The objective of this observational study is to predict healing at 26 weeks after the first visit in patients affected by the first ulcer, by means of combined data monitoring of Laser Speckle Contrast Imaging (LSCI) and temperature measurements during patient visits in hospital. In order to achieve this objective, study aims to produce a logistic regression model and then evaluate its prognostic ability by means of the area under the curve (AUC) of the receiver-operating-characteristics (ROC) curve. Patients with diabetes mellitus and suffering from ulcer and receiving health care will undergo regular microcirculatory measurements including LSCI scans in and around the ulcer location and thermography.
The study aims to predict healing at 26 weeks after the first visit in patients affected by the first ulcer, by means of combined data monitoring of Laser Speckle Contrast Imaging (LSCI) and temperature measurements during patient visits in hospital. In order to achieve this objective, the study aims to produce a logistic regression model and then evaluate its prognostic ability by means of the area under the curve (AUC) of the receiver-operating-characteristics (ROC) curve. The variables evaluated for the model will include demographic and baseline characteristics of the patients, location and seriousness of the ulcer, alongside LSCI and temperature measurements at baseline and different timepoints. The development of the model will include a rigorous variables selection in order to produce a parsimonious model. In the method proposed by Mennes, LSCI measurements at baseline, biological zero, post occlusion peak, and other parameters like non-invasive blood pressure measurements were individually evaluated as possible prognostic factors of healing trajectory at 26 weeks. All these parameters, when assessed at their highest possible value of sensibility and specificity, produced AUCs always inferior to 0.65. The highest value of AUC (0.625) was reached by toe pressure parameter when calculated using a threshold value of 54 mmHg. From diagnostic literature, an AUC of ≥ 0.8 is considered excellent result, while an AUC \< 0.7 is considered less than acceptable. Since the Mennes's method showed these results, study investigators set the AUC resulting from the model under the null hypothesis (H0) to be equal to 0.65. By including all these parameters, alongside the other mentioned variables, in a single prognostic model, study investigators expect to increase this prognostic ability. Therefore, in this study, investigators would like to detect an AUC ≥ 0.8 (H1). With these hypotheses, a power of 80%, a one-sided I type error of 5% and a prevalence of 43% of healed patients at 26 weeks, a total of 82 patients should be enrolled.
Study Type
OBSERVATIONAL
Area Under ROC curve generated by the statistical model able to discriminate between healing and not healing ulcers
to discriminate between healing and not healing ulcers by means of combined data monitoring of Laser Speckle Contrast Imaging (LSCI) and temperature measurements during patient visits in hospital
Time frame: at 26 weeks after the first visit in patients affected by the first ulcer
The predictive positive and negative value related to the score test, based of calculation of likelihood ratio.
The predictive positive and negative value related to the score test, based of calculation of likelihood ratio.
Time frame: at 26 weeks after the first visit in patients affected by the first ulcer
The AUC of the models estimated according to different sites of ulcers.
The AUC of the models estimated according to different sites of ulcers.
Time frame: at 26 weeks after the first visit in patients affected by the first ulcer
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