Flap failure prediction in microvascular tissue reconstruction using machine learning algorithms

lNTRODUCTlON

Microvascular reconstruction surgery is a commonly used technique to treat various defects, including remodeling after mastectomy[1], head and neck trauma repair[2], and profound burn tissue remodeling[3]. However, even though this technique is quite robust, many adverse complications arise after the reconstruction procedure, such as postoperative incision infection and reoperation[4,5]. Among these complications, the failure of flap transplantation after microvascular tissue reconstruction is the most important event, as it is associated with the arterial blood supply, ischemia-reperfusion, and venous return of the flap[6,7]. Despite the rare occurrence of flap failure, it can result in devastating consequences for patients, such as permanent scarring of the face and breast. Moreover, it increases complication in postoperative care, length of hospital stays, financial burden, and mental stress to the patients[8,9]. Therefore, it is important to identify the relevant factors and screen out high-risk patients before surgery, which might result in flap failure.

Previous studies have analyzed the multifactorial aspect of flap failure[10,11]. Associated preoperative risk factors include, but are not limited to age, smoking, diabetes, hypertension, and obesity[12,13]. Related intraoperative factors included the surgeon’s lack of experience and the choice of free flaps[14]. However, these studies were based on traditional logistic regression methods and were limited to nonlinearity and variable set[15]. Of note, precise, logistic regression analysis assumes that variables are linearly correlated, and therefore potential nonlinear interactions can compromise the outcome[15,16]. Moreover, only a small number of variables could be included in the analysis,overlooking the many potentially relevant factors[16]. These deficiencies in the analytical methods needs to be addressed using an advanced algorithm. Therefore, the recently emerging algorithms of machine learning might be a better option for the data analysis.

Machine learning, a branch of artificial intelligence (AI), literally meaning where machines can understand and learn from data to make decisions like humans[17,18]. In 2017, an AI called AlphaGo won worldwide attention by beating the international GO master Li Shiming. Due to its advantages in computational capacity and problem-solving techniques, machine learning has been widely used in medicine for many purposes, including the interpretation of test results[19], diagnosis of skin diseases[20], pathology[21], prediction of adverse complications[22], and the prognosis of cancer patients[23].However, in plastic surgery the use of clinical application of machine learning is still rare[24]. Therefore,this study aimed to apply AI in the field of plastic surgery, assessing the factors associated with the prognosis of microvascular tissue reconstruction for identifying high-risk patients with flap failure.

The schoolbag which is on the sofa is Tom’s.（沙發上的那個書包是湯姆的。）which在此句中做主語。

MATERlALS AND METHODS

Study population

A total of 946 consecutive patients were recruited in the study from January 1, 2006 to December 12,2020. These recruited patients underwent microvascular tissue reconstruction of free flap for head and neck, breast, back, and extremity at the Department of Plastic Surgery of Affiliated Hospital in Guangdong Medical University. Exclusion criteria included: patients with more than 30% data loss and who refused surgical treatment. Inclusion criteria/variables included: (1) Preoperative variables such as sex, age, body mass index (BMI), smoking, alcohol use, blood pressure, medication history, complications, laboratory findings, preoperative chemotherapy, preoperative radiotherapy, free flap location,and recipient surgical site; and (2) Intraoperative variables like duration of operation, duration of anesthesia, hypotensive events, use of vasoactive agents, duration of flap ischemia, number of vascular anastomoses, use of venous grafts, and surgeon’s experience measured as the time since the flap procedures.

Statistical analysis

Open-source software Python (version 3.6) and Scikit-learn package (https://scikit-learn.org/) were used for the data processing and analysis. Univariate analyses were done using the

and Fisher’s exact tests for categorical variables, whereas the

-test and Mann-Whitney

tests were used for the continuous variables. A subset of data was usually selected from the entire database for model training to train a suitable algorithm. The rest of the subset was used for the performance test of the model.Conceptually, the whole data set was divided into a training and testing subset according to the ratio of 5:5. Then, GridSearch was performed with the 5-fold cross-validation, where the training data set was further split into five parts and five repetitions. At each repetition, there were four random parts that served as the training set, whereas the remaining part served as the testing set. Multivariable regression was performed for the most critical variables in the random forest model to identify the risk factors in the traditional logistic regression model. A

value less than 0.05 was considered statistically significant.

Machine learning technique

Gradient boosting is a supervised machine learning technique for solving regression and classification problems that yield predictive models in the form of an ensemble of weak predictive models (

, decision trees). Through pooling weak predictive models into a more powerful and reliable prediction model, the gradient tree boosting technique incorporated in the eΧtreme Gradient Boosting system becomes a robust machine learning classifier.

The random forest classifier, one of the most used techniques in the data mining or automatic learning, was developed from the training data set using the python programming software.Random forest, introduced by Ishwaran, was used as decision tool based on a binary tree. It uses a branching structure like a binary tree to form a decision model and analyze possible results. Each node in these binary tree structures represents a decision (based on selected variables), whereas the two branches of the node represent the two kinds of classification results. Each branch produces two leaf nodes and other subtrees, depending on the classification when the variable is analyzed. For assessing the variables importance, variables in the random forest are determined by the average distance of the branching nodes in the tree structure from the roots. Thus, the higher a variable is in an inverted binary tree, the closer it would be to the root, with the higher ranking.

在設計階段加強對BIM技術的應用同樣可以提高建筑工程造價管理的有效性，并且設計階段是工程造價管理中不可或缺的一部分，要想真正提高工程造價管理的效率，需要積極做好設計階段的造價管理。就目前而言，在建筑工程造價管理中，限額設計得到了有效的應用，主要是根據投資估算額進行設計，利用限額設計對投資支出加以控制，保證資金得到有效分配，其中將BIM技術應用其中，可以對信息進行整合，使各個部門與單位參與其中，經過討論與分析，實現工程設計的完善性，減少后期因為設計變更所導致的成本浪費現象。

We employed the following machine learning methods:

Performance evaluation

There were some limitations for our study. First, this was a single-center retrospective study. Thus,although the model achieved high accuracy, relatively few factors and limited cases were included.Second, we did not perform the external validation of the samples from other institutions, so there might be differences that occur in the results obtained from other institutions and larger data sets. Third,we only developed one machine learning model of the random forest classifier, which may become more efficient if we would have used more algorithms during the data analysis. Finally, although we showed the importance of ranking variables in the random forest models, the black-box effect of the predictive models and the analytical decision on the samples remain ambiguous.

RESULTS

Patient characteristics

A total of 946 patients who underwent free flap transplantation for head and neck (40.2%), breast(38.3%), and extremity reconstruction (21.5%) were recruited. Overall, 58.3% of the recruited population was female, with an average age of 42 years (range: 13-65 years). The average BMI of the studied population was 24.9 ± 6.3 (mean ± standard deviation). Other potential factors for flaps failure were obesity (23.4%), smoking (30.3%), diabetes (6.3%), insulin (1.3%), hypertension (16.2%), preoperative tumor chemotherapy (25.3%), and preoperative tumor radiotherapy (19.2%). Table 1 showed the clinical characteristics of the patients in the training and the test sets. However, no significant statistical difference was observed between the two subsets.

Clinical outcomes

Major complications after flap transplantation were hematoma in 69 cases (7.3%), infection in 49 cases(5.2%), and damaged flap circulation in 65 cases (6.9%). Salvage measures were implemented for 95 cases (10%), where 61 cases were successfully saved, with a success rate of 64.2%. Finally, 34 patients(3.6%) had flap failure, with the most common cause of postoperative infection followed by hematoma formation.

Model performance

We developed three machine learning-based models based on the various preoperative and intraoperative data for analyzing the potential risk factors associated with the flap failure after microvascular tissue reconstruction. A total of 473 patients and 16 events were included in the training set, while a total of 473 patients and 18 events were included in the test set. The receiver operating characteristic graph was drawn based on model sensitivity and specificity, whereas the random forest model yielded the highest AUC score in the test set (AUC = 0.770, 95% confidence interval: 0.726-0.854) (Figure 1). The random forest model maintained a very high predictive ability for predicting the flap failure events,indicating that the classification model based on the binary tree could accurately divide the samples into with and without flap failure events. Other model indicators in the random forest were: (1) The value of precision based on the true positive divided by the sum of true positive and false positive was 0.82; (2)The values of recall obtained by dividing true positive by the sum of the true positive and false negative was 0.69; and (3) The values of the F1 score obtained by the precision-recall curve was 0.75 (Table 2).

Importance rank

3.1 護士的睡眠狀況令人擔憂 表1顯示，臨床護士經常失眠的比例較高，其中護師最高，護士其次，主管護師最低，這可能與護理人員工作崗位性質及三班制有一定的聯系。表2顯示，三班制護士睡眠評估分數較高，在6分以上者達37.1%;長日班護士相對較低，占22.9%。護士與護師由于年齡、工作年限等原因大多數在翻班，目前大部分醫院采用1周翻班的形式，頻繁的倒班導致護士生物鐘混亂，引起睡眠的失調。此外，不同科室的護士由于工作強度及壓力的不同，睡眠狀況也有差別。表3顯示，護士經常失眠的發生率以監護室和急診室最高，這與監護室、急診室工作節奏快、危重患者多、應對突發緊急狀況多等導致的護士精神長期高度緊張有關。

Multivariate regression

Table 3 outlines the statistical analysis results of the top ten variables of the random forest model in the traditional logistic regression analysis. Of note, among the top ten variables, only age, BMI, and ischemic time were significantly associated with the outcomes of the multivariate analysis. For the remaining seven variables,

values for diabetes and prior chemotherapy were 0.06 and 0.07,respectively. Interestingly, surgeon’s experience was not found to be statistically significant in the multivariate analysis.

Support vector machine is an algorithm for creating nonlinear discriminative classifier, governed by an optimal hyperplane that separates examples of different classes (the notable kernel trick).

DlSCUSSlON

Free flap transplantation is a robust technique, ensuring the success of tissue reconstruction even with various postoperative complications through timely rescue attempts[25]. However, though the incidence of flap failure is relatively low, once it occurs it is generally detrimental for the patient, as it results in the permanent scarring of the graft area, especially at the region of the face and breast[26].Therefore, in this study a random forest model based on machine learning was used for a series of preoperative and intraoperative variables, aiming to assess and analyze the risk factors associated with the flap failure after microvascular tissue reconstruction and to screen out the high-risk groups in clinical practice. To best of our knowledge, this is the first report about the application of the random forest model for flap failure after microvascular tissue reconstruction.

When the event-to-variable ratio was greater than 10, an ideal prediction model in multivariate logistic regression analysis was successfully constructed[27]. However, owing to the low incidence of flap failure, the event-to-variable ratio in this study was approximately 1. Therefore, even reducing the variables of the analysis could not achieve the ideal ratio value. Moreover, the traditional logistic regression could not consider the nonlinear relationship between the variables[15]. Therefore, in this study, due to the potential overfitting phenomenon, the utility of the prediction model based on the traditional multivariate analysis might be compromised. The phenomenon partially explains that only three factors, including age, BMI, and ischemia time, were considered statistically significant for flap failure using the multivariate analysis.

回顧往昔，成績令人鼓舞；展望未來，仍需再接再厲。黨的十八屆三中全會及中央經濟工作會議、中央城鎮化工作會議、中央農村工作會議為全面深化水利改革、做好下一步水利工作指明了方向，水利人責任重大，任務艱巨。作為全面深化水利改革的踐行者，我們應該認識到，深化水利改革不僅是一項歷史使命，更是一個廣闊舞臺。要切實把思想和行動統一到深化水利改革的決策部署上來，牢牢把握“增進人民福祉”的出發點和落腳點，用忠誠和智慧，靠勇氣和決心，奮力推動水利改革發展實現新跨越，為實現“兩個一百年”奮斗目標和中華民族偉大復興續寫絢麗多彩的水利篇章。

Other research has widely explored the comparison between the traditional logistic regression and emerging machine learning for the data analysis. In 2018, Lee

[15] published a study for predicting acute kidney injury after liver transplantation. Their research indicated that the AUC score of the prediction model based on machine learning could reach up to 0.90, while that of the logistic regression model was only 0.61[15]. In 2020, Arkin

[28] analyzed 30-d survival prediction of cancer patients by comparing the machine learning and logistic regression to understand the better statistical methods for the relatively accurate prediction of survival. Their results showed that the machine learning-based artificial neural network yielded a higher AUC score of 0.86 than the AUC score of 0.76 in the logistic regression model[28]. Considering the abovementioned pitfall of event-to-variable ratio, several techniques, such as GridSearch, 5-fold cross-validation, and oversampling to avoid potential overfitting defects were used in the current study. We found the adopted machine learning models achieved thehighest AUC score of 0.772 in the random forest classifier, as to the interesting outcome of flap failure.The effectiveness of our machine learning model was similar to that of the predictive models demonstrated in other studies. Formeister

[29] yielded a decision tree model that could correctly classify outcomes with an accuracy ranging from 65% to 75%. O’Neill

[30] achieved an AUC of 0.95 in the training set and 0.67 in the testing set for 2012 patients within microvascular breast reconstruction.

Machine learning models were successfully developed for identifying the potential factors and screening out the high-risk patients for the interesting outcome of flap failure.

利用微信公眾號開展實驗室安全知識宣傳、安全設施使用、事故案例分析和安全文化建設，此舉突破了時空限制，可結合實驗室實際情況和實驗教學進程編寫圖文并茂、針對性強的信息資料，及時傳送給學生，使學生能及時、持續地獲取豐富的學習內容，激發學生的學習興趣，時時保持實驗安全的警惕性，從而達到更好的安全教育目的，這種方式也是傳統的安全教育方法的有效補充[12]。

Figure 2 represents the importance ranking of tested variables in the random forest model to predict flap failure. The variables were ranked based on the average distance from the split branch to the tree root in the binary tree. The line length measured the variable importance in the random forest model(Figure 2). The top ten variables in the random forest model were age, BMI, ischemia time, smoking,diabetes, experience, prior chemotherapy, hypertension, insulin, and obesity.

The model evaluation used performance indicators used in the machine learning. The primary evaluation method was the receiver operating characteristic curve and the area under the curve (AUC)score. Other relevant indicators included accuracy, precision, recall, and F1 score. The higher value indicators represented the better predictive performance of the model.

CONCLUSlON

In our study, the random forest machine learning technique predicted the flap failure in patients following the microvascular tissue reconstruction for head and neck, breast, and extremities. We also identified the relevant risk factors of the outcome and further analysis in the traditional multivariate logistic regression. The findings of our study will help plastic surgeons to identify the potential risk factors associated with the flap failure and in screening high-risk events. These observations will eventually assist the clinician in decision-making by understanding the underlying pathologic mechanisms of the disease and improving the long-term outcome of the patients. Future multicentric research is required to develop an AI-based, big-data-driven clinical decision support system with a larger sample size.

ARTlCLE HlGHLlGHTS

Research objectives

The objective of the current study was to develop machine learning-based predictive models for the flap failure to identify potential factors and screening the high-risk patients.

Research methods

To establish machine learning classifiers, we used a data set with 945 consecutive patients who underwent microvascular tissue reconstruction. Model performances were evaluated by the indicators including area under the receiver operating characteristic curve, accuracy, precision, recall, and F1 score.A multivariable regression analysis was also performed for the essential variables in the random forest model.

開完記者招待會，葉曉曉兩眼燒得通紅地回到宿舍，把挎包往茶幾上一扔，就倒在沙發上。這回也沒有人跟她爭辯，沒有人給她命令，甚至罵她了。涂當搬走了，甚至連他用過的不要的東西都裝在垃圾袋里一股腦帶走了。他真是走得徹徹底底、干干凈凈，連挽留的機會都不留給葉曉曉。

Research results

The flap failure event occurred in 152 patients (1.9%) after the operation. The random forest classifier based on various preoperative and intraoperative variables performed the best, with an area under the curve score of 0.770 in the test set. The top variables in the random forest were age, body mass index,ischemia time, smoking, diabetes, experience, prior chemotherapy, hypertension, insulin, and obesity.

Research conclusions

The potential risk factors involved in the development of flap failure and the contribution ranking in a random forest classifier is shown in Figure 2. These observations are suggestive that the occurrence of flap failure is a multifactor-driven event with the identified numerous factors. Reported preoperative risk factors included BMI, ischemia time, and limited surgical experience[31-33]. Specifically, it is widely accepted that there was an increase in the postoperative complications for the free flap transplantation in the obese patients[34]. At the same time, Chang

[35] recommended that the microvascular tissue reconstruction should be performed with full discretion of patients with high BMI. Additionally,prolonged ischemia time of the free flap and subsequent ischemia-reperfusion injury can also increase the risk of postoperative complications and eventually flap failure[36].

Research perspectives

In our study, the machine learning technique correctly predicted flap failure in the patients who followed microvascular tissue reconstruction. Results from our research will help the clinician in decision-making by better understanding the underlying pathologic mechanisms of the disease and improving the long-term outcome of patients.

加拿大拉格朗德河干流下游已建成4座水電站，自下而上分別為Ⅰ級、Ⅱ級、Ⅲ級和Ⅳ級。其中拉格朗德Ⅱ級水電站1期工程最早建成，2期工程完成后總裝機容量7 326MW，年發電量380億kW·h，是加拿大已建的最大水電站。

FOOTNOTES

Shi YC and Li J contributed equally to this work; Shi YC and Li J were responsible for conceptualization, data curation, and methodology and wrote the original draft; Li SJ, Li ZP and Zhang HJ analyzed the data and edited the manuscript; Wu ZY was responsible for validation and supervision and reviewed the manuscript; All authors approved the final submission.

This study was approved by the Ethics Committee of the Affiliated Hospital of Guangdong Medical University.

The data used in this study were not involved in the patients’ privacy information, so the informed consent was waived by the Ethics Committee of the Affiliated Hospital of Guangdong Medical University.

The authors have no conflicts of interest to declare.

No additional data are available.

This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BYNC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is noncommercial. See: https://creativecommons.org/Licenses/by-nc/4.0/

China

Yu-Cang Shi 0000-0002-7643-1734; Jie Li 0000-0003-3003-5346; Shao-Jie Li 0000-0003-0103-8812; Zhan-Peng Li 0000-0002-8982-3972; Hui-Jun Zhang 0000-0002-8438-4544; Ze-Yong Wu 0000-0001-5510-3638; Zhi-Yuan Wu 0000-0001-9908-5283.

Gong ZM

Nif組：大鼠按每天的實測體質量以50 mg/kg的劑量胃飼給藥，1次/d[10]；CsA組：大鼠按每天的實測體質量以10 mg/kg的劑量行皮下注射，1次/d[11]；Nif+CsA組：大鼠按每天的實測體質量，以上述藥物劑量同時給藥，1次/d；空白對照組：大鼠不進行任何藥物刺激。

Filipodia

我國政府必須加快完善社會保障體系，逐步提高中央和地方財政對社會保障的支出比重；統籌推進國家基本養老保險、企業補充養老保險和個人儲蓄性養老保險體系改革；完善失業保險制度，擴大覆蓋面；積極穩妥地推進城鎮醫療保障制度改革；推進農村社會保障工作并對老弱病殘、鰥寡孤獨等實行社會救濟，建立新型農村合作醫療和醫療救助制度。建立包括養老、醫療、失業、工傷和生育保險在內的社會保障體系，使全體人民學有所教、勞有所得、病有所醫、老有所養、住有所居，推動以改善民生為重點的和諧社會建設，使改革發展的成果惠及更多的勞動者。

Gong ZM

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