display of individual classifications but a compressed
display, containing the same information, is preferable.in case of multiple
classifications.
|
drug |
classific |
perc (%) | trainp (sp/se) | spect (%) | senst (%) | npvt (%) | ppvt (%) | validp (sp/se) | specv (%) | sensv (%) | npvv (%) | ppvv (%) |
Sita | 26/0wks | 5/95 | 38/35 | 97.4 | 62.9 | 74.0 | 95.6 | 10/8 | 100.0 | 75.0 | 83.3 | 100.0 |
Sita | 26/0wks | 25/75 | 38/34 | 92.1 | 50.0 | 67.3 | 85.0 | 10/8 | 90.0 | 50.0 | 69.2 | 80.0 |
Glime | 26/0wks | 5/95 | 43/38 | 95.3 | 65.8 | 75.9 | 92.6 | 12/11 | 83.3 | 54.6 | 66.7 | 75.0 |
Glime | 26/0wks | 30/70 | 43/38 | 72.1 | 76.3 | 77.5 | 70.7 | 12/11 | 70.7 | 83.3 | 71.4 | 77.8 |
5. Classifier Robustness: Cross Validation
CLASSIF1 classifiers are inherently robust as shown by cross-validating
(tab.4)
the embedded validation patients.
|
drug |
classific |
perc (%) | trainp (sp/se) | spect (%) | senst (%) | npvt (%) | ppvt (%) | validp (sp/se) | specv (%) | sensv (%) | npvv (%) | ppvv (%) |
Sita | p_56,60,65... | 5/95 | 38/35 | 97.4 | 62.9 | 74.0 | 95.6 | 10/8 | 100.0 | 75.0 | 83.3 | 100.0 |
Sita | p_57,61,66... | 5/95 | 38/33 | 97.4 | 66.7 | 70.1 | 95.7 | 10/10 | 100.0 | 60.0 | 71.4 | 100.0 |
Sita | p_58,62,67... | 5/95 | 38/35 | 97.4 | 62.9 | 74.0 | 95.7 | 10/8 | 100.0 | 75.0 | 83.3 | 100.0 |
Sita | p_59,63,68... | 5/95 | 38/34 | 97.4 | 64.7 | 75.5 | 95.7 | 10/9 | 100.0 | 66.7 | 76.9 | 100.0 |
Sita | p_60,64,69... | 10/90 | 38/34 | 97.4 | 64.7 | 75.5 | 95.7 | 10/9 | 100.0 | 66.7 | 76.9 | 100.0 |
6. Sitagliptin/Glimepiride prior Therapy (0weeks)
Patients groups in clinical studies are selected to match as good as
possible for a certain number of parameters like age, sex or previous
medical history but certain differences concerning investigaed molecular
parameters may still exist.
Sitagliptin patients were classified against Glimepiride
patients prior to therapy start (0weeks) to detect differences
between the patient groups.
|
perc (%) | trainp (sp/se) | spect (%) | senst (%) | npvt (%) | ppvt (%) | validp (sp/se) | specv (%) | sensv (%) | npvv (%) | ppvv (%) |
10/90 | 43/38 | 86.1 | 44.7 | 63.8 | 73.9 | 12/10 | 75.0 | 40.0 | 60.0 | 57.1 |
30/70 | 43/38 | 88.4 | 50.0 | 66.7 | 79.2 | 12/10 | 83.3 | 40.0 | 58.8 | 60.0 |
Classification at the parameter picking 30/70% percentile pair provided a 9 parameter classification mask -+++-+-+- for parameter differences prior to therapy in the Sitagliptin patient group comprising (-) FMD (flow-mediated dilation), (+) BMI (body mass index), (+) HBA1C (glycated hemoglobin A1c), (+) PROINS (pro-insulin), (-) TRIGL (triglycerides), (+) TOTPAI-1 (total plasminogen activator inhibitor-1), (-) NTPROBND (N terminal prohormone of brain natriuretic peptide), (+) TNF-alpha (tumor necrosis factor-alpha). (-) U-ALB (albuminuria) (p<0.05, <0.10, >0.100) when compared to the Glimepiride reference group classification mask: 000000000 (training, validation, parameters, means,statistics).
7. Sitagliptin/Glimepiride after Therapy (26weeks)
|
perc (%) | trainp (sp/se) | spects (%) | sensts (%) | npvts (%) | ppvts (%) | validp (sp/se) | specvs (%) | sensvs (%) | npvvs (%) | ppvvs (%) |
20/80 | 38/34 | 81.6 | 52.9 | 66.0 | 72.0 | 11/7 | 63.6 | 42.9 | 63.6 | 42.9 |
|
perc (%) |
trainp (sp/se) | spect (%) | senst (%) | npvt (%) | ppvt (%) | validp (sp/se) | specv (%) | sensv (%) | npvv (%) | ppvv (%) |
25/75 p_1,5,10.. | 38/34 | 84.2 | 55.9 | 68.1 | 76.0 | 11/7 | 90.9 | 28.6 | 66.7 | 66.7 |
25/75 p_2,6,11.. | 39/32 | 89.7 | 53.1 | 70.0 | 81.0 | 10/9 | 90.0 | 10.1 | 52.8 | 50.0 |
25/75 p_3,7,12.. | 38/33 | 100.0 | 39.4 | 65.5 | 100.0 | 9/8 | 88.8 | 0.0 | 50.8 | 0.0 |
25/75 p_4,8,13.. | 40/32 | 85.0 | 46.9 | 66.7 | 71.9 | 9/9 | 77.8 | 33.3 | 53.8 | 60.0 |
25/75 p_5,9,14.. | 39/32 | 84.6 | 46.9 | 66.0 | 71.4 | 10/9 | 80.0 | 33.3 | 57.1 | 60.0 |
8. Manual Classification
Standardized classifications
can be performed manually with known
percentile thresholds
of: <7.06, >1.66, >58.46, >5.83, >28.06 for the parameters selected by the
preceding classification process, like for example with the values
of patient 72 Hb1Ac=7.20, CPEPTIN=1.70, HDL=46.0, SOD=8.80, BAP=29.877
yielding the classification mask: 0+0++, classifying as "T" .
This is correct as can be verified in the
training
set (rec.51).
9. References:
1.
Nomoto1 H, Miyoshi H, Furumoto T et al
A Randomized Controlled Trial Comparing
the Effects of Sitagliptin and Glimepiride on
Endothelial Function and Metabolic
Parameters: Sapporo Athero-Incretin Study 1
(SAIS1).
PLoS ONE (2016) 11(10): e0164255. doi:10.1371/journal.pone.0164255
Data 0 weeks: https://doi.org/10.1371/journal.pone.0164255.s004
Data 26 weeks: https://doi.org/10.1371/journal.pone.0164255.s005
2.
Oyama J-i, Murohara T, Kitakaze M, Ishizu, Sato Y, Kitagawa K, et al.
The Effect of Sitagliptin on Carotid Artery Atherosclerosis in Type 2
Diabetes: The PROLOGUE Randomized Controlled Trial.
PLoS Med (2016) 13(6): e1002051. doi:10.1371/journal.pmed.1002051
Data: https://datadryad.org/stash/dataset/doi:10.5061/dryad.qt743
3.
Berchialla1 P, Lanera C, Sciannameo V, Gregori D, Baldi T.
Prediction of treatment outcome in clinical trials under a personalized medicine
perspective.
Scient Rep (2022) 12:4115 | https://doi.org/10.1038/s41598-022-07801-4
4.
Valet.G
Human cytome project: A new potential for drug discovery.
In: Las Omicas genomica, proteomica, citomica y metabolomica:
modernas tecnologias para desarrollo de farmacos.
Ed: Real Academia Nacional de Farmacia, Madrid (2005) p 207-228
5.
Valet G, Valet M, Tschöpe D, Gabriel H, Rothe G,
Kellermann W, Kahle H.
White cell and thrombocyte disorders: Standardized, self-learning
flow cytometric list mode data classification with the CLASSIF1
program system.
Ann NY Acad Sci (1993) 677: 233-251
© 2025 G.Valet |