*2.1. Patients*

In this prospective, non-randomized, clinical study, patients with free flaps for reconstruction of the oro-maxillofacial complex were included. The patient population consists of 40 males and 23 females. The average patient age was 62.2 ± 12.6 years. Due to its design, the sensor unit of the hyperspectral camera system was not able to image the posterior portion of the oropharynx. Therefore, free flaps of the anterior-lateral region of the maxilla and mandible, as well as exposed flaps of the facial region, were included in the present study. All flaps that did not have regular monitoring (t0–t10 for HSI and t4–t10 for clinical monitoring) were not included.

#### *2.2. Monitoring*

#### 2.2.1. Clinical Monitoring

Clinical monitoring started with the first postoperative control (t4) and was always performed prior to hyperspectral imaging to avoid examination bias. The measurement time points are shown in Figure 1. In each case, clinical parameters of flap perfusion (color, temperature, re-capillarization time and tissue turgor) were assessed and documented by an experienced physician. Point values were assigned to each clinical category (color, temperature, re-capillarization time, turgor). Criteria for surgical re-exploration were met if total was ≥9 OR two variables scored 6 points OR flap color remained pale white or blue >60 min AND re-capillarization time was not detectable or <1 s (Table 1). Because clinical monitoring is still considered the gold standard, the final decision for re-exploration was always based on clinical assessment.


**Table 1.** Clinical flap evaluation system with point values.

*J. Pers. Med.* **2021**, *11*, 1101 3 of 16

**Figure 1.** Graphical study protocol shows the measurement time points (t0–t3) and time intervals (t4– t10), as well as the process of decision making for re-exploration surgery. Created with BioRender.com (accessed on 13 October 2021). **Figure 1.** Graphical study protocol shows the measurement time points (t0–t3) and time intervals (t4–t10), as well as the process of decision making for re-exploration surgery. Created with BioRender. com (accessed on 13 October 2021).

#### **Table 1.** Clinical flap evaluation system with point values. 2.2.2. Hyperspectral Monitoring

**Flap Color Flap Temperature Re-Capillarization Time Flap Turgor Total**  Pale white (3) Cold (2) Approx. 1 s (2) Soft (2) Pink (1) Body temperature (1) >2 s (1) Elastic (1) Red (2) Superheated (2) <1 s (3) Plump (2) Blue (3) No capillary refill (3) 2.2.2. Hyperspectral Monitoring In this study, a hyperspectral sensor system (TIVITATM Tissue System, Diaspective Vision GmbH, Pepelow, Germany) was used. The HSI sensors generate a three-dimensional (3D) data cube (hyperspectral cube), with the spatial information contained in the first two dimensions (resolution: 0.1 mm/pixel at 50 cm distance) and the spectral information in the third dimension (resolution: 5 nm). The method includes conventional as well as spectroscopic approaches to capture both spatial and spectral information of an image scene. While conventional RGB methods (red, green and blue) cover a limited wavelength spectrum, HSI is able to process electromagnetic wavelength spectra >740 nm [14]. Briefly, HSI is based on the assessment of contiguous spectra (i.e., light of different wavelengths) individually reemitted by molecules. These physicochemical raw data are then processed by computerized algorithms, specific for the respective molecule of interest (hyperspectral signatures), particularly haemoglobin, oxygenated haemoglobin and water [15,16]. Following HS-image recording over 10 s (s), additional 8 s are needed to compute a RGD (red, green and blue) truecolor image and additional four pseudo-color images, representing the parameters: tissue oxygen saturation [StO2 (0–100%)], near infrared perfusion index [NPI as arbitrary units (0– 100)] as well as distribution of haemoglobin [THI as arbitrary units (0–100)] and water [TWI as arbitrary units (0–100)] (Figure 2) [1,17]. Haemoglobin and its differentiation between its oxygenated and deoxygenated form plays a central role in HSI perfusion monitoring [15]. Since the absorbance of haemoglobin in the range from 570 to 590 nm is high, electromagnetic radiation of a shorter wavelength shows a lower penetration depth into tissue, thus In this study, a hyperspectral sensor system (TIVITATM Tissue System, Diaspective Vision GmbH, Pepelow, Germany) was used. The HSI sensors generate a three-dimensional (3D) data cube (hyperspectral cube), with the spatial information contained in the first two dimensions (resolution: 0.1 mm/pixel at 50 cm distance) and the spectral information in the third dimension (resolution: 5 nm). The method includes conventional as well as spectroscopic approaches to capture both spatial and spectral information of an image scene. While conventional RGB methods (red, green and blue) cover a limited wavelength spectrum, HSI is able to process electromagnetic wavelength spectra > 740 nm [14]. Briefly, HSI is based on the assessment of contiguous spectra (i.e., light of different wavelengths) individually re-emitted by molecules. These physicochemical raw data are then processed by computerized algorithms, specific for the respective molecule of interest (hyperspectral signatures), particularly haemoglobin, oxygenated haemoglobin and water [15,16]. Following HS-image recording over 10 s (s), additional 8 s are needed to compute a RGD (red, green and blue) truecolor image and additional four pseudo-color images, representing the parameters: tissue oxygen saturation [StO<sup>2</sup> (0–100%)], near infrared perfusion index [NPI as arbitrary units (0–100)] as well as distribution of haemoglobin [THI as arbitrary units (0–100)] and water [TWI as arbitrary units (0–100)] (Figure 2) [1,17]. Haemoglobin and its differentiation between its oxygenated and deoxygenated form plays a central role in HSI perfusion monitoring [15]. Since the absorbance of haemoglobin in the range from 570 to 590 nm is high, electromagnetic radiation of a shorter wavelength shows a lower penetration depth into tissue, thus microcirculation is detected at a depth up to 1 mm. StO<sup>2</sup> describes the relative oxygen saturation of blood in the microcirculatory system within superficial tissue layers, captures arterial and venous blood, and shows changes in oxygen supply and consumption directly in the tissue area measured. Thus, StO<sup>2</sup> represents the tissue oxygen saturation, which is mainly based on the blood volume in the venous part (75%) of the microcirculation and its oxygen saturation after delivery of oxygen to the tissue. Reference values are between 50–70% [18]. However, there are no thresholds, although corresponding studies are currently being conducted. Near-Perfusion-Index (NPI) describes the quality of blood flow which is determined by the relative oxygen saturation of the haemoglobin and the relative haemoglobin content in deep tissue layers (4 to 6 mm) [1]. In the software from the manufacturer, parameters are displayed in false colors from

red = high, through yellow and green, to blue = low. The Tissue-Haemoglobin-Index (THI) describes the relative amount of haemoglobin in the microcirculatory system. This parameter gives information on inflow and/or outflow disorders. Tissue Water Index (TWI) describes the relative water content in the assessed region of interest. We have described the importance of the parameters and their combination for perfusion assessment in detail in a previous publication [1]. haemoglobin content in deep tissue layers (4 to 6 mm) [1]. In the software from the manufacturer, parameters are displayed in false colors from red = high, through yellow and green, to blue = low. The Tissue-Haemoglobin-Index (THI) describes the relative amount of haemoglobin in the microcirculatory system. This parameter gives information on inflow and/or outflow disorders. Tissue Water Index (TWI) describes the relative water content in the assessed region of interest. We have described the importance of the parameters and their combination for perfusion assessment in detail in a previous publication [1].

microcirculation is detected at a depth up to 1 mm. StO2 describes the relative oxygen saturation of blood in the microcirculatory system within superficial tissue layers, captures arterial and venous blood, and shows changes in oxygen supply and consumption directly in the tissue area measured. Thus, StO2 represents the tissue oxygen saturation, which is mainly based on the blood volume in the venous part (75%) of the microcirculation and its oxygen saturation after delivery of oxygen to the tissue. Reference values are between 50–70% [18]. However, there are no thresholds, although corresponding studies are currently being conducted. Near-Perfusion-Index (NPI) describes the quality of blood flow which is determined by the relative oxygen saturation of the haemoglobin and the relative

*J. Pers. Med.* **2021**, *11*, 1101 4 of 16

**Figure 2.** HSI shows the blood flow measured in the left ulnar flap raise site (t0) with the region of interest (ROI) marked manually (green line, RGB image). The quality of blood flow is indicated by false colors ranging from blue (low) to red (high). On the left, quantification of ROI is listed and shows mean values of the respective parameters (StO2, NPI, THI, TWI). The number of assessed pixels and the corresponding amount of StO2 and NPI within ROI (*Y*-axis) is represented as a bar chart. *2.3. Statistics*  **Figure 2.** HSI shows the blood flow measured in the left ulnar flap raise site (t0) with the region of interest (ROI) marked manually (green line, RGB image). The quality of blood flow is indicated by false colors ranging from blue (low) to red (high). On the left, quantification of ROI is listed and shows mean values of the respective parameters (StO<sup>2</sup> , NPI, THI, TWI). The number of assessed pixels and the corresponding amount of StO<sup>2</sup> and NPI within ROI (*Y*-axis) is represented as a bar chart.

#### Raw data sets were saved in Excel® sheets (Microsoft Corporation, Redmond, WA, USA) *2.3. Statistics*

and subsequently transferred into SPSS Statistics® (version 23.0.0.2, MacOS X; SPSS Inc., IBM Corporation, Armonk, NY, USA). Data were expressed as mean (m), standard deviation (SD±), minimum (min), maximum (max) and standard error of the mean (SEM). Normal distribution was checked using non-parametric Shapiro–Wilk-test(+) and Kolmogorov– Smirnov Test. In addition to the descriptive analysis, the dependency analysis included tests to detect/exclude differences and correlations. Results were analysed for statistical significance by the use of analysis of variance (ANOVA(#)), unpaired non-parametric Mann– Whitney U-tests(\$), Wilcoxon Signed Ranks test(§) and Students' *t*-test(\*). To investigate whether the means of several dependent samples differ, Wilcoxon matched-pairs signed rank tests(\*\*) were performed. Correlations between two categorical variables were tested using the Pearson Chi-Square Test (+) or, in the case of expected cell frequencies <5, using Fisher's Exact Test(++). The eta-squared coefficient as a measure of correlation measures the extent to which the total variance of a dependent metric variable is explained by an independent nominal variable. The partial Eta square (Eta) shows how much % of the variation of Raw data sets were saved in Excel® sheets (Microsoft Corporation, Redmond, WA, USA) and subsequently transferred into SPSS Statistics® (version 23.0.0.2, MacOS X; SPSS Inc., IBM Corporation, Armonk, NY, USA). Data were expressed as mean (m), standard deviation (SD±), minimum (min), maximum (max) and standard error of the mean (SEM). Normal distribution was checked using non-parametric Shapiro–Wilk-test(+) and Kolmogorov–Smirnov Test. In addition to the descriptive analysis, the dependency analysis included tests to detect/exclude differences and correlations. Results were analysed for statistical significance by the use of analysis of variance (ANOVA(#)), unpaired non-parametric Mann–Whitney U-tests(\$), Wilcoxon Signed Ranks test(§) and Students' *t*-test(\*). To investigate whether the means of several dependent samples differ, Wilcoxon matched-pairs signed rank tests(\*\*) were performed. Correlations between two categorical variables were tested using the Pearson Chi-Square Test(+) or, in the case of expected cell frequencies < 5, using Fisher's Exact Test(++). The eta-squared coefficient as a measure of correlation measures the extent to which the total variance of a dependent metric variable is explained by an independent nominal variable. The partial Eta square (Eta) shows how much % of the variation of "Duration of surgery" can be explained by the revision status (Group-1 vs. Group-2). The *p*-values of ≤0.05 were termed significant. Line charts with plotted means ±SD, pie charts, aligned dot plots and boxplots were used for illustration purposes. Due to the small number of similar studies, as well as the lack of cut-off values, case number planning could not be performed [19,20].

### **3. Results**

A total of 54 primary and 11 secondary reconstructions were performed. Flaps included Radial-(RFF, 24) and Ulnar-(UFF, 16) Forearm flaps, Osteocutaneous Fibula flaps (OMFF, 16), Latissimus Dorsi flaps (LDF, 4), Osteocutaneous Scapula flaps (OMSF, 3) and 2 Upper Arm flaps (UAF). Affected regions included the tongue (4), cheek (18), floor of the mouth (10), alveolar ridge (2), soft (3) and hard palate (4), mandible (18), midface (4) and

neurocranium (2). Recipient vessels were as follows: superior thyroid (43) (end-to-end), lingual (11) (end-to-end), external carotid (7) (end-to-side) and the facial artery (4) (endto-end). Out of 65 flaps (two patients received 2 microvascular flaps each), re-exploration was performed in 19 cases (Group-2(R+)) due to a clinically apparent perfusion disturbance, of which in one case no cause could be found intraoperatively. In the latter, because there was improved flap perfusion after reopening of the neck in both, clinical assessment and HSI, this case was considered kinking of the pedicle. A total of seven complete flap failures occurred, resulting in a salvage rate of 63% regardless of cause (haematoma, kinking, arterial or venous thrombosis). There was no correlation between the reconstruction regime (primary or secondary)(++), the irradiation status(++), the arterial recipient vessel type(+) or the duration of surgery (Eta = 0.06) and the occurrence of poor perfusion (need for revision) (Table 2).


**Table 2.** Baseline data showing patient and flap characteristics.

<sup>+</sup> = Chi-Square test; ++ = Fisher's Exact Test; Eta = Partial Eta square (explained in Section 2.3. *Statistics*).

#### *3.1. Monitoring*

3.1.1. Clinical Monitoring Characteristics

The true-positive rate of clinical assessment for detection of perfusion defect was 100% with 19/19 flaps. The distribution of scores obtained using the clinical scoring system is shown in Figure 3.

The true-positive rate of clinical assessment for detection of perfusion defect was 100% with 19/19 flaps. The distribution of scores obtained using the clinical scoring system is shown

**Figure 3.** Bar chart shows the point total of clinical assessment from each impaired flap (Group-2(R+) that led to re-exploration. **Figure 3.** Bar chart shows the point total of clinical assessment from each impaired flap (Group-2(R+) that led to re-exploration.

3.1.2. General HSI Characteristics for Non-Revised and Revised Flaps Oxygen Saturation of Haemoglobin (StO2) 3.1.2. General HSI Characteristics for Non-Revised and Revised Flaps Oxygen Saturation of Haemoglobin (StO2)

*J. Pers. Med.* **2021**, *11*, 1101 6 of 16

Cause for malperfusion

3.1.1. Clinical Monitoring Characteristics

*3.1. Monitoring* 

in Figure 3.

Superior thyroid 30 (70%) 13 (30%) 43 Lingual 8 (72.7%) 3 (27.3%) 11 External carotid 5 (71%) 2 (29%) 7 Facial 3 (75%) 1 (25%) 4 Duration of surgery (minutes) 543.7 ± 126.5 527.3 ± 128.1 0.06(Eta)

Venous thrombosis 8 (42.1%) 8 Arterial thrombosis 8 (36.8%) 8 Haematoma 3 (4.6%) 3 Kinking 1 (5.3%) 1 + = Chi-Square test; ++ = Fisher's Exact Test; Eta = Partial Eta square (explained in Section 2.3. *Statistics*).

Except t0 (origin), t1 (flap raise), t2 (anastomosis) and t3 (flap inset), StO2 was significantly lower in Group-2(R+) at any time after flap inset including t4 (0–12 h; *p* < 0.001), t5 (12–24 h; *p* = 0.034), t6 (24–36 h; *p* < 0.001), t7 (36–48 h; *p* = 0.008), t8 (48–60 h, *p* < 0.001), and t10 ( > 72 h, *p* = 0.004) (Figure 4A). Compared with the reference site (StO2Δreference), StO2 decreased (%) significantly in Group-2(R+) during t4 (*p* = 0.004), t5 (*p* < 0.001), t6 (*p* < 0.001), t7 (*p* = 0.002), t8 (*p* = 0.002), and t10 (*p* = 0.006( \*) ) compared to Group-1(R−) (Figure 4B). Compared with flap inset (t3), StO2 significantly (*p* = 0.011( \*) ) decreased at t4 in Group-2(R+) when compared to Group-1(R−) (Figure 4C). Regarding the difference to the pre-value (StO2Δprevalue), StO2 decreased significantly in Group-2(R+) during t4 (*p* < 0.001( \*) ), t5 (*p* = 0.021( \*) ), and t6 (*p* < 0.001( \*) ) when compared to Group-1(R−) , respectively (Figure 4D). Except t0 (origin), t1 (flap raise), t2 (anastomosis) and t3 (flap inset), StO<sup>2</sup> was significantly lower in Group-2(R+) at any time after flap inset including t4 (0–12 h; *p* < 0.001), t5 (12–24 h; *p* = 0.034), t6 (24–36 h; *p* < 0.001), t7 (36–48 h; *p* = 0.008), t8 (48–60 h, *p* < 0.001), and t10 (>72 h, *p* = 0.004) (Figure 4A). Compared with the reference site (StO2∆reference), StO<sup>2</sup> decreased (%) significantly in Group-2(R+) during t4 (*p* = 0.004), t5 (*p* < 0.001), t6 (*p* < 0.001), t7 (*p* = 0.002), t8 (*p* = 0.002), and t10 (*p* = 0.006( \* ) ) compared to Group-1(R−) (Figure 4B). Compared with flap inset (t3), StO<sup>2</sup> significantly (*p* = 0.011( \* ) ) decreased at t4 in Group-2(R+) when compared to Group-1(R−) (Figure 4C). Regarding the difference to the pre-value (StO2∆pre-value), StO<sup>2</sup> decreased significantly in Group-2(R+) during t4 (*p* < 0.001( \* ) ), t5 (*p* = 0.021( \* ) ), and t6 (*p* < 0.001( \* ) ) when compared to Group-1(R−) , respectively (Figure 4D). *J. Pers. Med.* **2021**, *11*, 1101 7 of 16

**Figure 4.** Bar chart with means (±SD) show StO2 at different measurement timepoints (T0–T3)/time intervals (t4–t10) as (**A**) (StO2 mean), (**B**) StO2 drop to reference (%) StO2 drop to the respective reference site (StO2Δreference), (**C**) StO2 drop to t3 (StO2Δt3), and (**D**) in relation to the pre-measurement (StO2Δpre-value). Means are shown scalar. Asterisk (\*) marks existing significance. **Figure 4.** Bar chart with means (±SD) show StO<sup>2</sup> at different measurement timepoints (T0–T3)/time intervals (t4–t10) as (**A**) (StO<sup>2</sup> mean), (**B**) StO<sup>2</sup> drop to reference (%) StO<sup>2</sup> drop to the respective reference site (StO2∆reference), (**C**) StO<sup>2</sup> drop to t3 (StO2∆t3), and (**D**) in relation to the pre-measurement (StO2∆pre-value). Means are shown scalar. Asterisk (\*) marks existing significance.

). The same was seen within t6 (*p* = 0.008(

greater decrease in NPI (%) (NPIΔreference) at t4 (*p* = 0.023(

\*)

anastomosis (t2), and after flap insertion (t3). Within the first 12 h after flap inset, the Near-Infrared Perfusion-Index (NPI) was significantly lower in Group-2(R+) compared to Group-1(R−)

period containing only two compromised flaps that were subsequently re-explored (Figure 5A). Compared with the reference measurement site, Group-2(R+) revealed a significantly

5B). Compared with t3 (NPIΔt3), NPI decreased significantly more in Group-2(R+) at t4 (*p* =

measurement (NPIΔpre-value), the NPI drop was significantly higher in Group-2(R+) within

) than in Group-1(R−)

\*)

\*)

\*)

), t6 (*p* = 0.044(\$)) and t8 (*p* = 0.002(

) and t8 (*p* = 0.045(

\*)

\*)

) compared to Group-1(R−)

(Figure 5C). Compared with the pre-

), with the latter

) than in Group-

(Figure

NIR-Perfusion Index (NPI)

(*p* = 0.024(

0.016( \*)

1(R−)

\*)

(Figure 5D).

) and t6 (*p* = 0.011(

the measurement intervals t4 (*p* < 0.001(

NIR-Perfusion Index (NPI)

There was no difference in Near-Infrared Perfusion Index (NIR-P) between Group-1 (R−) and Group-2(R+) before flap harvesting (T0), after flap raise (t1), after microvascular anastomosis (t2), and after flap insertion (t3). Within the first 12 h after flap inset, the Near-Infrared Perfusion-Index (NPI) was significantly lower in Group-2(R+) compared to Group-1(R−) (*p* = 0.024( \* ) ). The same was seen within t6 (*p* = 0.008( \* ) ) and t8 (*p* = 0.045( \* ) ), with the latter period containing only two compromised flaps that were subsequently reexplored (Figure 5A). Compared with the reference measurement site, Group-2(R+) revealed a significantly greater decrease in NPI (%) (NPI∆reference) at t4 (*p* = 0.023( \* ) ) compared to Group-1(R−) (Figure 5B). Compared with t3 (NPI∆t3), NPI decreased significantly more in Group-2(R+) at t4 (*p* = 0.016( \* ) ) and t6 (*p* = 0.011( \* ) ) than in Group-1(R−) (Figure 5C). Compared with the pre-measurement (NPI∆pre-value), the NPI drop was significantly higher in Group-2(R+) within the measurement intervals t4 (*p* < 0.001( \* ) ), t6 (*p* = 0.044(\$)) and t8 (*p* = 0.002( \* ) ) than in Group-1(R−) (Figure 5D). *J. Pers. Med.* **2021**, *11*, 1101 8 of 16

**Figure 5.** Bar chart with means (±SD) show NPI at different measurement timepoints (t0–t3)/time intervals (t4–t10) as (**A**) (NPI mean), (**B**) NPI drop to reference (%) NPI drop to the respective reference site (NPIΔreference), (**C**) NPI drop to t3 (NPIΔt3), and (**D**) in relation to the pre-measurement (NPIΔpre-value). Means are shown scalar. Asterisk (\*) marks existing significance. ANOVA (#). (**A**) (NPI mean), (**B**) NPI drop to reference (%) NPI drop to the respective reference site (NPI∆reference), (**C**) NPI drop to t3 (NPI∆t3), and (**D**) in relation to the pre-measurement (NPI∆pre-value). Means are shown scalar. Asterisk (\*) marks existing significance. ANOVA (#).

Tissue-Haemoglobin-Index (THI) At the measurement time points (T0-t3)/within the measurement intervals (t4 and t5), the Tissue-Haemoglobin-Index (THI)

Tissue-Haemoglobin Index (THI) was not different between Group-2(R+) and Group-1(R−) . At t6 (*p* = 0.010( \*) ), t7 (*p* = 0.006( \*) ) and t8 (*p* = 0.033( \*) ), THI was significantly increased in Group-2(R+) (Figure 6A). In proportion to the reference site (THIΔreference), THI was significantly increased in Group-2(R+) compared with Group-1(R) during t6 (*p* = 0.048(\$)) and t7 (*p* = 0.048(\$)) (Figure 6B). Compared with flap inset (THIΔt3), THI was significantly higher in Group-2(R+) than in Group-1(R−) during t4 (*p* = 0.011(\$)) and t7 (*p* = 0.012(\$)) (Figure 6C). Compared with the pre-measurement (THIΔpre-value), the percentage increase in THI was significantly higher in Group-2(R+) within the measurement interval t4 (*p* < 0.001(\$)) than in Group-1(R−) (Figure 6D). **Figure 5.** Bar chart with means (±SD) show NPI at different measurement timepoints (t0–t3)/time intervals (t4–t10) as At the measurement time points (T0-t3)/within the measurement intervals (t4 and t5), the Tissue-Haemoglobin Index (THI) was not different between Group-2(R+) and Group-1 (R−) . At t6 (*p* = 0.010( \* ) ), t7 (*p* = 0.006( \* ) ) and t8 (*p* = 0.033( \* ) ), THI was significantly increased in Group-2(R+) (Figure 6A). In proportion to the reference site (THI∆reference), THI was significantly increased in Group-2(R+) compared with Group-1(R) during t6 (*p* = 0.048(\$)) and t7 (*p* = 0.048(\$)) (Figure 6B). Compared with flap inset (THI∆t3), THI was significantly higher in Group-2(R+) than in Group-1(R−) during t4 (*p* = 0.011(\$)) and t7 (*p* = 0.012(\$)) (Figure 6C). Compared with the pre-measurement (THI∆pre-value), the percentage increase in THI was significantly higher in Group-2(R+) within the measurement interval t4 (*p* < 0.001(\$)) than in Group-1(R−) (Figure 6D).

**Figure 6.** Bar chart with means (±SD) show THI at different measurement timepoints (t0–t3)/time intervals (t4–t10) as (**A**) (THI mean), (**B**) THI drop to reference (%) THI drop to the respective reference site (THIΔreference), (**C**) THI drop to t3 (THIΔt3), and (**D**) in relation to the pre-measurement (THIΔpre-value). Means are shown scalar. Asterisk (\*) marks existing significance. **Figure 6.** Bar chart with means (±SD) show THI at different measurement timepoints (t0–t3)/time intervals (t4–t10) as (**A**) (THI mean), (**B**) THI drop to reference (%) THI drop to the respective reference site (THI∆reference), (**C**) THI drop to t3 (THI∆t3), and (**D**) in relation to the pre-measurement (THI∆pre-value). Means are shown scalar. Asterisk (\*) marks existing significance.

#### Tissue-Water-Index (TWI) From t0 to t6, there was no difference in the water content (TWI) comparing Group-1(R−) Tissue-Water-Index (TWI)

and Group-2(R+). At t7 and t8, TWI was found to be significantly lower (*p* = 0.045( \*) ; *p* = 0.007( \*) ) in the group of poorly perfused flaps (Figure 7A). Compared with the reference site (TWIΔreference), TWI was significantly lower at t8 (*p* = 0.035(\$)) in Group-2(R+) than in Group-1(R−) (Figure 7B). Compared to flap inset (THIΔt3), TWI was significantly lower in Group-2(R+) at t6 (*p* = 0.015(\$)), t7 (*p* = 0.008(\$)) and t8 (*p* = 0.002(\$)) (Figure 7C). Compared with the premeasurement (TWIΔpre-value), TWI decreased significantly more in Group-2(R+) at t8 (*p* = 0.002(\$)) (Figure 7D). From t0 to t6, there was no difference in the water content (TWI) comparing Group-1 (R−) and Group-2(R+). At t7 and t8, TWI was found to be significantly lower (*p* = 0.045( \* ) ; *p* = 0.007( \* ) ) in the group of poorly perfused flaps (Figure 7A). Compared with the reference site (TWI∆reference), TWI was significantly lower at t8 (*p* = 0.035(\$)) in Group-2(R+) than in Group-1(R−) (Figure 7B). Compared to flap inset (THI∆t3), TWI was significantly lower in Group-2(R+) at t6 (*p* = 0.015(\$)), t7 (*p* = 0.008(\$)) and t8 (*p* = 0.002(\$)) (Figure 7C). Compared with the pre-measurement (TWI∆pre-value), TWI decreased significantly more in Group-2 (R+) at t8 (*p* = 0.002(\$)) (Figure 7D).

#### Duration until Signs of Malperfusion

Independent of the time point/interval, mean values of StO<sup>2</sup> and NPI as well as their drop rate (∆reference) differed significantly between Group-1(R-) and Group-2(R+) (Figure 8). StO2, as well as StO2∆reference were significantly lower in Group-2(R+) (32.6% <sup>±</sup> 9.8; <sup>−</sup>38.1% <sup>±</sup> 18.2) than in Group-1(R−) (43.2% ± 10.3; −18.3% ± 15.9) (*p* < 0.001). The same was seen with NPI (Group-1(R−) : 42.8 <sup>±</sup> 9.8; Group-2(R+): 32.9 <sup>±</sup> 12.8) and NPI∆reference (Group-1(R−) : 8.8 <sup>±</sup> 25.7; Group-2(R+): <sup>−</sup>13.4% <sup>±</sup> 36.9) (*<sup>p</sup>* < 0.001( \* ) ).

10 of 16

**Figure 7.** Bar chart with means (±SD) show TWI at different measurement timepoints (t0–t3)/time intervals (t4–t10) as (**A**) (TWI mean), (**B**) TWI drop to reference (%) TWI drop to the respective reference site (TWIΔreference), (**C**) TWI drop to t3 (TWIΔt3), and (**D**) in relation to the pre-measurement (TWIΔpre-value). Means are shown scalar. Asterisk (\*) marks existing significance. ANOVA (#). **Figure 7.** Bar chart with means (±SD) show TWI at different measurement timepoints (t0–t3)/time intervals (t4–t10) as (**A**) (TWI mean), (**B**) TWI drop to reference (%) TWI drop to the respective reference site (TWI∆reference), (**C**) TWI drop to t3 (TWI∆t3), and (**D**) in relation to the pre-measurement (TWI∆pre-value). Means are shown scalar. Asterisk (\*) marks existing significance. ANOVA (#). *J. Pers. Med.* **2021**, *11*, 1101 11 of 16

Duration Until Signs of Malperfusion

**Figure 8.** Boxplot showing the distribution of StO2, StO2Δreference, NPI and NPIΔreference comparing flaps with and without revision following poor perfusion. Asterisk (\*) marks existing significance. **Figure 8.** Boxplot showing the distribution of StO2, StO2∆reference, NPI and NPI∆reference comparing flaps with and without revision following poor perfusion. Asterisk (\*) marks existing significance.

Of a total of 19 compromised flaps, 12 (36.8%) occurred within the first 24 h postoperatively. The exact distribution is shown in Figure 9A. To calculate the duration from

\*\*)

) (Figure 9B).

dichotomization. If there was a negative deviation (<StO2; <NPI; >StO2Δreference; >NPIΔreference) from one of the parameters, the respective measurement time was counted as the detection time. Overall, the mean time from flap inset to decision making for re-exploration based on clinical assessment was 23.1 ± 21.9 h. In contrast, the average time from flap insertion to the appearance of hyperspectral criteria of inferior perfusion (StO2 ≤ 32% OR StO2 diff > −38% OR NPI ≤ 32.9 OR NPI diff. ≥ −13.4%) was

18.2 ± 19.4 h, resulting in a difference of 4.8 ± 5 h (*p* < 0.001(

Of a total of 19 compromised flaps, 12 (36.8%) occurred within the first 24 h postoperatively. The exact distribution is shown in Figure 9A. To calculate the duration from flap inset (t3) to the detection of malperfusion in HSI, the mean values of StO<sup>2</sup> (32.6%), StO2∆reference (−38.1%), NPI (32.9) and NPI∆reference (−13.4%) were used for dichotomization. If there was a negative deviation (<StO2; <NPI; >StO2∆reference; >NPI∆reference) from one of the parameters, the respective measurement time was counted as the detection time. Overall, the mean time from flap inset to decision making for re-exploration based on clinical assessment was 23.1 ± 21.9 h. In contrast, the average time from flap insertion to the appearance of hyperspectral criteria of inferior perfusion (StO<sup>2</sup> ≤ 32% OR StO<sup>2</sup> diff > −38% OR NPI ≤ 32.9 OR NPI diff. ≥ −13.4%) was 18.2 ± 19.4 h, resulting in a difference of 4.8 <sup>±</sup> 5 h (*<sup>p</sup>* < 0.001( \*\*) ) (Figure 9B). *J. Pers. Med.* **2021**, *11*, 1101 12 of 16

**Figure 9.** (**A**) Pie chart showing actual duration to clinical manifestation of flap malperfusion and subsequent re-exploration with associated time interval. (**B**) Aligned dot plot shows duration (h) from flap insertion to clinical (left) and hyperspectral detection of flap malperfusion. **Figure 9.** (**A**) Pie chart showing actual duration to clinical manifestation of flap malperfusion and subsequent re-exploration with associated time interval. (**B**) Aligned dot plot shows duration (h) from flap insertion to clinical (left) and hyperspectral detection of flap malperfusion.

#### **4. Discussion 4. Discussion**

In this study, monitoring of free flap perfusion in the head and neck region was compared between clinical assessment and hyperspectral imaging. As the major finding, malperfusion could be detected at a mean of 4.8 h earlier with the help of Hyperspectral Imaging (HSI) when compared to clinical examination. In addition, general information on the perfusion characteristics of the included flap types were presented. Postoperative flap monitoring is a key component for successful free tissue transfer In this study, monitoring of free flap perfusion in the head and neck region was compared between clinical assessment and hyperspectral imaging. As the major finding, malperfusion could be detected at a mean of 4.8 h earlier with the help of Hyperspectral Imaging (HSI) when compared to clinical examination. In addition, general information on the perfusion characteristics of the included flap types were presented.

whereby early detection of malperfusion is the pivotal criterion for treatment success as only early detection can ensure timely re-exploration to avoid flap failure [21]. One measure of this is the overall salvage rate which is reported to be from 60% up to 80% on average (63% in the present study) [22,23]. However, this neglects the subdivision according to the underlying cause (venous or arterial), with the salvage rate being significantly lower for arterial thrombosis [24]. To overcome the issue of delayed re-exploration due to late detection of malperfusion, several monitoring methods have been developed during the last decades, including the implantable Doppler, color duplex sonography, near-infrared spectroscopy, laser Doppler flowmetry, fluorescence angiography and microdialysis [10,25–28]. The ideal flap monitoring technique should be continuous, accurate, cost-efficient, non-invasive, safe, objective, recordable, reliable, reproducible, sensitive, highly spatially resolved, easy to use/interpret and applicable to all flap types [29]. As currently no single traditional monitoring technique meets all these requirements, clinical examination, as the least reproducible and little to no objective technique, still remains the most frequently used [10]. However, clinical assessment depends on evaluator experience and it is only reliable when the flap color changes significantly into pale or blueish [30]. In addition, technical monitoring support is only used by 30% of the surgeons whereas the Doppler (handheld or implanted) is the most commonly used method for free flap monitoring [31]. In addition to increased costs Postoperative flap monitoring is a key component for successful free tissue transfer whereby early detection of malperfusion is the pivotal criterion for treatment success as only early detection can ensure timely re-exploration to avoid flap failure [21]. One measure of this is the overall salvage rate which is reported to be from 60% up to 80% on average (63% in the present study) [22,23]. However, this neglects the subdivision according to the underlying cause (venous or arterial), with the salvage rate being significantly lower for arterial thrombosis [24]. To overcome the issue of delayed re-exploration due to late detection of malperfusion, several monitoring methods have been developed during the last decades, including the implantable Doppler, color duplex sonography, near-infrared spectroscopy, laser Doppler flowmetry, fluorescence angiography and microdialysis [10,25–28]. The ideal flap monitoring technique should be continuous, accurate, cost-efficient, non-invasive, safe, objective, recordable, reliable, reproducible, sensitive, highly spatially resolved, easy to use/interpret and applicable to all flap types [29]. As currently no single traditional monitoring technique meets all these requirements, clinical examination, as the least reproducible and little to no objective technique, still remains the most frequently used [10]. However, clinical assessment depends on evaluator experience and it is only reliable when the flap color changes significantly into pale or blueish [30]. In addition, technical monitoring support is only used by 30% of the surgeons whereas

due to consumables and acquisition, the experience-dependent classification of measurement results is also cited as a limitation of technology-based monitoring techniques [30]. When

use requires special training. The implantable doppler is placed distal to an anastomosis in contact to the vessel, to allow continuous measurement of blood flow [32]. However, it is an invasive technique that does not allow quantitative measurement. Laser Doppler flowmetry

the Doppler (handheld or implanted) is the most commonly used method for free flap monitoring [31]. In addition to increased costs due to consumables and acquisition, the experience-dependent classification of measurement results is also cited as a limitation of technology-based monitoring techniques [30]. When color duplex ultrasound is used, microanastomotic vessels can also be visualized in embedded flaps. It is a non-invasive and quantitative flap monitoring technique, whereby its use requires special training. The implantable doppler is placed distal to an anastomosis in contact to the vessel, to allow continuous measurement of blood flow [32]. However, it is an invasive technique that does not allow quantitative measurement. Laser Doppler flowmetry (LDF) is also frequently used for monitoring of free flaps whereby probes are implanted or applied to the flap surface. In this context, Yoshino et al. could not distinguish between arterial and venous malperfusion when monitoring 37 intraoral free flaps with LDF [33]. In contrast, Muecke et al. demonstrated in an animal study that the combination of at least two different technical monitoring methods (ICG and flowmeter) improved the monitoring of critical and/or buried flaps, whereas the use of the multispectral technique (O2C) diminished the predictive value [34]. In contrast, Hölzle et al. described the successful use of O2C (Oxygen-to-see, LEA-Medizintechnik GmbH, Giessen, Germany), a device combination of laser doppler flowmeter and tissue spectrophotometry, to monitor free flaps and to detect flap malperfusion at an early stage [35]. Compared to multispectral methods, the use of the complete and high-resolution spectrum (hyperspectral) results in significantly increased reliability and reproducibility of parameter determination [36]. Providing noncontact, non-invasive measurements, HSI allows perfusion monitoring status in different tissue layers/depths through pictorial representation of parameters calculated from the spectra (tissue oxygenation saturation (StO2), Near-Infrared Perfusion Index (NPI), Tissue Haemoglobin Index (THI), and Tissue Water Index (TWI)). Using the THI, additional conclusions can be drawn about the underlying cause of perfusion failure (venous versus arterial perfusion compromise) [1]. However, in the present study there was no significant correlation between StO<sup>2</sup> or NPI and THI. There were no signs of impaired flap perfusion at flap origin (t0), at flap/pedicle preparation (t1), directly after anastomosis (t2) or after flap inset (t3), as StO2, NPI, THI and TWI did not differ significantly between Group 1(R−) and 2(R+). The characteristics of the blood flow dynamic of free flaps prior to the actual tissue transfer as well as its possible reasons have already been presented in a previous publication [1]. During t4, StO<sup>2</sup> was significantly lower on average in Group 2(R+) (34.9 <sup>±</sup> 10%) than in Group 1(R−) (49.1 ±13.5% *p* < 0.001). This, in turn, is in line with the earlier findings of our group, as well as with those of Kohler et al., defining 40% as the lower limit of StO<sup>2</sup> in normal perfused flaps [1,37]. In both of these pilot studies, the low number of malperfused flaps (<10) must be taken into account with regard to their significance. Deep tissue perfusion (represented by NPI) was also significantly lower (37.3 ± 7.5 *p* < 0.001) in Group 2(R+) within the first 12 h (t4) in contrast to Group 1(R−) (43.75 ± 10.52). No significant differences were found for THI and TWI.

Since StO<sup>2</sup> and NPI are not independent of systemic total haemoglobin (regarding the relationship between Hb and StO2), we consider the drop rate (∆reference), as a measure of the systemic blood flow situation (adjacent reference site), providing high predictive value. This is in accordance with Keller et al. who stated the drop rate as a meaningful instrument [38]. In comparison between group 1(R−) and 2(R+), both StO<sup>2</sup> <sup>∆</sup>reference and NPI∆reference were overall (t4–t10) significantly lower in group 2(R+) (Figure 8). Previous studies on medical HSI were able to investigate its application in the field of visceral surgery, as well as plastic reconstructive surgery. In this context Barberio et al. implemented HSI as an intraoperative surgical guidance tool, using its capability of accurate detection and visualization of perfusion changes in the region of ischemic bowel segments [39]. The same group was able to demonstrate the successful usage of HSI and confocal laser endomicroscopy (CLE) for perfusion monitoring in esophageal surgery [40]. In a preclinical animal study, Chin et al. demonstrated the successful use of hyperspectral imaging for early detection of malperfusion in random axial flaps [41], as well as Grambow et al. revealed real-time perfusion monitoring of the rats' hind limb after vessel transection and re-anastomosis [12]. Recent approaches have been able to successfully perform automated tissue classification and differentiation ex- and in-vivo based on hyperspectral cubes using deep learning algorithms (neural networks and computer vision) [14,42,43].

Disadvantages of HSI are a relevant dependence on ambient illumination, as well as the lack of applicability in heavily pigmented individuals due to extended light absorption. The issue of illumination particularly affects intraoral skin islands, but these can probably be better examined in the near future with a newly developed endoscope variant of the system used. Furthermore, the number of microvascular flaps included must be mentioned as a study-specific limitation, although the crucial number of poorly perfused flaps (19), presents a valuable and representative collective of the main subject. While the sensitivity to detect malperfusion is the same for clinical monitoring and HSI (100%), we demonstrated that HSI indicates poor perfusion significantly earlier (4.8 h).
