KR20230119540A - Method and system for providing information for predicting the prognosis of breast reconstruction - Google Patents

Abstract

The present invention relates to a method for providing information for predicting a breast reconstruction prognosis and a system for predicting a prognosis of breast reconstruction. By using the information providing method or system for predicting the prognosis of breast reconstruction according to one embodiment of the present invention, the tissue expander insertion of a subject who is scheduled to perform tissue expander insertion among two-stage breast reconstruction along with total mastectomy is scheduled or has already been performed. Depending on the start time, complications can be predicted, appropriate treatment can be prepared, and information can be provided to select a surgery start time with a low possibility of complications.

Description

Method and system for providing information for predicting the prognosis of breast reconstruction

The present invention relates to a method and system for providing information for predicting the prognosis of breast reconstruction surgery.

Breast cancer is the most common cancer in women and the second most fatal cancer. In 2001, the prevalence of breast cancer was 90-100 per 100,000 in the United States and 50-70 per 100,000 in Europe. The incidence of this disease is on the rise worldwide. Risk factors for breast cancer include race, age, and mutations in the cancer suppressor genes BRCA-1, BRCA-2 and p53. Alcohol intake, high-fat diet, lack of exercise, exogenous postmenopausal hormones and ionizing radiation also increase the risk of breast cancer. Estrogen receptor- and progesterone receptor-negative breast cancer ("ER-" and "PR-", respectively), large tumor size, high-grade cytology, and poor prognosis in those under the age of 35 (Goldhirsch et al. (2001). J Clin. Oncol. 19: 3817-27).

The most essential treatment for breast cancer is surgery to remove the tumor. Breast cancer surgery can preserve the breast by removing only the cancer and a part of the normal tissue around the cancer if the cancer exists locally. can be implemented

Reconstructive surgery after mastectomy includes insertion of implants, tissue expanders, and implants, and flaps using autologous tissues such as skin, muscle, and fat of the back or abdomen. The timing and method of such reconstruction are determined in consideration of the patient's condition, mastectomy method, breast cancer stage, metastasis, and adjuvant therapy. Breast cancer patients who have undergone mastectomy experience not only physical changes but also psychological side effects such as low self-esteem and depression due to loss of femininity.

Breast reconstruction is divided into immediate reconstruction, which is performed simultaneously with mastectomy, and delayed reconstruction, which is performed after sufficient time has elapsed, depending on the reconstruction period. Immediate reconstruction is preferred, as it has been shown to provide significant benefits to patients in both aspects and overall quality of life. Patients who undergo two-step breast reconstruction, which is currently being performed most frequently, usually undergo a first-step operation (tissue expander insertion) and a second-step operation (tissue expander removal and replacement with an implant).

On the other hand, surgeons who perform surgeries have a duty to minimize surgical complications while improving patient safety. According to international reports on surgical outcomes, approximately 50 million people suffer from perioperative complications and more than 1.5 million die. Many studies have been conducted to identify potential risk factors posed by surgery and to improve patient safety.

Night surgery is usually performed in emergency situations where treatment cannot be delayed until the next regular time. there is. As the awareness of postoperative complications and patient safety increases, nighttime surgery is recognized as having a higher morbidity rate than regular surgery. There is also a study showing that the start time does not affect the postoperative outcome, so the relationship between the start time of surgery and the occurrence of postoperative complications has not been clarified. In addition, no study has been conducted on whether the prognosis of the first-stage surgery (tissue expander insertion) for breast reconstruction performed after mastectomy can vary depending on the start time of surgery.

Republic of Korea Patent No. 10-1896545

An object of the present invention is to provide a method for providing information for predicting the prognosis of patients who have undergone breast reconstruction.

Another object of the present invention is to provide a system for predicting the prognosis of patients who have undergone breast reconstruction.

One aspect of the present invention includes collecting clinical data; calculating a pattern including the same result by classifying the clinical data on the basis of a surgery start time and a treatment result; and predicting the subject's possibility of complications by applying the surgery start time among the subject's clinical data to the calculated pattern.

As used in the present invention, "breast reconstruction" refers to surgery performed to restore the shape of the breast after removing the breast to treat breast cancer, and the immediate reconstruction is performed by inserting a tissue expander immediately after mastectomy (1st stage surgery) It is a two-step breast reconstruction surgery in which tissue expander removal and implant replacement (two-step surgery) are performed after the expansion of the tissue expander is completed for a certain period of time.

"Prognosis" used in the present invention means determining whether or not recurrence, metastasis, complications, drug reactivity, resistance, etc. of the subject before/after treatment for subjects who have not yet been diagnosed or have been diagnosed. In the present invention, it means predicting whether or not there is a high possibility of complications after breast reconstruction using clinical data of a patient who has undergone breast reconstruction.

According to one embodiment of the present invention, the breast reconstruction surgery may be a first-step operation in which a tissue expander is inserted among two-step breast reconstruction surgery. The two-step breast reconstruction means that it includes a first step of inserting a tissue expander and a second step of replacing the tissue expander with an implant.

Collecting the clinical data is a process of obtaining information necessary for prognosis prediction. The clinical data is information related to patients who underwent breast reconstruction after mastectomy, including age, body mass index (BMI), presence or absence of comorbidities (diabetes, hypertension, dyslipidemia, liver disease, thyroid disease, stroke history). , ischemic heart disease, etc.), smoking status, ASA (american society of anesthesiologists) classification, preoperative chemotherapy, etc.

According to one embodiment of the present invention, information related to surgery includes the type of mastectomy, the weight of the resected specimen, the size of the tissue expander, and the artificial dermal matrix (ADM) for covering the inferolateral aspect of the tissue expander. ) may be used, the total operation time, and the plastic surgeon's career (more than or less than 5 years).

According to one embodiment of the present invention, the operation start time may be any one operation start time selected from the group consisting of between 8:00 and 18:00, between 18:00 and 20:00, and between 20:00 and 8:00.

Most surgeries usually start at regular time (usually between 8:00 and 18:00), but in the case of surgery that starts outside of regular time, the prognosis of surgery may be affected due to reasons such as fatigue of medical staff and lack of sleep. The onset time may affect the prognosis of surgery.

The step of classifying the clinical data based on the surgery start time and treatment result and calculating a pattern including the same result is based on the surgery start time and treatment result in the clinical data collected for predicting the prognosis of breast reconstruction. It is the process of calculating a pattern corresponding to the treatment outcome over time, that is, a prognostic prediction model.

According to one embodiment of the present invention, the treatment result includes any one complication selected from the group consisting of wound problems, seroma, hematoma, infection, unplanned reoperation, unplanned readmission, and early removal of a tissue expander. it may be

The wound problem refers to a problem occurring at a surgical site, including wound dehiscence in which a wound is opened or torn by an interlocking portion of the wound. The seroma refers to accumulation of tissue fluid from a surrounding wound without being absorbed. The hematoma refers to a state in which blood is collected in one place due to bleeding in an organ or tissue. The infection is a sign of local or systemic inflammation associated with an inserted tissue expander, and includes a case of antibiotic treatment or surgical treatment. The unplanned revision surgery includes a case in which a surgery other than the two-step surgery (tissue expander removal and implant replacement) that was planned prior to breast reconstruction is required due to postoperative complications. Unplanned readmission includes cases requiring hospitalization due to postoperative complications. This includes antibiotic treatment for surgical site infections and hospitalization for wound irrigation. Early removal of the tissue expander includes stopping maintenance or expansion of the tissue expander and removing the tissue expander due to infection or other problems at the surgical site.

The pattern can predict the possibility of complications by analyzing the complication rate corresponding to each operation start time by classifying and statistically analyzing the operation start time and treatment results.

The step of predicting the subject's possibility of complication by applying the surgery start time among the subject's clinical data to the calculated pattern includes the calculated pattern using information about the surgery start time among the clinical data for a subject whose prognosis needs to be predicted. It is a step of predicting the possibility of complication of the subject by comparing with.

The subject may be a breast cancer patient who has undergone two-step breast reconstruction after mastectomy.

According to one embodiment of the present invention, the complication may be any one selected from the group consisting of unplanned reoperation, unplanned readmission and early removal of the tissue expander.

According to one embodiment of the present invention, the step of predicting the possibility of complications may be to predict that the possibility of complications is high when the subject's surgery start time is between 20:00 and 8:00.

According to one embodiment of the present invention, when tissue expander insertion in subjects who underwent two-step breast reconstruction surgery started between 20:00 and 8:00, the total complication rate was 17.7%, and when started between 8:00 and 18:00 (10.1 %) and 8:00 to 20:00 (10.3%), the incidence of complications is higher than that of 20:00 to 8:00. It was confirmed that the rate of complications was significantly higher when starting at 13.1%, compared to cases starting between 8:00 and 18:00 (5.8%) and cases starting between 18:00 and 20:00 (6.1%).

The method of providing information for predicting the prognosis of breast reconstruction is to predict the occurrence of complications according to the start time of tissue expander insertion in subjects who are scheduled to undergo tissue expander insertion or have already performed tissue expander insertion among two-stage breast reconstruction along with total mastectomy. Appropriate treatment can be prepared for this, and information can be provided to select a surgery start time with a low possibility of complications.

The method for providing information for predicting the prognosis of breast reconstruction surgery described above may be implemented as a system using a computer device.

Another aspect of the present invention provides a system for predicting the prognosis of breast reconstruction surgery.

More specifically, referring to FIG. 2 , the system for predicting the prognosis of breast reconstruction surgery 100 includes an input unit 110 for receiving clinical data; a pre-processing unit 120 which classifies the clinical data based on a surgery start time and a treatment result and calculates a pattern including the same result; and an analysis unit 130 that predicts the possibility of complications of the subject by applying the surgery start time among the subject's clinical data to the calculated pattern.

Descriptions common to the foregoing here are omitted in order to avoid excessive complexity.

The input unit 110 receives clinical data and medical records from a medical record server or the like. The clinical data may include diagnosis information about the operation start time of a patient undergoing breast reconstruction after mastectomy and whether complications occur after tissue expander insertion.

According to one embodiment of the present invention, the breast reconstruction surgery may be a first-step operation in which a tissue expander is inserted among two-step breast reconstruction surgery. The two-step breast reconstruction means that it includes a first step of inserting a tissue expander and a second step of replacing the tissue expander with an implant.

The pre-processing unit 120 classifies the clinical data based on the operation start time and treatment result, and calculates a pattern including the same result, that is, a prognosis prediction model.

According to one embodiment of the present invention, the operation start time may be any one operation start time selected from the group consisting of 8:00 to 18:00, 18:00 to 20:00, and 20:00 to 8:00. .

According to one embodiment of the present invention, the treatment result includes any one complication selected from the group consisting of wound problems, seroma, hematoma, infection, unplanned reoperation, unplanned readmission, and early removal of a tissue expander. it may be

In the present invention, it is possible to analyze the complication rate by classifying various operation start times and treatment results in order to calculate a pattern.

The analyzer 130 analyzes the pattern calculated by the preprocessor 120 and the operation start time input to the input unit 110 to predict the possibility of complications after breast reconstruction surgery according to the start time of tissue expander insertion.

According to one embodiment of the present invention, the complication may be any one selected from the group consisting of unplanned reoperation, unplanned readmission and early removal of the tissue expander.

According to one embodiment of the present invention, the step of predicting the possibility of complications may be to predict that the possibility of complications is high when the subject's surgery start time is between 20:00 and 8:00.

According to one embodiment of the present invention, the system may further include a display unit for outputting a result of predicting the subject's possibility of complications in the analysis unit.

The display unit 140 may be a separate display screen or may be a certain space in an existing computer device in which the prognosis prediction system 100 is stored.

Referring to FIG. 3 , the system 100 for predicting the prognosis of breast reconstruction surgery inputs the surgery start time of a subject to predict the prognosis of breast reconstruction surgery, and presents the possibility of complications of breast reconstruction surgery according to the input surgery start time. can

The system for predicting the prognosis of breast reconstruction surgery according to the present invention may drive one or more programs and store them in a recording medium, etc., and may be implemented as a device such as a computer or server.

The program may include codes coded in various computer languages that can be executed by a processor of a computer or server. The codes may include codes such as functions defining functions necessary for predicting the prognosis of breast reconstruction surgery and control codes capable of controlling them.

In addition, the recording medium on which the program is stored is a medium readable by a computer or server, for example, ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical disk, USB memory, SD card, micro SD card, etc. , but is not limited thereto.

The system of the present invention is preferably implemented as a single hardware device, but may be implemented as an embedded device accommodated in an existing hardware device, if necessary, or as an application downloaded and installed in the form of software.

By using the information providing method or system for predicting the prognosis of breast reconstruction according to one embodiment of the present invention, the tissue expander insertion of a subject who is scheduled to perform tissue expander insertion among two-stage breast reconstruction along with total mastectomy is scheduled or has already been performed. Depending on the start time, complications can be predicted, appropriate treatment can be prepared, and information can be provided to select a surgery start time with a low possibility of complications.

1 is a flowchart illustrating a method of providing information for predicting a prognosis of breast reconstruction surgery according to an embodiment of the present invention.
Figure 2 is a diagram showing the components of a system for predicting the prognosis of breast reconstruction surgery according to an embodiment of the present invention.
3 is a diagram showing an example of predicting a prognosis of a breast reconstruction patient according to a surgery start time using a breast reconstruction prognosis prediction system according to an embodiment of the present invention.
Figure 4 shows the results of comparing the doses of analgesics (ketorolac and demerol) in groups 1, 2, and 3, classified according to the operation start time.

Hereinafter, one or more specific examples will be described in more detail through examples. However, these examples are intended to illustrate one or more specific examples, and the scope of the present invention is not limited to these examples.

Hereinafter, a process for predicting the possibility of complications after breast reconstruction surgery according to the present embodiment will be described.

One. preparation for study

1-1. study population

Data were prospectively collected for breast cancer patients who underwent total mastectomy at a tertiary general hospital between January 1, 2014 and December 31, 2019, and then underwent tissue expander insertion into one side of the breast for immediate breast reconstruction. . Patients who underwent bilateral breast reconstruction, patients who underwent delayed-type breast reconstruction, patients who underwent immediate reconstruction with direct implant implantation, and patients who had a tissue expander inserted into the prepectoral space were excluded for the homogeneity of the study population. All surgeries were performed by a breast surgeon who performed total mastectomy first, followed by a plastic surgeon who performed tissue expander insertion.

The study group was divided into 3 groups based on the start time of the tissue expander insertion in consideration of the regular working hours of Samsung Medical Center. Group 1 included patients who underwent tissue expander implantation, initiated between 8:00 and 18:00, the regular working hours of most medical staff. Group 2 included patients who underwent tissue expander implantation, initiated between 18:00 and 20:00 hours, when some medical staff on a day shift worked as an overtime form. Group 3 included patients who underwent tissue expander implantation initiated between 20:00 and 8:00, when most of the day shift staff were off work and a limited number of staff were on night duty.

1-2. Gather basic information

Patient and surgery-related data were collected retrospectively from patients' medical charts. Regarding the patient, age, body mass index (BMI), presence or absence of comorbidities (diabetes, hypertension, dyslipidemia, hyperthyroidism or hypothyroidism, liver cirrhosis, stroke, ischemic heart disease), American Society of Anesthesiologists (ASA) We collected information on physical condition classification, operation start time, total operation time, surgeon's experience (more or less than 5 years), and length of stay in hospital. Information on the incidence of postoperative complications and the amount of analgesics administered during hospitalization was also collected. This study was approved by the Institutional Review Board of Seoul Samsung Hospital.

During the six-year period, a total of 13,983 breast cancer patients were treated at Samsung Seoul Hospital. Of these, 5040 patients underwent total mastectomy with or without immediate breast reconstruction. Thus, mastectomy, including mastectomy or total mastectomy, had to be performed at least four times in one operating room per day. As a result, overcrowding occurred during day and night operations. The order of surgery was determined on a first-come, first-served basis, taking into account the earlier allocation of surgery for older patients or relatively vulnerable patients with comorbidities. Patients scheduled to undergo immediate reconstructive tissue expander implantation were assigned according to the plastic surgeon's possible surgical schedule. There was no significant association between the allocation of breast surgeons and plastic surgeons. During the study period, 9 plastic surgeons performed tissue expander implantation. Tissue expander insertion was performed in a conventional manner. Briefly, after total mastectomy was performed, a tissue expander was inserted into the subpectoral space. The size of the tissue expander to be inserted was determined considering the weight of the resected breast specimen and the width of the breast before surgery.

The tissue expander was inserted into the subpectoral space, covered on the sides with acellular dermal matrix (ADM) or the serratus anterior muscle fascia, depending on the preference of the attending physician. Two closed suction drainage tubes were inserted and removed when the daily drainage volume was less than 40 cc for two consecutive days. With financial support from the National Health Insurance, most patients were hospitalized until the suction drainage tube was removed. During the hospitalization period, patients managed their postoperative pain primarily by self-regulating analgesic administration and, in the case of uncontrolled pain, nonsteroidal anti-inflammatory drugs (NSAIDs), ketorolac, and/or Other analgesics, including opioids such as demerol, were administered as needed (pro re nata, PRN) considering the administration interval.

1-3. Gather information about complications

As a result of treatment, information on postoperative complications that occurred within 2 months after surgery was collected. These included infection, seroma, hematoma, wound problem, unscheduled reoperation due to complications, unscheduled readmission and premature removal of the tissue expander. Complications requiring reoperation and/or readmission and early removal of the tissue expander were classified as major complications. Complications that occurred after 2 months after surgery could be affected by adjuvant treatment including radiotherapy and chemotherapy and were not analyzed. In addition, information on the duration of hospital stay and the dose of additional analgesics including ketorolac and demerol were also collected.

1-4. statistical analysis

The patient-related and surgery-related characteristics of the three groups were compared. Complication profiles of the three groups were also compared. Pearson's Chi square test was used to analyze categorical variables and one-way analysis of variance was used to compare continuous variables. Univariate and multivariate binary logistic regression analyzes were performed to identify independent associations between each variable and outcome. for multivariate analysis A backward selection model was used. Statistical power analysis was performed on the incidence of overall complications. Statistical significance was set at p < 0.05. All statistical analyzes were performed using IBM SPSS version 20.0 (IBM Corporation, USA).

2. Results

2-1. Results of basic information collection

We studied 1,458 patients who underwent immediate breast reconstruction with a tissue expander according to the above-mentioned selection criteria. The mean age was 44.2 years (range: 18 to 71 years) and the mean BMI was 22.5 kg/m ² (range: 15.3 to 39.8 kg/m ² ). Most of the patients were in good physical condition, corresponding to the ASA classification Class I (81.3%) or Class II (18.4%).

Of the 1,458 tissue expander implantation procedures, 950 cases were operated between 8:00 and 18:00 in group 1, 358 cases were operated between 18:00 and 20:00 in group 2, and the remaining 130 cases were operated at 20:00. The surgery started between 8:00 and was classified into 3 groups. The basic information of the three groups is shown in Table 1 below. The patient-related information and surgery-related information of the three groups were generally similar. There were no significant differences between the three groups in the comorbidity rates and the distribution of ASA classifications. There was no significant difference between the three groups in the distribution of surgeon's experience (less than 5 years or more than 5 years) (p = 0.683). However, group 1 was found to be older than groups 2 and 3, showing a significant difference between groups.

Sum 1 group 2 groups 3 groups p value number of patients 1,458 970 358 130 patient-related variables age 44.2
(± 7.6) 44.6
(± 7.6) 43.1
(± 7.1) 43.8
(± 8.2) 0.006 BMI (kg/m ² )* 22.5
(± 3.0) 22.5
(± 3.1) 22.4
(± 2.9) 22.4
(± 2.8) 0.964 underweight 77 (5.3%) 48 (4.9%) 20 (5.6%) 9 (6.9%) 0.938 normal weight 1,126 (77.2%) 749 (77.2%) 277 (77.4%) 100 (76.9%) overweight 232 (15.9%) 156 (16.1%) 56 (15.6%) 20 (15.4%) obesity 23 (1.6%) 17 (1.8%) 5 (1.4%) 1 (0.8%) comorbidities diabetes 19 (1.3%) 15 (1.6%) 3 (0.8%) 1 (0.8%) 0.513 High blood pressure 61 (4.2%) 44 (4.5%) 13 (3.6%) 4 (3.1%) 0.616 dyslipidemia 22 (1.5%) 13 (1.3%) 7 (2.0%) 2 (1.5%) 0.717 thyroid disease 33 (2.3%) 21 (2.2%) 7 (2.0%) 5 (3.8%) 0.434 liver disease 6 (0.4%) 5 (0.5%) 1 (0.3%) 0 0.623 stroke history 4 (0.3%) 4 (0.4%) 0 0 0.365 ischemic heart disease 2 (0.1%) 2 (0.2%) 0 0 0.604 etc 9 (0.6%) 6 (0.6%) 1 (0.3%) 2 (1.5%) 0.292 smoker 15 (1.0%) 8 (0.8%) 4 (1.1%) 3 (2.3%) 0.285 ASA classification 0.834 Class I 1,185 (81.3%) 784 (80.8%) 297 (83.0%) 104 (80.0%) Class II 269 (18.4%) 182 (18.8%) 61 (17.0%) 26 (20.0%) Class III 3 (0.2%) 3 (0.3%) 0 0 Class IV 1 (0.1%) 1 (0.1%) 0 0 upfront chemotherapy 100 (6.9%) 61 (6.3%) 30 (8.4%) 9 (6.9%) 0.408 surgery-related variables Types of Mastectomy 0.739 skin preservation 1,133 (77.7%) 748 (77.1%) 282 (78.8%) 103 (79.2%) nipple preservation 325 (22.3%) 222 (22.9%) 76 (21.2%) 27 (20.8%) sample weight 393.6
(± 197.5) 389.3
(± 193.9) 403.1
(± 201.3) 391.9
(± 208.9) 0.513 size of tissue expander 411.0 (± 106.9) 409.6
(± 105.9) 415.6
(± 111.2) 410.2
(± 104.3) 0.780 Whether artificial dermis is used 880 (60.4%) 580 (59.8%) 225 (62.8%) 75 (57.7%) 0.486 Surgery duration (minutes)* 97.7 (± 27.3) 98.4
(± 28.8) 95.9
(± 24.3) 98.2
(± 23.1) 0.571 Plastic surgeon career 0.683 more than 5 years 660 (45.3%)
446 (46.0%)
159 (44.4%)
55 (42.3%) less than 5 years 798 (54.7%) 524 (54.0%) 199 (55.6%) 75 (57.7%) BMI: Body Mass Index,

2-2. Results of information collection on complications

Complications occurred in 158 patients (10.8%). The most common type of complication was wound problem (96 cases, 6.6%), followed by seroma, infection, and hematoma. Major complications were present in 95 patients. Referring to Table 2 below, complications occurred at a higher rate in group 3 (17.7%) than in groups 1 (10.1%) and 2 (10.3%), and the incidence of complications was found to be significantly different between the three groups. . Specifically, similar results were confirmed in the rates of readmission (p = 0.026), early removal of the tissue expander (p = 0.031), and major complications (p = 0.006). The hospitalization period was also significantly longer in group 3 than in the other two groups (p = 0.010). Post-hoc power analysis for all complications showed that the power was 0.48.

type of complication 1 group 2 groups 3 groups p value all complications 98 (10.1%) 37 (10.3%) 23 (17.7%) 0.031 infection 15 (1.5%) 7 (2.0%) 6 (4.6%) 0.057 Seroma 24 (2.5%) 7 (2.0%) 5 (3.8%) 0.493 hematoma 15 (1.5%) 6 (1.7%) 2 (1.5%) 0.985 wound problem 60 (6.2%) 22 (6.1%) 14 (10.8%) 0.131 revision surgery 48 (4.9%) 20 (5.6%) 14 (10.8%) 0.026 readmission 16 (1.6%) 8 (2.2%) 6 (4.6%) 0.079 Early removal of the tissue expander 8 (0.8%) 2 (0.6%) 4 (3.1%) 0.031 major complications 56 (5.8%) 22 (6.1%) 17 (13.1%) 0.006 Hospitalization period
(number of days, median) 9.0 9.0 10.0 0.010

2-3. Results of collecting information on analgesic use

As a result of analyzing the use of analgesics after surgery, group 3 had higher doses of ketorolac and demerol than the other two groups, and these differences were found to be statistically significant (FIG. 4). In the univariate analysis, the 3rd group showed a significantly higher overall complication rate than the 1st group. This difference was also maintained in multivariate analysis, indicating that the complication rate in group 3 was about twice that of group 1. There was no significant difference in the incidence rate of complications between groups 1 and 2.

2-4. Analysis of factors influencing complications

Referring to Table 3 below, age, presence of hypertension, ASA classification stage, type of mastectomy, and use of artificial dermis were also found to have an effect on the occurrence of complications. Similar results were obtained in the analysis of the occurrence of major complications.

variable Univariate analysis multivariate analysis Unadjusted p-value OR (95% CI) adjusted p-value OR (95% CI) age 0.003 1.034 (1.011 - 1.057) 0.047 1.023
(1.000 - 1.047) BMI normal Ref underweight 0.586 0.788 (0.335 - 1.857) overweight 0.005 1.770 (1.183 - 2.649) obesity 0.014 3.293 (1.272 - 8.527) comorbidities diabetes 0.965 0.968 (0.221 - 4.227) High blood pressure 0.009 2.339 (1.237 - 4.419) 0.003 2.659
(1.397 - 5.061) dyslipidemia 0.791 0.821 (0.190 - 3.544) thyroid disease 0.423 1.485 (0.565 - 3.902) Other diseases* 0.846 0.864 (0.199 - 3.746) smoker 0.999 0 (0 - ) ASA classification Class I Ref Class II <0.001 2.300 (1.595 - 3.316) 0.004 1.797 (1.212 - 2.664) Class III 0.188 5.037 (0.453 - 56.011) 0.232 4.613 (0.376 - 56.529) Class IV 0.999 0 (0 - ) 0.999 0 (0 - ) upfront chemotherapy 0.780 0.908 (0.462 - 1.785) Types of Mastectomy skin preservation Ref Ref nipple preservation <0.001 2.037 (1.430 - 2.901) <0.001 2.184 (1.503 - 3.173) sample weight <0.001 1.002 (1.001 - 1.002) <0.001 1.002 (1.001 - 1.002) tissue expander size <0.001 1.003 (1.002 - 1.005) Tissue expander type Siltex-textured Ref Ref Biocell-textured 0.124 0.700 (0.444 - 1.102) 0.092 0.662
(0.410 - 1.069) Whether artificial dermis is used <0.001 1.938 (1.339 - 2.805) 0.006 1.716 (1.171 - 2.514) surgery start time 1 group Ref Ref 2 groups 0.901 1.026 (0.688 - 1.529) 0.689 1.087 (0.721 - 1.640) 3 groups 0.010 1.913 (1.164 - 3.142) 0.008 2.006
(1.196 - 3.367) OR; odds ratio, odds ratio, CI; confidence interval,confidence interval, Ref; reference
*Includes other conditions such as liver disease, history of stroke, and ischemic heart disease

Referring to Table 4 below, after adjusting for other variables, it was found that the incidence of major complications in group 3 increased significantly compared to group 1.

variable Univariate analysis multivariate analysis Unadjusted p-value OR (95% CI) adjusted p-value OR (95% CI) age 0.003 1.042 (1.014 - 1.071) 0.063 1.027
(0.999 - 1.057) BMI normal Ref underweight 0.136 0.219 (0.030 - 1.611) overweight 0.353 1.292 (0.753 - 2.217) obesity 0.012 1.892 (1.148 - 3.118) comorbidity diabetes 0.114 2.745 (0.786 - 9.592) High blood pressure 0.038 2.273 (1.048 - 4.931) dyslipidemia 0.624 1.444 (0.332 - 6.272) thyroid disease 0.196 2.022 (0.696 - 5.876) other diseases 0.576 1.521 (0.349 - 6.630) smoking or not 0.999 0 (0 - ) ASA classification Class I Ref Ref Class II <0.001 2.577 (1.649 - 4.025) 0.007 1.944 (1.198 - 3.152) Class III 0.071 9.213 (0.824 - 103.015) 0.098 8.119 (0.652 - 101.076) Class IV 0.999 0 (0 - ) 0.999 0 (0 - ) upfront chemotherapy 0.829 0.910 (0.388 - 2.135) Types of Mastectomy skin preservation Ref Ref nipple preservation 0.006 1.855 (1.189 - 2.894) 0.004 1.989 (1.244 - 3.181) sample weight <0.001 1.002 (1.001 - 1.003) 0.001 1.002 (1.001 - 1.003) tissue expander size < 0.001 1.004 (1.002 - 1.006) Tissue expander type Siltex-textured Ref Biocell-textured 0.201 0.684 (0.382 - 1.224) Whether artificial dermis is used <0.001 2.599 (1.569 - 4.307) 0.001 2.449 (1.461 - 4.106) surgery start time 1 group Ref Ref 2 groups 0.798 1.069 (0.643 - 1.777) 0.545 1.175 (0.697 - 1.982) 3 groups 0.002 2.455 (1.379 - 4.372) 0.001 2.733
(1.497 - 4.990)

Referring to Table 5 below, in the multivariate analysis of infection, revision surgery, and early removal of the tissue expander, the incidence rate of group 3 was significantly higher than that of group 1 or 2, showing consistent results.

type of complication group Adjusted p-value OR (95% CI) infection 1 group Ref 2 groups 0.440 1.443 (0.569 - 3.663) 3 groups 0.014 3.503 (1.290 - 9.513) revision surgery 1 group Ref 2 groups 0.452 1.237 (0.711 - 2.151) 3 groups 0.009 2.383 (1.237 - 4.591) reconstruction failure
(Early Removal of Tissue Expanders) 1 group Ref 2 groups 0.672 0.709 (0.145 - 3.475) 3 groups 0.031 3.994 (1.134 - 14.071)

2-5. Results of comparative analysis of complications in group 1 and groups 2 and 3

An additional analysis was conducted to compare the complication rate between the group 1 and 2 combined (hereinafter referred to as 'combined group') and the 3 group. There was no significant difference in basic information, including age, BMI, and presence or absence of comorbidities. In the complication profile, the complication rate of the 3 groups was total complication (p = 0.008), infection (p = 0.019), wound problem (p = 0.044), reoperation (p = 0.008), readmission (p = 0.031), Major complications (p = 0.001) and early removal of the tissue expander (p = 0.010) were significantly higher than the combination group. Significantly more demerol was administered in the 3 groups than in the combined group (p = 0.026).

In multivariate analysis, similar to the above analysis, 3 groups were divided into overall complications (OR; 2.032, adjusted p-value = 0.006), infections (OR; 3.051, adjusted p-value = 0.021), and wound problems. (OR; 1.983, adjusted p-value = 0.030), reoperation (OR; 2.326, adjusted p-value = 0.008), major complication (OR, 2.667, adjusted p-value = 0.001), and early removal of the tissue expander (OR, 4.527, adjusted p-value = 0.015) showed a significant increase.

In summary, information and a system for predicting the possibility of complications according to the surgery start time of the tissue expander insertion corresponding to the first stage of the two-stage breast reconstruction surgery can be provided according to the criteria derived from the above-described embodiment.

So far, the present invention has been looked at with respect to its preferred embodiments. Those of ordinary skill in the art to which the present invention pertains will understand that the present invention can be implemented in a modified form without departing from the essential characteristics of the present invention. Therefore, the disclosed embodiments should be considered from an illustrative rather than a limiting point of view. The scope of the present invention is shown in the claims rather than the foregoing description, and all differences within the equivalent scope will be construed as being included in the present invention.

100: prediction system
110: input unit
120: pre-processing unit
130: analysis unit
140: display unit

Claims (13)

collecting clinical data;
calculating a pattern including the same result by classifying the clinical data on the basis of a surgery start time and a treatment result; and
Predicting the possibility of complications of the subject by applying the surgery start time among the subject's clinical data to the calculated pattern
A method for providing information to predict the prognosis of breast reconstruction.
The breast reconstruction according to claim 1,
Stage 1 surgery to insert a tissue expander during stage 2 breast reconstruction,
A method for providing information to predict the prognosis of breast reconstruction.
The method according to claim 1, wherein the operation start time,
Any one operation start time selected from the group consisting of between 8:00 and 18:00, between 18:00 and 20:00, and between 20:00 and 8:00,
A method for providing information to predict the prognosis of breast reconstruction.
The method of claim 1, wherein the treatment result includes any one complication selected from the group consisting of wound problems, seroma, hematoma, infection, unplanned reoperation, unplanned readmission, and premature removal of the tissue expander. ,
A method for providing information to predict the prognosis of breast reconstruction.
The method according to claim 1, wherein the complication includes any one complication selected from the group consisting of unplanned reoperation, unplanned readmission, and early removal of the tissue expander.
A method for providing information to predict the prognosis of breast reconstruction.
The method according to claim 1, wherein the step of predicting the possibility of complications is predicting that the possibility of complications is high when the subject's surgery start time is between 20:00 and 8:00.
A method for providing information to predict the prognosis of breast reconstruction.
an input unit for receiving clinical data;
a pre-processing unit that classifies the clinical data based on the operation start time and treatment result and calculates a pattern including the same result; and
An analysis unit for predicting the possibility of complications of the subject by applying the surgery start time among the subject's clinical data to the calculated pattern
A prognosis prediction system for breast reconstruction.
The method according to claim 7, wherein the breast reconstruction,
Stage 1 surgery to insert a tissue expander during stage 2 breast reconstruction,
A prognosis prediction system for breast reconstruction.
The method according to claim 7, wherein the operation start time,
Any one operation start time selected from the group consisting of between 8:00 and 18:00, between 18:00 and 20:00, and between 20:00 and 8:00,
A prognosis prediction system for breast reconstruction.
The method of claim 7, wherein the treatment result includes any one complication selected from the group consisting of wound problems, seroma, hematoma, infection, unplanned reoperation, unplanned readmission, and premature removal of the tissue expander. ,
A prognosis prediction system for breast reconstruction. The method according to claim 7, wherein the complications include any one selected from the group consisting of unplanned reoperation, unplanned readmission and early removal of the tissue expander,
A prognosis prediction system for breast reconstruction.
The method according to claim 7, wherein the analysis unit predicts that the possibility of complications is high when the subject's surgery start time is between 20:00 and 8:00.
A prognosis prediction system for breast reconstruction.
The method according to claim 7, wherein the system further comprises a display unit outputting a result of predicting the possibility of complications of the subject in the analysis unit,
A prognosis prediction system for breast reconstruction.