In reconstructive surgery, adequate perfusion of flaps is essential for successful outcomes. Assessing normal flap perfusion and differentiating it from problematic perfusion is important and time-consuming. Laser speckle contrast imaging (LSCI), an optical technique for quantitative microcirculation assessment, can be used to non-invasively monitor flap perfusion. We designed a wireless handheld LSCI device and investigated its clinical feasibility in Deep Inferior Epigastric Perforator (DIEP) flap reconstruction surgery. In a case series study with 15 patients and 20 DIEP flaps, perfusion was measured perioperatively across the 4 Hartrampf zones at 4 specific time points and on the first post-operative day. In unilateral reconstructions, the perfusion in dissected flaps showed a perfusion gradient across the zones, with the highest perfusion in zone I and lowest perfusion in zone IV. The decrease in perfusion between unilateral flap elevation and temporary occlusion measurements was detected in unilateral flaps, but not in bilateral flaps. The device could detect flap failure in 2 cases by measuring anomalous perfusion values. The results indicate that our device is potentially valuable for flap monitoring. It detected differences in perfusion throughout the flap zones, a decrease in perfusion when clamping the pedicle and anomalous perfusion in flaps that failed. Motion artefact correction is needed to measure reliably during motion caused by patient breathing, pulse and operator motion. Further studies are needed to determine whether the wireless perfusion imager enables the early detection of complications, which could aid in prevention or prompt reintervention to salvage a flap.

Background/Objectives: Traditional glycemic monitoring in type 2 diabetes is limited, whereas continuous glucose monitoring (CGM) offers better insights into glucose fluctuations. This study aimed to determine the correlations and relative contributions of fasting plasma glucose (FPG) and postprandial plasma glucose (PPG) levels to hyperglycemia. Methods: We utilized CGM and recorded carbohydrate intake data from lifestyle diaries of 59 patients enrolled in the Diabetes and Lifestyle Cohort Twente (DIALECT-2). Correlations between FPG and the glucose management indicator (GMI), FPG and Time Above Range (TAR), PPG and GMI, and PPG and TAR were conducted. Daily and mealtime relative contributions of PPG and FPG to glycated hemoglobin (HbA_1c) and GMI were determined, considering two ranges: on target (<7.0%, 53 mmol/mol) and not on target (≥7.0%, 53 mmol/mol). Correlations between mealtime PPG and carbohydrate consumption were examined. Results: FPG and PPG correlated with GMI (r = 0.82 and 0.41, respectively, p < 0.05). The relative contribution of PPG in patients with HbA_1c, GMI, and TAR values not on target was lower than in patients with HbA_1c, GMI, and TAR values on target. When analyzing different mealtimes, patients with target GMI values had a higher PPG (73 ± 21%) than FPG after breakfast (27 ± 21%, p < 0.001). Individuals with elevated GMI levels had lower PPG after lunch (30 ± 20%), dinner (36 ± 23%), and snacks (34 ± 23%) than FPG. PPG after breakfast positively correlated (r = 0.41, p < 0.01) with breakfast carbohydrate intake. Conclusions: Both PPG and FPG contribute to hyperglycemia, with PPG playing a larger role in patients with better glycemic control, especially after breakfast. Targeting PPG may be crucial for optimizing glucose management.